Error Research Articles

Validation of the 21st Century Skills Assessment Scale for public health students in Thailand: a methodological study.

This study aimed to develop and validate the 21st Century Skills Assessment Scale (21CSAS) for Thai public health (PH) undergraduate students using the Partnership for 21st Century Skills framework. A cross-sectional survey was conducted among 727 first- to fourth-year PH undergraduate students from 4 autonomous universities in Thailand. Data were collected using self-administered questionnaires between January and March 2023. Exploratory factor analysis (EFA) was used to explore the underlying dimensions of 21CSAS, while confirmatory factor analysis (CFA) was conducted to test the hypothesized factor structure using Mplus software (Muthén & Muthén). Reliability and item discrimination were assessed using Cronbach's α and the corrected item-total correlation, respectively. EFA performed on a dataset of 300 students revealed a 20-item scale with a 6-factor structure: (1) creativity and innovation; (2) critical thinking and problem-solving; (3) information, media, and technology; (4) communication and collaboration; (5) initiative and self-direction; and (6) social and cross-cultural skills. The rotated eigenvalues ranged from 2.12 to 1.73. CFA performed on another dataset of 427 students confirmed a good model fit (χ2/degrees of freedom=2.67, comparative fit index=0.93, Tucker-Lewis index=0.91, root mean square error of approximation=0.06, standardized root mean square residual=0.06), explaining 34%-71% of variance in the items. Item loadings ranged from 0.58 to 0.84. The 21CSAS had a Cronbach's α of 0.92. The 21CSAS proved be a valid and reliable tool for assessing 21st century skills among Thai PH undergraduate students. These findings provide insights for educational system to inform policy, practice, and research regarding 21st-century skills among undergraduate students.

Assessing the Psychometric Properties of Academic Engagement Questionnaire in Nursing Students

Background: Academic Engagement is considered as one of the important factors related to the learners’ academic achievement. It is known as a significant research issue in higher education. The present study aimed to make an instrument for investigating academic engagement among nursing students of Ilam University of Medical Sciences. Methods: The present study is a methodological and cross-sectional research. To localize the academic engagement questionnaire, first, it was translated into Persian using forward-backward method. Then, its content and face validity were evaluated and approved. In order to determine the validity and reliability, the questionnaire was delivered to 120 nursing students and their comments were collected and analyzed. SPSS and AMOS software, statistical tests of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used for data analysis. Results: According to the results, the internal consistency of the questionnaire was confirmed by calculating Cronbach's alpha coefficient (α = 0.906). The EFA and CFA were used to examine the dimensionality of three domains of vigor, dedication, and absorption. Values of Goodness of Fit Index (GFI), Non-Normed Fit Index (NFI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA) indices were 0.89, 0.92, 0.90 and 0.97, respectively Conclusion: The reliability and validity of the translated academic engagement questionnaire were relatively acceptable in reviewing the academic engagement of medical students, especially nursing students. Future studies should focus on more samples and examine the impact of nurses' background and educational variables on academic engagement.

Predicting and optimizing the wear behavior of centrifugal cast β-TCP/ Mg–2Zn–1Mn alloys: A hybrid artificial neural network—Grey relational analysis approach

This study investigates the wear behavior of temporary implant such as plates and screws made by Mg–2Zn–1Mn alloy reinforced with β-tricalcium phosphate (β-TCP) under both dry and wet sliding conditions. Grey relational analysis (GRA) and an artificial neural network (ANN) model were utilized to predict wear and optimize performance, considering process parameters such as β-TCP content (0%, 2.5%, 5%), load (20, 40, 60 N), sliding speeds (2, 4, 6 m/s), and sliding distances (800, 1600, 2400 m). The wear rate ( Ws) and coefficient of friction (COF) were evaluated using an orthogonal array L27 to optimize both dry and wet conditions. The optimal process parameters obtained through GRA for dry conditions are 5% β-TCP, 20 N, 4 m/s, and 1600 m, while for wet conditions, they are 0% β-TCP, 20 N, 2 m/s, and 2400 m. The experiment was conducted to calculate Ws and COF using the above process parameters. The values of Ws and COF are 0.010149 mm³/m and 0.109 for dry conditions, and 0.001143 mm³/m and 0.128 for wet conditions. The ANN predicted values for dry conditions are 0.010471 mm³/m for Ws and 0.11045 for COF, with error percentages of 3.17% and 1.33%, respectively. Similarly, for wet conditions, the predicted values are 0.001107 mm³/m for Ws and 0.125 for COF, with error percentages of 3.13% and 2.77%. The ANOVA results for the Grey relational grade (GRG) show that load was the most significant factor influencing wear performance, contributing 51.91% in dry conditions and 34.69% in wet conditions. The close alignment of predictions from both models, with overall error percentages below 3.17%, demonstrates their reliability and effectiveness. Field emission scanning electron microscope (FESEM) analysis of worn surfaces under optimal conditions provided insights into the wear mechanisms. This study confirms that GRA and ANN models are highly effective for predicting and optimize the wear behavior.

RBNN with Cycle-based Parameter Updates and its Application to RUL Prediction

Abstract Bayesian neural networks (BNNs) combine Bayesian theory with deep learning, providing a probabilistic interpretation of deep learning models. Traditional BNNs typically assume that network’s parameters follow a standard Gaussian distribution, which may not accurately reflect the actual situation. To address this issue, this paper proposes a Recurrent Bayesian Neural Network (RBNN) that employs two trainable Gaussian distributions as priors. Furthermore, an alternating cycle model parameter updation algorithm is designed accordingly, which not only prevents overfitting to specific data during training but also enhances the robustness of model parameters in error approximation. Additionally, this RBNN module can be integrated with classical neural network architecture conveniently. By integrating the RBNN with a temporal convolutional network (TCN), the effectiveness of the proposed approach was validated using a dataset for remaining useful life (RUL) prediction of gear and bearing components in civil aircraft. Comparative studies show that the integrated model outperforms traditional methods in terms of RUL prediction and uncertainty quantification, offering superior accuracy and robustness, and the RMSE of the RBNN prediction results is reduced by 17.7% and score is increased by 27.5% compared to BNN.

Strict Discretization Error Bounds on Quantities of Interest in Transient Dynamics

ABSTRACTThis work proposes a guaranteed error estimator for linear transient elastodynamics, accounting for both time and space discretization errors. The key lies in the definition of a novel dynamic constitutive relation error formulation, which is proven to be a strict bound of the discretization error. Moreover, based on the established dynamic constitutive relation error and the goal‐oriented error estimation framework, strict upper and lower bounds on quantities of interest are also obtained. Numerical examples are conducted to verify the proposed strict bounds and to explore the application of these bounds to adaptive time stepping and mesh refinement.

Synthesis and Characterization of Hydrazine Bridge Cyclotriphosphazene Derivatives with Amide–Schiff Base Linkages Attached to Decyl and Hydroxy Terminal Groups

New cyclotriphosphazene derivatives featuring amide–Schiff base linkages with a hydrazine bridge and different terminal ends, such as decyl alkyl chains and hydroxy groups, were successfully synthesized and characterized. Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), and CHN elemental analysis were used to characterize the structures of these compounds. The formation of hydrazine-bridged cyclotriphosphazene derivatives with amide–Schiff base linkages was confirmed by the FTIR spectra, showing a primary amine band for the amide linkage around ~3300 cm−1 and a band for the Schiff base linkage near ~1595 cm−1. This was further supported by NMR analysis, which displayed an amide proton (H-N-C=O) at ~δ 10.00 ppm and an azomethine proton (H-C=N) within the δ 8.40–8.70 ppm range. The 31P NMR spectra of cyclotriphosphazene compounds display a singlet at ~δ 8.20 ppm, indicating an upfield shift that suggests the complete substitution of all phosphorus atoms with identical side chains. Furthermore, CHN analysis verified the purity of the synthesized compounds, with a percentage error below 2%. The introduction of hydrazine bridges and amide–Schiff base linkages into the cyclotriphosphazene core significantly enriches the molecular structure with diverse functional groups. These modifications not only improve the compound’s stability and reactivity, but also expand its potential for a wide range of applications.

Thermal analysis of mineral oil‐based nanofluids of distribution transformers exposed to simultaneous current and voltage harmonics

AbstractThe exact thermal evaluation of distribution transformers (DTs), which are critical and costly pieces of equipment for the power grids, may contribute to preventing the respective failures. Therefore, the present study non‐uniformly investigated DT for correct anticipation of hotspot temperature (HST). Optical fibre sensors (OFSs) were applied for assessing our newly developed non‐uniform 3D computational fluid dynamic (CFD)‐based modelling while performing the temperature rise test (TRT). It should be noted that this new 3D CFD‐based thermal analysis showed an error percentage of 0.11% (0.1°C) in comparison to the OFS measurement, reflecting the ideal efficiency and accuracy of the model. Moreover, thermography for both top‐oil temperature (TOT) and bottom‐oil temperature (BOT) was employed to validate the results from non‐uniform 3D (three‐dimensional) CFD‐based thermal evaluations. The results indicated an acceptable level of relationship between thermography and thermal analysis of 3D CFD at the specified two spots, with an error percentage of <0.65%, demonstrating the acceptable accuracy of the new non‐uniform 3D CFD‐based model. In the following, yet importantly, the new non‐uniform 3D model was subjected to the total harmonic distortions (THD) for the current and voltage of 5%, 10%, and 15%, which raised the HST more than the original model without harmonics by 3.3°C, 7.1°C, and 10.3°C, respectively. Ultimately, different mineral oil‐based nanofluids’, such as multi‐walled carbon nanotubes (MWCNTs) and diamond nanoparticles, influence on the HST decrement of DT in simultaneous current and voltage harmonics was investigated.

Associations of war-related PTSD symptoms with sleep disturbance: The explanatory roles of emotion regulation difficulties and intolerance of uncertainty.

Although associations between posttraumatic stress disorder (PTSD) and sleep disturbance are well-established, relatively little work has examined mechanisms that may underlie this association. This study aimed to examine the explanatory role of emotion regulation difficulties and intolerance of uncertainty (IU) in associations between PTSD symptoms and sleep disturbance among a sample of war-exposed Iraqi individuals. We used structural equation modeling in a war-exposed nonclinical sample (N = 617; Mage = 27.63; SD = 4.81; 46.03% females) to model indirect effects from PTSD symptoms to the sleep disturbance via emotion regulation difficulties and IU. Participants completed PTSD symptoms, sleep disturbance, difficulties in emotion regulation, and IU scales. Significant correlations were found between PTSD symptoms and sleep disturbance. Those who reported higher levels of PTSD symptoms also reported higher levels of sleep disturbance. The structural model was an excellent fit to the data (χ² = 166.03; degrees of freedom = 32; comparative fit index = .960; goodness-of-fit index = .954; Tucker-Lewis index = .943; root-mean-square error of approximation = .082), and all hypothesized indirect effects were significant (ps < .001). Findings add to the emerging body of literature examining potential mechanisms that may help to explain the maintenance or even escalation of PTSD-related sleep disturbance. Findings have clinical implications in designing specialized treatments for individuals with PTSD and suggest focusing on emotion regulation difficulties and IU as potential therapeutic targets that putatively underlie PTSD-related sleep disturbance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sustainable Development Goals (SDG): Assessing The Online Homework Management Scale's Invariance Across Gender and College Year

Objective: This study is to validate the Online Homework Management Scale (OHMS) among a diverse sample of 1,192 undergraduate students, aiming to assess its effectiveness in measuring online homework management strategies in alignment with United Nations' Sustainable Development Goals (SDG), particularly Goal 4 (Quality Education). Theoretical Framework: The research is grounded in self-regulation theory, particularly focusing on volitional control, to understand how students manage homework in online environments. Method: A quantitative research design was employed to validate the OHMS. The scale measures six dimensions of online homework management: arranging the environment, managing time, monitoring motivation, emotion management, cognitive reappraisal, and handling distraction. Confirmatory factor analysis assessed the fit of the hypothesized six-factor model using indices such as the Comparative Fit Index and the Root Mean Square Error of Approximation. Measurement invariance across gender and college year was tested to confirm the scale's applicability across demographic groups. Results and Discussion: The results confirmed that the OHMS includes six distinct factors, with invariant latent means across gender and college year. A positive correlation was identified between OHMS, online homework interest, and completion rates. Research Implications: Findings suggest that the OHMS can serve as a valuable tool for educators and researchers to enhance students' online homework management and promote self-regulation skills, supporting educational outcomes aligned with the SDG. Originality/Value: This research provides robust evidence for the validity of the OHMS, underscoring its relevance in assessing online homework strategies among college students, contributing to the achievement of the SDG.

Tikhonov Regularization with Oversmoothing Penalty: Error Bounds in Terms of Distance Functions

In recent years error bounds for Tikhonov regularization of linear and non-linear ill-posed problems with oversmoothing penalty received attention. Here, oversmoothing means, that the exact solution does not admit a finite value for the used penalty. This problem was initiated by F. Natterer in “Error bounds for Tikhonov regularization in Hilbert scales”. Applicable Anal., 18(1–2):29–37, 1984. In this note we discuss the application of distance functions to derive order optimal error bounds. We specify these bounds in case that the smoothness is given in terms of source conditions.

Development of a Self-Care Scale for Compound Caregivers

Background: Japan and other nations are experiencing rising demands for care, owing to declining birth rates and aging populations. This particularly affects compound caregivers, people who provide informal care for multiple individuals. Compound caregivers face unique challenges and higher risks of physical and mental health problems. However, existing self-care scales do not cover their needs. This cross-sectional study aimed to develop and validate the Self-Care Scale for Compound Caregivers (SCSCC) to assess specific self-care practices and promote targeted support. Methods: A cross-sectional study was conducted through an online survey of 400 compound caregivers. Reliability was assessed via item analysis, exploratory and confirmatory factor analyses, and Cronbach’s alpha. Criterion validity was evaluated using a self-rated health scale. Results: Overall, 400 compound caregivers participated in the study. After item analysis, we excluded nine items, leaving eight for factor analysis. Exploratory factor analysis revealed a single-factor structure explaining 51.5% of the variance. Confirmatory factor analysis showed a good model fit after modifications (goodness of fit index = 0.964, adjusted goodness of fit index = 0.932, comparative fit index = 0.980, root mean square error of approximation = 0.054). The final version of the SCSCC demonstrated strong internal consistency (Cronbach’s alpha = 0.859) and was positively correlated with self-rated health (r = 0.387, p &lt; 0.001). Conclusions: The SCSCC is a reliable tool for assessing self-care in compound caregivers, supporting health promotion, interventions, and policies to improve their health within the community care system.

Adaptive disturbance observer-based fixed-time prescribed performance sliding-mode control of uncertain robot manipulators

This paper studies the fixed-time prescribed performance trajectory tracking issue of robot manipulators under uncertainties, actuator faults, and external disturbances. First, an adaptive disturbance observer (ADO) is developed, which does not need the exact upper bound information of unknown lumped disturbance and its derivative to effectively estimate the unknown lumped disturbance. Then, a novel fixed-time prescribed performance function (FTPPF) that can converge to a small region in a fixed time is proposed as the preset bound of tracking errors. Furthermore, a sliding surface that uses the prescribed performance tracking error is designed. Afterward, a nonsingular fixed-time prescribed performance terminal sliding-mode control (FTPPTSMC) combined with ADO is developed, to assure the transient and steady-state performance of the robot manipulator. The proposed FTPPTSMC can guarantee that the position tracking errors always keep within the prescribed performance bound and converge to a preset small region in a fixed time by the proposed FTPPF, asymptotically converging to the origin. Finally, the effectiveness of the proposed FTPPTSMC scheme for the robot manipulator is verified by simulations and experiments.

Psychometric properties of Farsi version of Demoralization Scale-II (DS-II) in Iranian cancer patients.

Demoralization, a prevalent form of psychological distress, significantly impacts patient care, particularly in terminally ill individuals, notably those diagnosed with cancer. This study aimed to assess psychometric properties of Farsi version of Demoralization Scale-II (DS-II) in Iranian cancer patients. This study was descriptive-analytical cross-sectional research. The statistical population was cancer patients who sought treatment at Imam Khomeini Hospital in Tehran throughout the 2021-2022. In the initial phase of the study, a preliminary sample comprising 200 patients was carefully selected through convenience sampling. After applying these criteria, 160 patients satisfactorily completed the questionnaires, forming the final study sample. They completed series of questionnaires that included sociodemographic information, DS-II, Scale of Happiness of the Memorial University of Newfoundland, and Beck Depression Inventory (BDI-II). The evaluation included exploratory factor analysis, confirmatory factor analysis (CFA), assessments of convergent validity, and internal consistency reliability. The CFA revealed a 2-factor model consistent with the original structure. The specific fit indices, including the Comparative Fit Index, Root Mean Square Error of Approximation, and Goodness-of-Fit Index, were 0.99, 0.051, and 0.86, respectively. Significant correlation coefficients (p < 0.05) were found between the DS-II and the Beck Depression and MUNSH Happiness scales. The internal consistency of the DS-II, as measured by Cronbach's alpha, yielded values of 0.91 for the meaning and purpose factor, 0.89 for the coping ability factor, and 0.92 for the total score. The Farsi version of DS-II has demonstrated reliability and validity in evaluating demoralization among cancer patients in Iran. This tool can offer valuable insights into the psychological problems of terminally ill patients. Further research opportunities may include conducting longitudinal studies to track demoralization over time and exploring the impact of demoralization on the overall well-being and care of terminally ill patients in Iranian society.

Factorial invariance of the Everyday Discrimination Scale among African, Caribbean, Black, and other racialized women living with and without HIV in British Columbia, Canada.

The Everyday Discrimination Scale (EDS) assesses the mistreatment racialized people experience daily. It is widely used in Canadian HIV research to measure racial discrimination among African, Caribbean, and/or Black (ACB), and other racialized women. However, no studies have examined whether the EDS provides valid estimates of racial discrimination that are objectively comparable across racial/ethnic groups or by HIV status in Canada. We aimed to address this gap. We analyzed EDS data from the British Columbia CARMA-CHIWOS Collaboration study of self-identifying women ≥ 16years of age living with and without HIV. We conducted confirmatory factor analysis (CFA) by race/ethnicity (ACB; Indigenous; other racialized; white) and also by HIV status to assess validity and reliability of the EDS within these groups. Next, we ran multigroup-CFA (mCFA) to determine whether the EDS measures the same construct to the same extent across race/ethnicity- and HIV status-based groups. Of 504 participants (202 with HIV), 62 were ACB, 152 Indigenous, 90 other racialized, and 200 white. CFA revealed adequate fit within race/ethnicity- and HIV status-based groups-all Cronbach's alpha > 0.9, factor loadings > 0.6, and acceptable fit indices, except for root mean square error of approximation. Residual correlations > 0.4 suggested redundancy between some item pairs. In the mCFA, the EDS was invariant across groups. The EDS provides racial discrimination estimates that are meaningfully comparable across race/ethnicity- and HIV status-based groups. Despite potential redundancy between item pairs that may warrant refinement, the EDS can be included in survey instruments that aim to examine and compare the prevalence and health impacts of racial discrimination among ACB and other racialized women in Canada.

Detecting psychometric and diagnostic performance of the RU_SATED v2.0 multidimensional sleep health scale in community-dwelling adults combining exploratory graph analysis and ROC analysis

ObjectiveThe RU_SATED scale is increasingly used across the globe to measure sleep health. However, there is a lack of consensus around its psychometric and diagnostic performance. We conducted an empirical investigation into the psychometrics of the Chinese version of the RU_SATED (RU_SATED-C) scale, with a focus on structural validity and diagnostic performance. Methods1171 adults were enrolled from three communities in Hangzhou, China in July 2022. The dataset was spilt in half, and we ran a bootstrapped exploratory graph analysis (bootEGA) in one half and a confirmatory factor analysis (CFA) in the other half to assess structural validity. Correlations with insomnia, wellness, anxiety, and depression symptoms were examined in order to assess concurrent validity; and Cronbach's α and McDonald's ω were calculated to assess internal consistency. Additionally, a Receiver Operating Characteristic (ROC) analysis established and externally validated the optimal score for identifying insomnia symptoms. ResultsA one-dimensional structure, as identified by bootEGA, was corroborated in the CFA [comparative fit index = 0.934, root mean square error of approximation = 0.088, standardized root mean square residual = 0.051]. A moderate correlation was shown with insomnia symptoms, while weak correlations were observed with wellness, anxiety, and depression symptoms. The RU_SATED-C scale displayed sub-optimal internal consistency where coefficients dropped if any item was removed. A recommended cutoff score of ≤13 was derived for probable insomnia with a satisfactory diagnostic performance. ConclusionThe RU_SATED-C scale displayed a one-dimensional model, along with adequate concurrent validity, internal consistency, and diagnostic performance. Further work necessitates multi-scenario testing and additional validation using objective sleep assessments.

Klotho protein: a new insight into the pathogenesis of essential hypertension

BackgroundEssential hypertension is a most prevalent global health concern. Despite extensive research, the exact mechanisms contributing to essential hypertension remain unclear. Several factors contribute to the pathogenesis of essential hypertension. Klotho, a membrane-bound and soluble protein, has been found to modulate hypertension through physiological processes like vascular function and sodium balance. This study aimed to determine the association of klotho protein with essential hypertension.MethodsThe study included 164 hypertensive cases and 164 normotensive controls, after imposing certain inclusion and exclusion criteria with written consent from all subjects. Subject’s details were obtained using structured proforma to account for potential confounding variables. To estimate klotho protein activity using sandwich enzyme-linked immunosorbent assay, 2 mL blood was collected in a plain vial. All data were tested at a 5% significance level.ResultsThe analysis revealed a significant decrease in klotho protein levels in cases compared to controls (1.52 ± 0.87 vs. 2.45 ± 0.90, P < 0.001), suggesting an inverse relationship of klotho protein with risk of essential hypertension. All indices in the structural equation model have suggested that the final model fitted the data reasonably (chi-square to df ratio, 1.153; goodness of fit index, 0.990; adjusted goodness of fit index, 0.945; normed fit index, 0.936; standardized root mean square residual, 0.953; root mean square error of approximation, 0.031). Also, klotho was negatively associated with blood pressure. The area under the receiver operating characteristic curve for klotho and blood pressure was 0.765 (95% confidence interval, 0.716–0.815; P < 0.001).ConclusionsKlotho levels were significantly reduced in essential hypertension cases compared to controls, Also, klotho had a negative direct association with essential hypertension indicating a potential role for klotho as a prognostic and predictive marker for essential hypertension. This suggests that klotho may have a role in the pathogenesis of essential hypertension. Understanding klotho’s role in essential hypertension may lead to the development of novel therapeutic strategies for this disease.Graphical

Ranking approaches for similarity-based web element location

Context:GUI-based tests for web applications are frequently broken by fragility, i.e. regression tests fail due to changing properties of the web elements. The most influential factor for fragility are the locators used in the scripts, i.e. the means of identifying the elements of the GUI. Objective:We extend a state-of-the-art Multi-Locator solution that considers 14 locators from the DOM model of a web application, and identifies overlapping nodes in the DOM tree (VON-Similo). We augment the approach with standard Machine Learning and Learning to Rank (LTR) approaches to aid the location of web elements. Method:We document an experiment with a ground truth of 1163 web element pairs, taken from different releases of 40 web applications, to compare the robustness of the algorithms to locator weight change, and the performance of LTR approaches in terms of MeanRank and PctAtN. Results:Using LTR algorithms, we obtain a maximum probability of finding the correct target at the first position of 88.4% (lowest 82.57%), and among the first three positions of 94.79% (lowest 91.86%). The best mean rank of the correct candidate is 1.57. Conclusion:The similarity-based approach proved to be highly dependable in the context of web application testing, where a low percentage of matching errors can still be accepted.

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.

A New Fast Root-finder for Black Box Polynomials

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our black box root-finder involves no coefficients and works for a black box polynomial, defined by an oracle (that is, black box subroutine) for its evaluation. Such root-finders have various benefits, e.g., are particularly efficient where a polynomial can be evaluated fast, say, is a sum of a small number of shifted monomials (x−c)a. With incorporation of a fast algorithm by the first author for compression of a disc on the complex plane without losing roots, our root-finder approximates all d complex zeros of a dth degree polynomial p(x) (aka roots of equation p(x)=0) by using near-optimal Las Vegas expected number of bit-operations,1 that is, the root-finder is expected to run almost as fast as one accesses the coefficients with a precision required for the solution within a prescribed error bound. The only other known near-optimal polynomial root-finder was presented by the first author at ACM STOC 1995. It is quite involved and has never been implemented, while already in its initial implementation our new root-finder competed with user's choice package of root-finding subroutines MPSolve, according to extensive numerical experiments with standard test polynomials. Furthermore we readily extend our black box root-finder to approximation of the eigenvalues of a matrix in record expected bit operation time, while the root-finder of STOC 1995, using the coefficients of p(x), does not support such extension.

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 %.