Search Method Research Articles

Quantum pairwise-parallel mismatch attack on Kyber

Abstract Quantum algorithm uses the quantum parallel method to calculate, which can better solve the encryption and decryption problems in cryptography and secure communication. This paper proposes a quantum pairwise-parallel mismatch attack on Kyber using the quantum binary search method. We first give quantum search methods for finding the secret key and show that our method can be applied to Kyber. Then, According to the proposed quantum search method, we compute the number of queries required and computational complexity for recovering the full key on Kyber. Compared with the existing results, our improved attack significantly reduces the number of queries and computational complexity.

Prediction of the Strength of the Concrete-Filled Tubular Steel Columns Using the Artificial Intelligence

Introduction. The machine learning algorithms are highly promising for predicting the load-bearing capacity of the building structures. The paper aims at building the predictive models for calculating the strength of the concrete-filled steel tubular (CFST) columns to enable a highly accurate prediction of the ultimate loads for the entire possible range of parameters affecting the load-bearing capacity of the eccentrically compressed columns.Materials and Methods. The article studies the eccentrically compressed short concrete-filled steel tubular (CFST) columns of circular cross-section. Model input parameters: column outer diameter, pipe wall thickness, yield strength of steel, compressive strength of concrete, relative eccentricity. Output parameters: the ultimate loads without taking into account and taking into account the random eccentricities. The models were trained on synthetic data generated based on the theoretical principles of the limit equilibrium method. Two machine learning models were built. When training the first model, the ultimate loads were determined at a given eccentricity of the longitudinal force without taking into account the additional random eccentricity. When training the second model, the additional random eccentricity was taken into account. The effect of the features on the model predictions was assessed using the Feature Importance function. The Optuna method was used to select the hyperparameters. The machine learning models were implemented in the Jupiter Notebook environment using the Gradient Boosting learning method. The total volume of the training sample was 179 025 samples.Results. The importance of the features most affecting the predictive values of the model have been determined. For both models, the outer diameter of the column and the relative eccentricity have proved to be the most important features, which is consistent with the existing experience of designing and calculating such structures. Optimisation of the hyperparameters using the Grid Search method enabled getting the improved results. The high accuracy of prediction has been ascertained by the low values of the regression metrics: MSE = 9.024; MAE = 9.250; MAPE = 0.004 — for the model built without taking into account the additional random eccentricity; MSE = 8.673; MAE = 8.673; MAPE = 0.004 — for the model built taking into account the additional random eccentricity.Discussion and Conclusion. The developed Gradient Boosting models for predicting the ultimate loads of the eccentrically compressed short concrete-filled steel tubular (CFST) columns of circular cross-section, both without taking into account and taking into account the additional random eccentricities, have demonstrated high accuracy and stability of prediction, they can be applied for assessing the strength of the columns during design and construction, which will reduce the time and resources involved in physical testing. In the future, it is planned to expand the data range by including other materials, different cross-section geometries of the columns and a slenderness parameter, which may improve the generalization ability of the model.

Criticality of preparation and equipment in hiking and trekking activities: a systematic review

Outdoor activities like hiking and trekking require appropriate preparation and equipment. However, many adventurers underestimate the importance of these factors, leading to increased risk and adverse events. This study aims to deliver a comprehensive review of existing literature on preparation and equipment requirements for hiking and trekking and to identify prevalent challenges during these activities. The review considered papers with English full-text, listed in MEDLINE/PubMed or Google Scholar, published between January 2000 and June 2023. The search method focuses on potential obstacles during the trip and equipment preparation and risks during hiking and trekking. Following the PRISMA 2021 guidelines, 14 papers were considered for detailed review. Two articles investigated hiking experts whereas the other studies included experts and beginners. The following preparative measures and equipment were identified as crucial: appropriate clothing, maps, compass, first aid kit, extra food and water, good physical fitness, and outdoor-related skills (e.g., orientation and medical proficiency). The reviewed studies had methodological flaws and were susceptible to bias. This review identifies common risks and best practices with respect to preparation and equipment for hiking and trekking. It contributes to outdoor adventure safety, potentially reducing risks and the likelihood of adverse events and enriching outdoor experiences. Keywords: Hiking, Trekking, Equipment Requirements, Preparedness, Outdoor Activities, Risk Management, Safety Practices, Adventure Tourism, Literature Review.

On the convergence of the fixed point method for solving neutron transport alpha eigenvalue problems

It was shown that the Fixed Point Method (also known as the Rayleigh Quotient Method) is several times faster than the Critical Search Method for solving neutron transport alpha eigenvalue problems. It was also shown that the Fixed Point Method is able to determine the alpha eigenvalues of sub-critical systems that are beyond the reach of the Critical Search Method. Despite these significant advances, the Fixed Point Method remains an unproven algorithm. This report provides a proof.

A Two-Stage Target Search and Tracking Method for UAV Based on Deep Reinforcement Learning

To deal with the complexities of decision-making for unmanned aerial vehicles (UAVs) in denial environments, this paper applies deep reinforcement learning algorithms to search and rescue (SAR) tasks. It proposes a two-stage target search and tracking method for UAVs based on deep reinforcement learning, which divides SAR tasks into search and tracking stages, and the controllers for each stage are trained based on the proposed deep deterministic policy gradient with three critic networks (DDPG-3C) algorithm. Simulation experiments are carried out to evaluate the performance of each stage in a two-dimensional rectangular SAR scenario, including search, tracking, and the integrated whole stage. The experimental results show that the proposed DDPG-3C model can effectively alleviate the overestimation problem, and hence results in a faster convergence and improved performance during both the search and tracking stages. Additionally, the two-stage target search and tracking method outperforms the traditional single-stage approach, leading to a more efficient and effective decision-making ability in SAR tasks.

A Proposed Ensemble Approach for Searching Hacking News Semantically

Efficient search of hacking information has been a topic of great discussion in recent years. Many challenges are encountered when searching for this information. In particular, researchers may encounter unfamiliar and potentially challenging terms, ideas, tools, and other items that are unique to hacking. Effective comprehension of synonyms and polysemy is necessary. These reasons serve as the driving force behind our efforts to develop a productive method for semantic hacking information searches. Semantic search, using advanced NLP techniques, has transformed information retrieval by improving search result accuracy and relevance. Unlike traditional lexical methods, neural models like sentence-transformers handle synonyms and polysemy efficiently. However, processing time increases with model size. This paper proposes a novel ensemble semantic search (NESS) approach that aggregates mini or small neural embedding models, leveraging their distinct advantages. Evaluated on a dataset with over 300,000 Hacker News stories, our proposed method significantly enhances ranking quality and retrieval accuracy compared to existing techniques, while requiring half the processing time of the best-performing large model. The findings underscore the trade-offs between model complexity, retrieval accuracy, and processing efficiency, offering insights for optimizing semantic search systems.

The efficient method for searching stable structures in herringbone-phase organic semiconductors using density functional theory

We present the herringbone structure search (HSS) method to predict the crystal structures of organic semiconductors from molecular structural formulas. The charge transport efficiency of small-molecule organic semiconductors is governed by molecular packing, often in a two-dimensional herringbone structure. This method predicts crystal structures within the herringbone framework using the density functional theory. As a demonstration, we successfully applied it to promising molecules, predicting crystal structures that agreed well with experimental data. This method offers an efficient approach for in silico screening of new organic molecules, aiding the development of high-performance organic semiconductors.

A fast search method of buckling load for telescopic boom structure

The telescopic boom structures are extensively utilized in engineering applications involving large mobile cranes, aerial work platforms of significant height, and similar mechanical devices. These are predominantly fabricated using solid-webbed box types, latticed truss designs, or a combination thereof. Under load, they exhibit pronounced geometric nonlinear effects, with the relationship between load and displacement demonstrating marked nonlinear characteristics. This paper focuses on the geometric nonlinear modeling of slender multi flexible beam structures. The overall structural system is divided into several substructures, and the concept of multi flexible system modeling is borrowed to establish a follow-up connected body foundation on each substructure. By decomposing the node displacement and rotation in the substructure, the large displacement and large rotation of the node are decomposed into rigid body motion of the connected base and small displacement and small rotation relative to the connected base, effectively representing the large displacement and large rotation of the substructure as rigid body motion of the connected base. A modeling method for the geometric nonlinear analysis of slender and flexible multibody beam structures is proposed. By decomposing the nodal displacement and rotation within the substructures, the large displacement and rotation of the nodes are broken down into the rigid body motion of the body-fixed coordinate and small displacement and rotation relative to the body-fixed coordinate. This approach establishes the application conditions for calculating the structure’s virtual power of deformation using traditional beam elements with linear strain. A method for solving the instability load of slender and flexible multibody structures is proposed. This method integrates the characteristics of the system’s nonlinear equilibrium equations with respect to load, by deriving the system equations with respect to a load increment control parameter. This paper converts nonlinear equilibrium equations into first-order ordinary differential equation (ODE). The method proposed in this paper provides a more efficient solution for the buckling load of the telescopic boom. It can be used to systematically and quickly calculate the structure of the telescopic boom.

A radioactive source-seeking method based on angle constraint and particle diffusion

The deployment of mobile robots to conduct search for radioactive sources plays a crucial role in preventing radiation pollution and safeguarding public health. And it is a significant challenge to accurately locate radioactive sources in unknown environments. Herein, a particle filter based on mobile robot search method is proposed to localize radiation sources. By applying an angle constraint technique, the complexity of this algorithm is reduced, the high accuracy of source estimation is maintained, and the range of search is expanded. Additionally, a particle diffusion technology is applied to address particle loss that occurs during filtering. Furthermore, an adaptive gradient descent strategy is adopted to enhance the search efficiency. Experimental results demonstrate that this method achieves a success rate of over 95 % within a rectangular area (e.g., 40m by 40m), outperforming traditional methods, and it could perform effectively in dual radioactive source search tasks.

Experimental measurements and thermodynamic modeling of dissociation pressure of CO2 hydrate in sulfate solutions

Carbon capture and storage (CCS) has been a popular strategy to mitigate climate change and has attracted significant attention from both industry and academia. CO2 can be stored in the form of CO2 hydrate in deeper locations in an ocean, which makes it a potential option for CO2 sequestration. Dissolved salts in the ocean brine can significantly affect the dissociation pressure of CO2 hydrate. Sulfate salts are one of the most common salt species in the oceanic brine. Although various experimental studies have been conducted to investigate the phase behavior of CO2 hydrate in brine, few studies focus on the effect of sulfate salts on the dissociation pressure of CO2 hydrate. In this study, we build an in-house experimental setup to investigate the effect of monovalent and divalent sulfate salts on the dissociation pressure of CO2 hydrate. The dissociation pressure of CO2 hydrate in Na2SO4, K2SO4, MgSO4, and CuSO4 aqueous solutions is measured using the isochoric pressure search method at different concentrations over the temperature range of 274.36–––282.15 K and the pressure range of 1.50–––4.03 MPa. A hybrid methodology incorporating the Soave-Redlich-Kwong Equation of State (SRK EOS), the van der Waals-Platteeuw (vdW-P) model, and the Pitzer model is applied to predict the dissociation pressure of CO2 in sulfate solutions. In addition, the dissociation enthalpies of CO2 hydrate in these sulfate solutions are calculated using the Clausius-Clapeyron equation based on the measured dissociation points. The experimental results show that the dissociation pressure of CO2 hydrate in Na2SO4, K2SO4, and MgSO4 solutions is higher than that in pure water, and the dissociation pressure of CO2 hydrate in sulfate solutions increases with an increasing salt concentration. Conversely, CuSO4 barely affects the dissociation pressure of CO2 hydrate, which is mainly attributed to the lower molar concentration of the ions compared with the other salt solutions and the ion specificity of Cu2+. The prediction results are in alignment with the experimental data measured in this study, which proves the feasibility of the thermodynamic model in predicting the dissociation pressure of CO2 hydrate in sulfate solutions. Additionally, the calculated dissociation enthalpies of CO2 hydrate show a dependence on both temperature and salt concentration. It is also revealed that Cu2+ exhibits ion specificity in affecting the dissociation enthalpy of CO2 hydrate, likely due to its distinct ability to impact the cage occupancy of CO2 hydrate compared with the other ions. These findings enhance our understanding of the impact of sulfate salts on the dissociation behavior of CO2 hydrate and offer valuable insights for CO2 sequestration.

Spin states of X-complex asteroids in the inner main belt

Context. Based on the V-shape search method, two families, Athor and Zita, have been identified within the X-complex population of asteroids located in the inner main belt. The Athor family is ~3 Gyr old while the Zita family could be as old as the Solar System. Both families were found to be capable of delivering near-Earth asteroids (NEAs). Moreover, the Athor family was linked to the low-iron enstatite (EL) meteorites. Aims. The aim of our study is to characterise the spin states of the members of the Athor and Zita collisional families and test whether these members have a spin distribution consistent with a common origin from the break up of their respective family parent asteroids. Methods. To perform this test, our method is based on the well-established asteroid family evolution, which indicates that there should be a statistical predominance of retrograde-rotating asteroids on the inward side of family’s V-shape, and prograde-rotating asteroids on the outward side of family’s V-shape. To implement the method, we used photometric data from our campaign and the literature in order to reveal the spin states, and hence their rotation sense (prograde or retrograde), of the asteroids belonging to these families. We combined dense and sparse-in-time photometric data in order to construct asteroid rotational light curves; we performed the light curve inversion method to estimate the sidereal period and 3D convex shape along with the spin axis orientation in space of several family member asteroids. Results. We obtained 34 new asteroid models for Athor family members and 17 for Zita family members. Along with the literature and revised models, the Athor family contains 60% (72% considering only the family’s core) of retrograde asteroids on the inward side and, conversely, 76% (77% considering only the family’s core) of prograde asteroids on the outward side. We also found that the Zita family exhibits 80% of retrograde asteroids on the inward side. In addition, the Zita family presents an equal amount of prograde and retrograde rotators (50% each) on the outward side. However, when we applied Kernel density estimation (KDE), we also found a clear peak for prograde asteroids on the outward side, as expected from the theory. Conclusions. The spin states of these asteroids validate the existence of both families, with the Athor family exhibiting a stronger signature for the presence of retrograde-rotating and prograde-rotating asteroids on the inner and outer side of the family, respectively. Our work provides an independent confirmation and characterisation of these very old families, whose presence and characteristics offer constraints for theories and models of the Solar System’s evolution.

Kinetics model with experimental validation for optimal microalgae generation in double-skin façades

Microalgae photobioreactors integrated into double-skin façades in cold climates enhance growth conditions for biomass generation, CO2 fixation, and O2 production, while reducing thermal loads. However, shading imposed by double-skin façade's assembly limits light interception for the photobioreactors. This study employs kinetics and shading models to estimate variations in biomass concentration within the light-constrained photobioreactor. To validate the accuracy and reliability of the model, the results were compared to experimental data collected by the authors from a full-scale constructed system. Biomass concentration was monitored using a developed RGB image-based monitoring technique. Direct search methods were employed to optimize the design and operational variables of the microalgae photobioreactors, with the goal of maximizing annual areal and volumetric productivities across 14 Canadian cities under various climatic conditions. Hooke-Jeeves algorithm was 65.6 % faster than Powell's conjugate direction method. This showed the system's adaptability by generating 1.3–3.0 kg/m2.year of biomass in different climates. The study established a framework for optimizing microalgae photobioreactors into façades to maximize biomass harvest.

A novel TD3 for solving multi-level imperfect maintenance optimization problem

Abstract As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.

Mindfulness Intervention for Post-Stroke Patients: Literature Review

Introduction: Physical dysfunction can increase the psychological burden that hinders daily life and affects the quality of life in stroke patients. Appropriate management and treatment are needed to provide optimal recovery. One intervention that can be implemented is mindfulness. Aims: To identify and determine the benefits obtained by applying mindfulness interventions in stroke patients. Methods: This study used the method of literature review study and article search method using electronic databases Science Direct, Scopus, PubMed, and Google Scholar with a range of 2018 to 2024. Findings: The review of 7 articles showed that mindfulness interventions have benefits for post-stroke patients in improving cognitive skills, reducing post-stroke anxiety and depression, improving social and emotional well-being, and improving quality of life. Conclusion: Mindfulness intervention can be one of the holistic approaches that can be done in the chronic phase and post-stroke rehabilitation.

From efficiency to equity: A multi-user paradigm in mobile route optimization

This study addresses the challenge of optimizing vehicle mobility in urban environments, which is significant for the advancement of smart city initiatives and spatial data analysis. We introduce a novel mobile recommendation system designed for multi-user scenarios, aiming to achieve a balance between effectiveness and fairness. The system prioritizes maximizing the profitability of vehicle service providers while ensuring an equitable distribution of recommended routes among users. Our approach features a redefined objective function that integrates a fairness criterion alongside path quality optimization. We further propose PSA-DLMA (Parallel Simulated Annealing with Deep Learning-Guided Move Adaptation), a stochastic path search method that leverages deep learning to guide move and strategy selection, alongside a dynamic termination mechanism and a parallel processing strategy. We validate our methodology using recent yellow taxi data from New York City and its surroundings, conducting comprehensive experiments to assess the performance of the system. The results demonstrate the superiority of PSA-DLMA over existing state-of-the-art solutions, offering significant contributions to improving urban vehicle mobility within the smart city framework.

Solving Multi-Objective Optimal Control Problems Using a Multiresolution Approach

This paper presents an adaptive multiresolution strategy for multi-objective optimal control problems. The optimal control problem is solved using a direct approach, with individualistic grid adaptation facilitated by a local error analysis at element boundaries. Multiple objectives are considered using a dominance-based approach applying both local and global search methods to a collaborative population of unique solutions. These aspects are simultaneously incorporated via a novel application of evolutionary algorithms for adaptive optimal control problems. Together, this avoids the need for a priori specification of the quantity and temporal location of element boundaries and the set of scalarization weights defining the multi-objective descent directions. Solution fidelity can thus increase concurrently with the exploration of the design space, which leads to increased numerical efficiency while propagating and maintaining population diversity. The benefits of the proposed approach over traditional uniform-grid implementations are demonstrated. Results show that the multiresolution approach is capable of striking an effective balance between solution fidelity, population diversity, and computational cost unachievable using uniform grids.

AI-PEDURO – Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository

BackgroundArtificial intelligence (AI) and machine learning (ML) methods are increasingly being applied in pediatric urology across a growing number of settings, with more extensive databases and wider interest for use in clinical practice. More than 30 ML models have been published in the pediatric urology literature, but many lack items required by contemporary reporting frameworks to be high quality. For example, most studies lack multi-institution validation, validation over time, and validation within the clinical environment, resulting in a large discrepancy between the number of models developed versus the number of models deployed in a clinical setting, a phenomenon known as the AI chasm. Furthermore, pediatric urology is a unique subspecialty of urology with low frequency conditions and complex phenotypes where clinical management can rely on a lower quality of evidence. ObjectiveTo establish the AI in PEDiatric UROlogy (AI-PEDURO) collaborative, which will carry out a living scoping review and create an online repository (www.aipeduro.com) for models in the field and facilitate an evidence synthesis of AI models in pediatric urology. Methods and AnalysisThe scoping review will follow PRISMA-ScR guidelines. We will include ML models identified through standardized search methods of four databases, hand-search papers, and user-submitted models. Retrieved records will be included if they involve ML algorithms for prediction, classification, or risk stratification for pediatric urology conditions. The results will be tabulated and assessed for trends within the literature. Based on data availability, models will be divided into clinical disease sections (e.g. hydronephrosis, hypospadias, vesicoureteral reflux). A risk assessment will be performed using the APPRAISE-AI tool. The retrieved model cards (brief summary model characteristics in table form) will be uploaded to the online repository for open access to clinicians, patients, and data scientists, and will be linked to the Digital Object Identifier (DOI) for each article. Discussion and ConclusionWe hope this living scoping review and online repository will offer a valuable reference for pediatric urologists to assess disease-specific ML models' scope, validity, and credibility to encourage opportunities for collaboration, external validation, clinical testing, and responsible deployment. In addition, the repository may aid in identifying areas in need of further research.

A single-pixel and non-redundant branching-based algorithm for nailfold capillary skeleton line extraction.

Static nailfold capillary parameters are important parameters that reflect the health of the human body. Disease onset or progression is often accompanied by changes in the physiological parameters of the nailfold. Hence, the physiological parameters of the nailfold are closely related to the study of disease, with their automated and high-precision measurements playing a crucial role in these studies. Currently, manually measured values of the nailfold's parameters are the gold standard; however, they are time consuming and labor intensive, making the development of automated measurement methods essential. Most automated measurement methods use skeleton lines; however, current skeleton-thinning algorithms have non-single pixels and redundant branches that lead to reduced measurement accuracy. This study proposes a single-pixel and non-redundant branching-based skeleton line extraction algorithm for nailfold capillaries, which is then applied to nailfold static parameter calculations to improve accuracy. The algorithm includes deletion and restoration templates combined with the depth-first search method to obtain single-pixel skeleton lines without redundant branches. These lines are applied to the static nailfold capillary parameter measurement method based on digital image processing to calculate the blood vessel diameter. The results show that the proposed method can obtain the single-pixel skeleton line without the redundant branches that are required for the parameter calculations and improve the accuracy of the nailfold capillary diameter measurement. Experiments showed that the root mean square errors (RMSEs) of the labeled apical diameter, arterial limb diameter, and venous limb diameter were 0.794, 0.756, and 0.830 µm, respectively, when the calculated results were compared with those of the manual calculations. According to the accuracy formula, the accuracy of the method in this study is 90%. We calculated the P values of the algorithmic and manual measurements to P<0.001 and found that the difference in the measurements of the proposed algorithm is statistically significant. Therefore, the method in this study has high sensitivity and specificity for the measurement of normal nailfold capillaries. The proposed algorithm could obtain single-pixel skeleton lines without redundant branches, thereby improving the nailfold static parameter measurement accuracy.

Meta-Analytical and Meta-Regression Evaluation of Subclinical Hyperthyroidism's Effect on Male Reproductive Health: Hormonal and Seminal Perspectives.

Subclinical hyperthyroidism (SCH) is a subtle thyroid dysfunction marked by decreased serum thyroid-stimulating hormone (TSH) levels while maintaining a normal thyroid hormone profile. Despite its mild nature, SCH can significantly impact various physiological functions, including male reproductive health. However, the effects of SCH on reproductive hormones and semen quality are less understood compared to overt thyroid disorders. This study employed extensive search methods across various databases from January 2000 to February 2024 to explore the relationship between SCH and Hormonal and Seminal Perspectives. Effect sizes, estimated using the standardized mean difference (SMD) and pooled with a Random-effect model, provided significant insights from 748 participants. Included studies adhered to the following criteria: Patients (male individuals with SCH), Intervention (assessment of reproductive hormones and semen quality), Comparison (SCH patients versus healthy controls), and Outcome (changes in reproductive factors). Significant alterations in reproductive hormones were observed in SCH patients, including reduced LH levels (SMD = - 0.20; p = 0.007), elevated FSH levels (SMD = 0.25; p = 0.002), and stable testosterone levels (SMD = - 0.05; p = 0.50). Regarding thyroid profile, SCH was associated with increased FT3 (SMD = 0.15; p < 0.001) and FT4 (SMD = 0.08; p = 0.002) levels, along with decreased TSH levels (SMD = - 2.00; p < 0.001). Adverse effects on semen quality were also observed. These findings underscore the need to incorporate thyroid health assessments in the evaluation of male infertility, recognizing the impact of minor thyroid hormone deviations on reproductive outcomes.

Machine learning aided prediction of martensite transformation temperature of NiTi-based shape memory alloy

The martensitic transformation temperature (Ms) of shape memory alloys plays a crucial role in their performance. To achieve rapid prediction of Ms in shape memory alloys, this study employs machine learning techniques, using NiTi-based SMAs, the most widely used type, as an example. The results show that the gradient boosting machine algorithm provides the best prediction accuracy, with an R² of 0.92 and a mean absolute error (MAE) of 23.42 °C on the test set. Through correlation analysis, recursive feature elimination, and exhaustive search methods, six key features were identified. Subsequently, SHapley Additive exPlanations (SHAP) values were introduced to analyze the model interpretability, revealing the feature importance ranking and the dependence relationship between features and SHAP values. To address the issue of low Ms in NiTi-based shape memory alloys, the concept of high-entropy alloys was introduced. The study designed a TiZrHfNiCu high-entropy shape memory alloy and efficiently screened alloy compositions using the predictive model. This approach successfully increased the Ms, with the Ti19Zr19Hf19Ni37Cu6 alloy exhibiting an Ms exceeding 400 °C, significantly enhancing high-temperature application performance.