http://ijaers.com/ Open Access international Journal to publish research paper en-us April This study aims to compare the upper body morphology of young Chinese and Bangladeshi women and to evaluate whether existing garment sizing methods adequately accommodate population specific upper body shape characteristics. An integrated anthropometric approach combining three dimensional (3D) body scanning, two dimensional (2D) image based measurement, and manual anthropometry was used to obtain 16 upper body descriptors (dimensional, circumferential, thickness, and angular) from women aged 18–30 years in China (n = 189) and Bangladesh (n = 20). Descriptive statistics, principal component analysis (PCA), factor analysis, and K means clustering were applied to extract dominant morphological factors and classify upper body shape types, which were then interpreted with reference to national and international sizing criteria. In this sample of young women, Chinese participants generally exhibited broader shoulders, greater chest convexity, wider thoracic dimensions, and comparatively flatter waist–hip profiles, whereas Bangladeshi participants tended to show more sloping shoulders, thicker neck regions, reduced anterior chest projection, deeper waists, and more prominent hips. Three principal components and three upper body shape categories were identified in each population, but the key contributing variables and the distribution of individuals across clusters differed between countries. Current sizing systems cover most Chinese body types in the sample, though they do not fully address extreme proportions, while in Bangladesh, the lack of a unified national sizing standard appears to be associated with greater size–shape mismatches. This research provides a cross national, data driven comparison of female upper body morphology using combined 3D and 2D measurement techniques and a shared set of upper body descriptors. The results support the development of population adapted sizing systems and body shape oriented garment design strategies aimed at improving apparel fit, wearer comfort, and production efficiency. http://ijaers.com/detail/comparative-analysis-of-upper-body-shape-for-chinese-and-bangladeshi-women-based-on-3d-and-2d-measurements/ Israt Jahan, Bingfei Gu, Nur E Nasiba, Shaik Faizur Rahman http://ijaers.com/uploads/issue_files/1IJAERS-0320261-Comparative.pdf This work compares fatigue analysis results presented in the literature with computational simulations performed using finite element software. To this end, two fatigue cases with distinct geometric configurations and loading conditions are analyzed. In the first case, the loading is multiaxial, with sinusoidal variation and the presence of a non-zero mean stress. In the second case, the loading presents alternating and mean stresses that vary over time, and is analyzed by counting load cycles using the Rainflow method. The results found in the literature for both cases, obtained through conventional theoretical fatigue approaches, are compared with the results of the computational simulations performed in this work using finite element software. It can be noticed that the use of numerical simulation computational tools offers great flexibility for the analysis of fatigue problems with complex geometries and loadings. Furthermore, the use of computational tools provides greater ease and speed in obtaining results, contributing to the development of more efficient designs. http://ijaers.com/detail/verification-of-high-cycle-fatigue-analyses-from-the-literature-by-using-finite-element-software/ Guilherme Depentor de Souza, Geraldo Creci http://ijaers.com/uploads/issue_files/2IJAERS-0320266-Verification.pdf To achieve low-carbon, economical, and secure operation of power systems under the “dual carbon” goals, virtual power plants (VPPs) serve as a key technology for aggregating flexible resources such as distributed energy, storage, and demand response, making multi-objective coordinated scheduling critically important. However, the high dimensionality, multiple constraints, and conflicting objectives of this problem pose challenges for traditional optimization methods. Therefore, this paper proposes a hybrid intelligent optimization framework based on an improved Dream Optimization Algorithm (DOA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II), termed DOA-NSGAII, to solve the multi-energy scheduling problem of VPPs with objectives of minimizing economic cost, carbon emissions, and peak-valley load difference. Through customized encoding, decoding, and constraint-handling mechanisms, the framework integrates DOA’s global search capability with NSGA-II’s multi-objective decision-making advantages. Simulation experiments on the IEEE 14-bus system with three comparative schemes show that, compared with the single-objective economic optimization scheme (S1), the proposed multi-objective coordinated optimization scheme (S3) achieves significant comprehensive benefits—reducing carbon emissions by 30.5% and improving the peak-valley load difference by 12.0%—at the cost of only a marginal 4.9% increase in economic cost. This study validates the effectiveness of the DOA-NSGAII framework in solving complex engineering optimization problems and provides a new approach for multi-objective intelligent scheduling of VPPs. http://ijaers.com/detail/multi-objective-coordinated-scheduling-of-virtual-power-plants-for-economic-low-carbon-and-stability-objectives-a-doa-nsgaii-hybrid-optimization-strategy/ Yu-Xuan Chen, Yan-Zuo Chang, Yan-Xiao Jia, Kai-Ming Chen, Zi-Rui He, Wen-Min Wen, Guan-Hong Xie, Hong-Rui Yang, Jie-Zhen Yang, Yong-Qing Wang, Zheng-Kuan Deng http://ijaers.com/uploads/issue_files/3IJAERS-0320268-Multi-Objective.pdf In the velocity potential solution of the Laplace equation obtained using the separation of variables method, three wave constants arise: wavelength, wave period, and the wave constant G. The wave constant G represents the rate of wave energy transmission. Unlike these constants, wave amplitude is not part of the solution constants but serves as an input parameter. Therefore, the wave constants should be expressed as functions of the wave amplitude. This study derives analytical expressions for wavelength, wave constant G, and wave period in deep water with wave amplitude as the governing variable. The relationship between wavelength and wave amplitude is obtained using the Kinematic Free Surface Boundary Condition. The relationship between wave constant G and wave amplitude is derived from a modified Euler momentum conservation equation together with a wave amplitude function, which relates the three wave constants to the wave amplitude. The wave period is then determined using the equations for wave constant G and the wave amplitude function. After establishing the wave constants as functions of wave amplitude, the study further formulates wave amplitude as a function of wave energy. The resulting amplitude is then used to calculate the three wave constants. This approach can also be applied to analyze waves generated by ship motion, where the input energy corresponds to the ship’s kinetic energy. The method is further extended to long waves, particularly tsunamis and sneaker waves. http://ijaers.com/detail/analysis-of-wave-constants-in-the-laplace-equation-solution-for-deep-water-with-wave-amplitude-or-wave-energy-as-input/ Syawaluddin Hutahaean http://ijaers.com/uploads/issue_files/4IJAERS-03202699-Analysis.pdf Aiming at the problems of the Long Short-Term Memory network (LSTM) in time series modeling, such as hyperparameter adjustment relying on experience, being prone to falling into local optimum, and slow convergence speed, an LSTM model optimized by the Love Evolution Algorithm (LEA), namely LEA-LSTM, is proposed. First, the gating mechanism and time series processing principle of the LSTM network are elaborated, and the influence of its core hyperparameters on model performance is analyzed. Second, the LEA algorithm is introduced, and the adaptive optimization of the key hyperparameters of LSTM is realized through the five-stage evolution mechanism of encounter, stimulation, reflection, value and role, which solves the defect of insufficient global search capability of traditional optimization algorithms. Finally, the Jena Climate Dataset, a general time series dataset, and scenario-specific dataset such as power load are used for performance verification. The proposed model is compared with LSTM, PSO-LSTM, WOA-LSTM, BWO-LSTM and IGWA-ADConv1D-LSTM models in three aspects: prediction accuracy, convergence speed and robustness. The results show that the Mean Absolute Error (MAE) of the LEA-LSTM model on the Jena Climate Dataset is reduced by 68.3% compared with LSTM, and by 42.1%, 37.5% and 29.8% compared with PSO-LSTM, WOA-LSTM and BWO-LSTM respectively; in the power load forecasting scenario, the MAE is reduced by 18.6% compared with IGWA-ADConv1D-LSTM; the convergence speed is increased by more than 35% compared with traditional optimized models, and the coefficient of determination (R²) remains 99.1% even in small sample scenarios. http://ijaers.com/detail/research-on-construction-and-performance-optimization-of-the-lea-lstm-model/ Wen-Min Wen, Yan-Zuo Chang, Jin-Ping Chen, Hong-Rui Yang, Yong-Qing Wang, Yu-Xuan Chen, Jie-Zhen Yang, Guan-Hong Xie, Zi-Rui He, Zheng-Kuan Deng, Kai-Ming Chen http://ijaers.com/uploads/issue_files/5IJAERS-0420261-Research.pdf Accurate prediction of the output power of distributed photovoltaic (PV) systems is crucial for achieving efficient renewable energy integration and ensuring stable grid operation. Given that the power output of distributed PV systems is significantly influenced by meteorological factors and exhibits strong randomness and volatility, this study takes a distributed PV system in a region of Guangdong Province as the research object and constructs a PV power generation calculation model based on real meteorological data and system parameters. For the prediction approach, traditional time series methods are first employed as a benchmark for comparison. Subsequently, a GWO-LSTM model is proposed, in which the Grey Wolf Optimizer (GWO) is used to optimize the hyperparameters of the Long Short-Term Memory (LSTM) model. The experimental evaluation employs mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) as performance metrics. The results indicate that the MSE of the GWO-LSTM model is reduced by approximately 85% compared with traditional methods, while the RMSE and MAE are reduced to around 38% and 33% of those of the traditional methods, respectively. This model demonstrates significantly higher prediction accuracy than conventional time series approaches, verifying the effectiveness and superiority of using GWO to optimize LSTM hyperparameters in distributed PV power forecasting. http://ijaers.com/detail/research-on-power-prediction-method-for-distributed-photovoltaic-power-generation-systems-based-on-lstm-optimized-by-grey-wolf-optimizer/ Kai-Ming Chen, Yan-Zuo Chang, Yan-Xiao Jia, Yu-Xuan Chen, Hong-Rui Yang, Wen-Min Wen, Zi-Rui He, Jie-Zhen Yang, Yong-Qing Wang, Zheng-Kuan Deng, Guan-Hong Xie http://ijaers.com/uploads/issue_files/6IJAERS-0420262-Research.pdf