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This study explores how Rashba and Dresselhaus spin-orbit interactions affect the ground state properties of a two-electron system confined within a two-dimensional GaAs quantum dot, particularly under a magnetic field and Gaussian confinement potential. Using a variational approach based on a Chandrasekhar-type wave function with three adjustable parameters and a modified Jastrow correlation factor, we compute the interaction energy of the electrons with high accuracy under strong confinement. Key ground-state physical quantities such as interaction energy, magnetic moment, magnetic susceptibility, and chemical potential are analyzed. To assess the stability of singlet bound states, we construct phase diagrams for various realistic quantum dot parameters, showing how confinement strength controls electron pairing. The electron pair density function reveals spatial correlations between electrons and their evolution under different magnetic fields, dot sizes, and spin-orbit strengths. By analyzing the peak positions of the pair density distribution, we gain insight into the effective pair size in different regimes. Additionally, we calculate the average inter-electronic distance to quantify how the quantum dot environment shapes electron pairing behavior. These findings provide a deeper understanding of spin-orbit-driven electron correlations and the tunability of quantum dot systems for spintronic and quantum computing applications.

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP[sup.2]-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP[sup.2]-RSA attains a 96.123% security level.

The dielectric relaxation study of Chlorobutane (CLB) and Chloropentane (CLP) in 1,4 Dioxane (DX) for various concentrations and temperatures, using Time Domain Reflectometry (TDR) technique have been achieved in the frequency range of 10 MHz to 30 GHz. The dielectric spectra of CLB and CLP in DX mixture have been fitted to Debye model. Luzar model is applied to compute static dielectric constant, Kirkwood correlation factor and number of hydrogen bonds between the molecules in the mixture. The study provides the valuable insights into the molecular interactions and dynamic behavior of the molecules in the binary mixtures of CLB-DX and CLP-DX systems.

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.

This study investigates the influence of geometric parameters of internally threaded tubes on heat transfer and resistance characteristics. Experimental analyses were conducted on pressure drop for 9.52 mm outer diameter tubes with various industry-standard geometric parameter combinations. Using R410A as the working fluid under turbulent flow conditions (Re = 20,000–60,000), experimental parameters included the following: mass velocity 50–600 kg/(m2·s), condensation temperature 45 ± 0.2 °C, and geometric ranges of thread height (e = 0.0001–0.0003 m), helix angle (α = 17–46°), crest angle (β = 16–53°), and number of ribs (Ns = 50–70). Results demonstrate that the newly developed correlation based on Webb and Ravigururajan friction factor models shows improved prediction accuracy for R410A condensation pressure drop in ribbed tubes. Model II achieved a mean absolute percentage error (MAPE) of 7.08%, with maximum and minimum errors of 27.66% and 0.76%, respectively. The standard deviation decreased from 0.0619 (Webb-based Model I) to 0.0362. Integration of SVR machine learning further enhanced tube selection efficiency through optimized correlation predictions.

The displacement evolution of concrete dams serves as a key indicator of their structural safety. Establishing an accurate and reliable model for predicting displacement is essential for effective dam monitoring. Nevertheless, current multi‐point forecasting approaches often overlook the interdependencies among deformation drivers and lack robust validation techniques to assess generalization capability and stability. This shortcoming hinders the accurate representation of deformation behavior under complex loading scenarios. To overcome these issues, this research introduces a spatiotemporal prediction model for concrete dams that combines hybrid clustering with adaptive decomposition and optimization strategies. Initially, the SOM‐K‐means method is employed to clusters monitoring points, followed by spatial correlation analysis to uncover interpoint relationships. Clustering performance is quantitatively evaluated using a composite assessment technique. During model development, hydrostatic pressures are derived through finite element simulation, and sensitivity analysis is applied to gauge the influence of environmental variables on deformation. Furthermore, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Mean Impact Value (MIV) techniques are employed to decompose and select deformation features. Tests show that the proposed model achieves superior predictive accuracy within clustered zones ( R 2 > 0.98, compared to Transformer: 0.9673 and CNN‐BiLSTM: 0.9501). Validation across multiple dam types further confirms the framework’s broad applicability and resilience. By incorporating spatiotemporal analysis, this method enables regionalized health monitoring and integrates data fusion under physical constraints, thereby significantly improving noise resistance and establishing a new benchmark for health prediction in high concrete dams.

Effective management of watershed risks and landslides necessitates comprehensive landslide susceptibility mapping. Support vector machine (SVM) and random forest (RF) machine learning models were used to map the landslide susceptibility in Morocco’s Taounate Province. Detailed landslide inventory maps were generated based on aerial pictures, field research, and geotechnical survey reports. Factor correlation analysis carefully eliminated redundant factors from the original 14 landslide triggering factors. As a result, 30% of the sites were randomly chosen for testing, whereas 70% of the landslide locations were randomly picked for model training. The RF model achieved an area under the curve (AUC) of 94.7%, categorizing 30.07% of the region as low susceptibility, while the SVM model reached an AUC of 80.65%, indicating high sensitivity in 53.5% of the locations. These results provide crucial information for local authorities, supporting sound catchment planning and development strategies.

Context Protected areas (PAs) serve as robust safeguards for the ecological safety of urban areas, and positively affect their socioeconomic development. However, limited research that integrates both ecological and socioeconomic aspects to evaluate the role of PAs. Objectives In this study, we aimed to establish an evaluation framework for PAs that applies the concept of landscape sustainability and integrates ecological and socioeconomic functions to enhance understanding of the role of PAs. Additionally, we aimed to develop analytical framework for identifying the correlation elements of landscape sustainability of protected areas (PA-LS) and improving the understanding of the mechanisms underlying PAs. Methods This study focused on 38 PAs in Beijing, China. We established the PA-LS evaluation framework to evaluate the role of PAs by analyzing changes in their overall landscape services from 2000 to 2019, and in ecological and socioeconomic functions. Subsequently, an analytical framework was established to identify the correlation factors of PA-LS, focusing on four aspects: the fundamental characteristics of PAs, landscape patterns of PAs, impact of urban areas on PAs, and human well-being within a 5 km buffer of PAs. Results The landscape sustainability evaluation of Beijings’ PAs revealed that 30 PAs (78.95% of the total) were strongly sustainable, eight (21.05%) were weakly sustainable, and none unsustainable. The results revealed that there was a positive correlation between several factors and PA-LS, including the density of the road network within a 1 km buffer of the PAs and the economic income and employment rate within a 5 km buffer of the PAs. Conversely, there was a negative correlation between one factor and PA-LS, its the distance between PAs and the urban center. Other factors, such as the category, area, classification of PAs, SHDI (ecological land), ED, LPI (forest) of PAs, and population density and residents’ health within a 5 km buffer of the PAs, were unrelated to PA-LS. Conclusions This study established a PA-LS evaluation framework and its correlation factor analytical framework, which significantly contributes to enhancing the value cognition of PAs and enriching landscape-sustainability evaluation methods. Furthermore, the study provides valuable support and serves as a reference for the conservation and management of PAs in Beijing and similar metropolitan cities.

Dielectric studies of propionitrile (PPN) and butyronitrile (BTN) with 1,4-dioxane (DX) have been obtained at 25 °C temperature in the frequency range from 10 MHz to 30 GHz using the time-domain reflectometry method. The frequency-dependent complex permittivity spectra (CPS) of PPN and BTN with DX mixtures were fitted to the Harvilik-Nigami equation. The least squares fit method was used to determine the dielectric parameters of binary mixtures. The interaction of nitriles (PPN and BTN) with non-polar dioxane solvents through dipole–dipole and heteromolecular hydrogen bonding has been discussed using excess dielectric properties, thermodynamic parameters, Kirkwood correlation factor, and Bruggeman factor. These studies provide information on the intermolecular and intramolecular interactions between solutes and solvent molecules.

This paper proposes a model of photography teaching literacy based on cross-cultural teaching by designing and implementing a photography curriculum based on “recording the fading colors of Suzhou” and combining it with cross-cultural teaching. Then, we set the gray correlation factor series, transformed the gray correlation factor series into a dimensionless pure quantity through mathematical transformation, calculated the correlation coefficients between the comparison series and the reference series, and applied the gray correlation analysis to analyze the intercultural photography teaching literacy index system. The results showed that teachers of different genders showed significant differences in intercultural photography teaching ability (F=2.774, p=0007<0.05) and no significant differences in intercultural photography teaching knowledge and intercultural photography teaching emotion. In terms of correlation, the correlation coefficient between research hard work and autonomous cooperation was 0.523 (**), and its correlation exists, indicating that teachers in colleges and universities at the prefecture level have a stronger awareness of the degree of correlation between research spirit and teaching spirit than those in colleges and universities at the county level. This study explores the good relationship between research literacy and photography teaching literacy so that it can be adapted to the development of teachers and students in the context of the new curriculum.