Error Research Articles

Assessing rating curve uncertainty

ABSTRACT Most applied hydrologists and investigators appear to bypass any serious consideration of the uncertainty in published daily discharges. This paper offers a relatively simple and easy to understand procedure to estimate relative uncertainty in daily discharges using stream gaugings and rating curve(s) from which the published daily discharges were computed. Uncertainty is assessed through two metrics: the root mean square error of the difference between gauged and rating curve discharges expressed as a ratio of the mean gauged daily discharge; and the percentage flow volume extrapolated above the maximum rated discharge. The first metric can be calculated separately for low and high discharges. These metrics have been computed for 459 gauging stations drawn from Australia’s high quality Hydrologic Reference Stations (HRS) (http://www.bom.gov.au/water/hrs/index.shtml). The two metrics can be computed by investigators which allow them to assess uncertainty in their daily discharge data against the metrics derived for the high-quality dataset.

In+115−Yb+172 Coulomb Crystal Clock with 2.5×10−18 Systematic Uncertainty

We present a scalable mixed-species Coulomb crystal clock based on the S01↔P30 transition in In+115. Yb+172 ions are cotrapped and used for sympathetic cooling. Reproducible interrogation conditions for mixed-species Coulomb crystals are ensured by a conditional preparation sequence with permutation control. We demonstrate clock operation with a 1In+−3Yb+ crystal, achieving a relative systematic uncertainty of 2.5×10−18 and a relative frequency instability of 1.6×10−15/τ/1 s. We report on absolute frequency measurements with an uncertainty of 1.3×10−16 and optical frequency comparisons with clocks based on Yb+171 (E3) and Sr87. With a fractional uncertainty of 4.4×10−18, the former is—to our knowledge—the most accurate frequency ratio value reported to date. For the In+115/Sr87 ratio, we improve upon the best previous measurement by more than an order of magnitude. We also demonstrate operation with four In+115 clock ions, which reduces the instability to 9.2×10−16/τ/1 s. Published by the American Physical Society 2025

Numerical analysis of low-velocity impact behavior on composite materials

AbstractThis study uses numerical modeling to explore the intricate relationship between low-velocity impact and composite materials. The study investigates the impact response of composite materials under low-speed loading circumstances in great detail. Two specimens were prepared for the current study the first one has a uniform thickness for each layer, and the second is prepared with altering layer thicknesses. A tensile test was employed to obtain the mechanical properties to use it in the numerical analysis. An impact test was employed in this analysis, where the impactor was modeled also on a height of 300 mm, the analysis time was taken as 0.08 s to capture all the impact. The numerical analysis was verified with other studies, and the error percentage did not exceed 2%. The results showed that the second specimen with alternating thickness had the best behavior.

Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data

The cultivation of saline–alkali-tolerant peanut (Arachis hypogaea L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut varieties, this research proposed a saline–alkali stress tolerance evaluation method based on deep learning and multimodal data. Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. Finally, the point prediction of the saline–alkali stress score were carried out by using the Gaussian process regression model. The experimental results show that the multimodal method is superior to the single-modal data and the BO algorithm significantly improves the performance of the model. The root mean squared error and relative percentage deviation of the BO-MFFNet model are 0.089 and 3.669, respectively. The model effectively predicted the salt–alkali stress tolerance of five varieties, and the predicted results were Huayu25, Yuhua31, Yuhua33, Yuhua32, and Yuhua164 from high to low. This research provides a new method for assessing crop tolerance under extreme environmental stress.

Accuracy of Fully Automated and Human-assisted AI-based CT Quantification of Pleural Effusion Changes after Thoracentesis.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023. Chest CTs were obtained just before and after thoracentesis. The volume of pleural fluid on each CT scan, with the difference representing the drained fluid volume, was measured by automated segmentation (fully-automated measurement). An expert thoracic radiologist then manually corrected these automated volume measurements (human-assisted measurement). Both fully-automated (median percentage error, 13.1%; maximum estimated 95% error range, 708 mL) and human-assisted measurements (median percentage error, 10.9%; maximum estimated 95% error range, 312 mL) systematically underestimated the volume of drained fluid, beyond the equivalence margin. The magnitude of underestimation increased proportionally to the drainage volume. Agreement between fully-automated and human-assisted measurements (intraclass correlation coefficient [ICC], 0.99), and the test-retest reliability of fully-automated (ICC, 0.995) and human-assisted (ICC, 0.997) measurements were excellent. These results highlight a potential systematic discrepancy between AI segmentation- based CT quantification of pleural effusion volume change and actual volume change. ©RSNA, 2025.

A constructible conductivity cloak via homogenisation

Abstract Abstract. We show that approximate cloaks can be constructed by a finite number of layers of isotropic materials. We detail a construction method, and we provide an upper bound on the number of layers required. Our approach is to view anisotropic matrix-valued materials obtained by the cloaking-by-mapping method as the effective limit of a multiple materials rank-one laminate, and to quantify precisely the approximation error. We illustrate our result and a variant in dimension two with an example.

Energy-preserving methods for gyrocenter system in strong magnetic field

Abstract We formulate second-order and fourth-order energy-preserving methods with two explicit corrections for gyrocenter system in a strong magnetic field 1/ϵ B(X) where the ∥B(X)∥2 = O(1) and ϵ << 1. We theoretically show that the error bound of the two methods are independent of ϵ. Numerical experiments illustrate the impact of the ϵ on the phase orbit and the global error. Additionally, the numerical results show that the energy-preserving methods with the first correction show its advantage in long-term energy conservation than the energy-preserving methods without correction. The energy-preserving methods with the second correction behaves better in energy conservation than those with the first correction as the energy error of the former ones can be bounded by the machine precision over long time.

An AI-Based Digital Scanner for Varroa destructor Detection in Beekeeping

Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the Varroa destructor mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R2 ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis.

Security of hybrid BB84 with heterodyne detection

Abstract Quantum key distribution (QKD) promises everlasting security based on the laws of physics. Most common protocols are grouped into two distinct categories based on the degrees of freedom used to carry information, which can be either discrete or continuous, each presenting unique advantages in either performance, feasibility for near-term implementation, and compatibility with existing telecommunications architectures. Recently, hybrid QKD protocols have been introduced to leverage advantages from both categories. In this work we provide a rigorous security proof for a protocol introduced by Qi in 2021, where information is encoded in discrete variables as in the widespread Bennett Brassard 1984 (BB84) protocol but decoded continuously via heterodyne detection. Security proofs for hybrid protocols inherit the same challenges associated with continuous-variable protocols due to unbounded dimensions. Here we successfully address these challenges by exploiting symmetry. Our approach enables truncation of the Hilbert space with precise control of the approximation errors and lead to a tight, semi-analytical expression for the asymptotic key rate under collective attacks. As concrete examples, we apply our theory to compute the key rates under passive attacks, linear loss, and Gaussian noise.

An a priori method for estimating the informativeness of the configuration of sensor placement when solving inverse problems of remote sensing

Abstract The question of finding the optimal location and number of sensors is important when solving applied inverse problems of remote sensing. An incorrect answer to this question can significantly affect (1) the accuracy of reconstructing the unknown parameters of the object under study, and/or (2) the computational complexity of the inverse problem being solved. The work proposes an economical algorithm that allows among all equivalent (in the sense of the computational complexity of obtaining a solution to the inverse problem) sensor configurations, to select the one that will give a more accurate result when processing experimental data. This algorithm also allows to make a conclusion about whether it is really necessary to process all the experimental data obtained in the experiment, or whether it is possible to limit ourselves to only part of them without a significant loss of accuracy in the reconstructed solution. The algorithm is based on (1) the application of the mosaic-skeleton approximation method to the matrix of the system of equations to which the inverse problem being solved is reduced, and (2) the calculation of the compression rate of the approximating matrix relative to the original dense matrix for a given permissible relative approximation error. Thus, the algorithm is a priori, that is, it does not require a preliminary search for a solution to the inverse problem for the sensor configuration under study. Moreover, if the formulas that define the elements of the system matrix are known, then the algorithm requires the calculation of not all of these elements, but only part of them.

Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling

The transient electromagnetic (TEM) method is a crucial tool for subsurface exploration, providing essential insights into the electrical resistivity structures beneath the Earth’s surface. Traditional forward modeling approaches, such as the finite-difference time-domain (FDTD) method and the finite-element method (FEM), are computationally intensive, limiting their practicality for real-time, high-resolution, or large-scale investigations. To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. Deep-TEMNet integrates the U-Net architecture with a tailored two-dimensional long short-term memory (2D LSTM) module, allowing it to effectively capture complex spatial-temporal relationships in TEM data. The U-Net component enables high-resolution spatial feature extraction, while the 2D LSTM module enhances temporal modeling by processing spatial sequences in two dimensions, thereby optimizing the representation of electromagnetic field dynamics over time. Trained on high-fidelity FEM-generated datasets, Deep-TEMNet achieves exceptional accuracy in reproducing electromagnetic field distributions across diverse geological scenarios, with a mean squared error of 0.00000134 and a root mean square percentage error of 0.002373019. The framework offers over 150 times the computational speed of traditional FEMs, with an average inference time of just 3.26 s. Extensive validation across varied geological conditions highlights Deep-TEMNet’s robustness and adaptability, establishing its potential for efficient, large-scale subsurface mapping and real-time data processing. By combining U-Net’s spatial resolution capabilities with the sequential processing strength of the 2D LSTM module, Deep-TEMNet significantly advances computational efficiency and accuracy, positioning it as a valuable tool for geophysical exploration, environmental monitoring, and other applications requiring scalable, real-time TEM analyses that are easily integrated into remote sensing workflows.

The effect of an interactive mobile health intervention to improve community-based essential neonatal care practices among postpartum women in northeast Ethiopia: a cluster randomized controlled trial.

Despite global declines in child mortality rates, Africa's reduction is lagging behind other regions. Neonatal survival remains a key priority in the sustainable development agenda. Promoting neonatal care practices at the individual and community levels is essential, and technology-based interventions can effectively reach potential future mothers. This study aimed to evaluate the effect of an interactive mobile health intervention on improving community-based essential neonatal care practices among postpartum women in northeast Ethiopia. This study was conducted in Dessie and Kombolcha city zones, in northeast Ethiopia. A cluster randomized controlled trial was implemented among 743 participants (376 intervention and 367 control) from 2 January to 15 June 2023. Pregnant women at 30-weeks' gestation in selected clusters were enrolled and followed up to 45days after childbirth. Data were collected using Open Data Kit and analysed with Stata version 17. Structural equation modelling through confirmatory factor analysis was employed. Model fitness was evaluated using the χ2:degree of freedom ratio, root mean square error of approximation and standardized root mean square residual, indicating a good model fit. Statistical significance was declared at a level <0.05 with a 95% confidence interval. The study revealed high narrow birth-to-pregnancy intervals of <24 months in both groups (48.5% control, 49.5% intervention). Awareness of neonatal care increased markedly in the intervention group, increasing from 62.0% to 85.9%, compared with an increase from 57.8% to 67.6% in the control group. Disagreement regarding immediate newborn bathing was more prevalent in the intervention group (73.9%) than in the control group (58.9%). Initiating breastfeeding within 1h after birth was higher in the intervention group (85.4%) compared with the control group (74.4%). Postnatal visits to health facilities were more frequent in the intervention group (79.6%) than in the control group (54.8%). Mobile health intervention (β=0.393, p=0.007) and knowledge of neonatal care (β=0.347, p=0.012) had a significant positive effect on neonatal care practices. There were no significant indirect pathways between the variables analysed. Mobile health intervention and knowledge of neonatal care remain significant predictors with a total effect of β=0.382, p=0.009 and β=0.347, p=0.012, respectively, in enhancing neonatal care practices. This study underscores the significant role of mobile health interventions and maternal knowledge in enhancing neonatal care practices. These findings should inform the design and implementation of maternal and child health programs, emphasizing the integration of technology and education to improve neonatal outcomes in resource-limited settings. Protocol Registration and Results System Clinical Trial Registry, www.ClinicalTrials.gov, NCT05666050. Registered on 23 December 2022.

Simplified Jaw Functional Limitation Scale for Temporomandibular Disorders: Psychometric Evaluation.

This study aimed to simplify the Jaw Functional Limitation Scale (JFLS) and evaluate its reliability and validity in assessing jaw function in patients with temporomandibular disorders (TMD) in primary care. TMD patients and non-TMD participants were assessed using Diagnostic Criteria for Temporomandibular Disorders (DC/TMD). Demographic data and the 8-item JFLS questionnaire were collected to quantify jaw functional restriction. Confirmatory factor analysis tested the validity of JFLS-8 using chi-square, comparative fit index (CFI), root mean square error of approximation (RMSEA), and root mean square residual (RMR). The study selected key items from each domain to create a 4-item JFLS for further testing. Exploratory factor analysis, reliability, and validity tests were conducted. Analysis included 671 valid questionnaires (502 TMD patients, 169 non-TMD participants). JFLS-8 exhibited satisfactory fit among TMD patients (RMSEA = 0.143, CFI = 0.881, RMR = 0.567). JFLS-4 retained items with high factor loadings: JFLS1 (0.935), JFLS2 (0.923), JFLS7 (0.766), and JFLS5 (0.713). The simplified JFLS-4 showed good reliability (Cronbach's alpha 0.714) and validity (Kaiser-Meyer-Olkin 0.558, Bartlett's test < 0.001) among TMD patients. A 4-item JFLS was developed and validated, demonstrating good reliability and validity in assessing mandibular function in TMD patients, exhibiting the potential to streamline evaluation processes in clinical practice.

Recycled used cooking oil (UCO) as a rejuvenator in high content reclaimed asphalt pavement (RAP) mixes: A life cycle assessment (LCA).

This study investigates the primary data collected at a used cooking oil (UCO) recycling facility to quantify its environmental impact when used as a rejuvenator in high content reclaimed asphalt pavement (RAP) mixes. Annual energy consumption data sets on transportation, storage, filtration, machinery, and purification are assessed using the life cycle assessment (LCA) methodology with the LCA software Simapro 9.4 to evaluate the influential parameters and processes in reducing emissions. Pearson correlations of the production process show a higher association of carbon footprint with two dominant recycling factors: (1) electricity consumption (r=0.89) and (2) fuel consumption for transportation (r=0.37). A comprehensive LCA conducted on asphalt pavements with various RAP contents shows that a 28.81% reduction of carbon emissions can be achieved during the construction phase by adding 60% rejuvenated RAP. However, the relative uncertainty around RAP moisture content at the time of usage and faster pavement deterioration over the service life when high RAP content is incorporated into asphalt mixes might hinder the environmental advantage of adding RAP during the maintenance phase. Non-properly treated, blended, and rejuvenated RAP can be susceptible to early cracking, thus resulting in increased periodic maintenance. Moreover, the break-even point of economic appraisal suggest over 50% RAP content is beneficiary even at the higher discount rates (12%). Conducted LCA and sensitivity analyses emphasise the importance of collecting primary data from recycling facilities and considering both the construction and maintenance phases for implementing effective sustainable strategies.

Comparing two-sample log-linear exposure estimation with Bayesian model-informed precision dosing of tobramycin in adult patients with cystic fibrosis.

Tobramycin dosing in patients with cystic fibrosis (CF) is challenged by its high pharmacokinetic (PK) variability and narrow therapeutic window. Doses are typically individualized using two-sample log-linear regression (LLR) to quantify the area under the concentration-time curve (AUC). Bayesian model-informed precision dosing (MIPD) may allow dose individualization with fewer samples; however, the relative performance of these methods is unknown. This single-center retrospective analysis included adult patients with CF receiving tobramycin from 2015 to 2022. Tobramycin concentrations were predicted using LLR or Bayesian estimation with two population PK models (Hennig and Alghanem). Then, both methods were used to estimate the AUC for simulated patients. For Bayesian estimation, AUC estimation with flattened priors and limited sampling strategies were also assessed. Predictions were evaluated using normalized root mean square error (nRMSE), mean percent error (MPE), and accuracy. The data set included 70 treatment courses, with 32 not evaluable by LLR due to detection limits or timing issues. Bayesian estimation demonstrated worse accuracy (47.1%-50.7% vs 75.7%), higher MPE (24.2%-32.4% vs -2.4%), and higher nRMSE (35.0%-39.4% vs 24.8%) than LLR for peak concentrations but performed better on troughs (accuracy: 92.0%-92.9% vs 84.6%). Bayesian estimation with flattened priors and a single sample at 4 h was comparable to LLR performance, with better accuracy (42.9%-68.0% vs 41.1% LLR), comparable MPE (-2.3% to -3.7% vs -0.5%) and nRMSE (11.3%-21.6% vs 17.3%). Bayesian estimation with one concentration and flattened priors can match LLR prediction accuracy. However, popPK models must be improved to better estimate peak samples.

Assessment of transpulmonary thermodilution, ultrasound dilution, and esophageal Doppler against direct pulmonary artery flow for cardiac output monitoring in anesthetized cats.

To assess the agreement of transpulmonary thermodilution (TPTD) and transpulmonary ultrasound dilution (TPUD) against direct measurement of main pulmonary artery flow with an ultrasound transit time flow probe (UTF) over a wide range of conditions in anesthetized cats. Additionally, the trending ability of TPTD, TPUD, and esophageal Doppler ultrasonography (EDU) was evaluated against UTF. 12 purpose-bred cats were used. One cat was used for a pilot study. The cats were instrumented according to the manufacturer's recommendation. Paired measurements were made over a wide range of cardiac output. Agreement was analyzed with Bland-Altman analysis with repeated measures, and trending ability was assessed by 4-quadrant plot and concordance rate. Interchangeable was defined as percentage error less than simplified total error. Acceptable trending was defined as concordance rate > 95%. Bias and percentage error were -0.084 L·min-1 and 38.2% for TPTD and -0.041 L·min-1 and 52.9% for TPUD. The concordance rate was 100%, 95.5%, and 64% to 65% for TPTD, TPUD, and EDU, respectively. Both TPTD and TPUD were not interchangeable to UTF. Both TPTD and TPUD provided acceptable trending ability but not EDU. Transpulmonary thermodilution and TPUD allow detection of the direction of cardiac output changes in anesthetized cats but not EDU. Clinical and research use of these 3 devices warrants careful consideration of the limitations presented in this study.

Assessing Internalized Beliefs: Psychometric Evidences for the Pathogenic Belief Scale in Turkish Culture.

This study was conducted to test the reliability and validity of the Pathogenic Belief Scale (PBS) on adult individuals. Participants consisted of 299 adults, including 189 (63.2%) females and 110 (36.8%) males, with a mean age of 21.16 years (SD = 3.68). Personal Information Form, Pathogenic Belief Scale-R, Cognitive Distortions Scale, and Dysfunctional Attitude Scale were used as data collection tools. The three-dimensional structure of the PBS was confirmed by confirmatory factor analysis on the Turkish sample (χ2 = 204.990, df = 186, χ2/df = 1.102, comparative fit index = 0.997, normed fit index = 0.997, standardized root mean square residual = .058, and root-mean-square error of approximation = .019 (90% confidence interval [.001, .032]). Factor loadings were found to be between .470 and .761. The analyses also showed that the internal consistency reliability coefficients were at a quite good level for the undeserving dimension (α = .874, ω = 0.875), the cannot rely on others dimension (α = 0.794, ω = 0.799), and the interpersonal guilt dimension (α = .847, ω = 0.857). The Turkish version of the PBS is a valid and reliable tool that can be used to evaluate the pathogenic beliefs of individuals. The research results were discussed, and certain inferences were made for the future in line with the limitations.

Simplified Medication Adherence Questionnaire (SMAQ) for People Living With HIV in a National Hospital in Mexico: Instrument Validation Study.

Adherence to antiretroviral therapy is a critical component in achieving viral suppression in people living with HIV in addition to increasing overall quality of life. Several indirect methods have been used to measure adherence including the Simplified Medication Adherence Questionnaire (SMAQ). The objective of this study is to evaluate the reliability and validity of the SMAQ in men living with HIV/AIDS attending a Mexican national hospital. A cross-sectional analytical design study was carried out in a Mexican National Hospital in Jalisco, including men aged >18 years with at least 3 months of antiretroviral treatment, excluding those with cognitive difficulties in answering the survey. A minimum sample size was calculated to detect the contribution of the variables within the model. The analysis included descriptive tests, confirmatory factor analysis, reliability and validity assessment, correlation between adherence and viral load, and association between viral load and adherence. The final analysis included a total of 260 patients with a mean age of 43 (SD 12) years and an average of 8.97 (SD 6.33) years on antiretroviral treatment. The SMAQ showed sufficient structural validity (comparative fit index=1, root-mean-square error of approximation=0, 90% CI 0-0.085) with satisfactory factor loadings on most questions except item 2 (Do you always take your medication at the prescribed time?). The reliability of the scale is acceptable (Cronbach α=0.702, ω=0.718). Adherence correlated with viral load significantly but not with recent TCD4 lymphocyte levels. Patients classified as adherent were three times more likely to be undetectable than nonadherent patients (odds ratio 3.31, 95% CI 1.13-9.64, P=.04). The SMAQ represents an adequate tool to assess adherence in men living with HIV in the Mexican context, this will contribute to this study and compression of adherence to establish future intervention programs.

A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts

The lattice-based multi-identity fully homomorphic encryption scheme combines the quantum security of lattice cryptography with the advantage of identity-based encryption. However, existing schemes face challenges such as large key sizes, inefficient ciphertext expansion processes, and reliance on outdated trapdoor designs, limiting their compactness and practicality. In this study, we propose a novel Compact Multi-Identity Fully Homomorphic Encryption Scheme (WZ-MIBFHE) that eliminates the need for fresh ciphertexts during expansion. First, we construct a compact identity-based encryption scheme by combining the YJW23 trapdoor and ABB10 under the standard model, proving its IND-sID-CPA security. The scheme is then adapted to ensure correctness and security when integrated with the decomposition method for ciphertext expansion. This adaptation also utilizes approximation errors to reduce overall noise. Finally, we expand the modified IBE scheme’s ciphertext using the decomposition method to construct the WZ-MIBFHE scheme. Compared to existing methods, WZ-MIBFHE reduces the lattice dimension to nlogq+logbq, improves public and private key sizes, and significantly lowers ciphertext expansion rates by removing the need for fresh ciphertexts. These improvements enhance both the compactness and efficiency of the scheme, making it a promising solution for multi-identity homomorphic encryption.

Development and Psychometric Validation of the Cyber-Self Scale (CSS) in Saudi Arabia.

Measuring adolescents' and youths' perception of their Cyber-Self can enhance the understanding of how digital technology influences identity formation. While psychological literature offers numerous measures of the self, there is a notable lack of studies addressing the measurement of the Cyber-Self. This study aims to evaluate the reliability, factorial- and criterion-related validity, and measurement invariance of the Cyber-Self Scale (CSS) across age and gender among the youth and adolescents in Saudi Arabia. The Cyber Relationship Motives (CRM) and E-Emotional Questionnaire (EEQ) were administered to students at Umm Al-Qura University (N = 335), aged 17-31 years (39.7% male, 60.3% female; mean (M) = 21.75, standard deviation (SD) = 2.17). The results indicated significant positive correlations between the sub-components of the CRM and EEQ. One item was selected based on two criteria: the highest correlation with other items and the highest correlation with the general factor. A total of 12 items were identified as the final form of the CSS, which demonstrated acceptable internal consistency for both male and female participants. Confirmatory factor analysis (CFA) revealed that the CSS model fit the data well, with all 12 items meeting the fit criteria for chi-square and root mean square error of approximation (RMSEA). The Arabic version of the CSS is sufficiently reliable and valid for use among Arabic-speaking adolescents and youth. Further research is recommended to examine its measurement invariance over extended periods.