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As China transitions into a deeply aging society, the elderly population's growth has driven a rapid increase in elderly care institutions and facilities during the "13th Five-Year Plan" period, rising by over 235% compared to the "12th Five-Year Plan". The ongoing normalization of COVID-19 prevention measures has underscored the urgent need to improve natural ventilation in elderly care residential facilities. This study conducted empirical surveys to assess the current state of natural ventilation in typical elderly care facilities and analyzed the architectural elements influencing it. By examining the needs and preferences of the elderly for natural ventilation, two basic living space layout types were identified through typological analysis. Suitable CFD simulation software was then employed to model key elements, and effective methods to enhance ventilation were summarized. This study found that modifying the floor plan to reduce the depth of south-facing rooms to 8.4 m, relocating external openings to the center of the rooms, adjusting the ratio of ventilation openings to room area to greater than 1/12, and adding 0.5 m high windows facing internal traffic roads can significantly improve indoor ventilation while maintaining privacy. Furthermore, placing the main activity and resting areas of the elderly in well-ventilated zones with minimal drafts can enhance both comfort and ventilation effectiveness. The research provides a scientific basis and methodological guidance for the construction and renovation of elderly care residential facilities. [ABSTRACT FROM AUTHOR]

As an integral part of urban forests, urban parks play a vital role in mitigating urban heat islands (UHI) and providing residents with comfortable outdoor recreational plots. For high-quality use of the trees in regulating the thermal comfort of urban parks, previous studies primarily focused on the microclimate variations caused by tree coverage and morphological features separately. However, there is still a lack of systematic understanding of how tree canopy coverage (TCC) and its morphological elements, including leaf area index (LAI), trunk height (TH), and crown diameter (CD), combined affect the thermal comfort in the urban park. This study employed an orthogonal experiment design and ENVI-met software to simulate the microclimate of various multi-factor combination models in the case of a typical urban park in a temperate continental climate zone in China, analyzing the simulated result through physiological equivalent temperature (PET). Results show that the contribution ratio of various elements to the thermal environment vary over time. In studied elements, the contribution ratio of TCC to PET is consistently higher than 50% during the morning, midday, and evening periods, reaching a peak of 67% in the evening. The maximum contribution ratios for CD, TH, and LAI occur during midday, morning, and midday, respectively, with corresponding contribution ratios of approximately 22%, 10%, and 9%, respectively. The ranking of elements affecting thermal comfort in the urban park generally is TCC, CD, LAI, TH throughout the day, apart from the morning, when the influence of TH is greater than LAI. The optimal combination of elements is 85% TCC, 4m TH, 3.9 LAI, and 7m CD, and thirteen combinations of element cases meet the thermal comfort requirements during summer. The research findings highlight the significance of optimizing the configuration of trees in creating a more comfortable and inviting space for human activities. [ABSTRACT FROM AUTHOR]

Scientific and reasonable planning of urban forests is crucial to mitigate the UHI effect and create a comfortable local climate. This study focused on maximizing the synergistic effect of landscape design parameters (i.e., Landscape Shape Index (LSI), Percentage of Green Area (PGA), Park Area (PA), and Percentage of Water Area (PWA)) through orthogonal experimental design and numerical simulation to improve the regulation of the thermal environment of urban parks in the surrounding blocks. For the neighborhood of low-rise buildings, the influence of PGA was the most significant in the morning with a contribution rate of 50.43%, while PWA was the most influential during midday and evening, with contribution rates of 73.02% and 52.30%, respectively. In contrast, PA was the most influential in the morning with a contribution rate of 62.11% for the high-rise building neighborhood, while the impact of PWA was the most significant in the midday with a contribution rate of 43.99%. In addition, the contribution of PA and PWA played vital roles at night. This study proposed combinations of landscape design parameters for urban parks that met the requirements of two types of neighborhoods, which would help guide the planning and construction of urban forests. [ABSTRACT FROM AUTHOR]

Simple Summary: The objective response rate of lenvatinib combined with transarterial chemoembolization for unresectable hepatocellular carcinoma is unsatisfactory. We aimed to develop predictive models using demographic characteristics, pre-treatment serum biomarkers and tumor characteristics of unresectable hepatocellular carcinoma patients by five machine learning algorithms to predict the response under combined treatments. We identified the 10 most important predictors, including K, low-density lipoprotein, D-dimer, red blood cell, alanine aminotransferase, albumin, monocyte, tumor size, triglyceride, and age. In addition, we applied the Shapley Additive exPlanation to explain the best-performing random forest predictive model to provide a reasonable explanation of the efficacy prediction at an individualized level. The combination of machine learning and Shapley Additive exPlanation can provide valuable suggestions for clinical decision making. Background: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. Methods: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4–12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model. Results: A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61–0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level. Conclusions: A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction. [ABSTRACT FROM AUTHOR]

Silicon nanowires are appealing structures to enhance the capacity of anodes in lithium-ion batteries. However, to attain industrial relevance, their synthesis requires a reduced cost. An important part of the cost is devoted to the silicon growth catalyst, usually gold. Here, we replace gold with tin, introduced as low-cost tin oxide nanoparticles, to produce a graphite–silicon nanowire composite as a long-standing anode active material. It is equally important to control the silicon size, as this determines the rate of decay of the anode performance. In this work, we demonstrate how to control the silicon nanowire diameter from 10 to 40 nm by optimizing growth parameters such as the tin loading and the atmosphere in the growth reactor. The best composites, with a rich content of Si close to 30% wt., show a remarkably high initial Coulombic efficiency of 82% for SiNWs 37 nm in diameter. [ABSTRACT FROM AUTHOR]

Defect engineering is one of the effective ways to improve the electrochemical property of electrode materials for lithium-ion batteries (LIB). Herein, an organic functional molecule of p-phenylenediamine is embedded into two-dimensional (2D) layered TiO2 as the electrode for LIB. Then, the 2D carbon/TiO2 composites with the tuning defects are prepared by precise control of the polymerization and carbothermal atmospheres. Low valence titanium in metal oxide and nitrogen-doped carbon nanosheets can be obtained in the carbon/TiO2 composite under a carbonization treatment atmosphere of N2/H2 gas, which can not only increase the electronic conductivity of the material but also provide sufficient electrochemical active sites, thus producing an excellent rate capability and long-term cycle stability. The prepared composite can provide a high capacity of 396.0 mAh g−1 at a current density of 0.1 A g−1 with a high capacitive capacity ratio. Moreover, a high specific capacity of 80.0 mAh g−1 with retention rate of 85% remains after 10,000 cycles at 3.0 A g−1 as well as the Coulomb efficiency close to 100%. The good rate-capability and cycle-sustainability of the layered materials are ascribed to the increase of conductivity, the lithium-ion transport channel, and interfacial capacitance due to the multi-defect sites in the layered composite. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % . [ABSTRACT FROM AUTHOR]

According to the IEEE 802.15.6 standard, interference within each wireless body area network (WBAN) can be well addressed by the time division multiple access (TDMA)-based media access control (MAC) protocol. However, the inter-WBAN interference will be caused after multiple WBANs are gathered together. This paper proposes a priority-aware price-based power control (PPPC) scheme for mitigating the inter-WBAN interference. Specifically, to maximize the transmission data rate of sensors and control the aggregate interference suffered by coordinators, a Stackelberg game is established, in which the coordinators issue interference prices and the active sensors adjust their transmission power accordingly. On the other hand, since the information about the identities of the active sensors in a specific time slot is kept private, a Bayesian game is designed to model the interaction among sensors. Moreover, the timeliness and reliability of data transmission are guaranteed by designing the sensors' priority factors and setting a priority-related active probability for each sensor. At last, a power control algorithm is designed to obtain optimal strategies of game players. Simulation results show that compared with other existing schemes, the proposed scheme achieves better fairness with a comparable network sum data rate and is more energy efficient. [ABSTRACT FROM AUTHOR]