Low Fluctuations Research Articles

A Personalized Muscle–Tendon Assessment and Exercise Prescription Concept Reduces Muscle–Tendon Imbalances in Female Adolescent Athletes

BackgroundImbalances between muscle strength and tendon stiffness influence the mechanical demand on the tendon (i.e., tendon strain) and may increase tendon injury risk. The purpose of this study was to identify muscle–tendon imbalances and to promote a more balanced musculotendinous adaptation through a personalized assessment and exercise prescription concept in female adolescent handball athletes (13–16 years).MethodsAt four measurement time points during a competitive season, we used dynamometry and ultrasonography to assess knee extensor muscle strength, patellar tendon stiffness and strain. Tendon micromorphology was assessed with a peak spatial frequency (PSF) analysis of proximal tendon ultrasound images. Muscle–tendon imbalances were identified based on tendon strain during maximum voluntary contractions. A control group (n = 15) followed their usual training. In the intervention group, athletes with a deficit in tendon stiffness (strain ≥ 9%; n = 6) or no muscle–tendon imbalances (strain between 4.5% and 9%; n = 15) performed exercises (3x/week for 32 weeks) with a personalized load to reach ~ 6.2% tendon strain to predominantly promote tendon or both muscle and tendon adaptation. Individuals with a muscle strength deficit (strain ≤ 4.5%; n = 1) trained with submaximal loads to failure to promote muscle strength.ResultsIn the intervention group we found lower fluctuations of maximum tendon strain (p = 0.005) and a decrease in tendon strain over time (p = 0.010), which was more pronounced in individuals with initially high tendon strain. While there were no systematic changes in muscle strength or tendon stiffness at the group level (p > 0.05), the marked decrease in tendon strain in individuals with a deficit in tendon stiffness was caused by a predominant increase in tendon stiffness. Overall, the prevalence of muscle–tendon imbalances was reduced in the intervention group, while it temporarily increased in the control group. PSF did not differ between groups at baseline but decreased significantly in the intervention group (p = 0.013).ConclusionsThe findings suggest that the personalized concept is suitable to promote a more uniform adaptation of knee extensor muscle strength and patellar tendon stiffness and to reduce the prevalence of musculotendinous imbalances in female adolescent athletes, which may have important implications for tendon injury prevention.Trial RegistrationDRKS, DRKS00035110. Registered 20 September 2024–retrospectively registered, https://drks.de/search/de/trial/DRKS00035110.

Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany

State-of-the-art hydrogeological investigations use transient calibrated numerical flow and transport models for multiple scenario analyses. However, the transient calibration of numerical flow and transport models still requires consistent long-term groundwater time series, which are often not available or contain data gaps, thus reducing the robustness and confidence of the numerical model. This study presents a data-driven approach for the reconstruction and prediction of gaps in a discontinuous groundwater level time series at a monitoring station in the Allertal (Saxony-Anhalt, Germany). Deep Learning and classical machine learning (ML) approaches (artificial neural networks (TensorFlow, PyTorch), the ensemble method (Random Forest), boosting method (eXtreme gradient boosting (XGBoost)), and Multiple Linear Regression) are used. Precipitation and groundwater level time series from two neighboring monitoring stations serve as input data for the prediction and reconstruction. A comparative analysis shows that the input data from one measuring station enable the reconstruction and prediction of the missing groundwater levels with good to satisfactory accuracy. Due to a higher correlation between this station and the station to be predicted, its input data lead to better adapted models than those of the second station. If the time series of the second station are used as model inputs, the results show slightly lower correlations for training, testing and, prediction. All machine learning models show a similar qualitative behavior with lower fluctuations during the hydrological summer months. The successfully reconstructed and predicted time series can be used for transient calibration of numerical flow and transport models in the Allertal (e.g., for the overlying rocks of the Morsleben Nuclear Waste Repository). This could lead to greater acceptance, reliability, and confidence in further numerical studies, potentially addressing the influence of the overburden acting as a barrier to radioactive substances.

Classification of young adult motor fluctuation phenotypes in gait.

Stride-to-stride fluctuations are natural in gait. These fluctuations are marked by inter-individual variability, suggesting that different fluctuation strategies (i.e., phenotypes) may exist. This study investigates the presence of gait fluctuation phenotypes. Whole-body kinematics were measured from young, healthy males and females (N = 51) while walking on a treadmill at their preferred speed. Motor fluctuation metrics (i.e., magnitude of variability, local dynamic stability, and regularity) were measured for 32 joint angles across the upper and lower body. These metrics were reduced to principal components (PCs) via principal component analysis and then grouped into clusters using the k-means method. One-way ANOVAs were conducted to test for cluster differences in motor fluctuation PCs. Three PCs were extracted, explaining 39.7% of all 96 motor fluctuation metrics. Higher PC1 scores represent more fluctuation across all joints, higher PC2 scores represent greater upper limb fluctuations with fewer fluctuations in the lower limb, and PC3 scores represent less regularity in fluctuations. PC scores best grouped into four clusters in 54.0% of iterations. Clusters 1-4 each had a significantly different PC1 score (p<0.022), and Cluster 3 had a higher PC2 score than all other clusters (p<0.022). Motor fluctuations in treadmill gait of young adults were characterised by four gait fluctuation phenotypes, interpreted as repeaters, replacers, moderate fluctuators, and mixed fluctuators (i.e. more upper limb but fewer lower limb fluctuations); extending the repeaters vs replacers hypothesis. The identified phenotypes add a new perspective that may help clarify the link between motor fluctuations and gait instability.

Understanding the formation of a low-pressure pedestal in the presence of a strong internal transport barrier in DIII-D high βp plasmas

Abstract As a promising scenario for fusion reactors, the high poloidal-beta ( β P ) scenario is characterized by a strong large radius internal transport barrier (ITB), which significantly enhances the overall confinement quality and the bootstrap current fraction for fully non-inductive operation. It is frequently observed that in the presence of a strong ITB, the pedestal height is lower and is accompanied by small edge localized modes (ELMs), which further improves the compatibility of a high performance core with an edge solution. A mechanism for the formation of the low pedestal is proposed in this paper. It is found that the strong ITB creates an off-axis bootstrap current to clamp the local safety factor q, and thus the magnetic shear in the outer core/pedestal region is increased. Gyrokinetic simulations with the CGYRO code show that the higher magnetic shear brings the experimental profiles into the range where the growth rate of drift-wave instabilities and thus transport is higher, and therefore a lower pedestal gradient is expected. The combination of low pedestal and high magnetic shear further enhances the turbulent transport across the whole pedestal, consistent with power balance analysis. Such a positive feedback mechanism ultimately results in a lower pressure pedestal as observed in experiments. Under such a low pedestal, linear simulations with BOUT++ predict the growth rates of peeling–ballooning modes to be lower across the whole toroidal mode number spectra, and the nonlinear BOUT++ simulation exhibits lower saturated fluctuation intensity as well, consistent with the experimentally observed lower ELM size.

Transfer Learning-Based Health Monitoring of Robotic Rotate Vector Reducer Under Variable Working Conditions

Due to their precision, compact size, and high torque transfer, Rotate vector (RV) reducers are becoming more popular in industrial robots. However, repetitive operations and varying speed conditions mean that these components are prone to mechanical failure. Therefore, it is important to develop effective health monitoring (HM) strategies. Traditional approaches for HM, including those using vibration and acoustic emission sensors, encounter such challenges as noise interference, data inconsistency, and high computational costs. Deep learning-based techniques, which use current electrical data embedded within industrial robots, address these issues, offering a more efficient solution. This research provides transfer learning (TL) models for the HM of RV reducers, which eliminate the need to train models from scratch. Fine-tuning pre-trained architectures on operational data for the three different reducers of health conditions, which are healthy, faulty, and faulty aged, improves fault classification across different motion profiles and variable speed conditions. Four TL models, EfficientNet, MobileNet, GoogleNet, and ResNET50v2, are considered. The classification accuracy and generalization capabilities of the suggested models were assessed across diverse circumstances, including low speed, high speed, and speed fluctuations. Compared to the other models, the proposed EfficientNet model showed the most promising results, achieving a testing accuracy and an F1-score of 98.33% each, which makes it best suited for the HM of robotic reducers.

Operational properties of cement-free concrete with porous aggregate

The article presents the results of technology development and research on the operational properties of porous aggregate and cement-free concretes based on it. The purpose of the work is to study lightweight concretes containing a liquid–glass porous aggregate for resistance to various aggressive influences. The porous granular aggregate was obtained by firing a mixture of liquid glass with the ash of thermal power plants and an ash aluminosilicate microsphere. Binders based on caustic magnesite and liquid glass with the addition of thermal energy waste were used to produce coarse-pored concretes. The choice of cement-free binders is due to the high adhesion to the filler. The behavior of the developed concretes in various aggressive environments, under the influence of low and elevated temperatures, has been studied. The resistance of magnesia concrete to the effects of water and salt solutions has been revealed. The technological and operational advantages of liquid-glazed concrete are shown, featuring increased thermal insulation ability, satisfactory resistance to aggressive media and resistance to low and high-temperature fluctuations. The developed concretes can be used in the enclosing structures of objects for various purposes.

Exploring caffeine as a disruptor of membrane integrity and genomic stability in Staphylococcus aureus: functional and in silico analysis.

To explore the mechanistic underpinnings of caffeine as a potent antibacterial against Staphylococcus aureus ATCC 25923 via in vitro functional assays, whole-genome sequencing, and in silico docking studies. In vitro studies established that caffeine's minimum inhibitory concentration (MIC) against S. aureus ATCC 25923 is 0.01544mmol/mL. Functional assays along with Scanning Electron Microscopy confirmed that caffeine at 0.030089mmol/mL (2MIC) released nucleotide constituents (nucleotide leakage assay) and effluxed potassium ions (potassium efflux assay) thereby, further validating caffeine's role as a membrane-active antimicrobial agent. Whole genome sequencing of control versus caffeine treated samples revealed a significant drop in read mapping percentage from 99.96 to 23.68% and GC content from 30.69 to 6.93%. This massive reduction in the treated sample was a consequence of single nucleotide polymorphisms (SNPs, 50,303), along with insertions and deletions (InDels, 62). Several of these caffeine-induced mutations were found to be harbouring the coding regions of genes involved in processes such as cell membrane organization, bacterial virulence, and DNA repair processes. Thus, implying a caffeine-mediated genomic rearrangement and instability. In silico docking studies revealed a strong binding affinity of caffeine to key cell wall proteins ltaA (-6.9kcal/mol) and ltaS (-6.5kcal/mol) respectively. The dynamic simulation studies revealed caffeine's interaction with receptor ltaS remained stable, with low deviations and minimal fluctuations. Although caffeine has been widely investigated for its antibacterial properties, its specific mechanisms of action, notably its effects on the cell membrane and genomic integrity in S. aureus ATCC 25923, are little understood. This study thus offers a comprehensive functional genomic analysis of caffeine as an antibacterial against S. aureus.

Assisting recolonization of near‐shore seagrasses

Natural recolonization of seagrasses may take decades after disturbances and is particularly challenging in near‐shore environments, where sediment mobility inhibits seagrass establishment. We assisted recolonization in fifteen 4x10 m unvegetated experimental plots in a Mexican Caribbean near‐shore fringe where the seagrasses had died due to massive inundations of holopelagic Sargassum species, and where a containment barrier was placed to avoid future inundations. The applied treatments were: artificial substrate (AS: 90 belowground, artificial, biodegradable, 15x15 cm‐sized substrates, cut from a 0.91 x 0.45m Biodegradable EcoSystem Engineering sheet), transplant (TR: 90 cores, 4.5 cm diameter, with Halodule wrightii), and control (C: no manipulation), each with five replicates. After 6 months, 63% of H. wrightii transplants survived, presenting mean rhizome extension of 7.6 cm, and H. wrightii from natural surrounding patches started to colonize the plots. After approximately 8 months, AS and TR plots already showed higher light conditions and lower fluctuations in sediment levels than the controls. After 14 months, the AS and TR plots reached higher mean (± SE) density (respectively, 4024 ± 620 and 3484 ± 360 shoots/m2) and cover (60.3 ± 3.85 and 53.7 ± 2.84 %), compared to the control plots (2043 ± 381 shoots/m2, 35.3 ± 5.7 % cover). Higher density of H. wrightii likely favored the natural establishment of Thalassia testudinum seedlings, with an average of 2.07 (± 0.15) and 1.87 (± 0.18) seedlings in AS and TR plots, respectively, compared to 0.48 (± 0.23) in the controls. Both techniques accelerated seagrass recolonization, but artificial substrates required less effort and avoided harvesting of donor meadows.

Association between gynecological disorders and insomnia and depression trajectories: a longitudinal study of middle-aged women.

Insomnia and depression often receive inadequate attention regarding their association with common menopausal gynecological disorders (GDs), and there is a lack of longitudinal epidemiological evidence. Furthermore, the specific disorders that exhibit the strongest correlation with depression, as well as the potential mediating role of insomnia, remain poorly understood. Using data from the Study of Women's Health Across the Nation (SWAN) spanning 1996 to 2008, this study analyzed a sample of 2217 racially diverse premenopausal women (aged 42 to 53 at baseline). Longitudinal trajectory analysis, employing latent class mixture models (LCMM), was used to identify optimal patterns for insomnia and depression. Logistic regression explored associations between pelvic pain, pelvic prolapse or relaxation, abnormal bleeding, and insomnia/depression trajectories. A causal mediation model investigated whether insomnia mediated the link between gynecological disorders and depression. The analysis included 2217 participants for insomnia trajectories and 1767 for depression trajectories. Insomnia and depression showed similar patterns, with a single high and low trajectory and minimal fluctuations. Logistic regression revealed a significant positive correlation between pelvic pain, abnormal bleeding, number of GDs, and an increased risk of high insomnia and depression trajectories. Insomnia trajectories mediated 23.6%, 14.3%, and 11.9% of the association between pelvic pain, abnormal bleeding, and number of GDs, respectively, with depression trajectories. This study found the significant associations between pelvic pain, abnormal bleeding, and comorbidity with an elevated risk of insomnia and depression during the menopausal transition.

Clustering Countries of the World Based on the Trend of the COVID-19 Incidence: An application of shape-based k-means algorithm

Background: the urban Health Commission of Wuhan City, China, issued an emergency notice due to an incidence of viral pneumonia of unknown cause in December 2019. The World Health Organization officially named it the 2019 novel coronavirus. Since the course of the disease is not the same in the countries and regions of the world, and the study of this diversity is an important source of information for policymakers and researchers. The current study aims to cluster selected countries of the world based on its incidence and it was done according to the trajectories shape. Methods: the data set analyzed, included new cases of COVID-19(per million people) in 13 countries of the world, which were published on the World Health website from March, 2020, to October, 2021 monthly then analyzed by the k-means clustering method using Fréchet distance and R software V4.0.5. in addition, clustercrit and kmlshape packages were utilized for trajectory clustering. Result: Research results show that, 13 countries of the world were classified into 2 clusters with high and low incidence. The cluster with high incidence included 8(62%) countries. The 2 cluster exhibits the highest outlier in COVID-19 incidence. In the analyzed nations, United States and Brazil exhibited the highest incidence rate in clusters 1 and 2, respectively. Conclusion: The present findings showed that there are 2 patterns in the epidemic of COVID-19. The first pattern includes severe fluctuations and the next pattern includes low fluctuations. The results revealed that the method used in this article has the potential to understand incidence trends regardless of the time of disease onset. Since the Covid-19 infection process experiences fluctuations over time that vary based on when the pandemic began in each country, it's essential to analyze the similarity and shape of infection trends collectively, independent of their starting points.

Thermo-energetic performance of two dwellings during a heat wave in the Buenos Aires province, Argentina: On-site response assessment and adaptation strategies testing

This study evaluates the effects of heat waves on habitability conditions, energy consumption and cooling expenses through on-site measurements in two houses in Buenos Aires Province: an urban house located in La Plata, with low thermal quality of the envelope and two air conditioners; and a rural one in Brandsen, with an intermediate thermal quality of the envelope and one fan. Energy strategies were simulated on both houses that, simultaneously, allow the adaptation to heat waves and reduce, or generate the least possible, impact on electricity grids: a photovoltaic (PV) system and a ground source heat pump (GSHP) system. Both locations experienced similar average outdoor temperatures (∼28°C), but La Plata had fewer fluctuations due to heat accumulation in built surroundings. Indoor temperatures were comparable (∼29°C living spaces/∼27°C bedrooms), but the house in La Plata experienced lower thermal fluctuations due to its air-conditioning (ACs) and outdoor temperatures. Average heat index values were alike (∼30°C living spaces/∼29°C bedrooms). Although La Plata’s dwelling had ACs, results show the influence of envelope performance and nighttime ventilation in the Brandsen’s rural setting. Adaptation assessment revealed that PV and GSHP systems are adequate measures to face heat waves; however, they require important incentives to achieve massiveness.

The Petrology and Geochemistry of the 2021 Fagradalsfjall Eruption, Iceland: An Eruption Sourced From Multiple, Compositionally Diverse, Near‐Moho Sills

AbstractMagmatic processes at the crust‐mantle boundary (i.e., Moho) are commonly studied post facto at fossil ophiolites, oceanic core complexes, or inferred from the compositions of crystals or melt inclusions. The 2021 eruption at Fagradalsfjall on the Reykjanes Peninsula, Iceland, was supplied from magma bodies near the Moho and offers a unique opportunity to study the timescales, structure, and syn‐eruptive processes of near‐Moho magmatic systems at ∼15 km depth. Here, we present a comprehensive petrological and geochemical investigation of the full 183 day eruption that is based on frequent sampling of the eruption. Lavas erupted in the first 45 days displayed significant and sudden changes in geochemistry, followed by lower amplitude fluctuations until the end of the eruption. This variability can be explained by contribution from multiple magma bodies, as best distinguished using Sr‐Nd‐Hf‐Pb isotope systematics. The lavas display unusual trace element and radiogenic isotope compositions compared to other Icelandic basalts, but are similar to other rare, highly incompatible element enriched lavas on the Reykjanes Peninsula, and thus these lavas may represent a distinct suite of Reykjanes Peninsula basalts. Our geochemical and petrological observations show that numerous, compositionally variable bodies of magma must exist in the lowermost crust or at the crust‐mantle boundary. These near‐Moho magma bodies transfer magma between one another on timescales as short as days‐to‐months, but partially crystallize over longer time periods, and periodically inject into the overlying crust.

Reliable wavelength detection method of sapphire fiber Bragg gratings using added multimode fiber

Sapphire fiber Bragg gratings (SFBGs) are promising high-temperature sensors, which can be applied to measure temperature and strain in extreme environments. However, the multimode operation of SFBGs is susceptible to disturbance, leading to unreliable wavelength detection. Here, we propose by using added multimode fibers (AMMF) and tracing the longwave edge of reflection envelope to enhance the stability of wavelength detection for SFBG. The near-field profiles of transmission modes are investigated in sapphire fiber with different lengths of AMMF. It is found that the mode-field distribution of sapphire fiber can be improved by using AMMF with a length of 1000 m, which results in a reduction of relative standard deviation (RSD) from 57 % to 10 %. Then, the signal-to-noise ratio (SNR) in the reflection spectrum of SFBG is improved to 16 dB by polishing inclined end faces of sapphire fiber using the removal mechanism of hard-brittle materials. Furthermore, we detect the wavelengths of both the longwave edge and peak on the reflection envelope, which reveals lower fluctuations (i.e., SD = 0.02 nm) of the longwave edge, since lower-order modes are more stable during transmission. The effect of external disturbances (i.e., torsion and vibration) on demodulation of SFBG is also evaluated, with a maximum fluctuation of 0.06 nm (SD = 0.01 nm). A temperature experiment is carried out with the assembly and polynomial fitting curves with high fitness are obtained. Thus, our proposed methods enhance the reliability of wavelength detection in the reflection spectrum of SFBG, which is beneficial to improving the sensing performance of SFBG-based sensors.

A novel multi-objective robust optimization method based on improved Gray Wolf optimizer and Kriging model

A novel multi-objective robust optimization method for mechanical structure products is introduced. Firstly, the method introduces Bernoulli Dynamic Adaptive Gray Wolf Optimizer (BDAGWO) to address inherent limitations within the original Gray Wolf Optimizer (GWO), such as falling into local optimal solutions and low convergence rate. Subsequently, BDAGWO is utilized to search optimal correlation coefficients of Kriging. The proposed surrogate model exhibits high fitting accuracy on different test cases, with coefficients of determination reaching above 0.99 on the test set, and mean relative error, mean absolute error, mean absolute percentage error and root mean squared error are all close to 0. To solve multi-objective optimization problems, an improved NSGA-II is introduced to amplify search capabilities. Then based on robust optimization method, combine BDAGWO-Kriging and improved NSGA-II to establish a multi-objective robust optimization framework with high accuracy and high solution efficiency. The proposed method is implemented to an EMU bogie frame, demonstrating that the robust optimization scheme reduces the maximum equivalent stress and mass, and exhibits lower fluctuation compared to deterministic optimization. This substantiates the method’s effectiveness and provides an optimization approach of large and complex mechanical products.

Transcriptomic, mutational and structural bioinformatics approaches to explore the therapeutic role of FAP in predominant cancer types

Fibroblast activating protein (FAP) is a cell surface marker of cancer-associated fibroblasts with a distinct pro-tumorigenic role. The present study analyzed the pan-cancer expression; and clinical and mutational profiles of the FAP coding gene. Molecular dynamics simulation (MDS) deciphered the backbone dynamics and energetics of FAP. Virtual screening and subsequent pharmacokinetic-profiling (PK) filtered lead molecules, which were subjected to molecular docking. MDS projected a stable trajectory for the protein, as dynamics evidenced by low residue-level fluctuations, stable backbone dynamics, and energetics. Around five stabilization and deleterious mutations in the catalytic domain were identified. The low binding energy (BE) profiles from molecular docking studies screened the top five lead molecules for site-specific intermolecular interaction studies. Lead-16 (ZINC000245289699) exhibited a significant BE and inhibition constant of −6.87 kcal/mol and 12.27 μM, respectively, across FAP and its mutants. Interestingly, the docked complexes of Lead-16 interacted with the catalytic triad residues (S624, D702, and H734). The docked complexes of Lead-16 with FAP showed lower average root-mean-square fluctuations compared to the unbound protein, suggesting a stable ligand–protein complex. The tumor-specific expression and its critical overall survival suggest the inhibitors of FAP for potential cancer therapeutic intervention and hindering tumor microenvironment-driven cancer progression.

Impact of sample preparation on bitumen content measurement using laser-induced breakdown spectroscopy

The impact of sample preparation on bitumen content measurement using LIBS was investigated by collecting spectra from wet and dry tailings. A multivariate data analysis model was developed using optimal wavelength selection for bitumen content classification and prediction in tailings. Wet tailings can be classified into three classes (low, medium, and high bitumen) with 12.1 % error, while dry tailings have a classification error of 6.1 %. Quantitative analysis showed a bitumen content prediction error of 4.7 % for wet tailings and 8.9 % for dry tailings. Wet tailings showed a 1.8–2.5 times improvement in the limit of detection range compared to dry tailings. Plasma density and crater size measurements revealed that plasma density fluctuation was 2.7 times lower in wet tailings due to consistent crater formation from laser-tailings interaction. The lower plasma density fluctuation indicates a stable mass ablation for wet samples, which is attributed as the primary reason for significant LIBS performance improvement on wet tailings.

(Invited) Investigation of Plasma Defect Engineering in ZrO2-Based RRAM Devices for Low Power in-Memory Computing

Resistive random-access memory (RRAM) is a promising candidate to be used for in-memory computing applications due to its low power consumption, high scalability, high density, significant endurance, good retention, and low cost [1-2]. The usage of zirconium dioxide (ZrO2) as the switching layer in RRAM stacks has garnered attention due to its high thermodynamic stability, simple structure, and CMOS process compatibility [3-5]. In addition, introducing a hydrogen (H) plasma treatment to the switching layer reduced the forming power since it improved the oxygen vacancies (Vo) and defects distribution near the top electrode (TE) and switching layer interface [6].In this work, we investigated five ZrO2-based RRAM devices with titanium nitride (TiN) as top and bottom electrodes. Figure 1 shows the schematics for the devices that have been investigated in this work. Device-A that has stochiometric ZrO2 switching layer was compared with devices B, C, D, and E that have been treated with H-plasma. The treated devices vary by the insertion position of the plasma during the deposition of the oxide layer, optimized to reduce initial defect levels [7]. The oxide layer of device-B - 7nm of ZrO2 - was fully deposited, followed by the H-plasma. While device-C had the H-plasma at the midpoint of the 7nm oxide. Device-D oxide was deposited as 3.5nm ZrO2, followed by H-plasma followed by 4nm ZrO2 that has the interface with the TE. Device-E was deposited as 3.5nm ZrO2, followed by H-plasma, followed by 3nm ZrO2 that has the interface with the TE. We studied the leakage current of the devices to investigate the number of defects in the oxide layer since it plays a significant role in reducing the forming power, as previously reported [8]. While it is well-known that the devices with higher leakage current have more defects [9], devices from wafer-A (7nm stochiometric ZrO2) show the lowest leakage current. The devices were then formed with a DC voltage sweep and compliance current between 1nA-100mA and observed a trend between the leakage current (number of defects) and the forming power, device-A consumed the highest forming power (122mW) as expected due to fewer defects in the oxide layer, which matched with the leakage current measurements.The leakiest devices - device-C (TiN/3.5nmZrO2/H-plasma/3.5nmZrO2/TiN) and device-D (TiN/4nmZrO2/H-plasma/3.5nmZrO2/TiN) - consumed the lowest power while forming (0.46nW) and (0.24nW) respectively- which confirms that more defects inside the oxide layer support the electron transport and lead to reduction of the forming power [10]. While device-B (TiN/H-plasma/7nmZrO2/TiN) has higher leakage current and lower forming power (2.5nW) than device-E (TiN/3nmZrO2/H-plasma/3.5nmZrO2/TiN) that consumes (4.7nW) to form the conductive filament. A thinner layer can't passivate the defects, resulting in a uniform spread of Vo related defects throughout the dielectric thickness, which increases the forming voltage as observed in device-E (TiN/3nmZrO2/H-plasma/3.5nmZrO2/TiN). A thicker cap layer, on the other hand, allows for reoxidation, and the migration of oxygen vacancies towards the top, ultimately reducing the forming voltage as observed in device-D (TiN/4nmZrO2/H-plasma/3.5nmZrO2/TiN) [7].Since the devices are expected to be used in memory computing, a pulse operation was performed with amplitude variation to quantize the conductance states. Device-C and device-D show a good quantization, with no overlap between the states and low fluctuations for each state compared to device-B, which shows degraded fluctuations and overlap between the states due to reduced defect distribution near the interface with the TE [11]. REFERENCE: Waser, R. et al 2009 ECS Trans. 21(1) ,2632–2663Chen, Y. S. et al 2009 IEEE Int. Electron Devices Meeting pp.105-108Sun, B. et al 2009 Appl. 105:61630 Lin, C.-C. et al 2016 AIP Adv 6:35103 Lin, C.-L. et al 2016 AIP Adv 6:35103Patel, Y. et al 2021 ECS Trans. 104, 3Consiglio, S. et al 2021 ECS Trans. 102, 19Young-Fisher et al 2013 IEEE Electron Device Letters, vol. 34, no. 6Maleeswaran, P. et al 2013 Appl. Phys. 113, 184504Bersuker, G. et al 2011 Solid-State Electron. vols. 65–66, pp. 146–150Kim, B. et al 2022 Applied Surface Science 593 Figure 1

Crossover detection based on variances of slope differences for multi-fractal detrended fluctuation analysis (MF-DFA)

AbstractMulti-fractal detrended fluctuation analysis (MF-DFA) describes long-range correlations across scales in time series in terms of the linear fittings of the fluctuation functions $$F_q(s)$$ F q ( s ) for every moment q considered. It is relatively common to find time scales, also called crossovers, where different linearities are found at each side of the $$F_q(s)$$ F q ( s ) , leading to different (multi-)fractal properties. Generally, crossovers are manually identified by experts, subject then to their skills and possible bias. In this work, a novel methodology based on the variances of the slope differences (CDV-A) is proposed to automatically detect crossovers. A set of synthetic fluctuation functions with different mono-fractal and multi-fractal features, different noise levels and known crossovers is generated to assess the CDV-A. Moreover, the proposed methodology is also applied to state of the art fluctuation functions in order to compare the proposed manual and automated locations of the crossovers. Results show the proposed methodology is highly accurate, specially in low noise synthetic fluctuation functions and in most of the real cases.

Effects of the intrayear fluctuation in serum albumin levels on the prognosis of patients on hemodialysis: a six-center, 3-year observational study in Japan

BackgroundMalnutrition and hypoalbuminemia are prognostic factors for mortality in patients undergoing hemodialysis (HD). We hypothesized that the monthly variability in albumin levels was a stronger prognostic factor than its baseline level. Therefore, in this study, we aimed to investigate the association between the 3-year prognosis and 1-year fluctuations in serum albumin levels of patients on HD.MethodsIn this retrospective follow-up study, we enrolled patients on HD from six centers between January 2014 and December 2014. Serum albumin levels were obtained every month during this period. We calculated the fluctuation rate of serum albumin levels using the following equation: (highest value – lowest value of the year)/average value of albumin during the year. We divided the patients into two groups on the basis of the median fluctuation rate. All patients were followed up from January 2015 to January 2017. The primary outcome of this study was all-cause mortality, and the secondary outcome was the combined endpoint of mortality and major cardio-cerebrovascular adverse events (MACCEs).ResultsOverall, 621 patients on HD were included in this study. The median rate of fluctuation in albumin levels in all patients was 14.5%. Compared with patients in the low fluctuation (LF) group (< 14.5%) (n = 308), those in the high fluctuation (HF) group (≥ 14.5%) (n = 313) were significantly older (67 versus 65 years; p = 0.009). During a mean follow-up period of 31.0 ± 10.1 months, there were 121 (19.5%) and 191 (30.5%) cases of all-cause death and combined endpoint, respectively. Kaplan–Meier analysis results showed that the HF group had a significantly worse prognosis in terms of all-cause mortality (log-rank test, p = 0.005) and combined endpoints (log-rank test, p = 0.001) than the LF group. Among patients with low albumin levels (< 3.7 g/dL), multivariate analysis results demonstrated that HF was independently associated with an increased risk of all-cause mortality and composite endpoint (hazard ratio [HR]: 1.02, 95% confidence interval [CI] 1.00–1.04, p = 0.011; HR: 1.02, 95% CI 1.00–1.03, p = 0.002).ConclusionIn patients on HD, high fluctuations in serum albumin levels were significantly associated with increased all-cause mortality risk. Thus, attention should be paid not only to the monthly albumin level but also to its fluctuations.

Investigation on the development of Novel PAM structure as high-performance clay inhibitor in HT/HP conditions by using functional groups

Polyacrylamide (PAM) molecular structures are not stable at higher temperatures, limiting their effectiveness in water-based drilling fluid (WBDF) applications. This study focuses on identifying functional groups that can enhance the thermal stability of PAM structure. It presents a comprehensive and comparative analysis of the swelling inhibition capacity of both low and high molecular weight (MW) PAM-based polymers at high temperature and high pressure (HT/HP). The mechanism by which modified PAM interacts with and adsorbs to the clay (Na-MMT) surface is examined, along with an analysis of the surface properties of the clay minerals. Therefore, molecular dynamic (MD) simulation results revealed that the Novel PAM polymers with high MW containing ethyl and benzyl groups demonstrate greater stability and more controlled deformation behavior compared to conventional PAM at HT conditions. Surface modification and hydrophobic effects were observed in the interactions between polymers and Na-MMT. Low MW PAM-based polymers showed larger fluctuations in d-spacing, indicating various or more disruptive interactions. In contrast, the insertion of high MW PAM-based polymer resulted in significantly narrower and lower d-spacing fluctuation ranges. The Novel PAM exhibited superior performance, providing lower d-spacing fluctuations compared to both low MW PAM-based polymers and standard PAM. Across a temperature range from 300 to 600°K, the Novel PAM consistently improved polymer adsorption on the Na-MMT surface compared to standard PAM. Indicating that the modifications effectively reduce the interaction between Na-MMT particles and water molecules while enhancing the interaction between the polymer and Na-MMT, potentially leading to more stable structures in HT/HP environments. The enhanced stability suggests that Novel PAM is more suitable for WBDF applications, as it is less prone to structural changes and can maintain its integrity under harsh conditions. The modifications provide the polymer with better resilience, potentially leading to improved performance in practical applications.