Lower Mean Research Articles

Optic nerve assessment with stereo photographs and ultra-widefield scanning laser ophthalmoscope images.

Clinical evaluation of the optic nerve using 3-D stereo disc photographs is considered the gold standard for estimating vertical cup-to-disc ratios. Ultra-widefield retinal imaging has gained increasing popularity to document and screen the health of the retina and optic nerve. Glaucoma is often first identified or suspected based on initial optic nerve assessment. Despite technological advancements in imaging, stereo disc photography remains the gold standard of optic nerve assessment. This study compared vertical cup-to-disc ratio (VCDR) estimations using ultra-widefield scanning laser ophthalmoscopy images with estimations using paired stereo disc images. Thirty-five healthy and 35 glaucomatous eyes were imaged using Zeiss VISUCAM® PRO NM and Optos Monaco® (optomap®) [Registered in U.S. Patent and Trademark Office] devices. Four experienced clinicians assessed each image in a masked fashion and graded the VCDR. Stereo viewers and review software tools were used to determine VCDRs. Bland-Altman plots and regression/correlation analyses were used to check VCDR agreement. Bland-Altman analyses showed low mean differences between devices with VCDR estimation for each grader (-0.03, +0.01, -0.02, and +0.03). Difference vs. means plots suggest stable differences across the range of measurement. VCDR estimations were comparable using optomap® images and VISUCAM® PRO NM stereo optic disc images.

Promoting Institutionalization of Research-Evidence Use in Maternal, Newborn and Child-Health Policymaking in Nigeria

The implementation of policies informed by research evidence in maternal newborn and child health (MNCH) is known to improve health outcomes. In this study we describe the process of the establishment of MNCH Research Days Event as a mechanism for the promotion of institutionalization of evidence-to-policy-to practice in MNCH in Nigeria. We also describe the current status of research evidence use in policymaking, level of policymakers-researchers interaction as well as identify the individual and organizational roles to promote evidence-informed policymaking in MNCH in Nigeria. The study was a cross-sectional survey carried out during a 3-day National MNCH stakeholders’ engagement meeting on Nigeria Research Days for MNCH, as part of the Implementation of Moving MNCH Evidence into Policy (MEP) Project. Data on the stakeholders’ perception regarding the study objective was collected using a semi-structured questionnaire administered in person. A total of 51 participants completed the questionnaire. Result showed low mean ratings (MNRs) for both research evidence use in MNCH policymaking (ranging from 2.23-3.14 in a 5-point scale) and policymakers-researchers interaction (2.98-3.43). Creating research capacity development platforms for individuals and institutions involved in policymaking (MNR 4.69) were among strategies that can enhance the evidence-to-policy process in MNCH. Sustainable interaction between policymakers and researchers can be achieved through the institutionalization of mechanisms such as ‘Research Day Event’ that will provide platform to consider issues around evidence-to-policy-to-practice. Urgent sustainable capacity development among stakeholders in the MNCH sector is highly recommended.

Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils

In the context of contaminated site remediation, the fate of chlorinated solvents in the subsurface and subsequent groundwater contamination is influenced by soil properties governing sorption. The solid–water distribution coefficient (Kd) is a key parameter for modeling contaminant distribution and transport, essential for risk assessment and remediation planning. This study evaluated tetrachloroethene sorption isotherms in 34 low-organic-carbon soils from the Czech Republic, assessing the influence of soil properties on Kd. Soil samples exhibited variability in organic carbon content (˂0.05–0.81%), with clay ranging from 0% to 64.9%, silt 5.1% to 71.2%, and sand 5.2% to 88.9%, specific surface area (0.41–64.39 m2 g−1), particle density (2.05–4.09 g cm−3), and porosity (43.5–67.3%). Batch experiments were conducted using standard procedures, with Kd values ranging from 0.379 to 2.272 L kg−1. Statistical analysis grouped the soils into three textural classes: sandy, clayey fine, and silty loam. The findings reveal that organic carbon content and specific surface area are the primary predictors of Kd, while clay and sand also play a significant role in shaping sorption behavior. Multivariate regression models explained 63.6% to 98.5% of Kd variability with high accuracy, as indicated by low root means square error (0.070–0.329) and mean absolute percentage error (3.8–28.8%) values. These models offer reliable predictions of sorption behavior, providing valuable tools for risk assessment and remediation strategies.

Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (R2) exceeding 97%, significantly improving prediction accuracy and efficiency.

Diffusion equation quantification: selective enhancement algorithm for bone metastasis lesions in CT images

Objective.Diffusion equation (DE) imaging processing is promising to enhance images showing lesions of bone metastasis (LBM). The Perona-Malik diffusion (PMD) model, which has been widely used and studied, is an anisotropic diffusion processing method to denoise or extract objects from an image effectively. However, the smoothing characteristics of PMD or its related method hinder extraction and enhancement of soft tissue regions of medical image such as computed tomography (CT), typically leaving an indistinct region with ambient tissues. Moreover, PMD expands the border region of the objects. A novel diffusion methodology must be used to enhance the LBM region effectively.Approach.For this study, we originally developed a DE quantification (DEQ) method that uses a filter function to selectively provide modulated diffusion according to the original locations of objects in an image. The structural similarity index measure (SSIM) and Lie derivative image analysisL-value map were used to evaluate image quality and processing.Main results.We determined superellipse function with its ordern=4as a better performing filter for the LBM region. DEQ was found to be more effective at contrasting LBM for various LBM CT images than PMD or its improved models when the filter was a positive exponential similar function. DEQ yields enhancement agreeing with the indications of positron emission tomography despite complex LBM comprising osteoblastic, osteoclastic, mixed tissues, and metal artifacts, which is innovative. Moreover, DEQ retained high quality of image (SSIM> 0.95), and achieved a low mean value of theL-value (<0.001), indicative of our intended selective diffusion compared to other PMD models.Significance.Our method improved the visibility of mixed tissue lesions, which can assist computer visional framework and can help radiologists to produce accurate diagnose of LBM regions which are frequently overlooked in radiology findings because of the various degrees of visibility in CT images.

Luteinizing hormone profiles during ovarian stimulation in assisted reproductive treatment.

Few data is available on the natural course of luteinizing hormone (LH) during ovarian stimulation, but it has been suggested that 'oversuppressed' LH could decrease fertility outcomes. Our aim with this study is to evaluate the changes in LH depending on the used stimulation protocol to better define LH oversuppressioin. Patients undergoing oocyte retrieval in a tertiary fertility center between 01-01-2015 and 30-09-2020 after stimulation with a short-agonist (SA) or antagonist (A) protocol were included. Data were retrospectively retrieved from 858 electronic patient records, of which 338 SA cycles and 783 A cycles. A continuous profile was set out to evaluate the pooled measurements of the mean LH in time during ovarian stimulation and linear mixed modeling was used to compare the change of LH between 4 time points: the day prior to start of gonadotrophins (T1), stimulation day 5 (T2), stimulation day 6 (T3) and on the day of oocyte maturation trigger (T4). Oversuppression of LH was defined as a decrease of LH > 50% after initiation of GnRH antagonist and LH levels < 1.2 IU/l after GnRH antagonist. A subanalysis was performed for type of gonadotrophin (recFSH vs hp-hMG). In the SA protocol, an initial LH peak was followed by a slow decrease of LH until triggering. In the A protocol, LH decreased after gonadotrophin initiation with a further rapid decrease after initiation of the antagonist and remained low until trigger. LH levels dropped > 50% in 26.2% of the antagonist cycles and LH levels were < 1.2 IU/l in 45.3% of cycles after initiation of GnRH-antagonist. The course of LH in the SA protocol differs from the A protocol where low mean LH levels are seen. Oversuppression of LH, or iatrogenic LH deficiency as described in earlier studies, may be a rather pervasive phenomenon during stimulation with an antagonist protocol and warrants further investigation to elucidate the clinical relevance of low LH levels during ovarian stimulation.

Triiodothyronine Is Associated with Incidence/Resolution of Steatotic Liver Disease: Longitudinal Study in Euthyroid Korean.

The positive relationship between triiodothyronine (T3) and steatotic liver disease (SLD) demonstrated only in crosssectional study. We aimed to evaluated whether total T3 (TT3) is associated with the development/resolution of SLD in longitudinal design. This retrospective, longitudinal, population-based cohort study included 1,665 South Korean euthyroid adults with ≥4 thyroid function test. We explored the impact of mean TT3 during follow-up on development/resolution of either SLD (diagnosed by ultrasound) or modified metabolic dysfunction-associated steatotic liver disease (MASLD) using Cox proportional hazards regression models. During about median 5 years follow-up, 807/1,216 (66.3%) participants among participants without SLD at baseline developed SLD, and 253/318 (79.5%) participants among participants with SLD at baseline SLD resolved fatty liver. Mean TT3 rather than thyroid stimulating hormone or mean free thyroxine was significantly related with development (adjusted hazard ratio [HR], 1.01; 95% confidence interval [CI], 1.00 to 1.02; P=0.002) and resolution (adjusted HR, 0.97; 95% CI, 0.96 to 0.99; P=0.005) of SLD. Compared with low mean TT3 group, high mean TT3 group was positively associated with development of SLD (adjusted HR,1.20; 95% CI, 1.05 to 1.38; P=0.008) and inversely associated with resolution of SLD (adjusted HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). The statistical significance remained for development (adjusted HR, 1.29; 95% CI, 1.10 to 1.51; P=0.001) and resolution (adjusted HR, 0.71; 95% CI, 0.54 to 0.94; P=0.018) of modified MASLD. In Korean euthyroid adults, TT3 level was associated with development and resolution of either SLD or modified MASLD.

Talent management in volatility, uncertainty, complexity, and ambiguity (VUCA) health environment, nurses’ psychological contract fulfillment, cordial relation and generation: moderation-mediation model

BackgroundThe VUCA in the healthcare environment requires combating volatility, uncertainty, complexity, and ambiguity through highly talented employees and implementing a talent management strategy. This encompasses a range of operations designed to find, attract, nurture, and utilize competent workers that impact how well nurses fulfill their psychological contracts. However, as the researchers had anticipated, several factors, such as cordial relationships and generation differences, may mediate or influence this correlation.MethodsFor a cross-sectional study, a non-probability convenience sampling technique was used to include n = 375 nurses from among the 600 total nurses at three private hospitals in Alexandria, Egypt. Three validated measures were used to measure the study variables and develop a mediation-moderation structural equation model.ResultsThe result of this study revealed that nurses perceived a moderate level of talent management with a mean score of (48.91 ± 18.15), a low mean score (8.89 ± 3.93) of psychological contract fulfillment, and a moderate mean score (185.11 ± 27.02) of cordial relation. Additionally, more than half of the participants were in the Gen Z. Also, this study concluded that there is a positive mediation role of cordial relation and a negative moderation role of Gen between talent management and psychological contract fulfillment.ConclusionsUsing Gen as a moderating variable and cordial relations as a mediating factor, a moderating mediating structural equation model is created and validates the important influence of talent management on nurses’ psychological fulfillment, confirming the mediating effect of cordial relations and the moderating effect of genes in this relationship. GEN negatively predicted psychological contract fulfillment, meaning baby boomers predicted more psychological contract fulfillment than Z gen. Furthermore, talent management could positively predict cordial relations and psychological contract fulfillment, and cordial relations partially mediated the relationship between talent management and psychological contract fulfillment. Nurse mangers should be aware of and implement effective and talent management strategies with respecting gen difference, and apply tailored strategies for fostering nurses’ cordial relation and psychological contract fulfillment to deal with the VUCA challenging healthcare environment.

Stock Price Prediction System LSTM Based on Deep Learning

Stock price prediction is a highly complex task due to the inherent volatility and non-linear patterns of financial markets. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have yielded promising results in time series forecasting. This paper provides an in-depth analysis of LSTM models applied to the stock price prediction of major technology companies, including Apple (AAPL), Tesla (TSLA), Microsoft (MSFT), and Nvidia (NVDA). The study utilizes historical stock data to train and evaluate LSTM models, comparing their performance against various other regression models such as Linear Regression, Support Vector Regression (SVR), Decision Tree Regression, and Gradient Boosting Decision Trees (GBDT).The results demonstrate that the LSTM model outperforms traditional models, particularly in its ability to capture long-term dependencies and manage complex, volatile stock data. Among the companies analyzed, the LSTM model achieved the most accurate predictions for Apple, as indicated by the lowest Mean Squared Error (MSE). Tesla's predictions, while less accurate due to high volatility, still showed improvement compared to other models. In conclusion, the LSTM network proved to be the most effective method for stock price prediction in this study, particularly excelling in forecasting long-term trends and reducing prediction errors for volatile stocks.

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

This paper offers a comprehensive investigation into the forward and inverse kinematics of a wrist rehabilitation robot, utilizing the Denavit-Hartenberg method for forward kinematics (FK) and a geometric approach, as well as artificial neural networks (ANN) and adaptive Neuro-Fuzzy inference systems (ANFIS) for inverse kinematics (IK) analysis. While the geometric method entails precise parameter measurements and faces uncertainties, ANN and ANFIS are explored as potential remedies to enhance accuracy and robustness. Evaluating 11 different training functions sourced from existing literature, our study conducts a thorough assessment of their performance within an ANN network. We aim to pinpoint the most suitable training function for achieving optimal IK solutions in the context of a wrist rehabilitation robotic. Additionally, the ANFIS model, trained using Fuzzy C-Means (FCM), sets itself apart from Grid Partitioning (GP) and Subtractive Clustering (SC). Among the ANN training functions, Bayesian regularization with 5 hidden layers emerges as the most effective, yielding low root mean square error (RMSE) values of 0.003, 0.004, and 0.007 degrees for pronation/supination (P/S), abduction/adduction (AB/AD), and flexion/extension (F/E), respectively. Conversely, ANFIS, trained with FCM, demonstrates satisfactory yet less precise results, with RMSE values of 0.191, 0.082, and 0.165 degrees for P/S, AB/AD, and F/E, respectively. Despite its adequacy, ANFIS trails behind ANN, showcasing RMSE reductions of 98.4%, 95.1%, and 95.7% for P/S, AB/AD, and F/E angles, respectively. This study contributes to leveraging ANN and ANFIS for IK analysis in wrist rehabilitation robotics, highlighting the efficacy of ANN, particularly when employing Bayesian regularization, to enhance accuracy.

Enhancing No Reference Laparoscopic Video Quality Assessment with Evolutionary ANFIS

Abstract Distortions in laparoscopic videos affect surgeon visibility and surgical precision, underscoring the need for sustained high video quality. This study presents a real-time laparoscopic video quality assessment algorithm independent of reference content availability. Statistical parameters derived from luminance, local binary pattern and motion-vector maps of video frames are observed to effectively discern distortion types and severities. These parameters are used to train an evolutionary adaptive neuro-fuzzy inference system (ANFIS) end-to-end with subjective score labels. Training and validation loss curves saturate at the 85th epoch, demonstrating the model’s efficient data fitting capability. Performance comparison with other state-of-the-art methods reveals superior results, with high correlation scores of 0.9989 and 0.9446 for experts and 0.9956 and 0.9847 for non-experts, alongside low root mean square errors of 0.0828 and 0.1685 for expert and non-experts, respectively. The model accurately replicates the expert and non-expert perceptual opinions, encouraging future research in stereoscopic, augmented, and virtual reality data.

A Fourth Planet in the Kepler-51 System Revealed by Transit Timing Variations

Kepler-51 is a ≲1 Gyr old Sun-like star hosting three transiting planets with radii ≈6–9 R ⊕ and orbital periods ≈45–130 days. Transit timing variations (TTVs) measured with past Kepler and Hubble Space Telescope (HST) observations have been successfully modeled by considering gravitational interactions between the three transiting planets, yielding low masses and low mean densities (≲0.1 g cm−3) for all three planets. However, the transit time of the outermost transiting planet Kepler-51d recently measured by the James Webb Space Telescope 10 yr after the Kepler observations is significantly discrepant from the prediction made by the three-planet TTV model, which we confirmed with ground-based and follow-up HST observations. We show that the departure from the three-planet model is explained by including a fourth outer planet, Kepler-51e, in the TTV model. A wide range of masses (≲M Jup) and orbital periods (≲10 yr) are possible for Kepler-51e. Nevertheless, all the coplanar solutions found from our brute-force search imply masses ≲10 M ⊕ for the inner transiting planets. Thus, their densities remain low, though with larger uncertainties than previously estimated. Unlike other possible solutions, the one in which Kepler-51e is around the 2:1 mean motion resonance with Kepler-51d implies low orbital eccentricities (≲0.05) and comparable masses (∼5 M ⊕) for all four planets, as is seen in other compact multiplanet systems. This work demonstrates the importance of long-term follow-up of TTV systems for probing longer-period planets in a system.

Optimising Control Device Luring Strategies for Invasive Predator Control: A Modelling Approach.

Invasive predators pose a serious threat to native biodiversity, with trapping being one of several methods developed to manage and monitor their populations. Many individuals in these predator populations have been found to display trap-shyness, which hinders eradication and results in inaccurate estimates of population size. Lures are used to help overcome trap-shyness by increasing the probability of interaction with the device, but the extent of trap-shyness in wild populations, and the best timing for the introduction of a new lure or combination of lures, are uncertain. A key challenge for wildlife managers is maximising the efficacy of invasive predator control, particularly in relation to baiting and trapping, so that pests are extirpated, or survivors are reduced to a minimum. We first use a Bayesian estimation method to quantify trap-shyness in a population of brushtail possum (Trichosurus vulpecula) in a New Zealand forest; the resulting estimated parameters are then used to calibrate a stochastic, individual-based model simulating the outcomes of different luring scenarios. We show that the brushtail possum population analysed was likely split into a smaller, very trappable group and a larger trap-shy group, with a low mean nightly probability of interaction with traps of 0.28 [0.14-0.56]. Furthermore, our results show that under the assumption of independent attraction levels towards different lures, using a combination of lures simultaneously can result in a greater and faster population knock-down than using a single lure, or than to switch from one lure to another. The model presented can be used to infer wildlife population trappability from capture data, and our simulation results highlight the potential of improved luring strategies to capture individuals in post-control residual populations.

Correlation and Conversion between the QuickDASH, Constant Score and Oxford Shoulder Score in Patients with Conservatively Treated Proximal Humerus Fractures

BackgroundThere is significant heterogeneity of outcome measures used in the research of proximal humerus fractures (PHFs). Current evidence regarding the correlation and conversion between the various outcome measures is sparse. This study aims to study the correlation and conversion between the QuickDASH, Constant Score (CS) and Oxford Shoulder Score (OSS) in conservatively treated PHFs. MethodsA prospective cohort study of patients (n=136) with conservatively treated PHFs between August 2017 and April 2020 was conducted. Patients had a minimum follow-up period of 1 year. The three outcome measures (QuickDASH, CS and OSS) were collected at four time points - 6 weeks, 3 months, 6 months and 1 year after injury. Changes in scores across time and correlation between each pair of outcome measures was calculated. A linear regression model was used to derive conversion equations which were then internally validated. ResultsA significant strong negative correlation was observed between the OSS and QuickDASH (coefficient: -0.746; p< 0.001), a significant moderate negative correlation was observed between the CS and QuickDASH (coefficient: -0.581; p< 0.001), and a significant moderate positive correlation was observed between the CS and OSS (coefficient: 0.697; p< 0.001). The six derived regression equations showed low mean differences between predicted and actual values (ranging from -1.21 to 2.51). The correlation between actual and predicted values was moderate to strong, ranging from a coefficient of 0.57 in the conversion from the CS to QuickDASH to 0.74 in the conversion from the CS to OSS and OSS to CS. ConclusionIn a cohort of patients with conservatively managed PHFs, moderate to strong correlations were seen in pairwise comparisons of the OSS, QuickDASH and CS. With linear regression analyses, six regression equations were derived to estimate one score from another. On internal validation, there was good agreement between the means of the predicted and actual scores but high within-individual variability. These formulae can help to compare studies with heterogeneous outcome measures and facilitate meta-analyses. However, these equations should not be used to predict one score from another in an individual due to variability when converting individual scores.

An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. This methodology represents a significant advancement over conventional approaches in UAV technology. Our experimental design involved comprehensive field data collection across two distinct altitude environments, employing a high-precision Digital Laser Distance Meter as the reference standard (Class II). This rigorous approach facilitated a thorough evaluation of our model’s performance across varied terrains, ensuring robust and reliable results. The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. These results not only indicate significant improvements over traditional methods, but also establish a new benchmark in UAVs altitude estimation accuracy. A key innovation in our approach is the elimination of costly additional hardware such as Light Detection and Ranging (LiDAR), offering a cost-effective, software-based solution. This advancement has broad implications, enhancing the accessibility of advanced UAVs technology and expanding its potential applications across diverse sectors including precision agriculture, urban planning, and emergency response. This research represents a significant contribution to the integration of AI and UAVs technology, potentially unlocking new possibilities in UAVs applications. By enhancing the capabilities of lightweight UAVs, we are not merely improving a technical aspect, but revolutionizing the potential applications of UAVs across industries. Our work sets the stage for safer, more reliable, and precise UAVs operations, marking a pivotal moment in the evolution of aerial technology in an increasingly UAV-dependent world.

Longevity in Carnaby’s Cockatoo (Zanda latirostris) Carnaby, 1948

Context Carnaby’s Cockatoo Zanda latirostris is an endangered species endemic to south-western Australia. It has been the subject of long-term studies of its ecology from 1969 to the present. Aims This paper examines the maximum recorded life of the species in the wild. Methods The life spans of eight wild Carnaby’s Cockatoos that were more than 20 years old were compared with longevity records of long-lived birds recorded on the banding registers in Australia, USA, and UK. Key results Eight wild Carnaby’s Cockatoos were recorded living from 21 to 35 years, putting the species in the top 2% of all bird species in the wild for which there are longevity records. Conclusions The longevity of Carnaby’s Cockatoo is similar to that of long-lived seabirds. Implications Several years of immaturity and low reproductive output mean that Carnaby’s Cockatoos must be long-lived for the species to be sustainable. Conservation actions need to minimise threats to the lives of adult cockatoos.

Nurse Compliance with the Use of Personal Protective Equipment While Handling Chemotherapy: A Multicenter Cross-Sectional Study in Palestine

Purpose: The purpose of this study was to assess the level of compliance among oncology nurses with personal protective equipment while handling chemotherapy and determine factors hindering its use. Design: A cross-sectional study with a quantitative descriptive approach. Method: 100 oncology nurses from six Palestinian hospitals participated in this study (68.5% response rate). Nurses involved in chemotherapy handling and working in cancer departments were purposively selected to complete a paperbased questionnaire. A bivariate correlation analysis was conducted. Sample size was determined based on the practical limitations of time and resources rather than a formal calculation. Study variables: The primary independent variables in this study include knowledge about chemotherapy exposure, perceived risk of harm from exposure, self-efficacy in using personal protective equipment, barriers to personal protective equipment use, workplace safety climate, interpersonal influences, and perceived conflict of interest. The dependent variable is compliance with the use of personal protective equipment. Results: Nurses were experienced in oncology, well-educated, received training on the use of personal protective equipment, and, on average, treated 1-5 patients per day. Knowledge on chemotherapy exposure was low (Mean = 6.77, Standard Deviation ± 1.87); as was self-efficacy (13.22 ± 3.32). Perceived barriers to using personal protective equipment were moderate (29.93 ± 9.00); as was workplace safety climate (54.11 ± 11.43). Total precaution use during chemotherapy administration was moderate (2.3 ± 0.67). Nurse characteristics did not predict personal protective equipment use. Conclusions: This study demonstrated that Palestinian oncology nurses exhibited a moderate level of compliance with the use of personal protective equipment. Compliance with personal protective equipment was highest during chemotherapy administration and lowest during disposal activities. Further research is necessary to comprehend the impact of nurses' beliefs and attitudes on compliance levels. Additionally, future studies should consider other healthcare providers inadvertently exposed to chemotherapy.

Probabilistic assessment of peak discharge and debris flow concentration in the Surma River using Monte Carlo simulation

ABSTRACT Rivers, vital for household and industrial needs, vary in size due to erosion and sedimentation and are significantly impacted by climate variations. Accurate estimation of river characteristics like discharge and debris flow concentration is essential for designing hydraulic systems in flood-prone areas. This study assesses these factors in the Surma River, located in Sylhet, Bangladesh, by using Monte Carlo simulation to reduce uncertainty. The simulation optimized the time of concentration against water level height. Peak discharge results for 5, 10, 25, 50, and 100-year return periods were 1,904.66, 2,174.77, 2,543.63, 2,846.72, and 3,124.25 m3/s, with corresponding debris flow concentrations (Cv) of 0.5499989, 0.5499976, 0.5499967, 0.5499940, and 0.5499147. During the monsoon season, the maximum peak discharge reached 5,095.58 m3/s with a Cv of 0.7886368. Model validation using the F test and R2 test demonstrated high accuracy, indicated by low mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE), along with a coefficient of determination (R2) of 0.7097. The p-value from the F test (0.197852) indicated strong alignment between predicted and observed discharges. This study enhances understanding of flow dynamics, improves prediction precision, and underscores the importance of historical data in flood risk assessment.

Intraoperative high and low blood pressures are not associated with delirium after cardiac surgery: A retrospective cohort study

Study objectiveTo evaluate the associations between high and low intraoperative time-weighted average mean arterial pressures before, during and after cardiopulmonary bypass on postoperative delirium. DesignSingle center retrospective cohort study. SettingOperating rooms and postoperative care units. Patients11,382 patients, 18 years of age or older who had cardiac surgery requiring cardiopulmonary bypass between January 2017 and December 2020 at the Cleveland Clinic Main Campus. InterventionsAll cardiac surgery requiring bypass except procedures requiring deep hypothermic circulatory arrest. MeasurementsPost operative delirium was assessed from 12 to 96 h postoperatively, using the Confusion Assessment Method and brief Confusion Assessment Methods. Hypotension and hypertension were defined as time-weighted average mean arterial pressure < 60 and > 80 mmHg. Main resultsPostoperative delirium occurred in 678 (6.0 %) of 11,382 patients. Confounder-adjusted associations, using multivariable logistic regression models, between hypotension (time-weighted average mean arterial pressure < 60 mmHg) and hypertension (time-weighted average mean arterial pressure > 80 mmHg) and postoperative delirium were not statistically significant or clinically meaningful before, during, or after the cardiopulmonary bypass. ConclusionsThis large single-center cohort analysis found no evidence that exposure to high or low blood pressures during various intraoperative phases of cardiac surgery are associated with postoperative delirium.

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. We applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.