Consistency Error Research Articles

Predictive Regression Modeling for Forecasting Graduation Duration in Online Offsite Degree Program

The demand for Information Technology (IT) professionals continues to rise across various sectors, where theyplay vital roles. However, the supply of IT graduates often fails to meet industry needs and this is a huge problem for the SriLankan IT Industry (National IT-BPM Workforce Survey – 2019). In this context, this study presents a predictive regressionmodelling approach to predict graduation duration in the Bachelor of Information Technology (BIT) degree program at theUniversity of Moratuwa, Sri Lanka. It integrates demographic data—student district, birth year, AL results, OL maths grade,gender, employability status, occupation, and AL stream—along with academic performance indicators like diplomacompletions and higher diploma completions. After evaluating the suggested features, the key findings indicate thesignificance of certain features, notably the number of semesters taken to complete the diploma, higher diploma, and thedegree. Additionally, demographic factors such as district, birth year, AL results, OL maths grade, gender, and employabilitystatus were found to be important. The regression analysis was carried out using the Orange data mining tool (Orange DataMining). Various algorithms, including random forest, neural network, linear regression, and k-nearest neighbours (kNN),were used to develop predictive models. By adjusting parameters such as metrics, weights, number of neighbours, numberof iterations, and training dataset size, the models were optimised to better fit the dataset. Training and testing the modelsrevealed consistent error metrics, including MSE, RMSE, MAE, and R^2, validating the accuracy of predictions. Byconsidering the least and reasonable error in each model, the most suitable model to fit the given dataset was selected.The prediction model accurately forecasted graduation duration for subsequent academic batches, demonstrating itseffectiveness in predicting student progress in the program. This research contributes to understanding the factorsinfluencing graduation duration in a distance learning context and provides insights for educational institutions to optimiseprogram planning and student support initiatives. Additionally, it is a good indicator to the companies to gain a betterunderstanding of the availability of future workforce.

On robust inference in time series regression

Abstract Least squares regression with heteroskedasticity consistent standard errors (“OLS-HC regression”) has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time series environments via heteroskedasticity and autocorrelation consistent standard errors (“OLS-HAC regression”). First, in plausible time-series environments, OLS parameter estimates can be inconsistent, so that OLS-HAC inference fails even asymptotically. Second, most economic time series have autocorrelation, which renders OLS parameter estimates inefficient. Third, autocorrelation similarly renders conditional predictions based on OLS parameter estimates inefficient. Finally, the structure of popular HAC covariance matrix estimators is ill-suited for capturing the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in HAC-based hypothesis testing, in all but the largest samples. We show that all four problems are largely avoided by the use of a simple and easily-implemented dynamic regression procedure, which we call DURBIN. We demonstrate the advantages of DURBIN with detailed simulations covering a range of practical issues.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Reliability and Construct Validity of Three Self-report Questionnaires Assessing Dual-Task Difficulties in People With Multiple Sclerosis: An International Study

ObjectiveTo evaluate the reliability and validity of the dual-tasking questionnaire (DTQ), dual-task screening list (DTSL), and dual-task impact on daily life activities questionnaire (DIDA-Q). DesignMulticenter, cross-sectional study SettingPersons with multiple sclerosis (pwMS) were recruited from 7 multiple sclerosis centers across 6 countries (Belgium, Chile, Italy, Israel, Spain, and Turkey). ParticipantsA total of 356 pwMS (mean age 47.5±11.5y, expanded disability status scale, 3.79±1.83) were enrolled. InterventionsNot applicable Main Outcome MeasuresThe reliability (internal consistency, test-retest, and measurement error) and construct validity (structural and convergent) were assessed. ResultsThe DTQ, DTSL, and DIDA-Q demonstrated excellent test-retest reliability (intraclass correlation coefficients [95% CI], 0.84 [0.80-0.87] to 0.90 [0.87-0.92]) and internal consistency (Cronbach α: 0.86-0.96). As hypothesized, the 3 questionnaires showed a strong correlation with each other, moderate-to-strong correlations with other self-report questionnaires (perceived walking difficulties, fatigue, and fear of falling), and low-to-moderate correlations with cognitive information processing speed, manual dexterity, and dual-task walking performance (walking with word list generation task), showing convergent validity. The DIDA-Q exhibited systematically superior properties. These results were also verified in subsets from 6 different countries. In the structural validity analysis, all questionnaires displayed 2 main factors, allocated as “motor-driven” and “cognitive-driven” subscales. ConclusionsThe DTQ, DTSL, and DIDA-Q have good-to-excellent measurement properties, with the highest properties observed in DIDA-Q. The use of these self-reported questionnaires can be used in research and clinical practice to assess the effect of dual-task difficulties on the daily life of ambulatory pwMS.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Extending geodetic networks for geo-monitoring by supervised point cloud matching

Abstract In this paper we propose a monitoring method that allows the inclusion of point clouds into a geodetic monitoring network. Consequently, network adjustment and a rigorous deformation analysis can be performed allowing consistent error propagation and trustful significance calculation. We introduce a supervised pipeline based on ICP-matching of small-scale laser scan patches. It is specially designed for geo-monitoring applications and allows to improve the spatial resolution as well as the network geometry of monitoring networks in inaccessible hazardous areas in the mountains, where the installation of a sufficient number of targets is difficult. We apply our method to two datasets. The first is a monitoring setup in the laboratory, where we establish the parameters for the supervised patch selection and demonstrate how the network geometry is improved. Second is the real case study of Mt. Hochvogel, where the proposed method helps to clearly improve the spatial resolution of deformation vectors.

Validity and reliability of the Checklist for Habitual Physical Activity for people 75 years and older in Japan.

Despite health benefits, many older adults struggle to meet physical activity guidelines, leading to stagnant activity levels. While integrating physical activity into daily routines has been proposed as a promising strategy for older adults, fit-for-purpose measurement tools to assess such routines are lacking. The Checklist for Habitual Physical Activity (CHaPA) was developed based on the positive deviance approach and socio-ecological model to assess daily behaviours encouraging physical activity among adults aged 75 and older. It has been confirmed for its content and face validity. However, to ensure its broader applicability and usefulness, assessing its construct validity and reliability is necessary. Therefore, this study aims to assess the construct validity and reliability of the CHaPA and accordingly update the tool. To validate the construct validity of the CHaPA 22-item version, we conducted item analysis and exploratory and confirmatory factor analyses. We removed inappropriate items based on predefined criteria. Then, we assessed the reliability, internal consistency, test-retest reliability, and measurement errors of the CHaPA final version. Item analyses and factor analyses resulted in the deletion of 11 items. The results of confirmatory factor analysis validated the CHaPA 11-item version with the three-factor structure based on model fit index with χ2/degree of freedom = 1.25, comparative fit index = 0.965, Tucker-Lewis index = 0.952, and root-mean-square error of approximation = 0.038. Omega coefficient (0.90) showed excellent internal consistency. Intraclass correlation coefficient (ICC) demonstrated good test-retest reliability (ICC (1, 2) = 0.77, 95% CI = 0.34-0.89, standard error of measurement = 1.75). We finalised the CHaPA 11-item version as a valid and reliable instrument for assessing daily behaviours conducive to physical activity among individuals aged 75 years and older. We need to examine the methods and effectiveness of disseminating this checklist to ensure its utilisation as a resource for promoting healthy ageing and aiding older adults in maintaining active lifestyles.

Failing Consistently and Expectedly: Effects of Error Consistency and Drivers’ Mental Models on Trust in Automated Vehicles

This ongoing study investigates how driver expectations and automation error consistency influence trust in Driving Automation Systems (DASs). Drawing on the Expectation Confirmation Theory (ECT), we hypothesize that trust increases when system performance aligns with or exceeds driver expectations on the DASs. Inconsistencies in error patterns may diminish trust. We conducted a 2 × 3 × 3 mixed-design experiment on STISIM Drive with 150 participants, manipulating expectations (high vs. low) and error patterns (no error, consistent, or inconsistent errors). Trust and response times to takeover requests (TORs) were measured three times, one at each drive. We predict higher trust and longer TOR times in low-expectation conditions. As for error patterns, we predict the highest trust to arise from scenarios with no errors and longer TOR times when errors are inconsistent. Our findings are expected to provide insights into enhancing DAS design by aligning system performance with user expectations and promoting error consistency to improve trust and user satisfaction.

Translation and Cross-Cultural Adaptation of the Osteoarthritis Knowledge Scale Into Turkish.

The Osteoarthritis Knowledge Scale (OAKS) is a validated tool for assessing knowledge about hip and knee osteoarthritis (OA). However, to date, there has been no translation and adaptation of the OAKS for the Turkish population. To translate and cross-culturally adapt the OAKS into Turkish and to assess its psychometric properties in the Turkish population with and without hip or knee OA. The OAKS was translated following accepted guidelines. A validation study assessed internal consistency, test-retest reliability and measurement error. An exploratory factor analysis was conducted to assess the factor structure. A total of 278 participants (n=70 with hip OA, n=105 with knee OA, and n=103 without OA) were included. Internal consistency was 0.72, 0.79 and 0.79 for participants with hip OA, knee OA, and no OA, respectively. The test-retest intraclass correlation coefficient was 0.72 (95% CI; 0.45-0.85), 0.89 (95% CI; 0.82-0.93) and 0.88 (95% CI; 0.79-0.93) for participants with hip OA, knee OA and no OA, respectively. It had three principal components accounting for 57.2% of the total variance. The Turkish version of the OAKS is a reliable and valid tool for measuring OA knowledge in the Turkish population, including those with and without hip and knee OA. Test-retest reliability was below acceptable levels in the population with hip OA only. Therefore, we recommend that the ICC be interpreted with caution when used in this population.

Comparative Study of Seafloor Topography Prediction from Gravity–Geologic Method and Analytical Algorithm

Seafloor topography prediction can fill in sea areas without ship sounding data. However, the dependence of various topographic prediction algorithms on ship soundings varies significantly. Hence, this study explores the impact of the number and distributions of ship soundings on topographic prediction using the gravity–geologic method (GGM) and an analytical algorithm. Firstly, this study investigates the influence of ship sounding coverage on the two algorithms. The simulation results demonstrate that increasing coverage from 5.40% to 31.80%, coupled with more uniform distributions across the study area, substantially reduces the RMS error of the GGM. Specifically, the RMS error decreases from 238.68 m to 42.90 m, an improvement of 82.03%. The analytical algorithm maintains a consistent RMS error of 40.39 m because it does not depend on ship soundings. Furthermore, we select a 1° × 1° sea area (134.8°–135.8°E, 30.0°–31.0°N), and the ship soundings are divided into two control groups, Part I and Part II, with coverages of 8.19% and 33.19%, respectively. When Part II is used for calculation, the RMS error of the GGM decreases from 204.17 m to 126.95 m compared to when Part I is used, while the analytical algorithm exhibits an RMS error of 167.94 m. The findings indicate that the prediction accuracy of the GGM is significantly affected by ship soundings, whereas the analytical algorithm is more stable and independent of ship soundings. Based on simulation experiments and realistic examples, when the effective ship soundings coverage exceeds 30%, the GGM may have more advantages. Conversely, the analytical algorithm may be better. This suggests that effectively combining and utilizing different algorithms based on the ship sounding coverage can improve the accuracy of topographic prediction. This will provide a basis for integrating multiple algorithms to construct a global seafloor topography model.

Evaluating the Efficacy of GARCH Models in Forecasting Volatility Dynamics Across Major Global Financial Indices: A Decade-long Analysis

This study investigates the volatility dynamics of five major global financial indices—FTSE 100, Hang Seng, NIKKEI 225, NSE 50, and S&P 500—using a range of GARCH models over a ten-year period from January 1, 2014, to December 31, 2023. The analysis involves preprocessing the data to ensure stationarity, calculating log returns, and conducting stationarity and ARCH effect LM tests. Various GARCH models, including GARCH (0,1), GARCH (1,1), GARCH (1,2), and GARCH (2,2), are applied to capture and forecast volatility. The study aims to determine the most effective model for accurately reflecting volatility dynamics while accounting for significant market events such as the COVID-19 pandemic. The findings reveal that the GARCH (1,1) model generally provides a robust balance between model simplicity and statistical significance, effectively capturing the time-varying volatility of the indices. Despite some complex models offering better fit measures according to the Akaike Information Criterion (AIC) and Schwarz Criterion (SC), the GARCH (1,1) model consistently demonstrates significant parameter estimates and reliable predictive performance, as evidenced by consistent Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This suggests that the GARCH (1,1) model is a preferred choice for volatility forecasting due to its effectiveness and parsimony, although future research might explore more advanced GARCH model variations for further refinement.

Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems

This study explores fault-tolerant consensus in leader–following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler–Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

The Accuracy and Consistency of Machine Translation Engines: Case Study of Translating Economic & Statistical Texts from English into Arabic

Objectives: This study assesses the precision and consistency of the Google Translate engine and tool in translating statistical and economic texts from English into Arabic. Methods: The descriptive analytical approach was employed in this project, and the study sample consists of five texts extracted from statistical and economic research. Results: The study revealed that the Google translation tool faced numerous challenges and contained lexical, grammatical, and consistency errors. Besides, a grand challenge might arise from the idioms and abbreviations, as Google Translate would use word-for-word translation and render them inaccurate meaning. In some cases, the abbreviations in the source text (ST) would be displayed as they are in the target text (TT), In other cases, Google Translate would do a deep search to render their meanings in the TT. Further, Google Translate has a significant problem rendering technical and specialized terms into the TT, and in some cases, it doesn’t consider the contexts and has a lexical choice challenge. Finally, Google Translate renders some terms with different meanings within the same text. Conclusions: Idioms and abbreviations present a grand challenge, as Google Translate would use word-for-word translation and render them in an inaccurate meaning. In some cases, the abbreviations in the ST are rendered as they are in the TT, and in other cases, Google Translate would do a deep search to render their meanings in the TT.

A class of efficient high-order time-stepping methods for the anisotropic phase-field dendritic crystal growth model

In this paper, we develop and analyze a novel class of arbitrarily high-order and unconditionally energy stable schemes for the anisotropic phase-field dendritic crystal growth model, which is a highly nonlinear system that combines the anisotropic Allen–Cahn equation with the thermal equation. The proposed schemes are based on an extrapolated and linearized Runge–Kutta method for an auxiliary variable reformulation of the crystal growth model. A delicate implementation demonstrates that the proposed method can be realized in a very efficient way, requiring only the solution of a coupled linear elliptic system at each time step. We prove that the constructed schemes satisfy the energy dissipation property and demonstrate the convergence order through a consistency error analysis. To the best of our knowledge, this is the first unconditionally stable scheme of arbitrarily high-order for the anisotropic phase-field dendritic crystal growth model. Numerical experiments for two and three dimensional dendritic crystal growth are simulated to verify our theoretical accuracy as well as the efficiency of the proposed method. Furthermore, to emphasize the superiority of the high-order schemes, we present a numerical comparison that directly contrasts its performance with the lower-order schemes.

Algorithm-Dependent Generalization of AUPRC Optimization: Theory and Algorithm.

Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning. Despite extensive studies on AUPRC optimization, generalization is still an open problem. In this work, we present the first trial in the algorithm-dependent generalization of stochastic AUPRC optimization. The obstacles to our destination are three-fold. First, according to the consistency analysis, the majority of existing stochastic estimators are biased with biased sampling strategies. To address this issue, we propose a stochastic estimator with sampling-rate-invariant consistency and reduce the consistency error by estimating the full-batch scores with score memory. Second, standard techniques for algorithm-dependent generalization analysis cannot be directly applied to listwise losses. To fill this gap, we extend the model stability from instance-wise losses to listwise losses. Third, AUPRC optimization involves a compositional optimization problem, which brings complicated computations. In this work, we propose to reduce the computational complexity by matrix spectral decomposition. Based on these techniques, we derive the first algorithm-dependent generalization bound for AUPRC optimization. Motivated by theoretical results, we propose a generalization-induced learning framework, which improves the AUPRC generalization by equivalently increasing the batch size and the number of valid training examples. Practically, experiments on image retrieval and long-tailed classification speak to the effectiveness and soundness of our framework.

Precise measurement of human heart rate based on multi-channel radar data fusion

To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [-4.78, 4.78] beats per minute, and a consistency error of -0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.

Lymphocyte radiosensitivity: An extension to the linear-quadratic model?

Background and purposeThe linear-quadratic (LQ) model has been pivotal for evaluating the effects of radiation on cells, but it is primarily characterized by linear responses, which has exhibited limitations when applied to lymphocyte data. The present research aims to address these limitations and to explore an alternative model extended from the conventional LQ model. Materials and methodsLiterature providing lymphocyte counts from assays investigating apoptosis and survival after in vitro irradiation was selected. To address the nonlinearity in lymphocyte responses to radiation, we developed a saturation model characterized by a negative exponential relationship between radiation dose and cellular response. We compared the performance of this saturation model against that of conventional models, including the LQ model and its variants (linear model LM and linear-quadratic-cubic model LQC), as well as the repair-misrepair (RMR) model. The models were evaluated based on prediction-residual plots, residual standard errors, and the Akaike information criterion (AIC). We applied the saturation model to two additional datasets: (1) a dataset from the existing literature that assessed stimulated and unstimulated human lymphocytes exposed to gamma irradiation in vitro and (2) a novel dataset involving T lymphocytes from rodent spleens after exposure to various radiation types (X-rays and protons). ResultsThe literature (n = 15 out of 2342) showed that lymphocyte apoptosis varies with dose, time and experimental conditions. The saturation model had a lower AIC of 718 compared to the LM, LQ, LQC and RMR models (AIC of 728, 720, 720 and 734, respectively). The saturation model had a lower residual error and more consistent error distribution. Integrating time as a covariate, the saturation model also had a better AIC for demonstrating time-dependent variations in lymphocyte responses after irradiation. For datasets involving unstimulated lymphocytes before irradiation, the saturation model provided a more accurate fit than did the LM, LQ, and RMR models. In these cases, the fit of the saturation model was comparable to that of the LQC model but offered an advantage when extrapolating to higher doses, where the LQC model might underestimate survival. For stimulated lymphocytes, which are radioresistant, all the models approximated the LM. Both the LQ and saturation models indicated greater radiosensitivity to protons in vitro. ConclusionThe new “saturation model” performed better than the LQ model in quantifying lymphocyte apoptosis and survival, estimating time dependency and assessing the role of radiation modalities or lymphocyte stimulation. Further experiments are warranted to experimentally explore the validity of the saturation model as a promising alternative in the clinical setting.

Measurement properties of the Iranian version of the breast cancer perception scale (BCPS) according to the COSMIN checklist

BackgroundBreast cancer is a prevalent cancer characterized by its aggressive nature and potential to cause mortality among women. The rising mortality rates and women’s inadequate perception of the disease’s severity in developing countries highlight the importance of screening using conventional methods and reliable scales. Since the validity and reliability of the breast cancer perception scale (BCPS) have not been established in the Iranian context. Therefore, this study aimed to determine the measurement properties of the BCPS in women residing in Tabriz, Iran.MethodsThe present study comprised a cross-sectional design, encompassing a sample of 372 Iranian women. The participants were selected through a multi-stage cluster random sampling technique conducted over a period spanning from November 2022 to February 2023. The measurement properties of the Iranian version of BCPS were assessed following the guidelines outlined in the COSMIN checklist. This involved conducting various steps, including the translation process, reliability testing (internal consistency, test-retest reliability, and measurement error), and methodological tests for validity (content validity, face validity, construct validity, and hypothesis testing). The study also investigated the factors of responsiveness and interpretability. The presence of floor and ceiling effects was assessed.ResultsThe internal consistency of the scale was assessed using Cronbach’s alpha, yielding a satisfactory value of 0.68. Additionally, McDonald’s omega (95% CI) was computed, resulting in a value of 0.70 (0.66 to 0.74). Furthermore, the test-retest reliability was evaluated, revealing a high intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94 to 0.99). The CVI, CVR, and impact scores of the BCPS were determined to be 0.98, 0.95, and 3.70, respectively, indicating favorable levels of content and face validity. To assess construct validity, an examination of the Exploratory Factor Analysis (EFA) was conducted on a set of 24 items. This analysis revealed the presence of six distinct factors, which collectively accounted for 52% of the cumulative variance. The fit indices of the validity model (CFI = 0.91, NFI = 0.96, RFI = 0.94, TLI = 0.90, χ2/df = 2.03, RMSEA = 0.055 and SRMR = 0.055) were confirmed during the confirmatory factor analysis (CFA). The overall score of BCPS exhibited a ceiling effect of 0.3%. The floor effect observed in the overall score (BCPS) was found to be 0.5%. Concerning the validation of the hypothesis, Spearman’s correlation coefficient of 0.55 was obtained between the BCPS and the QLICP-BR V2.0. This correlation value signifies a statistically significant association. Furthermore, it is worth noting that the minimum important change (MIC) of 3.92 exhibited a higher value compared to the smallest detectable change (SDC) of 3.70, thus suggesting a satisfactory level of response.ConclusionsThe obtained findings suggest that the Iranian version of the BCPS demonstrates satisfactory psychometric properties for assessing the perception of breast cancer among Iranian women. Furthermore, it exhibits favorable responsiveness to clinical variations. Consequently, it can serve as a screening instrument for healthcare professionals to comprehend breast cancer and as a reliable tool in research endeavors.

A Super Encryption Approach for Enhancing Digital Security using Column Transposition - Hill Cipher for 3D Image Protection

Image encryption is an indispensable technique in the realm of information security, serving as a pivotal mechanism to safeguard visual data against unauthorized access and potential breaches. This study scrutinizes the effectiveness of merging columnar transposition with the Hill Cipher methodologies, unveiling specific metrics from a curated set of sample images. Notably, employing column transposition with the key "JAYA" and the Hill Cipher with the key "UDINUSSMG," the encrypted images underwent rigorous evaluation. 'Lena.png' demonstrated an MSE of 513.32 with a PSNR of 7.89 dB, while 'Peppers.png' and 'Baboon.png' recorded MSE values of 466.67 and 423.92, respectively, with corresponding PSNR figures of 7.12 dB and 7.31 dB. Across all samples, a consistent BER of 50.00% indicated uniform error propagation, while entropy values settled uniformly at 7.9999, highlighting consistent data complexity. While the findings underscore a consistent error rate and complexity, there's a compelling need for further refinement to enhance image quality and security. Moreover, the study proposes future research avenues exploring a three-layer super encryption paradigm, amalgamating columnar transposition, Hill Cipher, and other robust algorithms. This approach aims to fortify encryption methodologies against evolving threats and challenges in data protection, offering heightened resilience and efficacy in safeguarding sensitive information.

On the (In)efficiency of gold and bitcoin: impact of COVID-19

PurposeWe aim to examine the impact of COVID-19 on the efficiency of Gold and Bitcoin returns. In particular, our efficiency tests are based on the popular calendar anomaly, the turn-of-the-month (TOM) effect in these markets.Design/methodology/approachWe define the TOM days as the final trading day of a month and initial three trading days of the immediate next month. To understand the TOM effect, we estimate the typical Ordinary Least Squares (OLS) regression model using the Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors and covariances.FindingsThough in the full sample, a positive and significant TOM effect is observed only for Bitcoin, during COVID period, the TOM effect appears in Gold returns and becomes stronger for Bitcoin, implying that the considered securities become inefficient during COVID period.Practical implicationsBased on these results, we create a trading strategy which is found to surpass the buy-and-hold strategy for both the full sample as well as the COVID period for Bitcoin while only during the COVID period for Gold. Our results provide useful implications for investors and policymakers as the Gold and Bitcoin markets can be timed by taking positions especially based on the behavior of the TOM effect.Originality/valueWe examine the TOM effect in the two important securities – Gold and Bitcoin. Though, a few studies have examined this anomaly in currency, equity and cryptocurrency markets, however, they have not considered the Gold market. Additionally, no study has examined the impact of COVID-19 on the TOM effect in these markets, and hence, market efficiency. We believe that our study is the first to examine the TOM effect in these markets simultaneously.