Accuracy Rate Research Articles

Investigating the Precise Identification of Citrullination Sites with High-Performance Score Metrics using a Powerful Computation Predicting Tool.

To elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs is time-consuming and costly. However, there is a limited scope of the current PCS predictors. In particular, most predictors are commonly used for PCS prediction and have limited performance scores. This work aims to provide an improved sophisticated predictor of citrullination sites using a benchmark dataset in a machine learning platform. This study presents a reliable citrullination site predictor based on a benchmark dataset containing a 1:1 ratio of positive and negative samples. We classified citrullination sites using the Composition of the K-Spaced Amino Acid Pairs (CKSAAP) and Support Vector Machine (SVM). We developed PCS predictors using integrated machine-learning methods that produced the highest average scores. Using 10-fold cross-validation on test datasets, the True Positive Rate (TPR) was 98.34%, the True Negative Rate (TNR) was 99.44%, the accuracy was 98.89%, the Mathew Correlation Coefficient (MCC) was 98.21%, the Area Under the ROC Curve (AUC) was 0.999, and the partial Area Under the ROC Curve (pAUC) was 0.1968. According to overall performance, our developed predictor has a significantly higher implementation in comparison with the current tools on the same benchmark dataset. Moreover, it showed better performance metrics on both test and training datasets. Our developed predictor is promising and can be implemented as a complementary technique for identifying fast and precise citrullination sites.

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics

The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

A clinical evaluation and acceptability study of the innovative SurePulse VS wireless heart rate monitor across the neonatal journey

BackgroundThe SurePulse vital signs (VS) device is an innovative wireless heart rate monitor designed for neonatal patients. This study evaluates the application of SurePulse VS technology in clinical practice.MethodsData were collected about the quantitative metrics of the device itself when deployed on real infants and qualitative feedback from perinatal professionals and parents regarding their experiences using this novel technology.ResultsThis study recruited 101 infants and achieved target completion rates of 101 healthcare professional (HCP) and 51 parent questionnaires over the seven-month study period. The SurePulse device was deployed across a range of gestational ages (34–39 weeks) and birth weights (1.8–3.5 kg). Device deployment was performed across a range of clinical environments, with 51% of deployments at delivery and 47% within the neonatal unit. The data show clinically acceptable timings from device deployment to heart rate signal acquisition [median 20 s (IQR 15–76 s)]. HCP feedback rated SurePulse monitoring as “Always” or “Mostly” reliable in 80% of cases. Parental feedback reported that having the SurePulse device was reassuring, convenient and beneficial to them. These positive comments were reflected across device deployment in the delivery room and within the neonatal unit.ConclusionsThe study findings show that the SurePulse device has potential to be a significant advancement in the way neonatal patients are monitored in a variety of post-delivery circumstances. This study has demonstrated that the SurePulse device has utility throughout the neonatal journey, enabling accurate heart rate monitoring in a manner that promotes parent-infant contact and bonding.

Snapshot of Anti-SARS-CoV-2 IgG Antibodies in COVID-19 Recovered Patients in Guinea.

Background: Because the regular vaccine campaign started in Guinea one year after the COVID-19 index case, the profile of naturally acquired immunity following primary SARS-CoV-2 infection needs to be deepened. Methods: Blood samples were collected once from 200 patients (90% of African extraction) who were recovered from COVID-19 for at least ~2.4 months (72 days), and their sera were tested for IgG antibodies to SARS-CoV-2 using an in-house ELISA assay against the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike1 protein (RBD/S1-IH kit). Results: Results revealed that 73% of sera (146/200) were positive for IgG to SARS-CoV-2 with an Optical Density (OD) ranging from 0.13 to 1.19 and a median value of 0.56 (IC95: 0.51-0.61). The median OD value at 3 months (1.040) suddenly decreased thereafter and remained stable around OD 0.5 until 15 months post-infection. The OD median value was slightly higher in males compared to females (0.62 vs. 0.49), but the difference was not statistically significant (p-value: 0.073). In contrast, the OD median value was significantly higher among the 60-100 age group (0.87) compared to other groups, with a noteworthy odds ratio compared to the 0-20 age group (OR: 9.69, p-value: 0.044*). Results from the RBD/S1-IH ELISA kit demonstrated superior concordance with the whole spike1 protein ELISA commercial kit compared to a nucleoprotein ELISA commercial kit. Furthermore, anti-spike1 protein ELISAs (whole spike1 and RBD/S1) revealed higher seropositivity rates. Conclusions: These findings underscore the necessity for additional insights into naturally acquired immunity against COVID-19 and emphasize the relevance of specific ELISA kits for accurate seropositivity rates.

Testing for distributional structural change with unknown breaks: application to pricing crop insurance contracts

Abstract Agriculture in developed countries is produced under heavily subsidized insurance. The pricing of these insurance contracts, termed premium rates, directly influences farmers profits, their financial solvency, and indirectly, global food security. Changing climate and technology have likely caused significant shifting of mass in crop yield distributions and, if so, has rendered some of the historical yield data irrelevant for estimating premium rates. Insurance is primarily interested in lower tail probabilities and as such the detection of structural change in tail probabilities or higher moments is of great concern for the efficacy of crop insurance programs. We propose a test for structural change with an unknown break(s) which has power against structural change in any moment and can be tailored to a specific range of the underlying distribution. Simulations demonstrate better finite sample performance relative to existing methods and reasonable performance at identifying the break. The asymptotic distribution is shown to follow the Kolmogorov distribution. Our proposed test finds structural change in most major U.S. field crop yields leading to significant premium rate differences. Results of an out-of-sample premium rating game indicate that incorporating structural change in crop yields leads to more accurate premium rates.

Numerical approach for investigating the influence of various flow and fluid parameters on the performance of a cryogenic two-phase flow meter

Despite the intricacy, inline metering of two-phase flow has a significant impact in multitudinous applications, including nuclear fusion reactors, particle accelerators, and other systems that use cryogenic fluids. In this regard, a new concept for two-phase flow metering has been developed using model fluids. However, the geometric configurations of the channels and fluid properties are pivotal in ensuring accurate flow rate measurement. In this study, numerical analysis are performed to investigate the influence of channel width, number of channels, and gravitational field on the performance of the flow meter. The liquid height extracted from the volume fraction contour using an image processing tool has been used for calculating the mass flow rate of the liquid. The measured flow rate has been compared with the theoretical data, and it shows acceptable correspondence within ±6 %. The investigations suggest that the increase in channel width governs the slope development. The appropriate width of the channel has been found to be ranging between 1 mm and 3 mm for the selected flow conditions and configurations. It is found that as the number of channels is increased from 10 to 20, the flow rate range that the system can measure within ±5 % error has increased from 250 g/s to 450 g/s. The analysis broadened the reliability of the supposition and, therefore, provided a sturdy base for developing a cryogenic two-phase flow meter.

Statistical evaluation of individual external exposure dose of outdoor worker and ambient dose rate at evacuation ordered zones after the Fukushima Daiichi nuclear power station accident.

Following the accident at the Fukushima Daiichi Nuclear Power Station, evacuation orders were issued for the surrounding communities. In order to lift the evacuation order, it is necessary to determine individual external doses in the evacuated areas. The purpose of this study was to determine the quantitative relationship between individual external doses and ambient dose rates per hour as conversion coefficients.
More specifically, individual external doses of Tokyo Electric Power Company Holdings employees in difficult-to-return zone were measured broadly over a long period (FY2020 to FY2022). To obtain highly accurate estimates, we used not only ambient dose rates based on airborne radiological monitoring data, but also Integrated dose rate map data that had been statistically corrected to correspond to local ambient dose rate gradients on the ground.
 As a result, the conversion coefficients based on the ambient dose rate map measured by airborne radiological monitoring were 0.42 for the Evacuation-Order Lifted Zones (ELZs), 0.37 for the Special Zones for Reconstruction and Rehabilitation (SZRRs), and 0.47 for the Difficult-to-Return Zones without a SZRRs (DRZs). On the other hand, the conversion coefficients based on the Integrated dose rate map which is a highly accurate dose rate map based on statistical analysis of various types of monitoring that have been studied in government projects in recent years, were 0.78 for the ELZs, 0.72 for the SZRRs and 0.82 for the DRZs. Using these conversion coefficients, the individual external dose can be estimated from two representative ambient dose rate maps provided by the government.&#xD.

Definitions and surveillance methods of running‐related injuries: A scoping review

AbstractInconsistent and restricted definitions of injury have contributed to limitations in determining injury rates and identifying risk factors for running‐related injuries (RRIs). The aim of this scoping review was to investigate the definitions and surveillance methods of RRIs. A systematic electronic search was performed using PubMed, Scopus, SPORTDiscuss, MEDLINE, and Web of Science databases. Included studies were published in English between January 1980 and June 2023 which investigated RRIs in adult running populations, providing a definition for a general RRI. Results were extracted and collated. 204 articles were included. Three primary criteria were used to define RRIs: physical description, effect on training and medical intervention, while three secondary criteria are also associated with definitions: cause/onset of injury, location, and social consequences. Further descriptors and sub‐descriptors form these criteria. The use of Boolean operators resulted in nine variations in definitions. Inconsistency is evident among definitions of RRIs. Injury definitions seem to be important for two main reasons: firstly, determining accurate injury rates, and secondly, in research examining risk factors. For the latter, definitions seem to be very limited, only capturing severe injuries and failing to recognise the full development process of RRIs, precluding the identification of conclusive risk factors. A potential two‐approach solution is the initial use of a broad definition acting as a gatekeeper for identifying any potential injury, and follow‐up with an extensive surveillance tool to capture the specific consequences of the varying severity of RRIs.

A new approach to predict pump transient phenomena in Molten salt reactor Experiment (MSRE) by missing data identification and regeneration

Molten Salt Reactor Experiment (MSRE) is being extensively used for validating the computational tools for Molten Salt Reactors (MSRs). The pump transient tests were performed during the operation of MSRE to obtain the reactivity response to flow perturbation. The transient flow rate during this set of tests is required as input to predict the reactivity response. Since the primary loop of MSRE was not equipped with a flow rate meter, the transient flow rate, which is the crucial factor for the reactivity change in MSRE, is missing. Achieving an accurate simulation of the reactivity response to the MSRE pump transient tests necessitates a precise estimation of the transient flow rate.This paper endeavors to reconstruct the missing flow rate with the aim of offering a valuable input for simulating reactivity responses in the pump transient test series. In order to obtain an accurate transient flow rate, we employed a centrifugal pump transient model based on the affinity laws and solved the model for the MSRE secondary pump to validate the underlying assumptions of the model. In addition, we constructed the homologous pump head curves based on the water test data for the same purpose. The affinity law approximation is proved to be adequate in predicting the MSRE pump startup transient with the root-mean-square error (RMSE) in the normalized flow rate to be 0.03. On the other hand, for the MSRE pump coastdown transient, the preliminary results indicate that neither the affinity law approximation nor the homologous head curve is sufficient to provide acceptable predictions. To overcome this noticeable modeling deficit, we propose an innovative approach based on a straightforward data mining technique to regenerate the MSRE pump coastdown homologous relations using the measured data of the secondary pump. These relations are transferred to the primary pump and used to simultaneously solve for the impeller speed and the flow rate of the pump in the primary pump coastdown. With the new approach, the estimated RMSE in the normalized pump speed for the primary pump coastdown is reduced 0.0052. This excellent agreement validates the accurate calculations for the flow rate during the pump coastdown transient. We also performed an uncertainty analysis to quantify the confidence interval of the predicted quantities, which further justifies the robustness of the proposed approach.

Lung cancer detection based on computed tomography image using convolutional neural networks.

Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Kinetic Computation of Cyclization Reactions of Large Keto-Hydroperoxide Radicals in Low Temperature Combustion.

The cyclization reactions of keto-hydroperoxide (KHP) radicals leading to the formation of keto cyclic ethers and OH radicals play an important role in low temperature combustion for hydrocarbon fuels or oxygenated hydrocarbon fuels. However, due to the lack of kinetic data of cyclization reactions of KHP radicals, researchers often derive high-pressure-limit rate constants of cyclization reactions of KHP radicals from analogous cyclization reactions of hydroperoxyl alkyl radicals during construction of the combustion mechanism. This study aims to systematically investigate the kinetics of cyclization reactions of KHP radicals involving short-to-large-sized radicals. The studied reactions are divided into 7 reaction classes, according to the size of the cyclic transition state, the conjugative effect (whether KHP radicals are resonance-stabilized or not), and the position of the carbonyl group (whether the carbonyl group is inside or outside of the reaction center). The isodesmic reaction method, in conjunction with transition state theory, is utilized for each reaction class to compute the energy barriers and high-pressure-limit rate constants at the DFT level. The study revealed that energy barriers calculated at the DFT level with correction by the isodesmic reaction method are close to the results from the benchmark CCSD(T) method. To develop more accurate rate rules, these reaction classes are further divided into subclasses based on the relative site of the OOH group with the carbonyl group, the type of carbon atoms where the OOH group is located, and the type of carbon atoms where the radical site is located. For each subclass, high-pressure-limit rate rules are derived by averaging the rate constants of reactions in the subclass, and it is found that the maximum absolute deviation of the energy barrier and the ratio of the largest rate constant to the smallest rate constant among reactions in each subclass are within chemical accuracy limits, indicating acceptable use of the developed rate rules. A comparison of the rate constants for cyclization reactions of KHP radicals with the values of analogous cyclization reactions of hydroperoxyalkyl radicals as provided in reported mechanisms is made. Additionally, a comparison is drawn between our developed rate rules for subclasses of the cyclization reactions of KHP radicals and the rate rules for analogous subclasses of cyclization reactions of hydroperoxyl alkyl radicals. These comparisons demonstrate significant differences and highlight the necessity for improved rate rules for cyclization reactions of KHP radicals to enhance the automatically generated combustion mechanisms for hydrocarbon and oxygenated hydrocarbon fuels.

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index.

Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models. Questionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors. The XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models. The XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty.

Impact of downstream obstructions on ogee weir efficiency: a regression analysis

ABSTRACT This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments the influence of various obstruction angles on Cd was scrutinized applying a suite of regression analysis to develop predictive models. The analysis was enriched by considering hydraulic parameters such as flow rate, water level, and weir geometry. Despite the established importance of Cd in hydraulic designs the nuanced effects of downstream obstructions have received limited attention highlighting a critical research gap. The findings highlight a strong correlation between obstruction angles and Cd, with developed regression models demonstrating notable predictive strength. Remarkably the models exhibited varying levels of accuracy, with the Random Forest regressor achieving an exceptionally low root mean square error (RMSE) of 0.005, indicating superior predictive performance. Conversely, traditional models like Decision tree and XG BOOST reflected higher RMSE values of 0.60, suggesting less predictive accuracy in this context. LASSO, Bayesian Ridge, and OMP regressors stood out with an RMSE of zero, denoting perfect predictions under the study's specific conditions.

Medicare Advantage Health Risk Assessments Contribute Up To $12 Billion Per Year To Risk-Adjusted Payments.

With Medicare Advantage (MA) enrollment surpassing 50percent of Medicare beneficiaries, accurate risk-adjusted plan payment rates are essential. However, artificially exaggerated coding intensity, where plans seek to enhance measured health risk through the addition or inflation of diagnoses, may threaten payment rate integrity. One factor that may play a role in escalating coding intensity is health risk assessments (HRAs)-typically in-home reviews of enrollees' health status-that enable plans to capture information about their enrollees. In this study, we evaluated the impact of HRAs on Hierarchical Condition Categories (HCC) risk scores, variation in this impact across contracts, and the aggregate payment impact of HRAs, using 2019 MA encounter data. We found that 44.4percent of MA beneficiaries had at least one HRA. Among those with at least one HRA, HCC scores increased by 12.8percent, on average, as a result of HRAs. More than one in five enrollees had at least one additional HRA-captured diagnosis, which raised their HCC score. Potential scenarios restricting the risk-score impact of HRAs correspond with $4.5-$12.3billion in reduced Medicare spending in 2020. Addressing increased coding intensity due to HRAs will improve the value of Medicare spending and ensure appropriate payment in the MA program.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

Deep Learning Models and Fusion Classification Technique for Accurate Diagnosis of Retinopathy of Prematurity in Preterm Newborn

اعتلال الشبكية الخداجي (ROP) هو السبب الأكثر شيوعًا لعمى الأطفال الذي لا رجعة فيه، ويعتمد تشخيصه وعلاجه على الدرجات الذاتية بناءً على سمات الأوعية الدموية في شبكية العين. ومع ذلك، فإن هذه الطريقة شاقة وعرضة للخطأ، لذا فإن الأساليب الآلية مرغوبة لمزيد من الدقة والإنتاجية. تهدف هذه الدراسة إلى تطوير نهج قائم على التعلم العميق للتشخيص الدقيق لمرض اعتلال الشبكية الخداجي الزائد عند الأطفال الخدج باستخدام نماذج التعلم التحويلية وتقنية تصنيف الاندماج. زودنا مركز الأمل للعيون في العيادة الخاصة في بغداد، العراق، بـ 2776 صورة لقاعدة فحص مرضى اعتلال الشبكية الخداجي بين عامي 2015 و2020، واستخدمنا هذه الصور لتدريب ثلاثة نماذج للشبكات العصبية التلافيفية العميقة (ResNet50 وDensenet161 وEfficientNetB5). تم استخدام نهج مصنف الاندماج لدمج النماذج الثلاثة من أجل تشخيص شامل ودقيق. تتميز النماذج الثلاثة بمعدلات دقة نسبية تبلغ 69.78٪ و80.57٪ و81.29٪ في تصنيفاتها الخاصة. ومع ذلك، زادت الدقة الإجمالية إلى 90.28 في المائة عند استخدام مصنف الاندماج. هذا يدل على أن الطريقة المقترحة مفيدة لتحديد اعتلال الشبكية الخداجي عند الخدج. تشير نتائج الدراسة إلى أن الطريقة المقترحة لديها القدرة على تعزيز الدقة والسرعة التي يتم بها تشخيص اعتلال الشبكية الخداجي بشكل كبير، مما قد يؤدي بدوره إلى الكشف المبكر عن المرض وعلاجه وتقليل احتمالية الإصابة بعمى الأطفال.

Exploration of the individual and combined effects of predictors of engagement, dropout, and change from digital interventions for recurrent binge eating.

Our ability to predict responsiveness to digital interventions for eating disorders has thus far been poor, potentially for three reasons: (1) there has been a narrow set of predictors explored; (2) prediction has mostly focused on symptom change, ignoring other aspects of the user journey (uptake, early engagement); and (3) there is an excessive focus on the unique effects of predictors rather than the combined contributions of a predictor set. We evaluated the univariate and multivariate effects of outcome predictors in the context of a randomized trial (n = 398) of digitally delivered interventions for recurrent binge eating. Thirty baseline variables were selected as predictors, ranging from specific symptoms, to key protective factors, to technological acceptance, and to online treatment attitudes. Outcomes included uptake, early engagement, and remission. Univariate (d) and multivariate (D) standardized mean differences were calculated to estimate the individual and combined effects of predictors, respectively. At the univariate level, few predictors produced an effect size larger than what is considered small (d > .20) across outcomes. However, our multivariate approach enhanced prediction (Ds = .65 to 1.12), producing accuracy rates greater than chance (63%-71% accuracy). Less than half of the chosen variables proved to be useful in contributing to predictions in multivariate models. Findings suggest that accuracy in outcome prediction from digitally delivered interventions may be better driven by the aggregation of many small effects rather than one or several largely influential predictors. Replication with different data streams (sensor, neuroimaging) would be useful. Our ability to predict who will and will not benefit from digital interventions for eating disorders has been poor. We highlight the viability of a multivariate approach to outcome prediction, whereby prediction may be better driven by the aggregation of many small effects rather than one or a few influential predictors.

Analysis and prediction of the performance and reliability of PV modules installed in harsh climates: Case study Iraq

The increasing global demand for renewable energy necessitates a comprehensive understanding of solar photovoltaic (PV) system performance and reliability, particularly in harsh climates such as Iraq. Despite ambitious targets to diversify its energy sector, Iraq faces challenges in the deployment of PV projects due to limited field experience. In this study, we assess the reliability and performance of two different PV systems installed in Basrah and Baghdad, aged 3.5 and 8 years, respectively. Field analysis reveals prevalent issues including glass and cell breakage, delamination, solder bond fatigue, and encapsulant discoloration, contributing to medium degradation rates of 0.91 %/year and 2.6 %/year in Basrah and Baghdad, respectively. Our investigation attributes higher degradation rates not only to ageing but also to suboptimal operation and maintenance (O&M) practices. Additionally, since the two systems are from different manufacturers, we verify that the measured higher degradation rates are mainly attributed to harsh operating conditions rather than differences in manufacturing processes. To extrapolate our findings countrywide, we employ a physics-based model to simulate the degradation rates. Based on the simulated degradation, we proposed four degradation rate zones across the country with degradation rates ranging from 0.62 %/year to 0.96 %/year. By applying these rates to estimate lifetime energy yield across different zones, we demonstrate the trade-offs between higher irradiance zones with reduced PV lifetime and low irradiance zones with longer PV lifetimes. In the study, we compared energy yield simulations using fixed degradation rates with those employing climate-dependent degradation rates. Our analysis revealed that in certain locations in Iraq, employing a fixed degradation rate underestimates the yield by approximately 9.7 %. Conversely, in other locations, it results in overestimations ranging from approximately 10.5 %–31.1 %, highlighting the importance of accurate degradation rate modelling for PV system assessment. Furthermore, we simulate the impact of soiling losses on energy yield, revealing potential losses of up to 70 % depending on location and cleaning schedules. Our findings contribute valuable insights into PV system degradation across harsh climates, addressing critical gaps in global degradation rate data and facilitating more accurate climate-dependent assessments of PV performance and reliability.

The Evolutionary Interplay of Somatic and Germline Mutation Rates.

Novel sequencing technologies are making it increasingly possible to measure the mutation rates of somatic cell lineages. Accurate germline mutation rate measurement technologies have also been available for a decade, making it possible to assess how this fundamental evolutionary parameter varies across the tree of life. Here, we review some classical theories about germline and somatic mutation rate evolution that were formulated using principles of population genetics and the biology of aging and cancer. We find that somatic mutation rate measurements, while still limited in phylogenetic diversity, seem consistent with the theory that selection to preserve the soma is proportional to life span. However, germline and somatic theories make conflicting predictions regarding which species should have the most accurate DNA repair. Resolving this conflict will require carefully measuring how mutation rates scale with time and cell division and achieving a better understanding of mutation rate pleiotropy among cell types.

Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets.

Detailed superiority of CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for polyp detection/diagnosis, compared to endoscopists is not well examined. We examined endoscopist's ability using movie sets of colorectal lesions which were detected and diagnosed by CAD EYE accurately. Consecutive lesions of ≤10 mm were examined live by CAD EYE from March-June 2022 in our institution. Short unique movie sets of each lesion with and without CAD EYE were recorded simultaneously using two recorders for detection under white light imaging (WLI) and linked color imaging (LCI) and diagnosis under blue laser/light imaging (BLI). Excluding inappropriate movies, 100 lesions detected and diagnosed with CAD EYE accurately were evaluated. Movies without CAD EYE were evaluated first by three trainees and three experts. Subsequently, movies with CAD EYE were examined. The rates of accurate detection and diagnosis were evaluated for both movie sets. Among 100 lesions (mean size: 4.7±2.6 mm; 67 neoplastic/33 hyperplastic), mean accurate detection rates of movies without or with CAD EYE were 78.7%/96.7% under WLI (p<0.01) and 91.3%/97.3% under LCI (p<0.01) for trainees and 85.3%/99.0% under WLI (p<0.01) and 92.6%/99.3% under LCI (p<0.01) for experts. Mean accurate diagnosis rates of movies without or with CAD EYE for BLI were 85.3%/100% for trainees (p<0.01) and 92.3%/100% for experts (p<0.01), respectively. The significant risk factors of not-detected lesions for trainees were right-sided, hyperplastic, not-reddish, in the corner, halation, and inadequate bowel preparation. Unique movie sets with and without CAD EYE could suggest it's efficacy for lesion detection/diagnosis.