Variation In Level Research Articles

Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning

Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level and variation in accuracy with noise level was determined for 5 machine learning algorithms. These attributes were compared to the traditional method of post-processing and a 6x–36 improvement in model performance was noted, depending on the dataset. No algorithm was a clear winner when considering these 4 criteria, as the lowest-error model (neural network) was also the slowest predictor; the algorithm with the lowest overfitting and fastest prediction time (linear regression) had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble algorithm was judged to be the best tradeoff between these criteria due to its error level, prediction time and low variation of error with noise. For the first time, a machine learning model was validated using a 2-qubit datapoint obtained from an IBMQ quantum computer. The best 2-qubit model predicted within 2% of the actual phase, while the traditional method possessed a 25% error.

LcDel: deletion variation detection based on clustering and long reads

Motivation: Genomic structural variation refers to chromosomal level variations such as genome rearrangement or insertion/deletion, which typically involve larger DNA fragments compared to single nucleotide variations. Deletion is a common type of structural variants in the genome, which may lead to mangy diseases, so the detection of deletions can help to gain insights into the pathogenesis of diseases and provide accurate information for disease diagnosis, treatment, and prevention. Many tools exist for deletion variant detection, but they are still inadequate in some aspects, and most of them ignore the presence of chimeric variants in clustering, resulting in less precise clustering results.Results: In this paper, we present LcDel, which can detect deletion variation based on clustering and long reads. LcDel first finds the candidate deletion sites and then performs the first clustering step using two clustering methods (sliding window-based and coverage-based, respectively) based on the length of the deletion. After that, LcDel immediately uses the second clustering by hierarchical clustering to determine the location and length of the deletion. LcDel is benchmarked against some other structural variation detection tools on multiple datasets, and the results show that LcDel has better detection performance for deletion. The source code is available in https://github.com/cyq1314woaini/LcDel.

Concept Design of a 15 MW TLP-Type Floating Wind Platform for Korean Offshore Installation

Offshore wind farms on the east offshore of Korea to produce multi-GW power from floating wind platforms are being planned. The objectives of the present study are to develop a new TLP-type floating wind platform with a 15 MW turbine for the planned site and to confirm the feasibility of the TLP design under extreme typhoon environments. The concept design of the 15 MW TLP floating platform was completed for installation at a water depth of 137 m. The platform was vertically moored with highly pretensioned wire rope tendons. The platform and tendons were designed to withstand extreme conditions for up to 50 years. Additionally, a platform with an integrated turbine was designed to be wet-towable from the quayside without dedicated vessels to minimize the pre-service cost and risk. An extreme response analysis was conducted to evaluate the platform motion, acceleration, airgap and tendon tension for wave variation, intact and damaged condition of the tendon, environment heading change, and water level variation. The platform design results were validated using the design criteria from the industry standards and recommendations, and the design was verified to comply with the design requirements for the planned sites.

Adaptive Evolution of the Greater Horseshoe Bat AANAT: Insights into the Link between AANAT and Hibernation Rhythms

Arylalkylamine N-acetyltransferase (AANAT) is a crucial rate-limiting enzyme in the synthesis of melatonin. AANAT has been confirmed to be independently duplicated and inactivated in different animal taxa in order to adapt to the environment. However, the evolutionary forces associated with having a single copy of AANAT remain unclear. The greater horseshoe bat has a single copy of AANAT but exhibits different hibernation rhythms in various populations. We analyzed the adaptive evolution at the gene and protein levels of AANAT from three distinct genetic lineages in China: northeast (NE), central east (CE), and southwest (SW). The results revealed greater genetic diversity in the AANAT loci of the NE and CE lineage populations that have longer hibernation times, and there were two positive selection loci. The catalytic capacity of AANAT in the Liaoning population that underwent positive selection was significantly higher than that of the Yunnan population (p < 0.05). This difference may be related to the lower proportion of α helix and the variation in two interface residues. The adaptive evolution of AANAT was significantly correlated with climate and environment (p < 0.05). After controlling for geographical factors (latitude and altitude), the evolution of AANAT by the negative temperature factor was represented by the monthly mean temperature (r = −0.6, p < 0.05). The results identified the gene level variation, functional adaptation, and evolutionary driving factors of AANAT, provide an important foundation for further understanding the adaptive evolution of the single copy of AANAT in pteropods, and may offer evidence for adaptive hibernation rhythms in bats.

Asymmetry response of storm surges along the eastern coast of the Taiwan Strait

Spatiotemporal variation of storm surges in the Taiwan Strait (TWS) is studied using water level datasets from 14 tidal gauge stations located in the TWS from summer to fall of 2016. The effects of bathymetry on storm surges and tropical cyclone (TC)-induced continental shelf wave (CSW) are explored. By comparing water level response along the east coast of TWS, it is found that storm surges are asymmetric on the north and south sides of the Zhan-Yun Ridge (ZYR), regardless of the different categories and tracks of TCs passing by. Observations indicate that the ZYR could modulate the storm surges and the CSW propagation; ZYR can not only amplify the storm surges that generally peak around the ZYR, but also block the CSW propagation by dramatically dissipating its kinetic energy as revealed by the dispersion relation for the first mode CSW. Moreover, local wind work and the remote forcing, which are induced by TCs and cold fronts, respectively, can also modulate the synoptic variations of water level in the TWS.

Enrichment mechanism of selenium in Lujiaping formation in central South Qinling: Controls from mixed sources, sedimentary environment, primary productivity and paleo-climate during Ediacaran to early Cambrian period

The Ediacaran to lower Cambrian Lujiaping Formation in South Qinling exhibited extremely high level of Se enrichment, which has provoked extensive concerns since the critical role of Se in paleo-biological evolution and diversification as well as its bidirectional effects on current ecological environment and human health. Because of complex geological setting within South Qinling during the Ediacaran–early Cambrian interval, distribution pattern and mechanism of Se enrichment in Lujiaping Formation have not been fully understood. In this study, therefore, stratigraphic variations of Se concentration and enrichment level together with enrichment mechanism in Lujiaping Formation in the central part of South Qinling were systematically explored. The result showed that, in the whole Lujiaping Formation, Se concentrations varied from 0.19 to 129 μg/g, with an average of 13.28 μg/g. According to the enrichment factor, most of samples can be classified as strong enrichment and anomaly levels (EF = 10–1000) which mainly occurred in organic matter-enriched siliciclatic rocks including mudstone, shale and stone coal seams. Meanwhile, Se enrichment was accompanied with enrichment of Ag, Mo, As, Cd and V in Lujiaping Formation. Various geochemical proxies were collectively employed to identify potential mechanism of Se enrichment. The results indicated that Se was derived from both terrestrial detrital materials and deep hydrothermal fluids. Strong Se anomaly was intimately linked to authigenic enrichment of U and Mo, manifesting advantageous role of euxinic benthic condition to Se accumulation. Besides, weak watermass restriction, intermittent upwelling, high primary productivity and warm and humid climate were also beneficial to ultimate Se enrichment in Lujiaping Formation. This study complemented fundamental data of Se concentration and enrichment degree in strata of the Ediacaran–early Cambrian period in central South Qinling. Meanwhile, preliminary insight of correlation among Se enrichment, sedimentary environment, paleo-climate and biological blooming during the Ediacaran to early Cambrian interval was obtained, which laid the foundation for further researches.

Investigating Carded Yarn Quality through Cross-Doubling in the Draw frame: A Comprehensive Analysis

A carding machine's performance has an impact on the quality of the carded sliver, which in turn affects the quality of the carded yarn produced. Several factors can influence a machine's efficiency, resulting in fluctuations in the slivers generated over time. To achieve consistent and reproducible quality levels, slivers from all carding machines must be blended homogeneously. This blending process is vital for achieving homogeneity in the final product and maintaining a high quality standard. This can be achieved by cross-doubling. In this technique, carded slivers are arranged into categories of good, medium and low quality based on the nep removal efficiency of the carding machine behind the breaker draw frame. This is a specific type of doubling process. The focus of this research was the effectiveness of the aforementioned practice. Three carding machines were selected out of seven based on the quality level of the produced carded sliver, which was measured by nep removal efficiency. Yarn samples of 29.5 tex were produced from the slivers of the individual carding machines and by cross-doubling them at the breaker draw frame. Samples were collected at each stage of processing and tested. The results showed that cross-doubling significantly reduced variation in the imperfection level and yarn strength, but had very little effect on the mass variation level of the strands. Furthermore, the categorization of the four samples for selection was carried out using the Analytic Hierarchy Process method. This study will provide a guideline for new spinners to enhance yarn quality through cross-doubling on a draw frame machine.

Experimental Comparison Of The Hydraulic Performance Of Overhanging And Vertical Parapets Under Limited Wave Breaking Conditions: The Case Of The New Offshore Ravenna Lng Terminal (It)

Harbors play a pivotal role in global trade, serving as vital gateways for the transportation of goods, fostering economic growth. These essential coastal infrastructures are subjected to relentless forces (e.g. wave action, storm surges, and sea level variation induced by climate change) which can jeopardize their functionality and safety. Safe working conditions are mandatory for the operability of all kinds of harbors. However, particular attention must be paid in the case of terminals dedicated to dangerous goods, as for example Liquefied Natural Gas (LNG). Composite vertical breakwaters may be efficiently used to protect this kind of terminal and have both advantages and disadvantages over rubble mound breakwaters. This work presents the results of a 2D experimental campaign carried out to optimize, from a hydraulic point of view, the parapet wall of the new composite vertical breakwater that will be built to protect the new offshore LNG terminal located in in the North Adriatic Sea in the South-East of the Port of Ravenna (Italy). In particular, two types of parapets walls have been compared: one overhanging (recurved in the shape of one fourth of a circumference with a ray of one meter) and one vertical. For each type of parapets, three different heights were evaluated for a total of six different configurations. The typical water depth in the area where the new terminal will be built is approximately -15.0 m with respect to the MSL and the total range of the astronomical tides does not exceed one metre. The terminal is located about 8.0 km far from the coast and is positioned on a very mild seabed foreshore slope of less than 0.05°

Variation of groundwater level due to land use, precipitation, and earthquake in Yogyakarta City from 2005 to 2020

Yogyakarta City not only experiences rapid land use conversion and precipitation decline but also experiences frequent earthquake events. These factors can have a significant impact on groundwater variation dynamics in this city. Therefore, the aim of this research is to analyze the factors that can influence groundwater oscillation based on precipitation, land use, earthquake, and river discharge changes. The method used to analyze spatio-temporal groundwater level and precipitation are by applying space-time Bayesian statistics since Bayesian has lower root mean square error compared to the space-time variogram and inverse distance methods. Land use is classified with false color composite for Landsat ETM and infrared color composite for Landsat 8 since these color composites well classify built-up and non-built-up areas. River discharge is analyzed as a primary indication of earthquake event impact on groundwater level variation.There is no significant variation of groundwater level from 2005 to 2020. Groundwater fluctuation is classified as stable since the magnitude of change is approximately less than 1.5 m from dry to wet period. It is also probably because there is abundances of groundwater resources in Yogyakarta City that shallow aquifer have very high hydraulic conductivity. The conversion speed from non-built-up area into built-up area is 26% from 2005 to 2015, while 8% from 2015 to 2020. At least 78% of Yogyakarta City is built-up areas in 2020. Abrupt changes in groundwater level is due to: a) declining trends of precipitation on May 2009 to July 2016 with average accumulative monthly residual rainfall ranging from 500 mm month−1 to 1300 mm month−1, and b) high magnitude earthquake occurrences on May 2006, November 2010 and 2015 when groundwater patterns and levels including discharge rivers showed distinct behavior. This study shows that precipitation and earthquake are the major contribution to groundwater level variation compared to land use.

Analysis of the contribution of obstructive sleep apnea/hypopnea syndrome and glycemic level variability to the development and progression of cardiac arrhythmias in patients with type 2 diabetes mellitus

In this review, information is presented within the triad: obstructive sleep apnea/hypopnea syndrome (OSA), glycemic variability, and cardiac arrhythmias in patients with type 2 diabetes mellitus (DM2). Epidemiological aspects, pathogenetic relationships, possible instrumental and laboratory diagnostic methods, as well as approaches to personalized therapy are analyzed. Research is being actively conducted in certain areas of the designated triad, however, no studies have been found that include simultaneous monitoring of indicators reflecting these disorders in patients with DM2. Many issues are still controversial. Sleep disturbances in patients with DM2 are actively studied, but more often questionnaires are used for diagnosis, rather than instrumental methods. There is insufficient data examining the effect of hypoxia on the progression of complications in patients with DM2. Rhythm disturbances are being actively studied in patients with DM2 in combination with various cardiological problems. Of greatest interest is the study of rhythm disturbances in patients with DM2 without concomitant comorbid conditions of the cardiovascular system, in order to identify early signs of diabetic cardiovascular autonomic neuropathy and cardiomyopathy, as well as additional early risk factors for the development and progression of cardiovascular diseases. Most of the studies are devoted to the study of the association of OSA and various arrhythmias in cardiac patients. However, there is no data on the combined effect of glycemic variability and OSA on the development of cardiac arrhythmias in patients with DM2. Additional studies are needed to identify the features of the effect of OSA on cardiac arrhythmias in patients with DM2.

A new model to predict the small-hole decompression process of long CO2 pipeline

CO2 pipeline transportation is considered as an economical and safe way of moving large quantities of CO2 in Carbon Capture Utilization and Storage (CCUS) projects. However, design and planning of CO2 pipelines passing through populated areas require careful risk assessment of accidental pipeline failure scenarios leading to CO2 pipeline decompression. In this study, a new zero-dimensional multi-stage outflow model describing transient decompression of CO2 is developed. The model accounts for the experimentally observed liquid level variation along the pipeline and uses the liquid volume fraction threshold parameter (αt) to mark the end of the saturated liquid flow decompression stage. Besides, the Lee model is applied to describe the mass transfer in gas-liquid phases. The model was validated against experimental data obtained large-scale pipeline decompression tests and can be recommended for simulation of outflow of supercritical and liquid CO2 from pipelines longer than 258 m and the orifice-to-pipeline diameter ratios smaller than 0.21. The advantage of this model lies in its use of the relatively simple Vessel Blowdown Model (VBM) to accurately model the depressurization process. It addresses the inconvenience posed by the heavy computational burden of high-dimensional models when predicting long-duration leakages in large-capacity pipelines.

Trade-off analysis between gm/ID and fT of GNR-FETs with single-gate and double-gate device structure

This study examines the operational parameters of field-effect transistors (FETs) using single-gate (SG) and double-gate (DG) graphene nanoribbons (GNRs) within the analog/RF domain. A detailed exploration is conducted through an atomistic pz orbital model, derived from the Hamiltonian of graphene nanoribbons, employing the nonequilibrium Green’s function formalism (NEGF) for analysis. The atomic characteristics of the GNRFETs channel are accurately described by utilizing a tight-binding Hamiltonian with an atomistic pz orbital basis set. The primary focus of the analysis revolves around essential analog/RF parameters such as transconductance, transconductance generation factor (TGF), output resistance, early voltage, intrinsic gain, gate capacitance, cut-off frequency, and transit time. Furthermore, the study assesses the gain frequency product (GFP), transfer frequency product (TFP), and gain transfer frequency product (GTFP) to evaluate the balance between transistor efficiency, gain, and cut-off frequency. The research outcomes indicate that double-gate GNRFETs exhibit superior analog/RF performance in comparison to their single-gate counterparts. However, both types of devices demonstrate cut-off frequencies in the gigahertz range. The extensive data presented in this study provides valuable insights into the characteristics of SG and DG GNRFETs, particularly in terms of the figure-of-merit (FoM) for analog/RF performance, offering a comprehensive analysis of the trade-offs in analog applications. In addition, the analysis has been extended be performing a high-performance hybrid 6T static random-access memory (SRAM) to get the impact in their circuit level variation as well as improvement in their circuit performance.

The use of once-daily LCP-Tacrolimus with adolescent and young adult solid organ transplant recipients.

Adolescent and young adult (AYA) solid organ transplant (SOT) recipients experience increased rates of rejection and graft loss surrounding the time of health care transition, in part due to poor medication adherence. This study aims to examine the impact of a once-daily formulation of tacrolimus, LCP-tacrolimus (LCPT), on medication adherence for AYA SOT patients. A retrospective descriptive analysis was performed for all patients who underwent SOT and were prescribed LCPT after the age of 12 at our single-center pediatric hospital. Medication adherence was assessed via provider documentation and the medication level variability index (MLVI). Twenty-nine patients were prescribed LCPT as part of their immunosuppression regimen. Twenty patients were converted to LCPT from immediate-acting (IR) tacrolimus; six patients were initiated immediately following transplant, and three patients were unable to receive LCPT due to insurance denial. There was a numeric improvement in medication adherence for converted patients when measured by provider assessment (45.0% vs. 68.4%, p = .140) and MLVI (40.0% vs. 71.4%, p = .276), though these did not reach statistical significance. There were no differences in episodes of rejection or adverse effects. LCPT prescription was not associated with decreased medication burden, and two patients transitioned back to IR tacrolimus due to increased cost. LCPT use did not significantly improve patient adherence; however, it resulted in numerically higher perceived and measured adherence rates. LCPT appears to be safe and effective in the management of SOT recipients; however, it may not affect pill burden and may result in a higher financial burden. Use may be considered for a select group of AYA SOT recipients.

Analysis of sea level variability and its contributions in the Bohai, Yellow Sea, and East China Sea

Understanding the sea level variability of the Bohai, Yellow Sea, and East China Sea (BYECS) is crucial for the socio-cultural and natural ecosystems of the coastal regions. In this study, based on satellite altimetry data, selected time range from 1993 to 2020, using the cyclostationary empirical orthogonal function (CSEOF) analysis method distinguishes the primary sea level variability modes. The analysis encompasses the seasonal signal, trend, and El Niño-Southern Oscillation (ENSO) associated mode of sea level anomaly. The amplitude of the annual cycle demonstrates a non-stationary signal, fluctuating between -15% and 15% from the average. Monsoons, atmospheric forcing, ocean circulation, wind-driven Ekman transport, and the Kuroshio emerge as the primary factors influencing BYECS variability on seasonal scales. The satellite altimetry sea level exhibits an average trend within the range of 3-4 mm/year, while the steric sea level trend is generally smaller, falling within the range of 0-2 mm/year. Throughout the entire period, the contribution of steric sea level to the mean sea level trend consistently remains below 25%. Furthermore, BYECS sea level variations have a sensitive response to strong El Niño years, with a clear regionalization of the response, which is related to the intricate atmospheric circulation and local wind pressures, as well as the influence of ocean circulation. In conclusion, we gained a more comprehensive understanding of sea level variability in the BYECS, especially the annual cycle of sea level amplitude and the response of ENSO. However, more studies still need to be done to differentiate the various factors in sea level variations.

Using Shelf‐Edge Transport Composition and Sensitivity Experiments to Understand Processes Driving Sea Level on the Northwest European Shelf

AbstractVariability in ocean currents, temperature and salinity drive dynamic sea level (DSL) variability on the Northwest European Shelf (NWES). It is dominated by mass variations, with steric signals relatively small. A mechanistic explanation of how ocean dynamics relates to the mass component of NWES sea level variability is required. We use regional ocean model experiments to isolate sources of variability and then investigate the effect on monthly to‐interannual DSL variability together with the simulated momentum budgets along the shelfbreak. Regional (local) wind and non‐regional (remote) forcing are important on the NWES. For the local wind forcing, the net mass flux onto the shelf, which drives a shelf‐mean mode of DSL variability, results from a combination of surface Ekman, bottom Ekman and geostrophic flows and explains 73% of the variance in transport across the shelf‐edge. The geostrophic flow is closely related to wind stress with a flow about half that of surface Ekman transport but in the opposite direction. For the remotely forced mass‐flux across the shelf‐edge, the geostrophic component explains 62% of the variance and bottom friction plays an important indirect role. The remotely forced variability, while relatively spatially uniform, is more important for explaining DSL variance over the western NWES. This mode of variability is sensitive to signals propagating northward via a thin strip of the southern boundary near the Portuguese coast, consistent with coastal trapped wave signals. It also appears to drive steric height in the Bay of Biscay, which is related to DSL on the shelf.

Design and experimental analysis of a new vertical ultra-low-head hydro turbine with the variation of outlet flow level on the head drop section of an open canal

Design and experimental analysis of a new vertical ultra-low-head hydro turbine with the variation of outlet flow level on the head drop section of an open canal

Seaway restriction, sea level drop and erosion in the Alboran Basin from a paleotopographic reconstruction for the Messinian Salinity Crisis

The Messinian Salinity Crisis (MSC) resulted from changes in the Atlantic-Mediterranean connectivity in the Alboran Basin, a region with a complex and debated geodynamic configuration. Since the MSC, this basin's topography and its record of the Messinian Erosional Surface have been subject to vertical motions due to sediment accumulation, tectonic deformation, isostasy, and latent effects of thermal cooling after extension and magmatic arc formation. The objective of this work is to restore these contributions to post-Messinian subsidence in order to quantify the original depth of formation of the MSC features. We do this by performing a pseudo-3D planform flexural isostatic reconstruction of the Messinian Erosion Surface mapped from an extensive set of seismic reflection data. We focus on identifying the most likely position of the gateway between Atlantic and Mediterranean, the effect of a drawdown on gateway topography and connectivity, and the depth of proposed erosional features related to the Messinian lowstand. The results indicate that the depth of the Alboran Basin by the end of the Messinian was about 500 m shallower than nowadays, but over 500 m deep on average, reaching depths of >1000 m in most subbasins, even when accounting for the possible ~300 m isostatic rebound caused by water unloading in a largely desiccated Alboran Sea during the MSC. Although these results are compatible with volcanic cones locally emerging above sea level at the East Alboran Volcanic Arc during the Messinian, several lows remaining in the reconstruction suggest that that region is unlikely to have been the sill between Atlantic and Mediterranean at that time, unless the basin saw unconstrained dynamic topography contributions of over −500 m. Full desiccation of the Alboran Basin implies an uplift of up to 100 m at the Strait of Gibraltar, and uplift rates too high to be counteracted by erosion, suggesting that full disconnection and the main corresponding evaporative drawdown took place only once. The terraces and canyons in the West Alboran are restored to depths between 250 and 550 m (shallowest terrace) and 750–1500 m (deepest terrace), and cannot be clearly linked to a single, stable water level during the MSC, pointing to climate-controlled variations in the water level during the isolation phase.

Region-specific and age-related differences in astrocytes in the human brain

Astrocyte heterogeneity and its relation to aging in the normal human brain remain poorly understood. We here analyzed astrocytes in gray and white matter brain tissues obtained from donors ranging in age between the neonatal period to over 100 years. We show that astrocytes are differently distributed with higher density in the white matter. This regional difference in cellular density becomes less prominent with age. Additionally, we confirm the presence of morphologically distinct astrocytes, with gray matter astrocytes being morphologically more complex. Notably, gray matter astrocytes morphologically change with age, while white matter astrocytes remain relatively consistent in morphology. Using regional mass spectrometry-based proteomics, we did, however, identify astrocyte specific proteins with regional differences in abundance, reflecting variation in cellular density or expression level. Importantly, the expression of some astrocyte specific proteins region-dependently decreases with age. Taken together, we provide insights into region- and age-related differences in astrocytes in the human brain.

MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure.

To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings. A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV). Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively. The findings suggest high potential for SPG-based BP estimation at the fingertip. This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.

Causality Analysis and Prediction of Riverine Algal Blooms by Combining Empirical Dynamic Modeling and Machine Learning Techniques

AbstractRiver algal blooms have become a global environmental problem due to their large impact range and environmental hazards. However, the complex mechanisms underlying these blooms make prediction and prevention challenging. Here, we employed empirical dynamic modeling (EDM) and machine learning to reveal the causes and predict diatom blooms from 2003 to 2017 in the Han River of China. The diatom cell density ranged from 0.1 to 5.1 × 107 cells L−1, whereas algal blooms often lasted for 10 days with density exceeding 107 cells L−1. The EDM results elucidated that, under consistent high nutrient concentrations, algal blooms were primarily regulated by eight environmental factors: water temperature in the Han River; water levels, flow velocities, and streamflow discharges in the Han River and the Yangtze River; and water level variation in the Han River. The poor performance (coefficient of determination R2 < 0) of the multiple linear regression, EDM, and random forest models indicated the challenge of predicting algae density. Therefore, we used machine learning classification models to predict algal blooms occurrences. With the resampling techniques to account for imbalanced data, machine learning models achieved perfect classification prediction (Kappa value = 1) of the 13 algae‐bloom events with a 10‐day lead time during a 15‐year period, providing an important reference for preemptive warning of riverine algal blooms.