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Urban and building morphologies, usually determined at the design stage, have been proven as important determinants of energy usage at later operational stages. However, questions remain regarding the identification of the key determinants that influence urban building energy usage. To address this, in this study, an urban building dataset of 539 residential buildings and 153 public buildings was used to extract the building morphology factors as the determinants. A principal component analysis was performed to identify the key determinants for three buildings groups—residential buildings, residential blocks, and public buildings. The results show that the key determinants for residential buildings are their orientation, ratio of obstruction height to the canyon width from the south and west directions, shape coefficient, perimeter-to-area ratio, and building aspect ratio. The key determinants for public buildings are similar to those for residential buildings with the exception of the ratio of the obstruction height to the canyon width from the south direction. The key determinants for residential blocks are the ratio of the obstruction height to the canyon width from the south and west directions, mass space proportion, building aspect ratio, and floor area ratio. The findings of this study provide insights into the key drivers of urban building energy usage and the strategies that could be used to improve urban energy planning.

How to accurately design a personalized matching implant that can induce skull regeneration is the focus of current research. However, the design space for the porous structure of implants is extensive, and the mapping relationships between these structures and their mechanical and osteogenic properties are complex. At present, the forward design of skull implants mainly relies on expert experience, leading to high cost and a lengthy process, while the existing inverse design approaches face challenges due to data dependence and manufacturing process errors. This study presents an efficient inverse design method for personalized multilevel structures of skull implants using a machine learning pipeline composed of a finite element method, topological optimization, and neural networks. Based on the mechanical response of the human body falls, this method can tailor multi-level structures for implants in various defect positions. The results show that the proposed method establishes a bidirectional relationship between topological parameters and mechanical properties, enabling the customization of mechanical behavior at low computational cost while accounting for manufacturing errors in the 3D printing process. Additionally, the design results are also mutually consistent with analytical relationships between lattice parameters and the elastic modulus obtained from experiments and finite element simulations. Thus, this study provides a general and practical approach to rapidly design skull osteoinductive implants. [ABSTRACT FROM AUTHOR]

This study investigates the spatial patterns of residential migration among older adults in the city center of Yancheng and the influencing factors using data on the home purchases of individuals aged 65 and older from 2016 to 2018, along with peripheral point of interest (POI) data, analyzed with ArcGIS and a decision tree model. The results indicated that persons aged 60–65 accounted for 42.8% of the total sample and primarily chose to migrate in the early stages of retirement. The intra-city migration of older adults exhibits both centripetal and centrifugal patterns, with a greater tendency toward the city center. House prices, floor levels, and commercial facilities significantly impact their choice of migration destinations. Among these, house prices were the most critical determinant, with the majority of older adults migrating to neighborhoods with lower house prices. This study contributes by integrating residential migration and location choice research and constructing an analytical framework based on facility accessibility. The findings provide insights into the key determinants of location choice for intra-city residential migration among older adults and the construction of livable neighborhoods for them. [ABSTRACT FROM AUTHOR]

The six‐switch inverter has gained significant attention due to its ability to achieve dual AC voltage outputs with fewer semiconductor power devices. However, with existing pulse width modulation (PWM) algorithms, the switching frequency of the two middle‐position power devices is twice that of the other four power devices at the upper and lower ends. Therefore, this paper proposes an improved PWM algorithm for the six‐switch converter based on the unipolar modulation principle. The proposed algorithm can significantly reduce the switching frequency of the two middle‐position power devices. Research results indicate that, under optimal conditions, the switching frequency of the two middle‐position power devices is as low as the frequency of the AC output voltages, and in the worst‐case scenario, it is only half the carrier frequency. Consequently, the efficiency of the inverter is effectively improved. Additionally, analysis results suggest that, given the same switching frequencies of power devices, the modulation algorithm proposed in this paper for controlling the converter also results in lower Total Harmonic Distortion (THD) for the output voltages compared to traditional modulation algorithms. [ABSTRACT FROM AUTHOR]

The double active bridge series resonant converter (DABSRC) has become a research hotspot in both scientific and engineering fields due to its high efficiency and high voltage gain characteristics demonstrated in energy storage systems and smart grid systems. In this paper, an improved linear active disturbance rejection control (LADRC) algorithm is proposed to improve the dynamic response performance of the DABSRC. Compared to the existing LADRC algorithms, the primary enhancements of the proposed LADRC involve optimizing the feedback control law and the introduction of error proportion in the linear extended state observer (LESO). These modifications effectively reduced the estimation error of perturbation of the LESO and consequently enhanced the stability and dynamic response performance of the converter. In the paper, simulations and experiments are conducted to investigate the performances of a DABSRC by using three different control strategies, that is, the PI controller, the traditional LADRC, and the improved LADRC proposed in this paper. The experimental results proved the feasibility and correctness of the improved LADRC strategy. The proposed algorithm is not only applicable to the DABSRC but also suitable for other converters to improve the dynamic response performance. [ABSTRACT FROM AUTHOR]

Background: The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. Purpose: To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast‐enhanced MRI (DCE‐MRI), along with clinical findings. Study Type: Retrospective. Subjects: 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. Field Strength/Sequence: Fat saturated T2‐weighted, Fat saturated T1‐weighted, and DCE‐MRI performed at 1.5 T and 3.0 T. Assessment: Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical–radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24‐month survival for HCC. Statistical Tests: The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan–Meier analysis. The discrimination performance of each model was quantified by the C‐index. A P value <0.05 was considered statistically significant. Results: The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C‐index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. Conclusion: The combined radiomic model provides superior ability to discern the possibility of recurrence‐free survival in HCC over the total radiomic and the clinical–radiological models. Evidence Level: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]