Prediction Of Curves Research Articles

A Pore-Scale Investigation of Oil Contaminant Remediation in Soil: A Comparative Study of Surfactant- and Polymer-Enhanced Flushing Agents

Pore-scale remediation investigation of oil-contaminated soil is important in several environmental and industrial applications, such as quick responses to sudden accidents. This work aims to investigate the oil pollutant removal process and optimize the oil-contaminated soil remediation performance at the pore scale to find the underlying mechanisms for oil removal from soil. The conservative forms of the phase-field model and the non-Newtonian power-law fluid model are employed to track the moving interface between two immiscible phases, and oil pollutant flushing removal process from soil pores is investigated. The effects of viscosity, interfacial tension, wettability, and flushing velocity on pore-scale oil pollutant removal regularity are explored. Then, the oil pollutant removal effects of two flushing agents (surfactant system and surfactant–polymer system) are compared using an oil content prediction curve based on UV-Visible transmittance. The results show that the optimal removal efficiency is obtained for a weak water-wetting system with a contact angle of 60° due to the stronger two-phase fluid interaction, deeper penetration, and more effective entrainment flow. On the basis of the dimensionless analysis, a relatively larger flushing velocity, resulting in a higher capillary number (Ca) in a certain range, can achieve rapid and efficient oil removal. In addition, an appropriately low interfacial tension, rather than ultra-low interfacial intension, contributes to strengthening the oil removal behavior. A reasonably high viscosity ratio (M) with a weak water-wetting state plays synergetic roles in the process of oil removal from the contaminated soil. In addition, the flushing agent combined with a surfactant and polymer can remarkably enhance the oil removal efficiency compared to the sole use of the surfactant, achieving a 2.5-fold increase in oil removal efficiency. This work provides new insights into the often-overlooked roles of the pore scale in fluid dynamics behind the remediation of oil-contaminated soil via flushing agent injection, which is of fundamental importance to the development of effective response strategies for soil contamination.

Predicting Photovoltaic Module Lifespan Based on Combined Stress Tests and Latent Heat Analysis

In this study, long-term reliability tests for high-power-density photovoltaic (PV) modules were introduced and analyzed in accordance with IEC 61215 and light-combined damp heat cycles, such as DIN 75220. The results indicated that post light soaking procedure, light-combined damp heat cycles caused a 3.51% power drop, while IEC standard tests (DH1000 and TC200) caused only 0.87% and 1.32% power drops, respectively. IEC 61215 failed to assess the long-term reliability of the high-power-density PV module, such as the passivated emitter rear cell. Additionally, based on the combined test, the latent heat (Qmod) of the module was introduced to predict its degradation rate and to fit the prediction curve of the product guaranteed by the PV module manufacturers. Qmod facilitates in predicting a PV module’s lifespan according to the environmental factors of the actual installation area. The Qmod values of the PV stations in water environments, such as floating and/or marine PVs, indicated that they would last 7.2 years more than those on a rooftop, assuming that latent heat is the only cause of deterioration. Therefore, extending module life and improving power generation efficiency by determining installation sites to minimize latent heat would be advantageous.

Nomogram model using serum Club cell secretory protein 16 to predict prognosis and acute exacerbation in patients with idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with poor prognosis, nomogram model for its prognosis and acute exacerbation was constructed.MethodsTwo hundred and sixty eight patients with IPF were grouped with different severity according to fibrosis area, serum Club cell secretory protein 16(CC16) was compared between these groups. All patients were randomly divided into training and testing sets. COX regression and LASSO algorithm were used to screen featured characteristics. Then nomogram models were constructed, ROC curve, calibration curve and decision curve analysis(DCA) were conducted to evaluate the performance of model. Expression of CC16 were detected in fibrotic human lung tissues, bronchoalveolar lavage fluid (BALF) and Bleomycin(BLM)-treated mouse lung tissues and serums.ResultsSerum CC16 gradually increased with the severity of fibrosis, and was especially high in AE-IPF group. CC16 and diffusion capacity for carbon monoxide (DLCO) were screened as characteristic variables to construct nomogram model for IPF prognosis. The survival was significantly lower in high-risk group scored by the model. The area under ROC curves(AUCs) for 1-year and 2-year mortality prediction were 0.866 and 0.916, respectively. This model performed better than gender-age-physiology (GAP) index for predicting 2-year and 3-year mortality. Another nomogram model for acute exacerbation of IPF based on CC16, Krebs von den Lungen-6(KL-6) and DLCO was developed, the AUC was 0.815. Expression of CC16 obviously up-regulated in fibrotic lung tissues, BALF and BLM-treated mice lung tissues and serums.ConclusionsThe nomogram model based on CC16 performed good predictive ability for prognosis and acute exacerbation of IPF.

ERBB3-related gene PBX1 is associated with prognosis in patients with HER2-positive breast cancer

BackgroundHER2-positive breast cancer (BC) is a subtype of breast cancer. Increased ERBB3 expression has been implicated as a potential cause of resistance to other HER-targeted therapies. Our study aimed to screen and validate prognostic markers associated with ERBB3 expression by bioinformatics and affecting the prognosis of HER2 staging.MethodsAnalyzing differences in ERBB3-related groups. ERBB3 expression-related differentially expressed genes (DEGs) were identified and intersected with survival status-related DEGs to obtain intersected genes. Three algorithms, LASSO, RandomForest and XGBoost were combined to identify the signature genes. we construct risk models and generate ROC curves for prediction. Furthermore, we delve into the immunological traits, correlations, and expression patterns of signature genes by conducting a comprehensive analysis that encompasses immune infiltration analysis, correlation analysis, and differential expression analysis.ResultsSignificant variability in ERBB3 expression and prognosis in high and low ERBB3 expression groups. Twenty-five candidate DEGs were identified by intersecting ERBB3-related DEGs with survival-related DEGs. Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. In addition, the risk model had better prognostic and predictive effects, and the immune infiltration analysis showed that IGHM, CXCL13 might affect the proliferation of BC cells through immune cells. Functional studies demonstrated that interference with PBX1 inhibited the proliferation, migration, and epithelial-mesenchymal transition process of HER2-positive BC cells.ConclusionPBX1, IGHM and CXCL13 are associated with the expression level of the ERBB3 and are prognostic markers for HER2-positive in BC, which may play an important role in the development and progression of BC.

Unveiling immunity gaps and determining a suitable age for a third dose of the measles-containing vaccine: a strategic approach to accelerating measles elimination.

In highly measles immunized countries, immunity gaps in adolescents and young adults are a key issue posing an obstacle to measles elimination. This study aims to identify the gaps by estimating the age-stratified probability of seropositivity, and to ascertain a suitable age for the administration of a third dose of a measles-containing vaccine (MCV3) to effectively fill these gaps. We retrospectively obtained measles serological results from hospital setting among among individuals aged 13-39 years and developed a serocatalytic dynamic probability model, stratifying seropositivity due to vaccination or natural infection. We calibrated the model to age-stratified seropositivity data within a Bayesian setting using the Metropolis-Hastings algorithm. A scenario analysis to determine a suitable age for MCV3 administration was also performed. The overall prevalence of measles seropositivity was 65.6% (95% confidence interval [CI]: 61.5-69.6). Posterior predictive curves for the age-stratified seroprevalence exhibited a decreasing trend from ages 13-20 years but an upward trend from 26 to 30 years. The age at which a given individual's serostatus reached a 50% probability of seronegativity was found to be approximately 18-20 years depending on the annual measles force of infection. Our findings highlight a significant measles immunity gap in young adults aged 20-26 years, posing an increased risk of transmission. A MCV3 at the age of 18-20 years potentially closes the gap and aids measles elimination programmes. This work was supported by Faculty of Tropical Medicine (MCTM, ICTM grant), Mahidol University (to T.N.) and APC fee was supported by Mahidol University (to T.N.). S.M. and W.P. were funded in whole, or in part, by the Wellcome Trust [Grant number 220211]. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Dynamic changes and prognostic value of glutathione S-transferase alpha in mild cognitive impairment and Alzheimer’s disease

ObjectivesGlutathione S-transferase alpha (GSTα) is an important antioxidant enzyme closely associated with the onset and progression of neurodegenerative diseases. The alterations in GSTα protein levels associated with Alzheimer’s disease and their impact on cognitive abilities remain unclear. Thus, investigating the fluctuations of GSTα protein levels in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is essential.MethodsDATA were enrolled from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and we studied healthy individuals (as controls, a total of 54), patients with mild cognitive impairment (345), and patients with Alzheimer’s disease (96) A one-year follow-up was conducted to collect data on the dynamic changes of GSTα protein levels in plasma and primary information data, and to analyze the correlation between the changes in GSTα protein levels before and after the follow-up and cognitive function and its predictive value.ResultsPlasma GSTα protein levels were significantly lower in the AD group than in the CN group (0.94 vs1.05, p = 0.04) and the MCI group (0.94 vs1.09, p < 0.001). Plasma GSTα protein level changes were positively correlated with altered MMSE levels in MCI and AD patients (r = 0.09, p = 0.04). The AUC (95% CI) of the area under the prediction curve of plasma GSTα protein levels for MCI was 0.63 (0.54–0.71), p = 0.02, and the AUC (95% CI) of the area under the prediction curve of plasma GSTα protein levels for AD was 0.74 (0.69–0.80), p < 0.001. At the same time, we plotted ROC curves for the difference in the change of plasma GSTα protein levels after 1 year of follow-up. The results showed that the AUC (95% CI) of the area under the prediction curve of plasma GSTα protein levels change for MCI was 0.76 (0.696–0.83), p < 0.001, and the AUC (95% CI) of the area under the prediction curve of plasma GSTα protein levels change for AD was 0.75 (0.69–0.80), p < 0.001.ConclusionThe findings of the study indicated notable differences in GSTα protein levels among patients with MCI and those with AD after a one-year follow-up period. Furthermore, a positive correlation was observed between changes in GST αprotein levels and the decline in both baseline and cognitive function. This suggests that GSTα protein could potentially act as a biomarker for both MCI and AD, offering fresh insights for early detection and intervention strategies.

Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions

Artificial intelligence promises to revolutionize mental health care, but small dataset sizes and lack of robust methods raise concerns about result generalizability. To provide insights on minimal necessary data set sizes, we explore domain-specific learning curves for digital intervention dropout predictions based on 3654 users from a single study (ISRCTN13716228, 26/02/2016). Prediction performance is analyzed based on dataset size (N = 100–3654), feature groups (F = 2–129), and algorithm choice (from Naive Bayes to Neural Networks). The results substantiate the concern that small datasets (N ≤ 300) overestimate predictive power. For uninformative feature groups, in-sample prediction performance was negatively correlated with dataset size. Sophisticated models overfitted in small datasets but maximized holdout test results in larger datasets. While N = 500 mitigated overfitting, performance did not converge until N = 750–1500. Consequently, we propose minimum dataset sizes of N = 500–1000. As such, this study offers an empirical reference for researchers designing or interpreting AI studies on Digital Mental Health Intervention data.

Sand Fly Fauna in Urban Parks in the Brazilian Western Amazon: Potential Areas for American Cutaneous Leishmaniasis Transmission.

Sand flies are a group of insects (Diptera, Psychodidae, Phlebotominae) in which some species may transmit agents of leishmaniasis. This study aimed to analyze the sand fly fauna in urban parks in Rio Branco, capital of Acre state, addressing its diversity, infection by Leishmania, and food sources in sand fly females. The sand flies were collected with CDC-type automatic light traps installed in three urban parks in Rio Branco: Captain Ciríaco, Horto Florestal and Chico Mendes. For comparison analysis of species richness and diversity between the parks, we analyzed rarefaction and prediction curves. For detection of Leishmania spp., molecular tests were performed by polymerase chain reaction (PCR) using ITS1 as a marker, followed by genetic sequencing. Identification of the blood meal source in engorged females was performed by PCR using a fragment of the cytochrome b gene (cyt b), followed by sequencing and phylogenetic analysis. A total of 2,672 specimens were collected, distributed in 14 genera and 35 species. Of these, Nyssomyia antunesi and Evandromyia walkeri predominated, and important proven and suspected vectors were also found. Horto Florestal was the park with the greatest diversity, richness, and evenness of species, while the Captain Ciríaco presented the lowest diversity and richness. A female of Ny. antunesi was PCR-positive for Leishmania guyanensis. Human blood was the most frequent food source detected, mainly in Ev. walkeri and Ny. antunesi. This research showed that the studied parks of Rio Branco can act as foci for the maintenance of Leishmania, and people who frequent these urban areas may be in risk for contracting leishmaniasis.

Hyperenhancement of LA Wall by Three-Dimensional High-Resolution Late Gadolinium-Enhanced MRI and Recurrence of AF After Catheter Ablation.

Background/Objectives: This study investigated the relationship between LA (LA) enhancement on three-dimensional (3D) late gadolinium enhancement (LGE) MRI and recurrence after catheter ablation in patients with AF (AF). Methods: A total of one hundred patients with AF (mean age: 68 ± 9 years, 50% with paroxysmal AF) were included in this study. Each patient underwent a high-resolution 3D LGE MRI prior to catheter ablation, allowing for detailed imaging of the LA wall. Quantitative analysis of the enhancement was performed using dedicated software designed for volumetric measurements of LA LGE. Recurrence of AF was monitored over a 90-day period following the ablation procedure. The primary outcome was the correlation between the volume of LGE in the LA and the recurrence of AF. Results: Multivariate analysis confirmed that the volume of LA LGE, defined as the volume exceeding 1SD above the mean signal intensity of the LA, was an independent predictor of recurrence [hazard ratio: 1.16 (95%CI: 1.04-1.29, p = 0.0057)]. The area under the curve for recurrence prediction using 3D LGE MRI was 0.74 (95%CI: 0.63-0.86), with an optimal threshold of 11.72 mL, providing a sensitivity of 55% (95%CI: 32-77%) and a specificity of 86% (95%CI: 77-93%). Conclusions: LA enhancement assessed by high-resolution LGE MRI may serve as a valuable imaging marker for predicting the recurrence in patients with AF following catheter ablation.

Comparison of Different Machine Learning Models for Predicting Long-Term Overall Survival in Non-metastatic Colorectal Cancers.

In recent years, machine learning (ML) methods have gained significant popularity among medical researchers interested in cancer. We aimed to test different (ML) models to predict both overall survival and survival at specific time points in patients with non-metastatic colorectal cancer (CRC). The clinicopathological and treatment data of non-metastatic CRC patients with more than 10 years of follow-up at a single center were retrospectively reviewed. 1, 2, 3, 5, and 10-year survival rates for all patients and stages I-III were statistically calculated using the Kaplan-Meier method. Five distinct machine-learning algorithms were employed to develop predictive models for patient survival at five designated time points. A total of 498 patients were included in the study. The decision tree model had the highest area under the curve (AUC) for 1-year survival prediction (0.89). The ensemble model had the highest AUC for predicting 2-year, 3-year, and 5-year survival predictions (0.86, 0.92, and 0.89, respectively), while the support vector machine model had the highest AUC (0.84) for predicting 10-year survival. When considering the stages separately and assessing survival for the designated time intervals, the accuracy of all five models was found to be similar, ranging around 70% or higher. ML models can predict short- and long-term survival in patients with CRC, both for the overall patient population and when stratified by stage.

Predictive Value of the Platelet-Lymphocyte Ratio for Intravesical Recurrence After Radical Nephroureterectomy: A Retrospective Study.

The aim of this study was to evaluate the impact of preoperative platelet lymphocyte ratio (PLR) on the prognosis of patients after radical nephrectomy (RNU). We retrospectively analyzed clinical data from 226 patients without a history of bladder cancer who underwent RNU at Beijing Chaoyang Hospital, Capital Medical University between January 2009 and December 2020. Patients were stratified into two groups (A low PLR group (n = 174) and a high PLR group (PLR ≥ 169.4) based on an optimal PLR threshold (PLR=169.4). The predictive accuracy of inflammatory biomarkers was assessed using receiver operating characteristic curves. Univariate and multivariate Cox proportional risk analyses were used to estimate the effect of PLR on intravesical recurrence-free survival (IVRFS), recurrence-free survival (RFS), and overall survival (OS). The effect of PLR on IVRFS, RFS and OS was further examined using Kaplan-Meier survival curve analysis. The study cohort comprised 226 individuals with a mean age of 67.2 ± 9.8, 113 (50%) males and 113 (50%) females, 68 (30.1%) low-grade tumors and 158 (69.9%) high-grade tumors. In this study, 81 patients (36.7%) relapsed and 73 patients (32.3%) died. The area under the curve for PLR prediction of IVRFS was 0.603, superior to other inflammatory biomarkers. Multivariate analysis showed that PLR > 169.4 independently increased the risk of IVR after RNU, resulting in lower IVRFS [2.028 (1.014-4.057), P = 0.046], RFS [1.900 (1.168-3.090), P = 0.010], and OS [1.866 (1.099-3.167), P = 0.021]. In addition, survival analysis showed lower IVRFS [8.815 (62.722-97.278), P = 0.007], RFS [12.084 (44.315-91.685), P = 0.003] and OS RFS [10.165 (62.077-101.923), P = 0.005] in the low PLR group. Elevated preoperative PLR is strongly associated with prognosis in patients with upper urothelial carcinoma (UTUC) after RNU without a history of bladder cancer.

Research on task scheduling algorithm for digital cloud platform of Ultra-High voltage substation with load balancing awareness

Abstract This article focuses on the potential task scheduling risks in the digital cloud platform of ultra-high voltage substations and conducts research on workload prediction, exploring the differences in computing power caused by node heterogeneity. The article aims to address the impact of single-core high load or multi-core heavy load on system stability. In this algorithm, wavelet packet decomposition is used to obtain a more stable CPU utilization sequence. ARIMA (Autoregressive Integrated Moving Average) is employed for evaluation, and the SVM (Support Vector Machine) model is used to revise the time series to improve the accuracy of load prediction results, ultimately yielding a sequence representing the load status of nodes. The prediction curve generated by the SARIMA algorithm in this paper better reflects the actual load conditions of the test sequence. When the SARIMA load evaluation algorithm is applied to task scheduling, the task completion times are reduced. Specifically, the completion time is reduced by 84 seconds for 50 tasks and by 881 seconds for 200 tasks, corresponding to reductions of 14% and 22%, respectively.

Investigation and validation of neurotransmitter receptor-related biomarkers for forecasting clinical outcomes and immunotherapeutic efficacy in breast cancer

PurposeThe prognostic role of neurotransmitters and their receptors in breast cancer (BC) has not been fully investigated. The aim of this study was to construct a survival model for the prognosis of BC patients based on neurotransmitter receptor-related genes (NRRGs). MethodsBC-related differentially expressed genes (DEGs) were screened and intersected with NRRGs. GO, KEGG and PPI analyses were performed. Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to construct prognostic models for biomarker expression levels. The model was validated using an external validation set. The receiver operating characteristic curves (ROC) for diagnostic value prediction and clinicopathologic characteristic nomogram were constructed. qRT-PCR was used for further in vitro validation experiments. ResultsForty-five overlapping genes were obtained by intersecting BC-related DEGs with 172 NRRGs. Univariate Cox, LASSO and multivariate Cox regression analyses were used to construct prognostic models for the expression levels of biomarkers including DLG3, SLC1A1, PSCA and PRKCZ. The feasibility of the model was validated by the GEO validation set. ROC curves were established for diagnostic value prediction. Patients in the high-risk group had a worse prognosis, higher TMB score, higher probability of gene mutation, and higher immune cell infiltration. RiskScore, M, N and Age were strongly correlated with survival. The mRNA expression levels of DLG3, PSCA and PRKCZ in the BC group were significantly higher than those in the control group. ConclusionRisk prediction model based on DLG3, SLC1A1, PSCA and PRKCZ, which are closely related to BC prognosis, was successfully constructed.

Research on Defect Detection for Overhead Transmission Lines Based on the ABG-YOLOv8n Model

In the field of smart grid monitoring, real-time defect detection for overhead transmission lines is crucial for ensuring the safety and stability of power systems. This paper proposes a defect detection model for overhead transmission lines based on an improved YOLOv8n model, named ABG-YOLOv8n. The model incorporates four key improvements: Lightweight convolutional neural networks and spatial–channel reconstructed convolutional modules are integrated into the backbone network and feature fusion network, respectively. A bidirectional feature pyramid network is employed to achieve multi-scale feature fusion, and the ASFF mechanism is used to enhance the sensitivity of YOLOv8n’s detection head. Finally, comprehensive comparative experiments were conducted with multiple models to validate the effectiveness of the proposed method based on the obtained prediction curves and various performance metrics. The validation results indicate that the proposed ABG-YOLOv8n model achieves a 4.5% improvement in mean average precision compared to the original YOLOv8n model, with corresponding increases of 3.6% in accuracy and 2.0% in recall. Additionally, the ABG-YOLOv8n model demonstrates superior detection performance compared to other enhanced YOLO models.

Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data

As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. CNNs are utilized for spatial feature extraction, while LSTMs handle temporal dependencies, resulting in significantly improved prediction accuracy and stability. By using Mean Absolute Error (MAE) as the loss function, the model demonstrates excellent performance in processing complex meteorological data, addressing challenges such as missing data and high-dimensionality. The results show a strong alignment between the prediction curve and test data, validating the model's potential in climate prediction. This study offers valuable insights for fields such as agriculture, energy management, and urban planning, and lays the groundwork for future applications in weather forecasting under the context of global climate change.

Risk factors for the occurrence of arytenoid dislocation after major abdominal surgery: A retrospective study.

The incidence of arytenoid dislocation in abdominal surgery is relatively high, the cause is unknown, and it has not received sufficient attention. To identify the risk factors of arytenoid dislocation after abdominal surgery, and to establish a clinical prediction model based on relevant clinicopathological characteristics. We retrospectively collected the clinical data of 50 patients with arytenoid dislocation (AD) and 200 patients without AD after abdominal surgery with general anesthetic tracheal intubation in our Hospital from January 2013 to December 2019. General information about the patients was collected. Univariate analysis of the factors was performed, and indicators that were statistically significant were included in multivariate logistic regression analyses to identify the relationship between clinicopathological characteristics and arytenoid dislocation. Meanwhile, a clinical prediction model was established. Multivariate logistic regression analyses showed that age, surgical methods, operative time and gastric tube were dependent predictive factors of AD after abdominal surgery. A clinical prediction model was constructed, and the AUC of the ROC curve was 0.88 (95%CI: 0.83-0.94). The calibration plot shows that the prediction curve was close to the ideal curve. Patients undergoing abdominal surgery with general anesthesia exhibit a significantly higher incidence of AD due to a combination of factors. Clinicopathological features can be used as an independent predictor of risk in patients with AD, and a clinical model has been developed that is a good predictor of AD.

Evaluation of the performance and effect of steel fiber reinforced cellular concrete for underwater blast protection under contact explosion loading

The reinforced concrete structure is more likely to be destroyed under the action of underwater explosion load, which makes the overall structure have the risk of instability. The purpose of this paper is to study the underwater anti-explosion protection performance of steel fiber reinforced cellular concrete protective layer. The FEM-SPH coupling method was used to study the underwater damage process, dynamic response, failure mode and protection effect of the protected structure strengthened by eight different proportions of protective layers. The results show that the addition of steel fiber reinforced cellular concrete protective layer can effectively improve the anti-explosion performance of the protected structure, and its anti-explosion performance is directly related to the volume fraction of steel fiber in the protective layer. Under different ratio protection schemes, the total energy absorption and protection rate of the protective layer increase first and then decrease with the increase of the volume fraction of steel fiber in the protective layer, while the peak pressure of the protected structure at the measuring point and the failure volume rate of the whole structure decrease first and then increase. Among them, the underwater wave attenuation and energy consumption effect of SAP10S15 protective layer is the most prominent, and its total energy absorption is 20 % higher than that of other protective layers. Under this ratio protection scheme, the shock wave pressure of the protected structure is greatly attenuated, and the peak pressure of each measuring point is reduced by about 35.8 %, 37.9 %, 23.7 %, 27.9 % and 35.1 % respectively compared with the unprotected scheme. The damage degree of the protected structure under the SAP10S15 ratio protection scheme is significantly reduced. The failure volume rate and damage index are 8.03 % and 0.21, respectively, which are 37.7 % and 42.5 % lower than those of the unprotected scheme. The damage level is reduced from serious damage to moderate damage. In addition, the damage grade prediction curve of the protected structure is constructed, and the predicted value of the curve is in good agreement with the simulation results, which can provide a reference for the rapid evaluation of the underwater explosion-proof performance of the steel fiber reinforced cellular concrete protective layer.

Multi-objective optimization of the design parameters in longitudinal corrugated tubes to improve crashworthiness performance

Longitudinal corrugated tubes (LCTs) are among the structures of interest to designers, because of their ability to improve the weaknesses of conventional simple tubes by reducing the maximum crushing force (Fmax) and providing a controllable and predictable force-displacement curve during energy absorption. In this study using finite element (FE) simulations, the effect of design parameters of LCTs, i.e., amplitude and number of folds on the crashworthiness criteria, in different deformation modes has been investigated. Crushing parameters of some simple and corrugated tubes have been examined experimentally to validate the FE simulations. Results of 400 FE models, revealed some geometries in LCTs that, in addition to reducing Fmax, can increase the specific energy absorption (SEA) compared to the simple tubes. Most of these geometries deform under compressive axial loading in the N-mode region. Using multi-objective optimization, specification of the optimal LCT, including number of folds and their amplitudes were determined. Then the optimal LCT was made by the ring-forming method and compared with the simple tube experimentally. The optimized LCT was experimentally evaluated and results showed that it can reduce Fmax by 27.2% and increase SEA by 21.6% compared to the simple tube. The lower SEA of the LCTs is usually considered as a disadvantage for the LCTs compared to the simple tubes, which is violated in this research. Optimized LCT may be a new idea for aerospace applications as crash-resistant structures.

Multi-scale analysis of voltage curves for accurate and adaptable lifecycle prediction of lithium-ion batteries

Health status prediction of lithium-ion batteries is critical for the stable operation of electrical equipment. The data-driven approach can fit the degradation laws based on the historical cyclic data and identify potential problems in time. However, existing prediction methods mainly focus on acquiring various cyclic degradation features, and excessive battery-related data types increase the complexity and indeterminacy, posing a challenge for efficient and accurate prediction during practical application scenarios. Herein, this study only uses cyclic voltages and proposes a novel multi-scale cyclic voltage data preprocessing technique, involving the selection of highly correlated feature parameters with battery life in both the time and frequency domains, and constructing graphical samples with dynamic node connectivity properties. The proposed multi-scale graph convolutional network model adeptly captures and amalgamates the evolving features among graphical samples with efficacy. The one-step prediction experiments demonstrate the capability to predict subsequent capacity degradation solely based on the voltages of any consecutive 20 cycles, with a root mean square error of 0.173 %, exhibiting adaptable capability across different battery datasets. Compared to existing methods that transform data into images for health status prediction, this study highlights the significance of a multi-scale approach to voltage analysis in cyclic data, leveraging advanced feature engineering and modeling techniques, not only to enhance the accuracy and adaptability of battery lifecycle predictions but also to offer a robust framework for overcoming prevailing challenges in monitoring accuracy and cost-effectiveness.

Research progress on the development of myopia prediction models and their predictive performance

The incidence of myopia is continuously increasing worldwide, especially in China, where the prevention and control of myopia is of great significance. Accurate prediction of myopia and early intervention are of great importance for slowing down the development of myopia and reducing the social burden. This article reviews the research progress of myopia prediction models and their predictive performance in recent years. It includes models based on refractive power and ocular biological characteristics, such as those using parameters such as refractive power, axial length, and percentile curves for prediction; models combined with environmental factors, which explore the relationship between myopia in school-age children and the age of onset, parents' myopia, education, and various living environmental factors; models based on genetic factors, which predict by evaluating parents' myopia status and single nucleotide polymorphisms discovered in genome-wide association studies. In addition, the article also introduces the application of artificial intelligence in myopia prediction, such as machine learning and deep learning algorithms that can predict changes in axial length growth and refractive power. These research advances provide a reference for establishing more accurate myopia prediction models and help achieve precise and personalized myopia prevention and control.