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The detailed study of the effect of the initial microstructure on its evolution under hydrostatic compression before, during, and after the irreversible α → ω phase transformation and during pressure release in Zr using in situ x-ray diffraction is presented. Two samples were studied: one is plastically pre-deformed Zr with saturated hardness and the other is annealed. Phase transformation α → ω initiates at lower pressure for a pre-deformed sample but for a volume fraction of ω Zr, c > --> 0.7 , a larger volume fraction is observed for the annealed sample. This implies that the proportionality between the athermal resistance to the transformation and the yield strength in the continuum phase transformation theory is invalid; an advanced version of the theory is outlined. Phenomenological plasticity theory under hydrostatic loading is outlined in terms of microstructural parameters, and plastic strain is estimated. During transformation, the first rule is suggested, i.e., the average domain size, microstrain, and dislocation density in ω Zr for c < 0.8 are functions of the volume fraction, c of ω Zr only, which are independent of the plastic strain tensor prior to transformation and pressure. The microstructure is not inherited during phase transformation. Surprisingly, for the annealed sample, the final dislocation density and the average microstrain after pressure release in the ω phase are larger than for the severely pre-deformed sample. The results suggest that an extended experimental basis is required for the predictive models for the combined pressure-induced phase transformations and microstructure evolutions. [ABSTRACT FROM AUTHOR]

One of the most important issues related to the strength of metals is the strain rate sensitivity of the flow stress. In this study, an analytical model of the flow stress as a function of strain rate is derived theoretically. The model can reproduce the strain rate sensitivity of the flow stress of copper over a wide range of strain rates (up to 109 s−1) quantitatively. Our theoretical derivations indicate that the strain rate sensitivity of the flow stress, especially that above 103 s−1, is a result of both the variation of the dislocation mobility mechanism with stress and the particular stress dependence of dislocation density but is not a result of each single mechanism. In particular, the stress dependence of the dislocation density and the initial dislocation density are critical to the quantitative relation of the flow stress–strain rate at high strain rate and the strain rate threshold, under which the upturn of the flow stress occurs, respectively. Moreover, experiments with copper of different initial dislocation densities at moderate and high strain rate are performed. The strain rate threshold of the flow stress upturn observed in the experiments grows considerably as initial dislocation density increases, which is in accordance with theoretical prediction by our model. [ABSTRACT FROM AUTHOR]

Meso-scale calculations of energy localization and initiation in energetic material microstructures must capture the deformation and collapse of pores and high-temperature shear bands, which lead to hotspots. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for β-HMX are examined in the context of shock-induced pore collapse, treating predictions from all-atom molecular dynamics (MD) simulations as ground truth. Using atomistics-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson–Cook (M-JC) strength model, which is shown to capture key aspects of the physics of shock-induced localization in HMX. The study culminates in a MD-guided strength model for β-HMX that produces continuum pore-collapse results in better alignment on several aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and the surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of meso-scale simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks. [ABSTRACT FROM AUTHOR]

Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) –derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. Methods: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values). Results: The FMP model (R2 =.60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 =.62, RMSE = 141.2 U/L) and the combination of the 2 (R2 =.62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 =.57 vs R2 =.37) and for 73% of the players than the individualized model. Conclusions: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization. [ABSTRACT FROM AUTHOR]

This article presents the possibility of controlling/managing the frequency selection ability of membrane-based broadband frequency sensors (MB2FS), exploiting systematic selection of membrane stiffness. MB2FS is a bio-inspired system mimicking the geometry and functionality of the basilar membrane (BM) in the mammalian cochlea. The actual BM is tapered in geometry (both length wise and thickness wise), which makes the stiffness of the membrane uniformly varied over its length. Because of varied stiffness, different locations of the BM show resonance deflection at different frequency inputs, which allow the BM to select/sense the entire sonic frequency band within its 35 mm length. While actual BM and conventional MB2FS possess homogeneous stiffness over the domain length, in this article, a comprehensive insight is provided of how the frequency selection ability of the sensors can be manipulated and controlled, predictively, using functionally graded structural stiffness. Therefore, this work is not intended to develop an artificial BM, rather is focused on developing frequency sensors inspired by the membrane stiffness of BM, which plays a vital role in the spatial selection of acoustic frequencies. The study is performed using a numerically validated predictive model developed in a semi-analytical interface to explain the effects of MB2FS stiffness variations. Based on biological occurrence of stiffness in natural BM, three functions (logarithmic, linear, and exponential) are assumed to predict the FSP. While a random sonic frequency band of 10–12 kHz is targeted in this study to demonstrate the stiffness grading principle of MB2FS, a similar process (e.g., choosing the appropriate stiffness distribution of the beam) can be used to develop both sonic and ultrasonic frequency sensors. This study presents a detailed framework of how the sensing parameters of a specific frequency band (e.g., sensing location of band start and end frequencies and membrane segment width necessary to sense the entire frequency band) are dependent on the function coefficients. Finally, a comprehensive guideline is provided to predictively determine the function coefficients for user-defined frequency selection parameters. While existing state-of-the-art only allows designing MB2FS for a specific frequency band, the work presented in this study will open the opportunity to select multiple frequency bands of an MB2FS without altering its geometric configuration. [ABSTRACT FROM AUTHOR]

Purpose: Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players. Methods: We designed a prospective cohort study over 2 seasons (2019–20 and 2021–22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload. Results: Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P <.05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10−4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index:.74). Conclusions: Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model. [ABSTRACT FROM AUTHOR]

Bike fitting aims to optimize riders' positions to improve their performance and reduce the risk of injury. To calculate joint angles, the location of the joint centers of the lower limbs needs to be identified. However, one of the greatest difficulties is the location of the hip joint center due to the frequent occlusion of the anterior superior iliac spine markers. Therefore, the objective of this study was to validate a biomechanical model adapted to cycling (modified pelvic model, MPM), based on the traditional pelvic model (TPM) with an additional lateral technical marker placed on the iliac crests. MPM was also compared with a widely used model in cycling, trochanter model (TM). Thirty-one recreational cyclists pedaled on a roller bike while the movement was captured with a 7-camera VICON system. The position of the hip joint center and knee angle were calculated and compared with the TPM continuously (along 10 pedaling cycles) and discreetly at 90° and 180° crank positions. No significant differences were found in the position of the hip joint center or in the knee flexion/extension angle between the TPM and the MPM. However, there are differences between TPM and TM (variations between 4.1° and 6.9° in favor of the TM at 90° and 180°; P <.001). Bland–Altman graphs comparing the models show an average difference or bias close to 0° (limits of agreement [0.2 to −8.5]) between TPM and MPM in both lower limbs and a mean difference of between −4° and −7° (limits of agreement [−0.6 to −13.2]) when comparing TPM and TM. Given the results, the new cycling pelvic model has proven to be valid compared with the TPM when performing bike fitting studies, with the advantage that the occluded markers are avoided. Despite its simplicity, the TM presents measurement errors that may be relevant when making diagnoses, which makes its usefulness questionable. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to cross-validate a recently proposed equation for the prediction of maximal oxygen uptake ( V ˙ O 2 max) in cycling exercise by using the average power output normalized by the body mass from a 5-minute time trial (RPO5-min) as the independent variable. Further, the study aimed to update the predictive equation using Bayesian informative prior distributions and meta-analysis. Methods: On different days, 49 male cyclists performed an incremental graded exercise test until exhaustion and a 5-minute time trial on a stationary cycle ergometer. We compared the actual V ˙ O 2 max with the predicted value obtained from the RPO5-min, using a modified Bayesian Bland–Altman agreement analysis. In addition, this study updated the data on the linear regression between V ˙ O 2 max and RPO5-min, by incorporating information from a previous study as a Bayesian informative prior distribution or via meta-analysis. Results: On average, the predicted V ˙ O 2 max using RPO5-min underestimated the actual V ˙ O 2 max by −6.6 mL·kg–1·min–1 (95% credible interval, −8.6 to −4.7 mL·kg–1·min–1). The lower and upper 95% limits of agreement were −17.2 (−22.7 to −12.3) and 3.8 (−1.0 to 9.5) mL·kg–1·min–1, respectively. When the current study's data were analyzed using the previously published data as a Bayesian informative prior distribution, the accuracy of predicting sample means was found to be better when compared with the data combined via meta-analyses. Conclusions: The proposed equation presented systematic bias in our sample, in which the prediction underestimated the actual V ˙ O 2 max. We provide an updated equation using the previous one as the prior distribution, which could be generalized to a greater audience of cyclists. [ABSTRACT FROM AUTHOR]

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. [ABSTRACT FROM AUTHOR]

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Nanoconfined water plays an important role in broad fields of science and engineering. Classical molecular dynamics (MD) simulations have been widely used to investigate water phases under nanoconfinement. The key ingredient of MD is the force field. In this study, we systematically investigated the performance of a recently introduced family of globally optimal water models, OPC and OPC3, and TIP4P/2005 in describing nanoconfined two-dimensional (2D) water ice. Our studies show that the melting points of the monolayer square ice (MSI) of all three water models are higher than the melting points of the corresponding bulk ice Ih. Under the same conditions, the melting points of MSI of OPC and TIP4P/2005 are the same and are ∼90 K lower than that of the OPC3 water model. In addition, we show that OPC and TIP4P/2005 water models are able to form a bilayer AA-stacked structure and a trilayer AAA-stacked structure, which are not the cases for the OPC3 model. Considering the available experimental data and first-principles simulations, we consider the OPC water model as a potential water model for 2D water ice MD studies. [ABSTRACT FROM AUTHOR]

Solar interfacial evaporation, as a novel practical freshwater production method, requires continuous research on how to improve the evaporation rates to increase water production. In this study, sets of data were obtained from molecule dynamics simulation and literature, in which the parameters included height, diameter, height–radius ratio, evaporation efficiency, and evaporation rate. Initially, the correlation between the four input parameters and the output of the evaporation rate was examined through traditional pairwise plots and Pearson correlation analysis, revealing weak correlations. Subsequently, the accuracy and generalization performance of the evaporation rate prediction models established by neural network and random forest were compared, with the latter demonstrating superior performance and reliability confirmed via random data extraction. Furthermore, the impact of different percentages (10%, 20%, and 30%) of the data on the model performance was explored, and the result indicated that the model performance is better when the test set is 20% and all the constructed model converge. Moreover, the mean absolute error and mean squared error of the evaporation rate prediction model for the three ratios were calculated to evaluate their performance. However, the relationship between the height- radius ratio and optimal evaporation rate was investigated using the enumeration method, and it was determined that the evaporation efficiency was optimal when the height–radius ratio was 6. Finally, the importance of height, diameter, height– radius ratio, and evaporation efficiency were calculated to optimize evaporator structure, increase evaporation rate, and facilitate the application of interfacial evaporation in solar desalination. [ABSTRACT FROM AUTHOR]

We propose a new work design model, SMART work design, that identifies five higher order categories of work characteristics, including stimulating work characteristics (task variety, skill variety, information processing requirements, and problem‐solving requirements), mastery work characteristics (job feedback, feedback from others, and role clarity), autonomous work characteristics (decision‐making autonomy, timing autonomy, and method autonomy), relational work characteristics (social support, task significance, and beneficiary contact), and tolerable work characteristics (low levels of: role overload, work–home conflict, and role conflict). Higher order confirmatory factor analysis of working participants provided initial evidence of this structure (Study 1, N = 1107), which was replicated in an additional dataset (Study 2, time 1, N = 709). To provide further evidence, we examined Study 2 data across three waves (N = 573) to show that each higher order factor at time 1 predicted time 3 job satisfaction either directly or via the theorized time 2 mediators (challenge appraisals, work meaningfulness, fulfillment of relatedness needs, and activated negative affect). In Study 3 (N = 108), employees' scores on specific higher order variables correlated with leader ratings of performance in the expected ways. The SMART work design model provides a unique integrating and multidimensional theory of work design that extends beyond existing models. The model can be used to facilitate the synthesis of research knowledge and guide scholars and practitioners to diagnose and address contemporary work design challenges. [ABSTRACT FROM AUTHOR]

We present a theoretical model to study the effect of counterion size on the effective charge, size, and thermodynamic behavior of a single, isolated, and flexible polyelectrolyte (PE) chain. We analyze how altering counterion size modifies the energy and entropy contributions to the system, including the ion-pair free energy, excluded volume interactions, entropy of free and condensed ions, and dipolar attraction among monomer–counterion pairs, which result in competing effects challenging intuitive predictions. The PE self-energy is calculated using the Edwards–Muthukumar Hamiltonian, considering a Gaussian monomer distribution for the PE. The condensed ions are assumed to be confined within a cylindrical volume around the PE backbone. The dipolar and excluded volume interactions are described by the second and third virial coefficients. The assumption of freely rotating dipoles results in a first-order coil–globule transition of the PE chain. A more realistic, weaker dipolar attraction, parameterized in our theory, shifts it to a second-order continuous transition. We calculate the size scaling-exponent of the PE and find exponents according to the relative dominance of the electrostatic, excluded volume, or dipolar effects. We further identify the entropy- and energy-driven regimes of the effective charge and conformation of the PE, highlighting the interplay of free ion entropy and ion-pair energy with varying electrostatic strengths. The crossover strength, dependent on the counterion size, indicates that diminishing sizes favor counterion condensation at the expense of free ion entropy. The predictions of the model are consistent with trends in simulations and generalize the findings of the point-like counterion theories. [ABSTRACT FROM AUTHOR]

Multi-scale predictive models for the shock sensitivity of energetic materials connect energy localization ("hotspots") in the microstructure to macro-scale detonation phenomena. Calculations of hotspot ignition and growth rely on models for chemical reaction rates expressed in Arrhenius forms; these chemical kinetic models, therefore, are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for commonly used energetic materials (e.g., HMX in this paper) there are a wide variety of reaction rate models available. These available reaction rate models produce reaction time scales that vary by several orders of magnitude. From a multi-scale modeling standpoint, it is important to determine which model best represents the reactive response of the material. In this paper, we examine three global Arrhenius-form rate models that span the range of reaction time scales, namely, the Tarver 3-equation, the Henson 1-equation, and the Menikoff 1-equation models. They are employed in a meso-informed ignition and growth model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker–Wasley curve) with experimental data for pressed HMX Class V microstructures. The results provide a guideline for model developers on the plausible range of time-to-ignition that are produced by physically correct Arrhenius rate models for HMX. [ABSTRACT FROM AUTHOR]

Background: Maximal lower-body strength can be assessed both dynamically and isometrically; however, the relationship between the changes in these 2 forms of strength following resistance training is not well understood. Purpose: To systematically review and analyze the effects of resistance training on changes in maximal dynamic (1-repetition-maximum back squat, deadlift, and power clean) and position-matched isometric strength (isometric midthigh pull and the isometric squat). In addition, individual-level data were used to quantify the agreement and relationship between changes in dynamic and isometric strength. Methods: Databases were systematically searched to identify eligible articles, and meta-analysis procedures were performed on the extracted data. The raw results from 4 studies were acquired, enabling bias and absolute reliability measures to be calculated using Bland–Altman test of agreement. Results: Eleven studies met the inclusion criteria, which resulted in 29 isometric–dynamic change comparisons. The overall pooled effect was 0.13 in favor of dynamic testing; however, the prediction interval ranged from g = −0.49 to 0.75. There was no evidence of bias (P =.825) between isometric and dynamic tests; however, the reliability coefficient was estimated to be 16%, and the coefficient of variation (%) was 109.27. Conclusions: As a range of future effects can be expected when comparing isometric to dynamic strength changes following resistance training, and limited proportionality exists between changes in these 2 strength qualities, there is strong evidence that isometric and dynamic strength represent separate neuromuscular domains. These findings can be used to inform strength-assessment models in athlete populations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss the use of topological indices (TIs) to anticipate the physical and biological aspects of innovative drugs used in the treatment of Alzheimer's disease. Degree-based topological indices are generated using edge partitioning to assess the drugs Tacrine, Donepezil, Ravistigmine, Butein, Licochalcone-A and Flavokqwain-A. Furthermore, using linear regression, a quantitative structure–property relationship (QSPR) model is developed to predict the characteristics such as boiling point (BP), flash point (FP), molar volume (MV), molecular weight, complexity and polarizability. The findings show that topological indices have the potential to be used as a tool for drugs discovery and design in the field of Alzheimer's disease treatment. [ABSTRACT FROM AUTHOR]

Objective. The number of cancer survivors is increasing, and the high prevalence of frailty not only reduces quality of life but also affects the treatment of cancer patients. This study aimed to identify the prevalence and risk factors of frailty in cancer patients and to construct a nomogram to predict the probability of frailty. Methods. Nine hundred fifty‐eight cancer patients were included in this retrospective study, randomly divided into a development set (n = 680) and a validation set (n = 278). Frailty was assessed using the Tilburg frailty indicator (TFI). Social support, medical coping styles, and psychological distress were assessed by the Social Support Self‐Rating Scale (SSRS), Medical Coping Modes Questionnaire (MCMQ), and distress thermometer (DT), respectively. Results. The prevalence of frailty in cancer patients was 45.93%. Cancer patients who exercised regularly, ate a balanced diet, and actively coped with diseases were less likely to become frail. The risk factors for frailty identified by a multivariate analysis were parenteral nutrition, advanced TNM staging, vegetarian diet, unemployment, psychological distress ≥4, low physical activity, and negative coping styles. These risk factors were used to construct a nomogram, and the C‐index, calibration curve, and decision curve analysis (DCA) were used to assess the performance of the nomogram. The C‐index was 0.762, and the calibration curve showed satisfactory coherence. The net benefit of the nomogram was better between threshold probabilities of 17%–96% in DCA. Conclusion. Special focus needs to be placed on frail cancer patients due to their high prevalence and severe outcomes. Clinical medical workers could use this nomogram to identify high‐risk patients and intervene early to prevent frailty. [ABSTRACT FROM AUTHOR]

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

Objective: Cognitive decline is often considered an inevitable aspect of aging; however, recent research has identified a subset of older adults known as "superagers" who maintain cognitive abilities comparable to those of younger individuals. Investigating the neurobiological characteristics associated with superior cognitive function in superagers is essential for understanding "successful aging." Evidence suggests that the gut microbiome plays a key role in brain function, forming a bidirectional communication network known as the microbiome-gut-brain axis. Alterations in the gut microbiome have been linked to cognitive aging markers such as oxidative stress and inflammation. This study aims to investigate the unique patterns of the gut microbiome in superagers and to develop machine learning-based predictive models to differentiate superagers from typical agers. Methods: We recruited 161 cognitively unimpaired, community-dwelling volunteers aged 60 years or from dementia prevention centers in Seoul, South Korea. After applying inclusion and exclusion criteria, 115 participants were included in the study. Following the removal of microbiome data outliers, 102 participants, comprising 57 superagers and 45 typical agers, were finally analyzed. Superagers were defined based on memory performance at or above average normative values of middle-aged adults. Gut microbiome data were collected from stool samples, and microbial DNA was extracted and sequenced. Relative abundances of bacterial genera were used as features for model development. We employed the LightGBM algorithm to build predictive models and utilized SHAP analysis for feature importance and interpretability. Results: The predictive model achieved an AUC of 0.832 and accuracy of 0.764 in the training dataset, and an AUC of 0.861 and accuracy of 0.762 in the test dataset. Significant microbiome features for distinguishing superagers included Alistipes, PAC001137_g, PAC001138_g, Leuconostoc, and PAC001115_g. SHAP analysis revealed that higher abundances of certain genera, such as PAC001138_g and PAC001115_g, positively influenced the likelihood of being classified as superagers. Conclusion: Our findings demonstrate the machine learning-based predictive models using gut-microbiome features can differentiate superagers from typical agers with a reasonable performance. [ABSTRACT FROM AUTHOR]

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of language models in software engineering tasks, promoting their use in production environments and ensuring that deployed software is defect-free efficiently. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta (YRD) is an important engine of national economic development and a leading region in international competition. As economic exchanges and resource flows in the YRD region become closer, the inter-regional industrial linkages continue to grow, resulting in the formation of an industrial network structure characterized by a "complex network". The strength of the links between industrial sectors and the value and significance of the existence of industries in the network change over time, thus causing the overall evolution of the industrial network in the YRD region. Based on the input-output tables of the YRD region in 2012 and 2017, this paper uses the prediction index of network structure similarity to construct the prediction model of industrial network link between the YRD regions, and calculates the possibility of future links between industries in the Yangtze River Delta region through comparative analysis and selection of the RWR index of random walk similarity with the best effect, and concludes that: (1) the homogeneity of industries among provinces and cities in the Yangtze River Delta region is relatively high, resulting in homogeneous competition; (2) the overall nature of the industrial layout of the YRD is not prominent, and the depth and intensity of cross-regional industrial cooperation are lacking. On the basis of analysis and research, the countermeasures and suggestions for effectively realizing industrial integration are put forward from the macro level of the government and the meso level of the industry, so as to achieve a more complete industrial network in the YRD region and a more extensive length and width of the cross-regional industrial chain. [ABSTRACT FROM AUTHOR]

Background: 3-Hydroxybutyrate, also called β-hydroxybutyrate, is a significant constituent of ketone bodies. Previous observational and experimental studies have suggested that ketogenic diet, especially 3-hydroxybutyrate, may have a protective effect against cardiovascular disease. However, the relationship between ketone bodies, especially 3-hydroxybutyrate, and aortic dissection remains uncertain. Materials and methods: Publicly accessible data from genome-wide association study (GWAS) was utilized to obtain information on ketone bodies, including 3-hydroxybutyrate, acetoacetate and acetone as exposure respectively, while GWAS data on aortic dissection was used as outcome. Subsequently, two-sample Mendelian randomization (MR) analysis was conducted to examine the potential relationship between ketone bodies and aortic dissection. Then, reverse and multivariate Mendelian randomization analyses were performed. Additionally, sensitivity tests were conducted to assess the robustness of MR study. Results: The inverse-variance weighted (IVW) method of Mendelian randomization analysis of gene prediction observed a negative correlation between 3-hydroxybutyrate and risk of aortic dissection (OR 0.147, 95% CI 0.053–0.410). Furthermore, consistent findings were obtained through the implementation of the weighted median, simple mode, Mendelian randomization-Egger (MR-Egger), and weighted mode methods. After adjusting acetoacetate (OR 0.143, 95% CI 0.023-0.900) or acetone (OR 0.100, 95% CI 0.025–0.398), MR analysis of gene prediction still observed a negative correlation between 3-hydroxybutyrate and risk of aortic dissection. No indications of heterogeneity or pleiotropy among the SNPs were detected. Conclusion: The findings from the MR analysis demonstrated that genetically predicted 3-hydroxybutyrate exhibits a protective effect against aortic dissection. [ABSTRACT FROM AUTHOR]

Neonatal sepsis is a severe medical condition that contributes significantly to neonatal mortality. However, early diagnosis and treatment can help manage the condition effectively. Predictive analytics can assist neonatal sepsis diagnosis and treatment and offer a reassuring solution. This study presents a systematic literature review of various predictive analytics methods for neonatal sepsis diagnosis and treatment. It thoroughly reviews 16 studies between 2014 and 2024, including retrospective and prospective data and utilizing various predictive modeling techniques, such as Machine Learning (ML) and Deep Learning (DL). The review unveils that the Predictive analytics models can rapidly detect early and late-onset neonatal sepsis. This detection aids clinicians in decision-making and improves healthcare management for neonates, especially in low-resource settings. This study lays the groundwork for utilizing advanced analytics technologies to address challenges in this domain. It inspires further research in innovative and unexplored methods for neonatal sepsis diagnosis. [ABSTRACT FROM AUTHOR]

Background: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient health outcomes. However, current screening studies for patients experiencing moderate-to-severe CRBD after waking from general anesthesia are insufficient. Constructing predictive models with higher accuracy using multiple machine learning techniques for early identification of patients at risk of experiencing moderate-to-severe CRBD during general anesthesia resuscitation. Methods: Eight hundred forty-six patients with indwelling urinary catheters who were resuscitated in a post-anesthesia care unit (PACU). Trained researchers used the CRBD 4-level assessment method to evaluate the severity of a patient's CRBD. They then inputted 24 predictors into six different machine learning algorithms. The performance of the models was evaluated using metrics like the area under the curve (AUC). Results: The AUCs of the six models ranged from 0.82 to 0.89. Among them, the RF model displayed the highest predictive ability, with an AUC of 0.89 (95%CI: 0.87, 0.91). Additionally, it achieved an accuracy of 0.93 (95%CI: 0.91, 0.95), 0.80 sensitivity, 0.98 specificity, 0.94 positive predictive value (PPV), 0.92 negative predictive value (NPV), 0.87 F1 score, and 0.07 Brier score. The logistic regression (LR) model has achieved good results (AUC:0.87) and converted into a nomogram. Conclusions: The study has successfully developed a machine learning prediction model that exhibits excellent predictive capabilities in identifying patients who may develop moderate-to-severe CRBD after undergoing general anesthesia. Furthermore, the study also presents a nomogram, which serves as a valuable tool for clinical healthcare professionals, enabling them to intervene at an early stage for better patient outcomes. [ABSTRACT FROM AUTHOR]

Introduction Material and Methods Results Conclusion Machine learning models have shown promising potential in individual‐level outcome prediction for patients with psychosis, but also have several limitations. To address some of these limitations, we present a model that predicts multiple outcomes, based on longitudinal patient data, while integrating prediction uncertainty to facilitate more reliable clinical decision‐making.We devised a recurrent neural network architecture incorporating long short‐term memory (LSTM) units to facilitate outcome prediction by leveraging multimodal baseline variables and clinical data collected at multiple time points. To account for model uncertainty, we employed a novel fuzzy logic approach to integrate the level of uncertainty into individual predictions. We predicted antipsychotic treatment outcomes in 446 first‐episode psychosis patients in the OPTiMiSE study, for six different clinical scenarios. The treatment outcome measures assessed at both week 4 and week 10 encompassed symptomatic remission, clinical global remission, and functional remission.Using only baseline predictors to predict different outcomes at week 4, leave‐one‐site‐out validation AUC ranged from 0.62 to 0.66; performance improved when clinical data from week 1 was added (AUC = 0.66–0.71). For outcome at week 10, using only baseline variables, the models achieved AUC = 0.56–0.64; using data from more time points (weeks 1, 4, and 6) improved the performance to AUC = 0.72–0.74. After incorporating prediction uncertainties and stratifying the model decisions based on model confidence, we could achieve accuracies above 0.8 for ~50% of patients in five out of the six clinical scenarios.We constructed prediction models utilizing a recurrent neural network architecture tailored to clinical scenarios derived from a time series dataset. One crucial aspect we incorporated was the consideration of uncertainty in individual predictions, which enhances the reliability of decision‐making based on the model's output. We provided evidence showcasing the significance of leveraging time series data for achieving more accurate treatment outcome prediction in the field of psychiatry. [ABSTRACT FROM AUTHOR]

Background: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analyze the risk factors of AEFV and develop and select the optimal risk prediction model for AEFV to provide guidance for the prevention of this condition and enhancement of clinical outcomes. Methods: This work included 383 patients with primary osteoporotic vertebral compression fracture (OVCF) who underwent PKP. The patients were grouped based on the occurrence of AEFV postsurgery, and data were collected. Group comparisons and correlation analysis were conducted to identify potential risk factors, which were then included in the five prediction models. The performance indicators served as basis for the selection of the best model. Results: Multivariate logistic regression analysis revealed the following independent risk factors for AEFV: kissing spine (odds ratio (OR) = 8.47, 95% confidence interval (CI) 1.46–49.02), high paravertebral muscle fat infiltration grade (OR = 29.19, 95% CI 4.83–176.04), vertebral body computed tomography value (OR = 0.02, 95% CI 0.003–0.13, P < 0.001), and large Cobb change (OR = 5.31, 95% CI 1.77–15.77). The support vector machine (SVM) model exhibited the best performance in the prediction of the risk of AEFV. Conclusion: Four independent risk factors were identified of AEFV, and five risk prediction models that can aid clinicians in the accurate identification of high-risk patients and prediction of the occurrence of AEFV were developed. [ABSTRACT FROM AUTHOR]

The control of quadrotor vehicles under state and parameter uncertainty is a well studied problem that is vitally important to the deployment of these systems under real world conditions. In this article, we propose a linearization‐based extension to nonlinear systems of the existing scenario model predictive control (MPC) framework, which quantifies the impact of uncertainty on the vehicle dynamics through repeated sampling of the uncertainty space. Given the computational costs of such an approach, we also propose two simplifications of the scenario MPC algorithm that are significantly more tractable. In order to evaluate the performance of the algorithms, the specific problem of the control of a bidirectionally actuated quadrotor vehicle is considered. Simulations are carried out for each scenario MPC scheme as well as for a reference deterministic MPC scheme. When a sufficiently large sample count is considered, each of the scenario MPC algorithms achieves safer performance than the deterministic formulation without sacrificing any optimality. Additionally, the approximate solution techniques conclusively outperform the original nonlinear scenario MPC formulation for the same computational cost. [ABSTRACT FROM AUTHOR]

In recent years, the vigorous development of e-commerce has also greatly promoted the development of the fashion multimedia field. Fashion compatibility modeling plays a very important role in a series of applications, such as fashion retrieval and personalized recommendation. Because fashion compatibility prediction usually contains multiple modal information, in this paper, we present a type-aware fashion compatibility prediction model based on a hybrid attention mechanism that is centered on using a priori category information for the fusion of multimodal information. Specifically, the model first designs a hybrid attention mechanism to fully explore the inner dependency of the visual modality while enriching with textual modalities to obtain a joint feature representation of multimodal information. Furthermore, the model matches fashion items as a distance measurement problem, and the joint feature representation of multimodal information realizes type-aware characteristics by the design of a distance measurement function with category-aware embedding. The experimental results obtained on the publicly available Polyvore and Polyvore-D datasets demonstrate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learning models, and XGBoost emerges as the most promising model, demonstrating superior accuracy in predicting moisture content based on factors like soil properties, cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive exPlanations) to understand how these features influence the model's predictions. UPV is the most significant factor affecting predicted moisture content, followed by cement content and soil properties like uniformity coefficient. Also, the study explores the possibility of using a reduced set of features for moisture content prediction. They demonstrate that a combination of UPV, cement content, and uniformity coefficient can achieve good accuracy, highlighting the potential for practical applications where obtaining all data points might be challenging. [ABSTRACT FROM AUTHOR]

Background: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence. Methods: In this study, we examined patients undergoing radical oral cancer resection and explored inherent relationships among clinical data, OCT images, and peripheral immune indicators for oral cancer prognosis. We firstly built a peripheral blood immune indicator-guided deep learning feature representation method for OCT images, and further integrated a multi-view prognostic radiomics model incorporating feature selection and logistic modeling. Thus, we can assess the prognostic impact of each indicator on oral cancer by quantifying OCT features. Results: We collected 289 oral mucosal samples from 68 patients, yielding 1,445 OCT images. Using our deep radiomics-based prognosis model, it achieved excellent discrimination for oral cancer prognosis with the area under the receiver operating characteristic curve (AUC) of 0.886, identifying systemic immune-inflammation index (SII) as the most informative feature for prognosis prediction. Additionally, the deep learning model also performed excellent results with 85.26% accuracy and 0.86 AUC in classifying the SII risk. Conclusions: Our study effectively merged OCT imaging with peripheral blood immune indicators to create a deep learning-based model for inflammatory risk prediction in oral cancer. Additionally, we constructed a comprehensive multi-view radiomics model that utilizes deep learning features for accurate prognosis prediction. The study highlighted the significance of the SII as a crucial indicator for evaluating patient outcomes, corroborating our clinical statistical analyses. This integration underscores the potential of combining imaging and blood indicators in clinical decision-making. Trial registration: The clinical trial associated with this study was prospectively registered in the Chinese Clinical Trial Registry with the trial registration number (TRN) ChiCTR2200064861. The registration was completed on 2021. [ABSTRACT FROM AUTHOR]

Background: Mind--body treatments can improve coping mechanisms to deal with pain, improve the quality of life of patients with fibromyalgia syndrome (FMS), and reduce perceived pain in some cases. However, responses to these treatments are highly variable, the mechanisms underpinning them remain unclear, and reliable predictors of treatment response are lacking. We employed resting-state blood oxygen level-dependent (rsBOLD) functional magnetic resonance imaging (fMRI) to examine changes in brain functional connectivity (FC) following mind--body treatment that may relate to and predict pain relief. Methods: We recruited patients with FMS who underwent either mindfulnessbased stress reduction (MBSR; n = 18) or a psychoeducational program (FibroQoL; n = 22) and a treatment-as-usual FMS group (TAU; n = 18). We collected rsBOLD data, alongside subjective pain, anxiety, depression, and catastrophizing measures prior to and following treatments. We examined behavioral changes and FC changes in the salience network (SN) and sensorimotor network (SMN) and performed regression analyses to identify predictors for treatment response. Results: The MBSR and FibroQoL groups experienced significant reductions in pain catastrophizing. After treatment, the FC of the sensorimotor cortex with the rest of the SMN became significantly reduced in the MBSR group compared to the TAU group. The FC between the SN and the SMN at baseline was negatively correlated with pain reductions following MBSR but positively correlated with pain reductions in the FibroQoL group. These results yielded large to very large effect sizes. Following MBSR, only for those patients with lower baseline SMN-SN FC, minutes of mindfulness practice were positively associated with clinical improvement (small to medium effect size). Conclusions: Different mind--body treatments are underpinned by discrete brain networks. Measures of the functional interplay between SN and SMN have the potential as predictors of mind--body treatment response in patients with FMS. [ABSTRACT FROM AUTHOR]

Long COVID shares a number of clinical features with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), including post-exertional malaise, severe fatigue, and neurocognitive deficits. Utilizing validated assessment tools that accurately and efficiently screen for these conditions can facilitate diagnostic and treatment efforts, thereby improving patient outcomes. In this study, we generated a series of random forest machine learning algorithms to evaluate the psychometric properties of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) in classifying large groups of adults with Long COVID, ME/CFS (without Long COVID), and healthy controls. We demonstrated that the DSQ-SF can accurately classify these populations with high degrees of sensitivity and specificity. In turn, we identified the particular DSQ-SF symptom items that best distinguish Long COVID from ME/CFS, as well as those that differentiate these illness groups from healthy controls. [ABSTRACT FROM AUTHOR]

An accurate prediction model can be required for the greenhouse air temperature, such as Model Predictive Control. Long short-term memory neural networks (LSTM) have been widely used to predict time series data, such as air temperature. However, the prediction accuracy of LSTM can be reduced for the long time series data, due to data forgetting. In this study, the LSTM model was combined with the attention mechanism to construct the LSTM-AT model. The query vector, key vector, and value vector were calculated, according to attention mechanism and output states of LSTM’s hidden layer. The similarity between the query and key vector was calculated to obtain the similarity score. Softmax function was used to obtain attention distribution for the normalization processing. The larger the attention value was, the higher the relevance of input information to the task objective was. The dot product operation was carried out with the normalized weight and value vector to obtain the output of the attention mechanism. The local information integration and data dimension transformation were carried out through the full connection layer. Finally, the output data was obtained in the output layer of the LSTM-AT model. The weights were assigned to the output states of LSTM’s hidden layer, according to the degree of importance. The forgetting of long time series data was effectively solved to improve the prediction accuracy of indoor air temperature. The prediction performances were verified and compared on the LSTM-AT, LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM) model in the different prediction horizons (12, 24 and 48 h). The results showed that the prediction accuracy of the five models shared a decreasing trend with the increase in prediction time. The maximum and minimum RMSE for the LSTM-AT model were 1.34 ℃ and 0.59 ℃, respectively. The maximum and minimum RMSE for the rest four models were 3.37 ℃ and 0.66 ℃, respectively. The maximum and minimum MAPE for the LSTM-AT model were 8.14% and 2.48%, respectively. The maximum and minimum MAPE for the rest four models were 38.7% and 2.90%, respectively. Therefore, the prediction accuracy of the LSTM-AT model was higher than that of rest four models. The average RMSE for LSTM-AT, LSTM, GRU, RNN, and BiLSTM were 0.89, 1.42, 1.89, 2.10, and 1.51 ℃, respectively. The average MAPE for LSTM-AT, LSTM, GRU, RNN, and BiLSTM were 4.26%, 8.96%, 13.57%, 17.70%, and 10.67%, respectively. The sort data of the prediction model was ranked in descending order of the LSTM-AT, LSTM, BiLSTM, GRU, and RNN. The prediction performances of the LSTM-AT and LSTM model were compared under different weather conditions (sunny, cloudy, and rainy), in order to further explore the universality of the LSTM-AT model. The minimum and maximum RMSE for LSTMAT were 0.26℃ and 0.70 ℃, respectively. The minimum and maximum RMSE for LSTM were 0.68 ℃ and 1.57 ℃, respectively. The minimum and maximum MAPE for LSTM-AT were 1.61% and 10.51%, respectively. The minimum and maximum MAPE for LSTM were 4.27% and 25.07%, respectively. The prediction accuracy of the LSTM-AT model was higher than LSTM in all weather conditions. The LSTM-AT model has a higher prediction accuracy to accurately predict the indoor air temperature. [ABSTRACT FROM AUTHOR]

Health disparities are ongoing challenges in the United States, with one of the underlying factors being the variation in health risk behaviors across different socioeconomic and demographic communities. In this cross-sectional ecological study, utilizing data from 26,781 US zip codes provided by the Centers for Disease Control and Prevention (CDC), we conducted generalized linear model regressions to explore the associations between race, poverty, education, and urban/rural status in areas. Our analysis indicated that areas with a higher prevalence of Black population and higher poverty levels are associated with a more significant number of unique health risk behaviors. Conversely, other racial groups were associated with fewer unique health risk behaviors, although they may still engage in higher levels of specific behaviors. These insights underscore the need for tailored public health strategies to address the disparities in health risk behaviors across different sub-populations. Additionally, we employed Finite Mixture Modeling (FMM) to identify distinct sub-populations in the United States based on health risk behaviors, specifically binge drinking, smoking, sleep deprivation, and physical inactivity, which are essential risk factors for chronic diseases such as cancer, diabetes, and cardiovascular diseases. We identified four clusters and conducted an in-depth analysis of the demographics and socioeconomic characteristics of the identified clusters. Our findings reveal that one cluster, characterized by the worst health risk behaviors, exhibited the highest poverty rate, the least favorable health insurance profile, and the lowest frequencies of post-secondary education attainment compared to the other three clusters. This cluster, therefore, warrants targeted public health interventions. [ABSTRACT FROM AUTHOR]

The precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim. [ABSTRACT FROM AUTHOR]

This article introduces a control method for trajectory tracking of underactuated unmanned marine vehicles (UMVs), employing the sliding mode predictive control (SMPC) scheme. To address the challenges of demonstrating system stability with a local feedback controller for underactuated UMVs in model predictive control (MPC), this article proposes an auxiliary controller design method based on sliding mode control. A sliding mode dynamic is derived through an error system and sliding surface equations. Compared to existing literature, which predominantly emphasizes demonstrating input‐state stability, this strategy ensures the asymptotic stability of the closed‐loop system by introducing a novel method for selecting weight matrices. Furthermore, extended terminal sets and feasible sets constructed via sliding variables are provided, thereby reducing conservatism. Ultimately, the SMPC scheme is validated through simulation and hardware experiments providing quantitative evidence of its effectiveness in real‐world applications. [ABSTRACT FROM AUTHOR]