Error Research Articles

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Performance prediction of flame-retardant clothing using correlations and artificial neural networks: Optimizing firefighter safety

Flame-Retardant Clothing serves as a protective shield for firefighters that safeguards them from exposure to heat, flames and other thermal hazards. To achieve an optimal design for the clothing, it is essential to simultaneously account for all the factors affecting the performance of the clothing. The present study employs data from a numerical model to explore heat and moisture transport through clothing subjected to flame exposure. Seventeen non-dimensionless parameters associated with the heat and moisture transport in flame-retardant clothing are obtained. A correlation is developed to link the dimensionless second-degree burn time with other non-dimensional parameters. This correlation provides a means to predict the thermal protective performance (TPP) of the clothing. Additionally, an Artificial Neural Network (ANN) method is employed to determine the TPP of the clothing. Multi-layer feedforward backpropagation networks are utilized to predict the TPP under specified exposure conditions. The findings indicate that both the correlations and the ANN approach adopted in the present study demonstrated promising results. However, the ANN model predictions show better agreement with model data in comparison to the results derived from the developed correlations. The maximum percentage error in the predicted non-dimensional second degree burn time using ANN is limited to 10 %.

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors.

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product of two vectors), where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-square approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures (e.g., manifolds) that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal components analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance deteriorates when the tensor's entries deviate from additive white Gaussian noise, we demonstrate that the performance of DeepTensor is robust to a wide range of distributions. We validate that DeepTensor is a robust and computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. In particular, DeepTensor offers a 6 dB signal-to-noise ratio improvement over standard denoising methods for signal corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster than a single DN equipped with 3D convolutions.

Uncertainty-Boosted Robust Video Activity Anticipation.

Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision and autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. This reduces the model generalization ability and deep understanding on video content, leading to serious error accumulation and degraded performance. In this paper, we address the uncertainty learning problem and propose an uncertainty-boosted robust video activity anticipation framework, which generates uncertainty values to indicate the credibility of the anticipation results. The uncertainty value is used to derive a temperature parameter in the softmax function to modulate the predicted target activity distribution. To guarantee the distribution adjustment, we construct a reasonable target activity label representation by incorporating the activity evolution from the temporal class correlation and the semantic relationship. Moreover, we quantify the uncertainty into relative values by comparing the uncertainty among sample pairs and their temporal-lengths. This relative strategy provides a more accessible way in uncertainty modeling than quantifying the absolute uncertainty values on the whole dataset. Experiments on multiple backbones and benchmarks show our framework achieves promising performance and better robustness/interpretability.

Derivation of fractured rock hybrid analytical equation for estimation of groundwater inflow into mine pit using major fault trigonometry

ABSTRACT In hydrogeology, equations describing exchange flux in sedimentary systems mainly focus on the geometry of the streambed–aquifer system and parameters. However, groundwater flow in fractured crystalline basement and volcanic rocks, which are mainly characterized by restricted flow, receives little attention in groundwater flow equations, possibly due to a lack of appropriate data. This study develops an analytical equation to estimate groundwater inflow into mine pits in volcanic rock, addressing a significant knowledge gap in the literature. The equation was validated through a case study evaluation at the Nsuta Mine, where it accurately estimated groundwater inflow into the pit. Results indicate that estimated inflow at the current pit floor level (−60 MSL) is 38 710 m3/day, which is close to the actual dewatering rate of 39 794 m3/day (error percentage of 2.8%). The study also projects groundwater inflow at a deeper pit level (−200 MSL) to be 82 400 m3/day with a corresponding flux of 0.331 m/day.

A Heat Sensitivity Scale for People with Parkinson's Disease.

People living with Parkinson's disease (PD) commonly experience heat sensitivity-worsening symptoms and restricted daily activities in heat. This study aimed to develop a scale of heat sensitivity for people with PD. Through a search of the scientific literature and online forums, we developed 41 items relating to experiences of heat for people with PD to assess heat sensitivity. A panel of experts was then consulted to review the scale items critically. After two rounds of review, the scale was refined to 36 items with an overall scale content validity index of 0.89. Via an online survey, 247 people with PD responded to the items. The items were examined with exploratory factor analysis to determine the underlying factors therein. After several iterations, a simple structure was achieved with 29 items loading uniquely onto one of four factors: daily activities, sweating and exercise, heat-related illness, and symptoms and medications. The model had acceptable to excellent fit statistics (root mean square error of approximation = 0.073 [90% confidence interval 0.067-0.081], root mean square of the residuals = 0.03, comparative fit index = 0.93, and Tucker-Lewis index = 0.91), and each factor showed high reliability (Cronbach's α ≥0.89). Factor and total scale scores were significantly higher among those reporting sensitivity to heat and poor health status. This new heat sensitivity scale for people living with PD can enable health professionals and clients to assess the severity and impact of heat sensitivity.

Block occurrences in the binary expansion

The binary sum-of-digits function [Formula: see text] returns the number of ones in the binary expansion of a nonnegative integer. Cusick’s Hamming weight conjecture states that for all integers [Formula: see text] the set of integers [Formula: see text] such that [Formula: see text] has asymptotic density strictly larger than [Formula: see text]. So far, the strongest results are due to Spiegelhofer and Wallner, who showed that for [Formula: see text] having sufficiently many blocks of [Formula: see text]s in its binary expansion the conjecture holds and the difference [Formula: see text] is approximately normally distributed. In this paper, we are concerned with a related block-counting function [Formula: see text], returning the number of (overlapping) occurrences of the block [Formula: see text] in the binary expansion of [Formula: see text]. Our main result is a similar central limit-type theorem for the difference [Formula: see text], where we give a stronger estimate of the uniform error of Gaussian approximation. As a corollary, we obtain a partial result toward an analogue of Cusick’s conjecture for [Formula: see text]: the set of [Formula: see text] satisfying [Formula: see text] has density [Formula: see text] for [Formula: see text] belonging to a set of density [Formula: see text].

A Caching-based Framework for Scalable Temporal Graph Neural Network Training

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have demonstrated remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, making them not scalable to large dynamic graphs. To address the limitations, we propose Orca , a novel framework that accelerates T-GNN training by caching and reusing intermediate embeddings. We design an optimal caching policy, named MRD , for the uniform cache replacement problem, where embeddings at different intermediate layers have identical dimensions and recomputation costs. MRD not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. For the general cache replacement problem, where embeddings at different intermediate layers can have different dimensions and recomputation costs, we solve this NP-hard problem by presenting a novel two-stage framework with approximation guarantees on the achieved benefit of caching. Furthermore, we have developed profound theoretical analyses of the approximation errors introduced by reusing intermediate embeddings, providing a thorough understanding of the impact of our caching and reuse schemes on model outputs. We also offer rigorous convergence guarantees for model training, adding to the reliability and validity of our Orca framework. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over state-of-the-art T-GNNs while achieving higher precision on various dynamic graphs.

Analysis of Microstructural and Wear Mechanisms for 3D-Printed PET CF15 Using Box–Behnken Design

We examined the impact of 3D-printing parameters, such as the deposition pattern, deposition speed, and layer height, on the tribological performance measured through the coefficient of friction and cumulative linear wear. Optimizing these factors can significantly influence material wear and friction, which is critical for ensuring durability and functionality in practical applications like a cylindrical gear assembly for a vertical-articulated robot. The purpose of the study was to investigate these relationships by employing the Box–Behnken design (BBD) method to systematically analyze the effects of these parameters, while also using scanning electron microscopy (SEM) for detailed microstructural characterization. The findings aim to provide insights that can guide the development of more efficient and wear-resistant 3D-printed materials. The strong impact of layer height on CLW was noted, showing that lower layer heights can either improve or worsen wear depending on the combination of speed and pattern, with layer height playing a dominant role in determining wear performance. Lower speeds and specific patterns, particularly lines and concentric patterns, tend to result in higher COF values. The validation test results, with a COF of 0.2215 and CLW of 29.2075, closely align with the predicted values of 0.2064 and 27.3, showing small percentage errors of 7.3% for COF and 6.5% for CLW.

Optimising Physics-Informed Neural Network Solvers for Turbulence Modelling: A Study on Solver Constraints Against a Data-Driven Approach

Physics-informed neural networks (PINNs) have emerged as a promising approach for simulating nonlinear physical systems, particularly in the field of fluid dynamics and turbulence modelling. Traditional turbulence models often rely on simplifying assumptions or closed numerical models, which simplify the flow, leading to inaccurate flow predictions or long solve times. This study examines solver constraints in a PINNs solver, aiming to generate an understanding of an optimal PINNs solver with reduced constraints compared with the numerically closed models used in traditional computational fluid dynamics (CFD). PINNs were implemented in a periodic hill flow case and compared with a simple data-driven approach to neural network modelling to show the limitations of a data-driven model on a small dataset (as is common in engineering design). A standard full equation PINNs model with predicted first-order stress terms was compared against reduced-boundary models and reduced-order models, with different levels of assumptions made about the flow to monitor the effect on the flow field predictions. The results in all cases showed good agreement against direct numerical simulation (DNS) data, with only boundary conditions provided for training as in numerical modelling. The efficacy of reduced-order models was shown using a continuity only model to accurately predict the flow fields within 0.147 and 2.6 percentage errors for streamwise and transverse velocities, respectively, and a modified mixing length model was used to show the effect of poor assumptions on the model, including poor convergence at the flow boundaries, despite a reduced solve time compared with a numerically closed equation set. The results agree with contemporary literature, indicating that physics-informed neural networks are a significant improvement in solve time compared with a data-driven approach, with a novel proposition of numerically derived unclosed equation sets being a good representation of a turbulent system. In conclusion, it is shown that numerically unclosed systems can be efficiently solved using reduced-order equation sets, potentially leading to a reduced compute requirement compared with traditional solver methods.

A study on solutions of fractional order equal-width equation using novel approach

The time-fractional equal-width equation plays a vital role in plasma physics, serving as an essential tool for elucidating the dynamics of hydromagnetic waves in cold plasma, a critical component for the comprehensive understanding of plasma-related phenomena. Furthermore, this equation finds application in fluid mechanics, which models the transmission of nonlinear waves across shallow waters. In this study, we employ an innovative technique to solve time-fractional equal-width equation, demonstrating the q-homotopy analysis transform technique. This considered technique was implemented to find the effective approximated solution of the considered equations. The obtained results are discussed through the 3D plots and graphs that express the physical representation. The results derived from the q-homotopy analysis transform method are presented in a series form, exhibiting swift convergence and capturing the behavior of the equal-width equation’s solution with minimal error, and the convergence analysis and uniqueness of the solution using the projected method have been secured using the fixed-point theory. The projected method we used does not require linearization, perturbations, or discretization, which significantly reduces computations. The results disclose that the proposed technique is highly effective, methodical, and easy to apply for complex and nonlinear systems, helping us to capture the associated behavior of diverse classes of phenomena. The numerical simulations presented ensure higher accuracy and are compared with other techniques to evaluate approximate errors. In comparison with other techniques, the considered method is a competent tool to get an analytical solution of an considered equation and the methodology employed herein for the considered equation is demonstrated to be both efficient and dependable, marking a significant advancement in the field.

Oxford Shoulder Instability Score: cross-cultural adaptation into Spanish and analysis of its methodological quality

BackgroundOxford Shoulder Instability Score (OSIS) is a patient reported outcome measure designed specifically to assess functional difficulties resulting from shoulder instability. The main aim of this study was to cross-culturally adapt the OSIS to Spanish. Secondary, it aimed to analyse its methodological quality.MethodsA cross-cultural adaptation to Spanish has been carried out following the recommendations of COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) with a sample of 167 cases of shoulder instability. Inclusion criteria were: subjects with instability symptoms in at least one shoulder with or without clinical diagnosis; aged between 18 and 60. The following psychometric properties were evaluated: validity (construct, internal and external), reliability (internal consistency, test-retest and measurement error), discriminant ability and feasibility. The methodological quality was addressed with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and COSMIN Risk of Bias checklist (COSMIN RoB).ResultsThe construct validity obtained an OSIS correlation with the Simple Shoulder Test of r = 0.636 and with the Western Ontario Shoulder Instability of r = 0.80. The unidimensionality of the OSIS was confirmed through second-order factor analysis. Cronbach’s alpha and intraclass correlation coefficient were both 0.93 (IC95%: 0.91–0.94). Standard error of measurement was 0.70, and the percentage of error and smallest detectable change were 1.46% and 1.94, respectively. No floor or ceiling effects were found. Assessing feasibility of OSIS, the participants answered all questions, had no questions and completion time was: mean 2 min 30 s; SD ± 1 min. Regarding methodological quality, the study showed low risk of bias in the areas of patient selection, index test, reference standard and flow and timing, as well as low concern regarding applicability in the domains of patient selection, index test and reference standard according to QUADAS-2. In relation to COSMIN RoB, all psychometric properties, except for content validity, obtained very good results.ConclusionsThe Spanish version of OSIS offers valid, reliable and feasible functional outcome measures for Spanish-speaking subjects with shoulder instability, based on its psychometric properties and its methodological quality.

Four New Patient-Reported Outcome Measures Examining Health-Seeking Behavior in Persons With Type 2 Diabetes Mellitus (REDD-CAT): Instrument Development Study.

The management of type 2 diabetes mellitus (T2DM) includes mastery of complex care activities, self-management skills, and routine health care encounters to optimize glucose control and achieve good health. Given the lifelong course of T2DM, patients are faced with navigating complex medical and disease-specific information. This health-seeking behavior is a driver of health disparities and is associated with hospitalization and readmission. Given that health-seeking behavior is a potentially intervenable social determinant of health, a better understanding of how people navigate these complex systems is warranted. To address this need, we aimed to develop new patient-reported outcome (PRO) measures that evaluate health-seeking behavior in persons with T2DM. These new PROs were designed to be included in the Re-Engineered Discharge for Diabetes-Computer Adaptive Test (REDD-CAT) measurement system, which includes several other PROs that capture the importance of social determinants of health. Overall, 225 participants with T2DM completed 56 self-report items that examined health-seeking behaviors. Classical Test Theory and Item Response Theory were used for measurement development. Exploratory factor analysis (EFA; criterion ratio of eigenvalue 1 to eigenvalue 2 being >4; variance for eigenvalue 1 ≥40%) and confirmatory factor analysis (CFA; criterion 1-factor CFA loading <.50; 1-factor CFA residual correlation >.20; comparative fit index ≥0.90; Tucker-Lewis index ≥0.90; root mean square error of approximation <0.15) were used to determine unidimensional sets of items. Items with sparse responses, low-adjusted total score correlations, nonmonotonicity, low factor loading, and high residual correlations of high error modification indices were candidates for exclusion. A constrained graded response model was used to examine item misfit, and differential item functioning was examined to identify item bias. Cronbach α was used to examine internal consistency reliability for the new PROs (criterion ≥0.70), and floor and ceiling effects were examined (criterion ≤20%). Four unidimensional sets of items were supported by EFA (all EFA eigenvalue ratios >4; variance for eigenvalue 1=41.4%-67.3%) and CFA (fit statistics all exceeded criterion values). This included (1) "Health-Seeking Behavior: PCP-Specific" (6 items); (2) "Health-Seeking Behavior: General Beliefs" (13 items); (3) "Health-Seeking Behavior: Family or Friends-Specific" (5 items); and (4) "Health-Seeking Behavior: Internet-Specific" (4 items). All items were devoid of differential item functioning for age, sex, education, or socioeconomic status factors. "Health-Seeking Behavior: General Beliefs" was developed to include both a computer adaptive test and a 6-item short form version; all other PROs were developed as static short forms. The psychometric reliability of these new PROs was supported; internal consistency ranged from acceptable to excellent (Cronbach α=.78-.91), and measures were free of significant floor or ceiling effects (floor effects range: 0%-8.9%; ceiling effects range: 0%-8.4%). The new REDD-CAT Health-Seeking Behavior PROs provide reliable assessments of health-seeking behaviors among those with T2DM.

Predictive model for congenital heart disease in children of Pakistan by using structural equation modeling

BackgroundThe structural abnormality of the heart and its blood vessels at the time of birth is known as congenital heart disease. Every year in Pakistan, sixty thousand children are born with CHD, and 44 in 1000 die before they are a month old. Various studies used different techniques to estimate the risk factors of congenital heart disease, but these techniques suffer from a deficiency of capacity to present human understanding and a deficiency of adequate data. The current study provided an innovative approach by defining the latent variables to handle this issue and building a reasonable model.MethodData used in this study has been collected from mothers and hospital records of the children. The dataset contains information on 3900 children who visited the OPD of the Chaudry Pervaiz Elahi Institute of Cardiology (CPEIC) Multan, Pakistan from October 2021 to September 2022. The latent variables were defined from the data and structural equation modeling was used to model them.ResultThe results show that there are 53.6% of males have acyanotic CHD and 54.5% have cyanotic CHD. There are 46.4% of females have acyanotic CHD and 45.5% have cyanotic CHD. The children who have no diabetes in the family are 64.0% and children who have diabetes in the family are 36.0% in acyanotic CHD, the children who have no diabetes in the family are 59.7% and children have diabetes in the family are 40.3% in cyanotic CHD. The value of standardized root mean residual is 0.087 is less than 0.089 which shows that the model is a good fit. The value of root mean square error of approximation is 0.113 is less than 0.20 which also shows the good fit of the model.ConclusionIt was concluded that the model is a good fit. Also, the latent variables, socioeconomic factors, and environmental factors of mothers during pregnancy have a significant effect in causing cyanotic while poor general health factor increases the risk of Acyanotic congenital heart disease.

Relationship among cancer treatment, quality of life, and oral function in head and neck cancer survivors: A cross-sectional study

PurposeTreatment for head and neck cancer (HNC), such as surgery and chemoradiotherapy, can reduce oral function and affect quality of life (QoL). However, whether HNC treatment affects QoL via the decline of oral function remains unclear. This study aimed to investigate the relationship among cancer treatment, QoL, and actual oral function in HNC survivors.MethodsA total of 100 HNC survivors who had completed definitive treatment for HNC at least 6 months prior to enrollment were enrolled in this cross-sectional study. QoL was evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 summary score. Oral diadochokinesis (ODK), tongue pressure, moisture level on the mucosal surface, and mouth opening were measured. Information on age, sex, tumor site, tumor stage, history of HNC treatment, height, body weight, and lifestyle were collected from medical records. Structural equation modeling (SEM) was conducted to analyze the indirect/direct associations among HNC treatment, QoL, and oral function.ResultsIn total, 100 HNC survivors (58 males and 42 females; age range, 30–81 years, median, 67 years) were analyzed. Overall, 63 patients (63.0%) were diagnosed as oral cancer, 66 (66.0%) developed advanced cancer (stage 3/4), and 58 (58.0%) underwent reconstruction surgery in 100 HNC survivors. The SEM results supported the hypothesized structural model (root mean square error of approximation = 0.044, comparative fit index = 0.990, Tucker-Lewis index = 0.986). Surgery with neck dissection and reconstruction for advanced cancer had indirect effects on lower QoL via ODK and mouth opening.ConclusionHNC treatment is indirectly associated with QoL via oral function in HNC survivors.

Rate coefficients for the gas‐phase reaction of OH radicals with the L4, L5, D5, and D6 permethylsiloxanes

AbstractRate coefficients, k(T), for the gas‐phase OH radical reaction with decamethyltetrasiloxane ((CH3)3SiO[Si(CH3)2O]2Si(CH3)3, L4, k1), dodecamethylpentasiloxane ((CH3)3SiO[Si(CH3)2O]3Si(CH3)3, L5, k2), and decamethylcyclopentasiloxane ([–Si(CH3)2O–]5, D5, k3), and dodecamethylcyclohexasiloxane ([–Si(CH3)2O–]6, D6, k4) were measured using a pulsed laser photolysis—laser induced fluorescence absolute method over the temperature range 270–370 K. The obtained room temperature rate coefficients, with quoted 2σ absolute uncertainties, and fitted temperature dependence are (cm−3 molecule−1 s−1): The 2σ absolute rate coefficient uncertainty, for all compounds included in this study, is conservatively estimated to be ∼10% over the entire temperature range. The cyclic permethylsiloxanes were found to be less reactive than the analogous linear compound, while both linear and cyclic compounds show increasing reactivity with increasing number of CH3‐ groups. A structure activity relationship (SAR) parameterization for the permethylsiloxanes is presented. The estimated atmospheric lifetimes due to OH reaction for L4, L5, D5, and D6 are 5.2, 4.4, 6.8, and 5.2 days, respectively.

Euclid preparation. LVII. Observational expectations for redshift z&lt;7 active galactic nuclei in the Euclid Wide and Deep surveys

We forecast the expected population of active galactic nuclei (AGN) observable in the Euclid Wide Survey (EWS) and Euclid Deep Survey (EDS). Starting from an X-ray luminosity function (XLF), we generated volume-limited samples of the AGN expected in the survey footprints. Each AGN was assigned a spectral energy distribution (SED) appropriate for its X-ray luminosity and redshift, with perturbations sampled from empirical distributions. The photometric detectability of each AGN was assessed via mock observations of the assigned SED. We estimate 40 million AGN will be detectable in at least one band in the EWS and 0.24 million in the EDS, corresponding to surface densities of $ and $. The relative uncertainty on our expectation for detectable AGN is 6.7&lt;!PCT!&gt; for the EWS and 12.5&lt;!PCT!&gt; for the EDS, driven by the uncertainty of the XLF. Employing colour selection criteria on our simulated data we select a sample of $) AGN in the EWS and $) in the EDS, amounting to 10&lt;!PCT!&gt; and 8&lt;!PCT!&gt; of the AGN detectable in the EWS and EDS. Including ancillary Rubin/LSST bands improves the completeness and purity of AGN selection. These data roughly double the total number of selected AGN to comprise 21&lt;!PCT!&gt; and 15&lt;!PCT!&gt; of the detectable AGN in the EWS and EDS. The total expected sample of colour-selected AGN contains 6.0\,times \,106 (74&lt;!PCT!&gt;) unobscured AGN and 2.1\,times \,106 (26&lt;!PCT!&gt;) obscured AGN, covering $0.02 z 5.2$ and $43 (L_ bol erg s 47$. With these simple colour cuts expected surface densities are already comparable to the yield of modern X-ray and mid-infrared surveys of similar area. The EWS sample is most comparable to the WISE C75 AGN selection and the EDS sample is most similar to the yield of the collated Spitzer cryogenic surveys when considering bands alone, or the XXL-3XLSS survey AGN sample when also considering selection with ancillary optical bands We project that 15&lt;!PCT!&gt; (7.6&lt;!PCT!&gt;) of the total detectable population in the EWS (EDS) will exhibit X-ray fluxes that could be detected in the XMM-COSMOS survey, showing that the vast majority of AGN would not be detectable in modern medium-depth X-ray surveys.

Accuracy of an Activity Monitor in Assessing Physical Activity of Hospitalized Geriatric Rehabilitation Patients With Slow Walking Speed Using a Walking Aid.

Accurate assessment of physical activity is essential in hospital settings, but difficult for geriatric rehabilitation patients who have slow walking speed and rely on walking aids. This study conducted an external validation of the MOX activity monitor accuracy in distinguishing between sedentary (lying, sitting), standing, and dynamic activities among hospitalized geriatric rehabilitation patients. Additionally, the study assessed clinical tolerance and ease of use of the monitor. Inclusion criteria were being hospitalized on the geriatric rehabilitation ward, aged 70years or older, and using a walking aid. The monitor was placed on the upper leg, and remained in place for 1 week. In a test-session, four activities of 4min each (lying, sitting, standing, and walking) were filmed. Video analyses created a direct observation timeline. Daily skin irritation assessments were conducted and participants noted their discomfort level at the week's end. The study included 20 participants who relied on walking aids, with 10 requiring additional physical support while walking. The MOX activity monitor exhibited excellent accuracy in classifying the four activities, with a zero mean percentage error for sedentary activity, 2.2% for standing, and 2.7% for walking. Participants reported minimal hindrance, and the monitor proved user-friendly. The MOX activity monitor demonstrated remarkable accuracy in distinguishing between these four activities in a test-session and displayed strong clinical applicability. The monitor shows substantial promise as a valuable tool for assessing physical activity in hospitalized geriatric rehabilitation patients using walking aids.

Big Data Generation and Comparative Analysis of Machine Learning Models in Predicting the Fundamental Period of Steel Structures Considering Soil–Structure Interaction

The computing of the fundamental period of structures during seismic design is well documented in design codes but is mainly dependent on the height of the structure, which is considered to be the most influential parameter. It is, however, important to consider a phenomenon called the soil–structure interaction (SSI), as this has been found to have a detrimental effect, especially for buildings founded on soft soils. A pilot research project foresaw the use of machine learning (ML) algorithms trained on relatively limited datasets for the development of a more accurate and objective fundamental period formula. Therefore, a dataset that consists of 98,308 fundamental period data points was created through the use of a High-Performance Computer (HPC), which is the largest dataset of its kind. The HPC results were then used to train, test, and validate different ML algorithms. It was found that XGBoost-HYT-CV with hyperparameter tuning performed the best with a correlation of 99.99% and a mean average percentage error (MAPE) of 0.5%. Furthermore, the XGBoost-HYT-CV model outperformed all under-study ML models when using an additional dataset that consisted of out-of-sample building geometries and soil properties, with a resulting MAPE of 9%. Finally, irregular buildings were also used to test the performance of the proposed predictive models.