Reliable Estimates Research Articles

How Many Deaths Can Statistically Be Attributed to Anti-SARS-CoV-2 Injections? An Analysis of German Health Data from 2021

While the efficacies of the newly developed mRNA and vector vaccines against SARS-CoV-2 have been widely advertised, their harm-to-benefit ratio remains almost completely ignored, though reports of possible “side effects” (some of them lethal) keep piling up. The most severe of those adverse events is sudden death. Until now, to the best of our knowledge, in Germany at least reliable estimates of how many deaths may actually have been caused by SARS-CoV-2 vaccination are still missing. Here, we fill this void and provide such an estimate for Germany during the course of 2021. Thereto, the number of deaths reported by the Paul-Ehrlich-Institut to have occurred within the group of persons suspectedly affected by a vaccine-induced adverse event is scaled by the factor of under-reporting, based on health insurance reports, and finally corrected by known all-cause mortality. Our best estimate for the year 2021 is that 16,817 deaths were caused by SARS-CoV-2 vaccination within the short-term observation period of 50 days after the last injection of a vaccine. Taking independent autopsy reports into account, the estimate of 11,194 deaths is a lower bound. Our hope is that this report may serve as a pivot for further investigations of the questions addressed here.

External validation of the KFRE and Grams prediction models for kidney failure and death in a Spanish cohort of patients with advanced chronic kidney disease.

The Kidney Failure Risk Equation (KFRE) is a 2- and 5-year kidney failure prediction model that is applied in chronic kidney disease (CKD) G3 + . The Grams model predicts kidney failure and death at 2 and 4years in CKD G4 + . There are limited external validations of the Grams model, especially for predicting mortality before kidney failure. We performed an external validation of the Grams and Kidney Failure Risk Equation prediction models in incident patients with CKD G4 + at Hospital Universitario Fundación Alcorcón, Spain, between 1/1/2014 and 31/12/2018, ending follow-up on 30/09/2023. Discrimination was performed calculating the area under the receiver-operating characteristiccurve. Calibration was assessed using the Hosmer-Lemeshow test and the Brier score. The study included 339 patients (mean age 72.2 ± 12.7years and baseline estimated glomerular filtration rate 20.6 ± 5.0ml/min). Both models showed excellent discrimination. The area under the curve (AUC) for Kidney Failure Risk Equation-2 and Grams-2 were 0.894 (95% CI 0.857-0.931) and 0.897 (95%CI 0.859-0.935), respectively. For Grams-4 the AUC was 0.841 (95%CI 0.798-0.883), and for Kidney Failure Risk Equation-5 it was 0.823 (95% CI 0.779-0.867). For death before kidney failure, the Grams model showed acceptable discrimination (AUC 0.708 (95% CI 0.626-0.790) and 0.744 (95% CI 0.683-0.804) for Grams-2 and Grams-4, respectively). Both models presented excellent calibration for predicting kidney failure. Grams model calibration to estimate mortality before kidney failure was also excellent. In all cases, Hosmer-Lemeshow test resulted in a p-value greater than 0.05, and the Brier score was less than 0.20. In a cohort of patients with CKD G4 + from southern Europe, both the Grams and Kidney Failure Risk Equation models are accurate in estimating the risk of kidney failure. Additionally, the Grams model provides a reliable estimate of the risk of mortality before kidney failure.

Assessment of approach separation with probabilistic aircraft wake vortex recognition via deep learning

Compared to static aircraft wake separation during landing/departure, reducing separation minima related to wake turbulence without compromising safety has initially demonstrated operational benefits, while dynamic pairwise separation remains under development. This paper proposes a two-stage probabilistic deep learning framework for wake vortex recognition and duration assessment, using wake images from the Light Detection and Ranging (LiDAR) instrument at Hong Kong International Airport. The first stage consists of vortex core locating utilising the Convolutional Neural Network (CNN), and the second stage predicts vortex strength within the Region of Interest (ROI), derived from raw images based on the initial core locating results. The existence of vortices is assessed upon the reliable probabilistic estimation of vortex movement under specific wind conditions and the estimation of its endurance in the final approach path. Furthermore, the prediction uncertainty is explained from the feature analysis aspect. Computational results indicate that the proposed two-stage CNN framework excels in estimating the spatial features and strength of coupled vortices. The wake duration measurement suggests a high potential for separation minima reduction when the crosswind exceeds (2–3)m/s. This will establishe conditions for onboard real-time wake monitoring, and the development of dynamic pairwise and meteorologically-related aircraft separation systems.

Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

ICESat-2 noise filtering using a point cloud neural network

The ATLAS sensor onboard the ICESat-2 satellite is a photon-counting lidar (PCL) with a primary mission to map Earth's ice sheets. A secondary goal of the mission is to provide vegetation and terrain elevations, which are essential for calculating the planet's biomass carbon reserves. A drawback of ATLAS is that the sensor does not provide reliable terrain height estimates in dense, high-closure forests because only a few photons reach the ground through the canopy and return to the detector. This low penetration translates into lower accuracy for the resultant terrain model. Tropical forest measurements with ATLAS have an additional problem estimating top of canopy because of frequent atmospheric phenomena such as fog and low clouds that can be misinterpreted as top of the canopy. To alleviate these issues, we propose using a ConvPoint neural network for 3D point clouds and high-density airborne lidar as training data to classify vegetation and terrain returns from ATLAS. The semantic segmentation network provides excellent results and could be used in parallel with the current ATL08 noise filtering algorithms, especially in areas with dense vegetation. We use high-density airborne lidar data acquired along ICESat-2 transects in Central American forests as a ground reference for training the neural network to distinguish between noise photons and photons lying between the terrain and the top of the canopy. Each photon event receives a label (noise or signal) in the test phase, providing automated noise-filtering of the ATL03 data. The terrain and top of canopy elevations are subsequently aggregated in 100 m segments using a series of iterative smoothing filters. We demonstrate improved estimates for both terrain and top of canopy elevations compared to the ATL08 100 m segment estimates. The neural network (NN) noise filtering reliably eliminated outlier top of canopy estimates caused by low clouds, and aggregated root mean square error (RMSE) decreased from 7.7 m for ATL08 to 3.7 m for NN prediction (18 test profiles aggregated). For terrain elevations, RMSE decreased from 5.2 m for ATL08 to 3.3 m for the NN prediction, compared to airborne lidar reference profiles.

Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments

Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.

Seismic behavior of eccentric new RCS joints with through columns

Architectural requirements often result in beam-column joints with eccentrically framing beams. However, the fabrication of eccentric through beam-type reinforced concrete column and steel beam (RCS) joint is extremely complex due to the presence of column longitudinal reinforcement and joint confinement reinforcement. To mitigate the constructability issues associated with through-beam type RCS eccentric joints, the current study proposed a through-column type New RCS joint. The proposed New RCS joint used joint cover plates and continuity plates as additional attachments while eliminating the need for joint confinement reinforcement. Two large-scale beam column subassembly specimens were tested to evaluate the seismic behavior. Both specimens had high-strength reinforced concrete columns (New RC columns) with a design concrete grade of 84 MPa and steel reinforcement of specified yield stress 690 MPa. The experimental results revealed the asymmetric shear stress development in the joint cover plates due to joint eccentricity. Based on experimental observations, a joint shear force-deformation model was presented, which was able to capture the joint shear response with reasonable accuracy. Finally, simplified expressions were presented, which were able to provide reliable estimates of joint shear strength, which is suitable for design purposes.

Unveiling Key factors governing seismogenic potential and seismogenic productivity of hydraulic fracturing pads: Insights from machine learning in the Southern Montney Play

One of the most challenging works in mitigating seismic hazard related to injection-induced earthquakes (IIE) is to adequately estimate the activity level of IIE caused by individual hydraulic fracturing (HF) pad. Reliable estimation is largely dependent on comprehensively deciphering the relationships between IIE and the controlling factors. To deal with this conundrum, in this study, we first build an enhanced earthquake catalog with more than 30,000 earthquakes for the southern Montney Play (SMP), in western Canada between 2014 and 2022. We then associate these detected earthquakes to their corresponding HF pads (357 in total). We train the supervised machine-learning algorithm “eXtreme Gradient Boosting” (XGboost) to systematically evaluate how seven geological and eight operational factors contribute to the seismogenic potential (occurrence of IIE) and seismogenic productivity (number of IIE) of these HF pads. Our XGBoost classification model first suggests that cumulative injected volume and the location of the HF pads are the key factors deciding whether IIE would occur or not. Next, using the Shapley Additive Explanations (SHAP) values to quantitatively interpret the outputs from XGBoost, we reveal that contributions from operational and geological factors could be comparable to the seismogenic productivity. The top 3 most influential features are the number of HF stages targeting the Lower Middle Montney formation, cumulative volume from preceding injections, and whether the HF pads are located within the Fort St. John graben. In the interim, the seismogenic potential model reveals that the ranking of determinants governing the seismogenic productivity of IIE is somewhat different, signifying distinct fundamental mechanisms that influence the two phenomena (occurrence vs. number). Our results provide comprehensive understanding on the key operational and geological factors controlling seismogenic pattern and pave the way for forecasting the activity level of HF-related IIE for individual HF pad in the SMP area.

Dual time-scale state-coupled co-estimation of state of charge, state of health and remaining useful life for lithium-ion batteries via Deep Inter and Intra-Cycle Attention Network

The reliable and safe battery operations demand accurate estimation of battery states in the battery management system (BMS). Due to the complex electrochemical process, it is challenging to directly measure SOH and RUL of batteries for online applications. Moreover, the battery aging can strongly influence SOC estimation. The paper proposes a SOC, SOH, and RUL co-estimation method based on Deep Inter and Intra-Cycle Attention Network (DIICAN). From BMS data, the dual time-scale inter-cycle and intra-cycle features are automatically extracted with neural networks. The state degradation attention (SDA) unit is employed to recognize the state evolution pattern and evaluate battery degradation degree based on inter-cycle features. In addition, the extracted inter-cycle and intra-cycle features are simultaneously coupled for the SOC estimation to calibrate the capacity degradation. The mean absolute error (MAE) and root mean square error (RMSE) of the state of health (SOH) estimation are 0.30% and 0.43%, respectively. For the remaining useful life (RUL) estimation, the MAE is 178.5, and the RMSE is 230.3. With state coupling, the MAE of the state of charge (SOC) estimation decreases from 2.36% to 1.00%, and the RMSE decreases from 3.27% to 1.36% over the entire lifespan. In summary, the proposed method can not only accurately co-estimate SOH and RUL, but improve SOC estimation accuracy over the entire battery life.

It takes a village: Caregiver diversity and language contingency in the UK and rural Gambia

IntroductionThere is substantial diversity within and between contexts globally in caregiving practices and family composition, which may have implications for the early interaction’s infants engage in. We draw on data from the Brain Imaging for Global Health (BRIGHT, www.globalfnirs.org/the-bright-project) project, which longitudinally examined infants in the UK and in rural Gambia, West Africa. In The Gambia, households are commonly characterized by multigenerational, frequently polygamous family structures, which, in part, is reflected in the diversity of caregivers a child spends time with. In this paper, we aim to 1) evaluate and validate the Language Environment Analysis (LENA) for use in the Mandinka speaking families in The Gambia, 2) examine the nature (i.e., prevalence of turn taking) and amount (i.e., adult and child vocalizations) of conversation that infants are exposed to from 12 to 24 months of age and 3) investigate the link between caregiver diversity and child language outcomes, examining the mediating role of contingent turn taking. MethodWe obtained naturalistic seven-hour-long LENA recordings at 12, 18 and 24 months of age from a cohort of N = 204 infants from Mandinka speaking households in The Gambia and N = 61 infants in the UK. We examined developmental changes and site differences in LENA counts of adult word counts (AWC), contingent turn taking (CTT) and child vocalizations (CVC). In the larger and more heterogenous Gambian sample, we also investigated caregiver predictors of turn taking frequency. We hereby examined the number of caregivers present over the recording day and the consistency of caregivers across two subsequent days per age point. We controlled for children’s cognitive development via the Mullen Scales of Early Learning (MSEL). ResultsOur LENA validation showed high internal consistency between the human coders and automated LENA outputs (Cronbach’s alpha’s all >.8). All LENA counts were higher in the UK compared to the Gambian cohort. In The Gambia, controlling for overall neurodevelopment via the MSEL, CTT at 12 and 18 months predicted CVC at 18 and 24 months. Caregiver consistency was associated with CTT counts at 18 and 24 months. The number of caregivers and CTT counts showed an inverted u-shape relationship at 18 and 24 months, with an intermediate number of caregivers being associated with the highest CTT frequencies. Mediation analyses showed a partial mediation by number of caregivers and CTT and 24-month CVC. DiscussionThe LENA provided reliable estimates for the Mandinka language in the home recording context. We showed that turn taking is associated with subsequent child vocalizations and explored contextual caregiving factors contributing to turn taking in the Gambian cohort.

The effect of strong nonlinearity on wave-induced vertical mixing

Abstract. A semi-analytical solution to an advection–diffusion equation is coupled with a nonlinear wavemaker model to investigate the effect of strong nonlinearity on wave-induced mixing. The comparisons with weakly nonlinear model predictions reveal that in the case of waves of higher steepness, enhanced mixing affects the subsurface layer of the water column. A fully nonlinear model captures the neglected higher-order terms from a weakly nonlinear solution and provides a reliable estimation of the time-mean velocity field. The corrected wave-induced mass-transport velocity leads to improved estimates of subsurface mixing intensity and ocean surface temperature.

Holistic Exploration of Structural, Electronic, Magnetic, Transport, Mechanical, and Thermodynamic Characteristics, Including Curie Temperature Analysis.

Through intricate calculations, the density functional theory (DFT) implemented in the Wien2k code was employed to comprehensively investigate a wide range of material characteristics. Our study encompasses an exhaustive analysis of structural stability, electronic properties, magnetic behaviors, transport phenomena, mechanical responses, and thermodynamic profiles of two notable instances of filled Skutterudites, namely, CeNi4P12 and DyCo4Sb12, which have been thoroughly explored. These computations were performed using the WIEN 2K code, combining local orbitals and the full-potential linearized augmented plane-wave approach. The findings provided insight into the wide range of properties of these materials. In this methodology, the exchange-correlation potential relies on the local-density approximation. We conducted the calculations with and without incorporating spin-orbit interactions. The results obtained provide information about the lattice constant, bulk modulus, and pressure derivative. The stability, as indicated by the P-V graphical plot, suggests that there are no structural phase transitions from the cubic symmetry structure. Notably, our work includes an examination of Curie temperatures, which are pivotal in understanding magnetic phase transitions. The validated elastic properties further support the material's stability and corroborate its ductile nature. These alloys should be considered for spintronic and thermoelectric applications due to their estimated transport characteristics and the observed ductile nature. To enhance our understanding of the thermal stability of antimony-based compounds, we have made reliable estimations of the thermophysical characteristics. By integrating theoretical insights with practical implications, we bridge the gap between fundamental understanding and material design applications. Using DFT in the Wien2k framework, we discover connections and patterns among different properties, showing how to create materials with specific functions and better performance. This approach not only advances our fundamental comprehension of materials but also promises innovation across various technological domains.

MEMS fault-tolerant machine learning algorithm assisted attitude estimation for fixed-wing UAVs

The utilization of Micro-electromechanical Systems (MEMS) sensors is widespread for directly detecting attitude angles, such as Accelerometer, Gyro, and Magnetometer readings. However, these MEMS sensors are prone to flaws, leading to inaccurate estimates of attitude angles and, consequently, causing UAVs to lose control. Given that UAVs are operated remotely over long distances, ensuring accurate attitude estimates becomes crucial. This study aims to address this issue by employing machine learning algorithms (MLA). These algorithms were trained and evaluated to overcome the problem by predicting missing data from a malfunctioning MEMS sensor using the available data from other MEMS sensors. To calculate the attitude angles, the study utilizes the Extended Kalman Filter (EKF) technique. Furthermore, a novel fault-tolerant machine learning-aided estimation algorithm has been proposed specifically for estimating the attitude angles (φˆ, θˆ, ψˆ) of fixed-wing UAVs. The significance of this research becomes even more prominent when considering the occurrence of MEMS sensor failure. In such cases, the machine learning algorithm plays a crucial role as it has been pre-trained specifically to handle these scenarios. The algorithm is equipped with the ability to effectively address and mitigate the challenges posed by MEMS sensor failures. By leveraging its pre-existing knowledge and learned patterns, the algorithm can accurately predict missing data caused by malfunctioning MEMS sensors. This capability proves invaluable in ensuring the reliable estimation of attitude angles, even in the face of sensor failures. Thus, the integration of machine learning into the estimation process enhances the resilience and robustness of the system, making it well-equipped to handle the uncertainties introduced by MEMS sensor failures.

Association between the MCP-1 -2518 A > G (rs1024611) polymorphism and susceptibility to type 2 diabetes mellitus and diabetic nephropathy: a meta-analysis

BackgroundStudies evaluating the association between monocyte chemoattractant protein-1 (MCP-1) -2518 A > G (rs1024611) polymorphism and type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) are contradictory. The present study aims to provide a comprehensive assessment and more reliable estimation of the relationship between the MCP-1 rs1024611 polymorphism and T2DM and DN risk.MethodsEligible articles were retrieved from the PubMed, Web of Science, EMBASE, Cochrane, and China National Knowledge Infrastructure databases. The effect summary odds ratios (ORs) and 95% confidence intervals (CIs) were obtained to calculate the summary effect size. Heterogeneity was analyzed by subgroup analysis and meta-regression. Publication bias was tested using funnel plots and Egger’s test.ResultsIn total, sixteen studies were included. Thirteen studies involving 2,363 patients with T2DM and 4,650 healthy controls found no significant association between the MCP-1 rs1024611 polymorphism and T2DM in the overall population. Ethnicity stratification found an association between the GG + GA genotype and decreased T2DM risk in Caucasians (OR = 0.79, 95% CI: 0.66–0.93, P = 0.006; PQ = 0.372). No significant risks were found in the Asian population for any genetic models. Seven studies found an association between the GG + GA genotype and DN risk in the Asian population (OR = 1.37, 95% CI: 1.11–1.71, P = 0.004, PQ = 0.222). No significant risks were found in the Caucasian population with any genetic models. There were no statistically significant differences in genotype distribution between patients with T2DM and DN in Asians or Caucasians. Meta-regression revealed that genotyping method was a major driver of heterogeneity in five genetic models (GG + GA vs. AA: P = 0.032; GG vs. GA + AA: P = 0.028; GG vs. AA: P = 0.035; GG vs. GA: P = 0.041; G vs. A: P = 0.041).ConclusionThe MCP-1 rs1024611 polymorphism is associated with susceptibility to T2DM in Caucasians and DN in Asians. Larger, well-designed cohort studies are needed in the future to verify this association.

Fitness-to-drive after adult civilian traumatic brain injury: protocol for a systematic review and meta-analysis.

Traumatic brain injury (TBI) poses significant challenges for assessing fitness-to-drive (FTD) and determining the appropriate timing for return-to-driving (RTD) in civilian adults. This systematic review and meta-analysis protocol is designed to offer a comprehensive assessment of RTD timelines post-TBI, examining the effects of injury severity as well as demographic and clinical factors that influence driving capabilities. In response to gaps identified in previous literature-namely, the absence of recent systematic search strategies and thorough quality assessments-this study employs rigorous methodologies for literature search, data extraction, and evaluation of study quality. Our approach aims to provide reliable estimates and detailed analyses of subgroups within the TBI population. The findings aim to support clinical decision-making, inform RTD readiness, and potentially impact policy and driving assessment protocols. Ultimately, this review seeks to contribute to public safety measures, reduce traffic-related harm, and improve life outcomes for individuals recovering from TBI, thereby filling a vital research niche in neurotrauma rehabilitation.

Improving preoperative prediction of surgery duration

BackgroundOperating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon’s estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement.MethodsWe used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon’s estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration.ResultsThe results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods.ConclusionThe results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.

Multi‐level Factor Analysis of Daily Data to Identify Subsyndromes of Behavioral and Psychological Symptoms

AbstractBackgroundBehavioral and psychological symptoms in dementia (BPSD) are dynamic phenomena with a high amount of intraindividual variability. Many studies have used single‐level factor analysis approaches to identify subsyndromes of BPSD based upon meaningful groupings of co‐occurring symptoms yet, proposed subsyndromes have not been successfully replicated and lack stability over time. This may be due to measurement issues which do not account for dependencies in the data and create temporal misalignment. We applied an innovative multi‐level framework to overcome limitations of prior research to identify subsyndromes of BPSD.MethodsThis study used an intensive longitudinal design in which co‐residing family caregivers to persons living with dementia in the community were recruited to proxy‐report on their care recipient’s daily symptom experiences of 23 different BPSD for 8 consecutive days. A multi‐level exploratory/confirmatory factory analysis was utilized to account for nested data and separate within‐person variances from between‐level factor estimates.ResultsFamily caregivers (N = 70) were on average African American (54%) females (69%) most often caring for a parent with dementia (62%) average 74 years old requiring assistance with 4.5/7 activities of daily living. Data were provided on 79% of sampled days (n = 443 diaries). Exploratory factor analysis identified a 4‐between 3‐within factor structure based on fit statistics and clinical interpretability. The between‐level subsyndromes include wandering/aggression/care resistance (F1), affective/apathy/psychosis/vocalizations (F2), cognitive/ lability/ euphoria/ impulsivity/ compulsions (F3), and hallucinations/uncooperative (F4). Reliability estimates were high: 0.88 (F1), 0.91 (F2), 0.83 (F3), and 0.79 (F4).ConclusionThe between‐level factor structure identified through the multilevel factor analysis framework provide a more valid and reliable estimation of clinical subsyndromes within a population by separating out the variance due to within‐person daily fluctuations. At the between‐level, hallucinations was the only BPSD to cross‐load, which may indicate they are a byproduct of unmanaged depression in the affective/psychosis factor and their own unique subsyndrome. Support for the between‐level subsyndromes can be found in studies on shared neurobiological mechanisms of BPSD. The within‐level factors are similar to structures found in a recent systematic review. We argue these within‐level factors are more representative of symptom clusters, as they are driven by fluctuating contextual factors.

Reliability Analysis and Its Applications for a Newly Improved Type-II Adaptive Progressive Alpha Power Exponential Censored Sample

Recently, a newly improved Type-II adaptive progressive censoring plan was devised, which can successfully ensure that the test length will not surpass a particular threshold period. In this study, we explore the statistical inference of the alpha power exponential distribution in the context of improved adaptive progressive Type-II censored data. The parameters, reliability, and hazard functions were estimated from both classical and Bayesian viewpoints using this censoring plan. To begin, we applied the maximum likelihood estimation approach to obtain parameter, reliability, and hazard function estimators. Following that, the approximate confidence intervals for the aforementioned metrics were derived, assuming the asymptotic normality traits of the maximum likelihood estimators. Additionally, by employing the Bayesian method via the Markov chain Monte Carlo technique, the point estimators and highest posterior density intervals of various parameters were created based on the symmetric squared error loss. A simulation study that incorporates numerous scenarios was used to assess the effectiveness of various estimation methodologies. The optimal progressive censorship plans are then discussed based on a set of criteria. Finally, three applications from the engineering and medical domains have been offered as examples.

Paired Satellite and NWP Precipitation for Global Flood Forecasting

Abstract Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.

High prevalence of subjective cognitive decline in older Chinese adults: a systematic review and meta-analysis.

Subjective cognitive decline (SCD) is considered a preclinical stage of Alzheimer's disease. However, reliable prevalence estimates of SCD in the Chinese population are lacking, underscoring the importance of such metrics for policymakers to formulate appropriate healthcare strategies. To systematically evaluate SCD prevalence among older Chinese adults. PubMed, Web of Science, The Cochrane Library, Embase, CNKI, Wanfang, VIP, CBM, and Airiti Library databases were searched for studies on SCD in older Chinese individuals published before May 2023. Two investigators independently screened the literature, extracted the information, and assessed the bias risk of the included studies. A meta-analysis was then conducted using Stata 16.0 software via a random-effects model to analyze SCD prevalence in older Chinese adults. A total of 17 studies were included (n = 31,782). The SCD prevalence in older Chinese adults was 46.4% (95% CI, 40.6-52.2%). Further, subgroup analyzes indicated that SCD prevalence was 50.8% in men and 58.9% among women. Additionally, SCD prevalence in individuals aged 60-69, 70-79, and ≥ 80 years was 38.0, 45.2, and 60.3%, respectively. Furthermore, SCD prevalence in older adults with BMI <18.5, 18.5-24.0, and > 24.0 was 59.3, 54.0, and 52.9%, respectively. Geographically, SCD prevalence among older Chinese individuals was 41.3% in North China and 50.0% in South China. In terms of residence, SCD prevalence was 47.1% in urban residents and 50.0% among rural residents. As for retired individuals, SCD prevalence was 44.2% in non-manual workers and 49.2% among manual workers. In the case of education, individuals with an education level of "elementary school and below" had an SCD prevalence rate of 62.8%; "middle school, "52.4%; "high school, "55.0%; and "college and above, "51.3%. Finally, SCD prevalence was lower among married individuals with surviving spouses than in single adults who were divorced, widowed, or unmarried. Our systematic review and meta-analysis identified significant and widespread SCD prevalence in the older population in China. Therefore, our review findings highlight the urgent requirement for medical institutions and policymakers across all levels to prioritize and rapidly develop and implement comprehensive preventive and therapeutic strategies for SCD.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023406950, identifier: CRD42023406950.