Correlation Coefficient Research Articles

Institutional and Regional Variation in Opioid Prescribing for Hospitalized Infants in the US

High-risk infants, defined as newborns with substantial neonatal-perinatal morbidities, often undergo multiple procedures and require prolonged intubation, resulting in extended opioid exposure that is associated with poor outcomes. Understanding variation in opioid prescribing can inform quality improvement and best-practice initiatives. To examine regional and institutional variation in opioid prescribing, including short- and long-acting agents, in high-risk hospitalized infants. This retrospective cohort study assessed high-risk infants younger than 1 year from January 1, 2016, to December 31, 2022, at 47 children's hospitals participating in the Pediatric Health Information System (PHIS). The cohort was stratified by US Census region (Northeast, South, Midwest, and West). Variation in cumulative days of opioid exposure and methadone treatment was examined among institutions using a hierarchical generalized linear model. High-risk infants were identified by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes for congenital heart disease surgery, medical and surgical necrotizing enterocolitis, extremely low birth weight, very low birth weight, hypoxemic ischemic encephalopathy, extracorporeal membrane oxygenation, and other abdominal surgery. Infants with neonatal opioid withdrawal syndrome, in utero substance exposure, or malignant tumors were excluded. Any opioid exposure and methadone treatment. Regional and institutional variations in opioid exposure. Overall, 132 658 high-risk infants were identified (median [IQR] gestational age, 34 [28-38] weeks; 54.5% male). Prematurity occurred in 30.3%, and 55.3% underwent surgery. During hospitalization, 76.5% of high-risk infants were exposed to opioids and 7.9% received methadone. Median (IQR) length of any opioid exposure was 5 (2-12) cumulative days, and median (IQR) length of methadone treatment was 19 (7-46) cumulative days. There was significant hospital-level variation in opioid and methadone exposure and cumulative days of exposure within each US region. The computed intraclass correlation coefficient estimated that 16% of the variability in overall opioid prescribing and 20% of the variability in methadone treatment was attributed to the individual hospital. In this retrospective cohort study of high-risk hospitalized infants, institution-level variation in overall opioid exposure and methadone treatment persisted across the US. These findings highlight the need for standardization of opioid prescribing in this vulnerable population.

Rational Design of Reliable Computational Protocols for Predicting Dielectric Constants of Gaseous Molecules.

A rapid prediction of the dielectric constants from a wide range of organic compounds is of paramount importance given the pressing need to find alternatives to SF6, one of the seven greenhouse gases. However, the availability of a universally applicable equation for predicting dielectric constants remains limited. This study endeavors to systematically develop a universal equation for predicting the dielectric constants of gaseous organic molecules in a systematic manner. The reliability of these newly developed equational protocols is evaluated through both quantitative (i.e., root-mean-squared deviation) and qualitative (i.e., Spearman's rank correlation coefficient) analyses. Equational optimization of the traditionally unreliable Clausius-Mossotti equation highlights the critical role of selecting a suitable variable to be incorporated into an adapted Clausius-Mossotti equation, ultimately enhancing the predictive accuracy. Furthermore, it is revealed that the nature of the chosen variable has a more significant impact on prediction accuracy than the quantity of variables introduced. These findings shed light on the ongoing efforts of developing a dependable protocol for predicting not only dielectric constants but also other vital insulating properties, such as dielectric strength.

Agreement between PSMA-RADS and E-PSMA systems in classifying [18F]PSMA-1007 PET/CT lesions among prostate cancer patients: exploring the correlation between lesion size and uptake.

To determine the agreement between the PSMA-RADS and E-PSMA standardized reporting systems in the classification of [18F]PSMA-1007-uptaking lesions identified on PET/CT scan in patients with prostate cancer (PCa) and post-prostatectomy with suspected recurrent disease (local recurrence, regional nodal involvement and distant metastases), based on biochemical recurrence, while also exploring the correlation between lesion size and tracer uptake. A retrospective cross-sectional study of 32 post-prostatectomy PCa patients who had suspected recurrent disease based on biochemical recurrence post-prostatectomy (prostate-specific antigen values that are 0.2 ng/mL or higher) underwent [18F]PSMA-1007 PET/CT scan. The recurrent disease PCa lesions were characterized and subsequently classified using two standardized reporting systems (PSMA-RADS and E-PSMA). The lesions were grouped based on anatomical site, their size and SUVmax were compared using Kruskal-Wallis test with Dunn-Bonferroni post hoc tests. Spearman correlation coefficients were calculated between the size of the lesions and their SUVmax of the radiotracer [18F]PSMA-1007 for all the lesions and when grouped by anatomical site. Additionally, the agreement between lesion classifications was assessed using Cohen's kappa index. Only 32 (69.98 ± 8.27, men) patients met the inclusion criteria, a total of 149 lesions with avid uptake of [18F]PSMA-1007 were identified. Positive correlation (r = 0.516, p < 0.001) was observed between the size of the metastatic prostate cancer lymph node lesions and their [18F]PSMA-1007 uptake. Substantial agreement was noted between the PSMA-RADS and E-PSMA classification system scores among all lesions (κ = 0.70, p < 0.001), with notable discrepancies primarily among lymph node lesions. Our findings revealed a positive correlation between the size of the metastatic prostate cancer lymph node lesions and [18F]PSMA-1007 uptake, and although there was substantial agreement between the PSMA-RADS and E-PSMA classification systems, there were discrepancies mainly among the lymph node lesions.

Reference values and reliability of occlusal force distribution and occlusal time measured by the T-Scan system in adults with healthy dentition.

Reference values of occlusal characteristics are needed to interpret the data obtained using the T-Scan System. This study aimed to establish reference values for and to assess the reliability of, occlusal force distribution in the maximal intercuspal position and the occlusion time in young adults with healthy dentition. In total 178 adults with natural dentition participated in this retrospective cross-sectional study, of whom 76 performed a retest session. Several occlusal recordings were obtained from each participant using the T-Scan system while asking them to bite two or three consecutive times (multi-bite) or only once (single-bite) at the maximal intercuspal position. The lateral and anteroposterior occlusal force distribution were determined as percentages in the right and posterior teeth . Occlusion time was measured in seconds. After the occlusal force distribution and occlusion time percentiles were calculated, reliability was assessed by the intraclass correlation coefficient. The 5th-95th percentiles for occlusal force distribution were 34%-67% on the right teeth and 55%-94% on the posterior teeth. The 90th percentile for multi-bite occlusion time was 0.17 s and for single-bite occlusion time was 0.27 s. The intraclass correlation coefficients for lateral occlusal force distribution, anteroposterior occlusal force distribution, multi-bite occlusion time, and single-bite occlusion time were 0.70, 0.68, 0.58, and 0.67, respectively. This study generated reference values for key occlusal characteristics (occlusal force distribution and occlusion time) when using the T-Scan system. These values showed moderate reliability.

Standing Balance Conditions and Digital Sway Measures for Clinical Trials of Friedreich's Ataxia.

Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test-retest reliability and correlations between sway measures and clinical scores. Twenty-eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test-retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P-values <0.05 and r-values (0.63-0.86) and (0.65-0.81), respectively. Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society.

Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT.

To test equivalency of deep-learning 3D lumbar spine MRI with "CT-like" contrast to CT for virtual pedicle screw planning and geometric measurements in robotic-navigated spinal surgery. Between December 2021 and June 2022, 16 patients referred for spinal fusion and decompression surgery with pre-operative CT and 3D MRI were retrospectively assessed. Pedicle screws were virtually placed on lumbar (L1-L5) and sacral (S1) vertebrae by three spine surgeons, and metrics (lateral deviation, axial/sagittal angles) were collected. Vertebral body length/width (VL/VW) and pedicle height/width (PH/PW) were measured at L1-L5 by three radiologists. Analysis included equivalency testing using the 95% confidence interval (CI), a margin of ± 1mm (± 2.08° for angles), and intra-class correlation coefficients (ICCs). Across all vertebral levels, both combined and separately, equivalency between CT and MRI was proven for all pedicle screw metrics and geometric measurements, except for VL at L1 (mean difference: - 0.64mm; [95%CI - 1.05, - 0.24]), L2 (- 0.65mm; [95%CI - 1.11, - 0.20]),and L4 (- 0.78mm; [95%CI - 1.11, - 0.46]). Inter- and intra-rater ICC for screw metrics across all vertebral levels combined ranged from 0.68 to 0.91 and 0.89-0.98 for CT, and from 0.62 to 0.92 and 0.81-0.97 for MRI, respectively. Inter- and intra-rater ICC for geometric measurements ranged from 0.60 to 0.95 and 0.84-0.97 for CT, and 0.61-0.95 and 0.93-0.98 for MRI, respectively. Deep-learning 3D MRI facilitates equivalent virtual pedicle screw placements and geometric assessments for most lumbar vertebrae, with the exception of vertebral body length at L1, L2, and L4, compared to CT for pre-operative planning in patients considered for robotic-navigated spine surgery.

Measurement properties of the EQ-5D-3L, EQ-5D-5L, and SF-6Dv2 in patients with late-onset Pompe disease.

The objective of this study was to evaluate the psychometric properties of the EQ-5D (3L and 5L) and SF-6Dv2 in a group of Chinese patients with late-on Pompe disease (PD), and compare their performance in this patient group. The data used in this study were obtained from a web-based and cross-sectional survey conducted in China. All participants completed the 3L, 5L, and SF-6Dv2. Information about their sociodemographic status and health conditions was also collected. The measurement properties were assessed by examining ceiling and floor effects, evaluating convergent validity, known-group validity, and test-retest reliability (Intraclass correlation coefficient [ICC] and Gwet's AC). A total of 117 PD patients completed the questionnaire. All dimensions of the 3L showed strong ceiling effects, ranging between 17.1 and 42.7%. All three measures showed good test-retest reliability, with ICC values ranging from 0.85 to 0.87. The Gwet's AC values showed that four out of five dimensions of the 3L showed very good agreement. All hypothesized correlations between the 3L, 5L, SF-6Dv2, and items of WHODAS were supported, indicating satisfactory convergent validity. The 5L showed stronger correlations (|r|= 0.53-0.84) with WHODAS than the other two measures. The outcomes of ANOVA indicated that the 5L had higher F-statistics than the 3L and SF-6Dv2, indicating a stronger discriminant ability to differentiate most condition groups. The 5L demonstrates lower ceiling and floor effects, higher discriminant ability, and better convergent validity than the SF-6Dv2 and 3L in patients with PD. In addition, the 5L may generate a larger utility gain compared to the other two instruments when conducting cost-effectiveness analysis.

Construction of dose prediction model and identification of sensitive genes for space radiation based on single-sample networks under spaceflight conditions

Purpose To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space. Methods and materials A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them. Results Our model exhibited excellent performance with the following metrics: R 2 = 0.980 , MSE = 6.74 e − 04 , and the Pearson correlation coefficient of 0.990 (p value <.0001) between predicted and actual values. We identified 20 key genes, the majority of which had been proven to be associated with radiation. However, we uniquely established them as space radiation sensitive genes for the first time. Through further analysis of the SSNs, we discovered that the different tissues exhibited distinct mechanisms in response to space stressors. Conclusions The topology structures of SSNs effectively predicted radiation doses under spaceflight conditions, and the SSNs revealed the gene regulatory patterns within the organisms under space stressors.

The Impact of Photopigment Bleaching on the Human Rod Photoreceptor Subretinal Space Measured Via Optical Coherence Tomography.

The purpose of this study was to investigate rod photopigment bleaching-driven intrinsic optical signals (IOS) in the human outer retina and its measurement repeatability based on a commercial optical coherence tomography (OCT) platform. The optical path length of the rod photoreceptor subretinal space (SRS), that is, the distance between signal bands of rod outer segment tips and retinal pigment epithelium, was measured in 15 healthy subjects in ambient light and during a long-duration bleaching white-light exposure. On 2 identical study days (day 1 and day 2 [D1 and D2]), light stimulation resulted in a significant decrease in rod SRS by 21.3 ± 7.6% and 19.8 ± 8.5% (both P<0.001), respectively. The test-retest reliability of the SRS maximum change of an individual subject was moderate for single measures (intraclass correlation coefficient [ICC] = 0.730, 95% confidence interval [CI] = 0.376, 0.900, P<0.001) and good for average measures (ICC = 0.844, 95% CI = 0.546, 0.947, P<0.001). The mean area under the stimulus response curve with values of 14.8 ± 9.4 and 15.5 ± 7.5µm × minutes (P=0.782) showed excellent agreement between the stimulus response on D1 and D2. Intermittent dark adaptation of the retina led to an initial increase of the SRS by 6.1% (P=0.018) and thereafter showed a decrease toward baseline, despite continued dark adaptation. The data indicate the potential of commercial OCT in measuring slow IOS in the outer retina suggesting that the rod SRS could serve as a biomarker for photoreceptor function. The presented approach could provide an easily implementable clinical tool for the early detection of diseases affecting photoreceptor health.

Mathematical modelling, analysis and numerical simulation of social media addiction and depression.

We formulate a mathematical model of social media addiction and depression (SMAD) in this study. Key aspects, such as social media addiction and depression disease-free equilibrium point (SMADDFEP), social media addiction and depression endemic equilibrium point (SMADEEP), and basic reproduction number (R0), have been analyzed qualitatively. The results indicate that if R0 < 1, the SMADDFEP is locally asymptotically stable. The global asymptotic stability of the SMADDFEP has been established using the Castillo-Chavez theorem. On the other hand, if R0 > 1, the unique endemic equilibrium point (SMADEEP) is locally asymptotically stable by Lyapunov theorem, and the model exhibits a forward bifurcation at R0 = 1 according to the Center Manifold theorem. To examine the model's sensitivity, we calculated the normalized forward sensitivity index and conducted a Partial Rank Correlation Coefficient (PRCC) analysis to describe the influence of parameters on the SMAD. The numerical results obtained using the Fourth-order Runge-Kutta (RK-4) scheme show that increasing the number of addicted individuals leads to an increase in the number of depressed individuals.

Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.

To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. 32 Patients (23M/9F; age 61.8 ± 10.6years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73cm) undergoing resection were prospectively recruited. All patients underwent 1.5T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.

Predicting the Efficacy of Novel Synthetic Compounds in the Treatment of Osteosarcoma via Anti-Receptor Activator of Nuclear Factor-κB Ligand (RANKL)/Receptor Activator of Nuclear Factor-κB (RANK) Targets.

Osteosarcoma (OS) currently demonstrates a rising incidence, ranking as the predominant primary malignant tumor in the adolescent demographic. Notwithstanding this trend, the pharmaceutical landscape lacks therapeutic agents that deliver satisfactory efficacy against OS. This study aimed to authenticate the outcomes of prior research employing the HM and GEP algorithms, endeavoring to expedite the formulation of efficacious therapeutics for osteosarcoma. A robust quantitative constitutive relationship model was engineered to prognosticate the IC50 values of innovative synthetic compounds, harnessing the power of gene expression programming. A total of 39 natural products underwent optimization via heuristic methodologies within the CODESSA software, resulting in the establishment of a linear model. Subsequent to this phase, a mere quintet of descriptors was curated for the generation of non-linear models through gene expression programming. The squared correlation coefficients and s2 values derived from the heuristics stood at 0.5516 and 0.0195, respectively. Gene expression programming yielded squared correlation coefficients and mean square errors for the training set at 0.78 and 0.0085, respectively. For the test set, these values were determined to be 0.71 and 0.0121, respectively. The s2 of the heuristics for the training set was discerned to be 0.0085. The analytic scrutiny of both algorithms underscores their commendable reliability in forecasting the efficacy of nascent compounds. A juxtaposition based on correlation coefficients elucidates that the GEP algorithm exhibits superior predictive prowess relative to the HM algorithm for novel synthetic compounds.

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment.

A Study on Agri-preneurial Behaviour of Kalanamak Rice Growers in Siddharth Nagar and Sant Kabir Nagar District of Uttar Pradesh, India

Kalanamak rice, hailed for its nutritional potency and health benefits, presents a promising avenue for alleviating these challenges. This study, titled “A Study on Agri- preneurial Behaviour of Kalanamak Rice Growers in Siddharth Nagar and Sant Kabir Nagar District of Uttar Pradesh, India,” delves into this potential within the precincts of Siddharth Nagar and Sant Kabir Nagar district. The investigation hones in on two blocks – Birdpur in Siddharth Nagar, and Santha in Sant Kabir Nagar. Methodologically rigorous, the study selected six villages from each block and engaged with a total of 120 Kalanamak growers. Data collection unfurled through personalized interviews, facilitated by a meticulously crafted questionnaire. To unravel patterns and nuances, statistical tools such as frequency, percentage, mean, and standard deviation orchestrated the classification of data. The coefficient of correlation `r` was invoked to fathom the interplay between Kalanamak growers` attributes, their knowledge, and their adoption of Kalanamak rice. Analysis of the respondent demographics spotlighted several noteworthy trends. A preponderance (70.83%) emerged from the middle age group, with educational attainments peaking at the middle school (34.17%). In terms of occupation, 45% were engaged in agriculture, The agrarian landscape was marked by a significant representation from the Other Backward Classes (OBC) category (49.17%), Landholding skewed towards the smaller end (49.17%), Family dynamics, as reflected in size and structure, were characterized by medium level (64.17% and 90.83%, respectively).

Influence of Air Pollution and Climate on Daily Health of Gabonese Students in the Capital (Libreville): A Pilot Study

Air pollution is a global issue affecting billions of people, especially urban populations in low- and middle-income countries. Since air pollution is a health problem that also involves the climate, this study assessed the relationship between the individual health of 50 college students living in Libreville (Gabon) and air quality, temperature, and humidity using low-cost equipment. The study utilised 81 days of data from the air pollution monitoring network in Libreville. These data were paired with measurements of heart rate, breathing, and stress measured using 5 Garmin vívosmart® smartwatches. In addition to the environmental and health data, participants were also asked about their state of health and their lifestyle during the study. We found average concentrations of PM2.5 (25.25 µg/m3) and PM10 (29.50 µg/m3) that exceed the 24-h WHO air quality standards. The daily average of PM1 was around 17.84 µg/m3. Temperatures observed during the study period varied between 27 and 35 °C (mean = 30.15 °C), and humidity was around 51-77% (mean = 59.5%). Overall, the relationships between the environmental conditions and the health observations were negligible, with correlation coefficients R ≤ 0.36. Despite their weakness, coefficients between 0.31 and 0.36 showed that stress levels are associated with temperature, PM2.5 and PM10. The average stress level was associated with PM10 (R = 0.34) and PM2.5 (R = 0.36). Finally, the study reveals that smokers, mosquito repellent users, and fan users have reduced breathing capacities compared to non-smokers, non-users of mosquito repellent products, non-users of fans, users and non-users of air conditioning. The study recommends the use of portable sensors to measure individual exposure to environmental parameters for similar studies, which will resolve the problem of spatial representativeness highlighted in this study. It is also recommended that Gabon's health policies incorporate early warnings of forecast high temperatures or pollution peaks, in order to limit health risks

Machine Learning Study on the Virtual Screening of Donor–Acceptor Pairs for Organic Solar Cells

The selection of electron donors and nonfullerene acceptors (NFAs) in organic solar cells (OSCs) is crucial for improving photovoltaic performance. Machine learning (ML) has brought a breakthrough solution. Herein, 292 donor‐NFA pairs with experimental OSC parameters from the reported articles are collected. The ML descriptors include device processing parameters, molecular properties, and molecular structure. The five ML regression models, random forest (RF), extra tree regression, gradient boosting regression tree, adaptive boosting, and artificial neural network (ANN) are trained. GridSearchCV is used for hyperparameter optimization of ML regression models. The SHapley Additive exPlanation approach is employed to analyze descriptor importance. Among the trained five ML models, the RF model shows superior performance, achieving Pearson's correlation coefficient (r) of 0.81 on the test set. Based on the donors and NFAs in constructed dataset, the 9779 donor–NFA pairs for OSCs are generated by randomly combining donor and acceptor molecules. The trained RF model is utilized to predict the power conversion efficiency (PCE) of new donor–acceptor pairs for OSCs. The results indicate that the OSC composed of PBDB‐TF as donor and L8‐BO as acceptor can achieve the remarkable PCE of 17.9%.

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE &lt; 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

Rationing of nursing care and assessment of work safety and a healthy work environment in intensive care units: A cross-sectional, correlational study

IntroductionRationing of nursing care, whichrefers to the aspects of care not delivered by nurses in an intensive care unit (ICU), has implicationsfor patient outcomes and experiences. ObjectivesThis study aimed to identify the extent to which nursing care is rationed in intensive care units, as well as asses quality of nursing care, and the level of job satisfaction and its correlation with an assessment of the climate of work safety, teamwork, and a healthy work environment. MethodologyA cross-sectional, correlational study was conducted. The study included 226 ICU nurses. It was conducted with the use of three instruments: the Perceived Implicit Rationing of Nursing Care (PRINCA) questionnaire on the rationing of nursing care, assessment of patient care quality and job satisfaction, American Association of Critical-Care Nurses Healthy Work Environment Assessment Tool (HWEAT) and the Safe Attitudes and Behaviours Questionnaire questionnaire in the version: Teamwork and Safety Climate (BePoZa). SettingsIntensive Care Units in Warmia and Mazury Region in Poland. Main Outcome MeasuresLevel of Nursing Care Rationing in Intensive Care Units. ResultsThe majority of participants were women (89.82 %) with a mean age of 42.47 years. The average score for nursing care rationing across all groups was 0.58. The mean score for the HWEAT was 2.7 and BePoZa was 3.72. The scores from the questionnaires were negatively correlated with the nursing care rationing scores, being −0.36 for the HWEAT and −0.45 for BePoZa. All correlation coefficients were statistically significant at a p-value of less than 0.05. ConclusionsIt is important to monitor work safety, teamwork climate, and standards of a healthy work environment in ICUs to minimise the risk of rationing nursing care. Implications for Clinical PracticeInterventions that enhance work organisation and teamwork can elevate nursing quality and job satisfaction in ICUs, while underestimating patient care tasks; thus, highlighting the need for further research on the factors influencing nursing performance.

Translation and validation of the Chinese version of the menstrual distress questionnaire

Background The Menstrual Distress Questionnaire (MDQ) is a commonly used questionnaire that assesses various symptoms and distress associated with the menstrual cycle in women. However, the questionnaire has not been completely translated into Chinese with rigorous reliability and validity testing. Methods This study translated the Menstrual Distress Questionnaire Form Cycle (MDQC) from English into Chinese: MDQCC in two stages. First, it was translated forward and backward using Jones’ model; second, to test the validity and reliability, 210 Chinese-speaking women were recruited through online announcements and posters posted between June 2019 and May 2020. Expert validity, construct validity, convergent validity, and factorial validity were determined using content validity index (CVI), intraclass correlation coefficient (ICC), composite reliability (CR), and exploratory factor analysis, respectively. For concurrent criterion validity, MDQCC score was compared with three existing pain scales. Reliability was evaluated using internal consistency across items and two-week test-retest reliability over time. Results The CVI for content validity was .92. Item-CVI for expert validities among the 46 items ranged from .50 − 1; scale-CVI for the eight subscales, from .87 − 1; ICC, from .650 − .897; and CRs, from .303 − .881. Pearson correlation coefficients between MDQCC and short-form McGill pain questionnaire, present pain intensity, and visual analog scale scores were .640, .519, and .575, respectively. Cronbach’s α for internal consistency was satisfactory (.932). ICC for test-retest reliability was .852 for the entire MDQCC. Conclusion MDQCC was valid and reliable for Mandarin Chinese-speaking women. It can be used to evaluate female psychiatric symptoms related to the menstrual cycle in future work.

Comparative Biomechanical Study Between Shooting with Two Points and Three Points from Specific Areas Inside and Outside the Basketball Shooting Area

The aim of the current research is to identify the relationships and significance of differences in displacements, horizontal and vertical velocities for the strength and accuracy of shooting by jumping with two and three points from specific areas inside and outside the basketball shooting zone at the moment of pivot. It also aims to identify the relationships and significance of differences in displacements, horizontal and vertical velocities for the strength and accuracy of shooting by jumping with two and three points from specific areas inside and outside the basketball shooting zone at the moment of shooting. The research methodology utilized a descriptive approach using survey studies with their tools and procedures, relying on video analysis as it suited the nature of the research. The research sample consisted of three players who were purposely selected to be similar in physical characteristics and skilled in shooting a basketball. Each player performed 10 attempts, divided into five two-point and five three-point shooting attempts. The best three attempts for each player in both types of shooting, which achieved the highest level of accuracy according to the test, were selected from specific areas inside and outside the basketball shooting zone. Thus, the actual attempts subjected to statistical processing were 18 attempts, divided into nine attempts for shooting with two points from areas within the shooting zone and nine attempts for shooting with three points from specific areas outside the shooting zone. The researcher concluded that the logarithmic differences between correlation coefficients for horizontal and vertical displacements and velocities for the strength and accuracy of shooting by jumping with two and three points from specific areas inside and outside the basketball shooting zone favored shooting with three points. Also, the logarithmic differences between correlation coefficients for horizontal and vertical velocities for the strength and accuracy of shooting by jumping with two and three points from specific areas inside and outside the basketball shooting zone favored shooting with three points.