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The optimization of mechanical properties in acrylonitrile butadiene styrene-like (ABS-like) photopolymer utilizing neural network techniques presents a promising methodology for enhancing the performance and strength of components fabricated through stereolithography (SLA) 3D printing. This approach uses machine learning algorithms to analyze and predict the relationships between various printing parameters and the resulting mechanical properties, thereby allowing the engineering of better materials specifically designed for targeted applications. Artificial neural networks (ANNs) can model complex, nonlinear relationships between process parameters and material properties better than traditional methods. This research constructed four ANN models to predict critical mechanical properties, such as tensile strength, yield strength, shore D hardness, and surface roughness, based on SLA 3D printer parameters. The parameters used were orientation, lifting speed, lifting distance, and exposure time. The constructed models showed good predictive capabilities, with correlation coefficients of 0.98798 for tensile strength, 0.9879 for yield strength, 0.9823 for Shore D hardness, and 0.98689 for surface roughness. These high correlation values revealed the effectiveness of ANNs in capturing the intricate dependencies within the SLA process. Also, multi-objective optimization was conducted using these models to find the SLA printer’s optimum parameter combination to achieve optimal mechanical properties. The optimization results showed that the best combination is Edge orientation, lifting speed of 90.6962 mm/min, lifting distance of 4.8483 mm, and exposure time of 4.8152 s, resulting in a tensile strength of 40.4479 MPa, yield strength of 32.2998 MPa, Shore D hardness of 66.4146, and Ra roughness of 0.8994. This study highlights the scientific novelty of applying ANN to SLA 3D printing, offering a robust framework for enhancing mechanical strength and dimensional accuracy, thus marking a significant benefit of using ANN tools rather than traditional methods. Full article

(This article belongs to the Special Issue Recent Advances in Optimization of Additive Manufacturing Processes)

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18 pages, 2984 KiB

Open AccessArticle

A Domain Adaptation Meta-Relation Network for Knowledge Transfer from Human-Induced Faults to Natural Faults in Bearing Fault Diagnosis

by Dong Sun, Xudong Yang and Hai Yang

Sensors 2025, 25(7), 2254; https://doi.org/10.3390/s25072254 (registering DOI) - 3 Apr 2025

Abstract

Intelligent fault diagnosis of bearings is crucial to the safe operation and productivity of mechanical equipment, but it still faces the challenge of difficulty in acquiring real fault data in practical applications. Therefore, this paper proposes a domain adaptive meta-relation network (DAMRN) to achieve diagnostic knowledge transfer from laboratory-simulated faults (human-induced faults) to real scenario faults (natural faults) by fusing meta-learning and domain adaptation techniques. Specifically, firstly, through meta-task scenario training, DAMRN captures task-independent generic features from human-induced fault samples, which gives the model the ability to adapt quickly to the target domain tasks. Secondly, a domain adaptation strategy that complements each other with explicit alignment and implicit confrontation is set up to effectively reduce the domain discrepancy between human-induced faults and natural faults. Finally, this paper experimentally validates DAMRN in two cases (same-machine and cross-machine) of a human-induced fault to a natural fault, and DAMRN outperforms other methods with average accuracies as high as 99.62% and 96.38%, respectively. The success of DAMRN provides a viable solution for practical industrial applications of bearing fault diagnosis. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)

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14 pages, 1939 KiB

Open AccessArticle

Marital Status-Specific Associations Between Multidomain Leisure Activities and Cognitive Reserve in Clinically Unimpaired Older Adults: Based on a National Chinese Cohort

by Cheng Cai, Junyi Wang, Dan Liu, Jing Liu, Juan Zhou, Xiaochang Liu, Dan Song, Shiyue Li, Yuyang Cui, Qianqian Nie, Feifei Hu, Xinyan Xie, Guirong Cheng and Yan Zeng

Brain Sci. 2025, 15(4), 371; https://doi.org/10.3390/brainsci15040371 (registering DOI) - 3 Apr 2025

Abstract

Background: It is unclear how marital status moderates the association between multidomain leisure activities and the progression of cognitive decline in community-dwelling older adults. Methods: Data from the Chinese Longitudinal Healthy Longevity Survey with up to 10 years of follow-up were used. The study included participants aged ≥65 years without cognitive impairment at baseline. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Linear mixed-effect models were used to evaluate the modifying effect of marriage on leisure activities (multiple types, frequency, and single type) and cognitive decline. Results: A total of 5286 participants (aged 79.01 ± 9.54 years, 50.0% women, and 61.4% rural residents) were enrolled. The results indicated that marital status moderates the relationship between leisure activities and cognitive decline. In the unmarried group, multi-type and high-frequency leisure activities were more strongly associated with slower cognitive decline. Specific activities such as gardening, reading, performing household chores, and playing cards were found to significantly contribute to cognitive protection exclusively within the unmarried group, with no such effect observed in the married group. Conclusions: Marital status affects the relationship between participation in multiple leisure activities and cognitive decline in cognitively intact elderly people. For unmarried older adults, regular participation in leisure activities may be an effective intervention. Full article

(This article belongs to the Section Cognitive, Social and Affective Neuroscience)

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11 pages, 840 KiB

Open AccessArticle

Sex Difference in the Associations of Socioeconomic Status, Cognitive Function, and Brain Volume with Dementia in Old Adults: Findings from the OASIS Study

by Sophia Z. Liu, Ghazaal Tahmasebi, Ying Sheng, Ivo D. Dinov, Dennis Tsilimingras and Xuefeng Liu

J. Dement. Alzheimer's Dis. 2025, 2(2), 9; https://doi.org/10.3390/jdad2020009 (registering DOI) - 3 Apr 2025

Abstract

Background: Sex differences in the association of cognitive function and imaging measures with dementia have not been fully investigated. Understanding sex differences in the dementia-related socioeconomic, cognitive, and imaging measurements is crucial for uncovering sex-related pathways to dementia and facilitating early diagnosis, family planning, and cost control. Methods: We selected data from the Open Access Series of Imaging Studies, with longitudinal measurements of brain volumes, on 150 individuals aged 60 to 96 years. Dementia status was determined using the Clinical Dementia Rating (CDR) scale, and Alzheimer’s disease was diagnosed as a CDR of ≥0.5. Generalized estimating equation models were used to estimate the associations of socioeconomic, cognitive, and imaging factors with dementia in men and women. Results: The study sample consisted of 88 women (58.7%) and 62 men (41.3%), and the average age of the subjects was 75.4 years at the initial visit. A lower socioeconomic status was associated with a reduced estimated total intracranial volume in men, but not in women. Ageing and lower MMSE scores were associated with a reduced nWBV in both men and women. Lower education affected dementia more in women than in men. Age, education, Mini-Mental State Examination (MMSE), and normalized whole-brain volume (nWBV) were associated with dementia in women, while only MMSE and nWBV were associated with dementia in men. Conclusions: The association between education and the prevalence of dementia differs in men and women. Women may have more risk factors for dementia than men. Full article

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22 pages, 9907 KiB

Open AccessArticle

Advanced Modular Honeycombs with Biomimetic Density Gradients for Superior Energy Dissipation

by Yong Dong, Jie He, Dongtao Wang, Dazhi Luo, Yanghui Zeng, Haixia Feng, Xizhen You and Lumin Shen

Biomimetics 2025, 10(4), 221; https://doi.org/10.3390/biomimetics10040221 (registering DOI) - 3 Apr 2025

Abstract

The honeycomb configuration has been widely adopted in numerous sectors owing to its superior strength-to-weight ratio, rigidity, and outstanding energy absorption properties, attracting substantial academic attention and research interest. This study introduces a biomimetic modular honeycomb configuration inspired by the variable-density biological enhancement characteristics of tree stem tissues. This study examined the out-of-plane compressive behavior and mechanical characteristics of modular honeycomb structures. A numerical model of the modular honeycomb was constructed utilizing finite element technology, enabling simulation studies at varying impact velocities. The improved weight-bearing and impact-absorbing properties of modular honeycomb structures are investigated using theoretical analysis and computer simulations. It also scrutinizes the effects of boundary and matching conditions on the honeycomb’s performance. The results indicate that adjusting the thickness of the walls in both the matrix honeycomb and sub-honeycomb structures can substantially improve their resistance to low-velocity out-of-plane compression impacts. Furthermore, the energy absorption capacity of modular honeycombs during high-velocity impacts is significantly influenced by multiple factors: the impact velocity, the density of the honeycomb structure, and the distribution of wall thickness within the sub-honeycomb and the primary honeycomb matrix. Notably, the modular honeycomb with an optimally designed structure demonstrates superior high-speed impact resistance compared to conventional honeycombs of equivalent density. These insights underscore the potential for advanced honeycomb designs to further advance material performance in structural applications. Full article

(This article belongs to the Special Issue Biomimetic Energy-Absorbing Materials or Structures)

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Graphical abstract

17 pages, 4201 KiB

Open AccessArticle

Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning

by Wenxiang Luo, Yang Shen, Zewen Li and Fangming Deng

Energies 2025, 18(7), 1796; https://doi.org/10.3390/en18071796 (registering DOI) - 3 Apr 2025

Abstract

In a distributed photovoltaic system, photovoltaic data are affected by heterogeneity, which leads to the problems of low adaptability and poor accuracy of photovoltaic power prediction models. This paper proposes a distributed photovoltaic power prediction scheme based on Personalized Federated Multi-Task Learning (PFL). The federal learning framework is used to enhance the privacy of photovoltaic data and improve the model’s performance in a distributed environment. A multi-task module is added to PFL to solve the problem that an FL single global model cannot improve the prediction accuracy of all photovoltaic power stations. A cbam-itcn prediction algorithm was designed. By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics’ information and improve the feature extraction ability of time series data in photovoltaic power prediction. The experimental analysis shows that CBAM-iTCN is 45.06% and 42.16% lower than a traditional LSTM, Mae, and RMSE. Compared with FL, the MAPE of the PFL proposed in this paper is reduced by 9.79%, and for photovoltaic power plants with large data feature deviation, the MAPE experiences an 18.07% reduction. Full article

(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)

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21 pages, 4349 KiB

Open AccessArticle

Research on Wind Power Grid Integration Power Fluctuation Smoothing Control Strategy Based on Energy Storage Battery Health Prediction

by Bin Cheng, Jiahui Wu, Guancheng Lv and Zhongbo Li

Energies 2025, 18(7), 1795; https://doi.org/10.3390/en18071795 (registering DOI) - 3 Apr 2025

Abstract

Due to the volatility and uncertainty of wind power generation, energy storage can help mitigate the fluctuations in wind power grid integration. During its use, the health of the energy storage system, defined as the ratio of the current available capacity to the initial capacity, deteriorates, leading to a reduction in the available margin for power fluctuation smoothing. Therefore, it is necessary to predict the state of health (SOH) and adjust its charge/discharge control strategy based on the predicted SOH results. This study first adopts a Genetic Algorithm-Optimized Support Vector Regression (GA-SVR) model to predict the SOH of the energy storage system. Secondly, based on the health prediction results, a control strategy based on the model predictive control (MPC) algorithm is proposed to manage the energy storage system’s charge/discharge process, ensuring that the power meets grid integration requirements while minimizing energy storage lifespan loss. Further, since the lifespan loss caused by smoothing the same fluctuation differs at different health levels, a fuzzy adaptive control strategy is used to adjust the parameters of the MPC algorithm’s objective function under varying health conditions, thereby optimizing energy storage power and achieving the smooth control of the wind farm grid integration power at different energy storage health levels. Finally, a simulation is conducted in MATLAB for a 50 MW wind farm grid integration system, with experimental parameters adjusted accordingly. The experimental results show that the GA-SVR algorithm can accurately predict the health of the energy storage system, and the MPC-based control strategy derived from health predictions can improve grid power stability while adaptively adjusting energy storage output according to different health levels. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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5 pages, 148 KiB

Open AccessCorrection

Correction: Wang, M.; Li, T. Pest and Disease Prediction and Management for Sugarcane Using a Hybrid Autoregressive Integrated Moving Average—A Long Short-Term Memory Model. Agriculture 2025, 15, 500

by Minghui Wang and Tong Li

Agriculture 2025, 15(7), 774; https://doi.org/10.3390/agriculture15070774 (registering DOI) - 3 Apr 2025

Abstract

In the original publication [...] Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

22 pages, 3385 KiB

Open AccessArticle

Malnutrition and Climate in Niger: Findings from Climate Indices and Crop Yield Simulations

by Benjamin Sultan, Aurélien Barriquault, Audrey Brouillet, Jérémy Lavarenne and Montira Pongsiri

Int. J. Environ. Res. Public Health 2025, 22(4), 551; https://doi.org/10.3390/ijerph22040551 (registering DOI) - 2 Apr 2025

Abstract

Malnutrition, particularly its impact on child morbidity and mortality, is one of the top five health effects of climate change. However, quantifying the portion of malnutrition attributed to climate remains challenging due to various confounding factors. This study examines the relationship between climate and acute malnutrition in Niger, a country highly vulnerable to climate change and disasters. Since climate’s effect on malnutrition is indirect, mediated by crop production, we combine rainfall data from TAMSAT satellite estimates with the SARRA-O crop model, which simulates the impact of rainfall variability on crop yields. Our analysis reveals a significant correlation between malnutrition and both rainfall and crop production from the previous year, but not within the same year. The strongest correlation (R = −0.72) was found with the previous year’s crop production. No significant links were found with temperature or intra-seasonal rainfall indices, like the start or duration of the rainy season. Although national correlations between global malnutrition, rainfall, and crop yields were stronger, they were weaker or absent at the regional level and, for Severe Acute Malnutrition crises, are less likely driven by climate variability. However, the one-year lag in the correlation allows for the prediction of future food crises, providing an opportunity to implement early intervention measures. Full article

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27 pages, 7104 KiB

Open AccessArticle

Crypto Asset Markets vs. Financial Markets: Event Identification, Latest Insights and Analyses

by Eleni Koutrouli, Polychronis Manousopoulos, John Theal and Laura Tresso

AppliedMath 2025, 5(2), 36; https://doi.org/10.3390/appliedmath5020036 (registering DOI) - 2 Apr 2025

Abstract

As crypto assets become more widely adopted, crypto asset markets and traditional financial markets may become increasingly interconnected. The close linkages between these markets have potentially important implications for price formation, contagion, risk management and regulatory frameworks. In this study, we assess the correlation between traditional financial markets and selected crypto assets, study factors that may impact the price of crypto assets and identify potentially significant events that may have an impact on Bitcoin and Ethereum price dynamics. For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. We then use the dates and probabilities of these change points to link them to specific events, finding that nearly all of the change points can be associated with known historical crypto asset-related events. The events can be classified into broader geopolitical developments, regulatory announcements and idiosyncratic events specific to either Bitcoin or Ethereum. Full article

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12 pages, 1880 KiB

Open AccessArticle

Combustion of High-Energy Compositions (HECs) Containing Al-B, Ti-B and Fe-B Ultrafine Powders (UFPs)

by Weiqiang Pang, Ivan Sorokin and Alexander Korotkikh

Nanomaterials 2025, 15(7), 543; https://doi.org/10.3390/nano15070543 (registering DOI) - 2 Apr 2025

Abstract

Metal and metalloid powders are widely used in high-energy compositions (HECs) and solid propellants (SPs), increasing their energetic characteristics in the combustion chamber. The particle size distribution, protective coatings of the particles and heat of combustion of the metal powders influence the ignition and combustion parameters of the HECs as well as the characteristics of the propulsion systems. Boron-based metallic fuels achieve high-energy potentials during their combustion. The effect of Al-B, Fe-B and Ti-B (Me-B) mixture ultrafine powders (UFPs) on the ignition and combustion characteristics of a model HEC based on a solid oxidizer and a polymer combustible binder was investigated. The Me-B mass ratios in the mixture UFPs corresponded to the phase composition of the borides AlB₂, FeB and TiB₂. It was found that replacing the aluminum UFP with Al-B, Fe-B and Ti-B UFPs in the HECs changed the exponent (n) in the correlations of the ignition delay time t_ign(q) and burning rate u(p). The maximum burning rate and n over the pressure range of 0.5–5.0 MPa were obtained for the HEC with Al-B UFPs due to the increase in the heat release rate near the sample surface during the joint combustion of the Al and B particles. Full article

(This article belongs to the Special Issue Advanced Nanomaterials and Energetic Application: Experiment and Simulation)

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23 pages, 4531 KiB

Open AccessArticle

Research on Active Avoidance Control of Intelligent Vehicles Based on Layered Control Method

by Jian Wang, Qian Li and Qiyuan Ma

World Electr. Veh. J. 2025, 16(4), 211; https://doi.org/10.3390/wevj16040211 (registering DOI) - 2 Apr 2025

Abstract

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles. Full article

(This article belongs to the Special Issue Vehicle System Dynamics and Intelligent Control for Electric Vehicles)

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24 pages, 653 KiB

Open AccessArticle

Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm

by Bo Wang, Xinyu Sun and Ying Song

Algorithms 2025, 18(4), 200; https://doi.org/10.3390/a18040200 (registering DOI) - 2 Apr 2025

Abstract

Edge computing, characterized by its proximity to users and fast response times, is considered one of the key technologies for addressing low-latency demands in the future. An appropriate edge server deployment strategy can reduce costs for service providers and improve the quality of service for users. However, most previous studies have focused on server coverage or deployment solution consumption time, often neglecting the most critical aspect: minimizing user-request response latency. To address this, we propose an edge deployment strategy based on the queuing search algorithm (QSA), which models the edge deployment problem as a multi-constrained nonlinear optimization problem. The QSA mimics the logic of human queuing behavior and has the ability to perform faster global searches while avoiding local optima. Experimental results show that, compared to the genetic algorithm, simulated annealing algorithm, particle swarm optimization, and other recent algorithms, the average number of “hopping” iterations in QSA is 0.1 to 0.6 times fewer than in the other algorithms. Additionally, QSA is particularly suitable for edge computing environments with a large number of users and devices. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

18 pages, 2776 KiB

Open AccessArticle

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach

by Martina Srutova, Vaclav Kremen and Lenka Lhotska

Sensors 2025, 25(7), 2253; https://doi.org/10.3390/s25072253 (registering DOI) - 2 Apr 2025

Abstract

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques—area under the ECG curve and wave transformation into the unit circle—to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches’ functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS. Full article

(This article belongs to the Special Issue Sensors for Heart Rate Monitoring and Cardiovascular Disease)

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20 pages, 3859 KiB

Open AccessArticle

Thermal Mitigation in Coastal Cities: Marine and Urban Morphology Effects on Land Surface Temperature in Xiamen

by Tingting Hong, Xiaohui Huang, Qinfei Lv, Suting Zhao, Zeyang Wang and Yuanchuan Yang

Buildings 2025, 15(7), 1170; https://doi.org/10.3390/buildings15071170 - 2 Apr 2025

Abstract

Amidst the rapid global urbanization and economic integration, coastal cities have undergone significant changes in urban spatial patterns. These changes have further worsened the complex urban thermal environment, making it crucial to study the interaction between human-driven development and natural climate systems. To address the insufficient quantification of marine elements in the urban planning of subtropical coastal zones, this study takes Xiamen, a typical deep-water port city, as an example to construct a spatial analysis framework integrating marine boundary layer parameters. This research employs interpolation simulation, atmospheric correction, and other techniques to simulate the inversion of land use and Landsat 8 data, deriving urban morphological elements and Land Surface Temperature (LST) data. These data were then assigned to 500 m grids for analysis. A bivariate spatial auto-correlation model was applied to examine the relationship between urban carbon emission and LST. The study area was categorized based on the influence of marine factors, and the spatial relationships between urban morphological elements and LST were analyzed using a multiscale geographically weighted regression model. Three Xiamen-specific discoveries emerged: (1) the marine exerts a significant thermal mitigation effect on the city, with an average influence range of 7.94 km; (2) the relationship between urban morphology and the thermal environment exhibits notable spatial heterogeneity across different regions; and (3) to mitigate urban thermal environments, connected green corridors should be established in the southern coastal areas of outer districts in regions significantly influenced by the ocean. In areas with less marine influence, spatial complexity should be introduced by disrupting relatively intact blue–green spaces, while regions unaffected by the ocean should focus on increasing green spaces and reducing impervious surfaces and water bodies. These findings directly inform Xiamen’s 2035 Master Plan for combating heat island effects in coastal special economic zones, providing transferable metrics for similar maritime cities. Full article

(This article belongs to the Special Issue Advanced Research on the Urban Heat Island Effect and Climate)

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