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  • MDPI AG  (10)
  • 1
    In: Sustainability, MDPI AG, Vol. 14, No. 10 ( 2022-05-20), p. 6254-
    Abstract: The reformation and development of the education system in China have led to environmental upgrades in a great number of universities. Amid this improvement, plant landscapes hold an important role in improving the environment and highlighting the campus culture. However, due to the lack of in-depth exploration of the relationship between plant landscape characteristics and the spatiotemporal aggregation of the population in current research, the design methods of campus plant landscapes are not thoroughly studied. Therefore, the mutual improvement between landscaping and population activity has not been maximized. In this study, we collected 52 plant landscape units from Northwest A & F University as the research objects. We investigated the patterns of population aggregation on campus plant landscapes through quantitative analysis of the characteristics of plant landscapes and the temporal and spatial aggregation of people. Multiple regression analysis was used to explore the complex relationship between the characteristics of each landscape and the spatial-temporal agglomeration of people. Traditional survey questionnaires and field surveys, kernel density analysis, Python crawler technology, raincloud plots analysis, correlation analysis, principal component analysis, and other methods were used to further measure and analyze plant landscape characteristics under the influence of population density from the two levels of various characteristic elements and different landscape units, and explain the mechanism affecting population aggregation, striving to provide a theoretical basis and practical support for the sustainable development of the campus environment and landscape design methods.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 2
    In: Cancers, MDPI AG, Vol. 15, No. 6 ( 2023-03-15), p. 1784-
    Abstract: Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2527080-1
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  • 3
    In: Micromachines, MDPI AG, Vol. 13, No. 11 ( 2022-11-17), p. 1992-
    Abstract: Magnesium-based amorphous alloys have aroused broad interest in being applied in marine use due to their merits of lightweight and high strength. Yet, the poor corrosion resistance to chloride-containing seawater has hindered their practical applications. Herein, we propose a new strategy to improve the chloride corrosion resistance of amorphous Mg65Cu15Ag10Gd10 alloys by engineering atomic-to-nano scale structural homogeneity, which is implemented by heating the material to the critical temperature of the liquid–liquid transition. By using various electrochemical, microscopic, and spectroscopic characterization methods, we reveal that the liquid–liquid transition can rearrange the local structural units in the amorphous structure, slightly decreasing the alloy structure’s homogeneity, accelerate the formation of protective passivation film, and, therefore, increase the corrosion resistance. Our study has demonstrated the strong coupling between an amorphous structure and corrosion behavior, which is available for optimizing corrosion-resistant alloys.
    Type of Medium: Online Resource
    ISSN: 2072-666X
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2620864-7
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  • 4
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  Coatings Vol. 12, No. 11 ( 2022-10-31), p. 1655-
    In: Coatings, MDPI AG, Vol. 12, No. 11 ( 2022-10-31), p. 1655-
    Abstract: Multifunctional super-repellent composite coatings play an important part in academic and industrial fields, while it is still a great challenge to effectively integrate a variety of functions into one material. Mg alloys having low density, high strength-to-weight ratio, and good shielding, are widely used in electronic devices, while it is susceptible to sever corrosion especially in moist air and ocean atmosphere. Here, a versatile superhydrophobic coating with organic-inorganic hybrid structure and hierarchical surface textures, integrating robust wettability with design manipulation is synthesized by assembling modified SiO2 nanoparticles on polytetrafluoroethylene (PTFE) layer on the AZ31 Mg alloy. The composite coating has good water repellency with a contact angle of 170.5°, due to the micro/nano textures and low surface energy. The composite coating increases the corrosion potential of AZ31 Mg from −1.483 V to −1.243 V, and reduces the corrosion current density by 3 orders of magnitude. Remarkably, the superhydrophobic coating displays enticing damage-resistance ( 〉 40 cycles), superior environmental stability (thermal shock and outdoor placement) and self-cleaning function. Moreover, the composite coatings display excellent electrical properties with superior voltage resistance ( 〉 30 V/μm), and high resistivity ( 〉 1012 Ω∙cm), as well the coating has a low dielectric constant (≈3.91) and dielectric loss (0.0094), which are great advantages for the electronic or electrical engineering applications. We expect that the versatile super-repellent coating can be used as candidates for novel advanced energy materials, especially in harsh environments.
    Type of Medium: Online Resource
    ISSN: 2079-6412
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662314-6
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  Actuators Vol. 11, No. 4 ( 2022-04-06), p. 105-
    In: Actuators, MDPI AG, Vol. 11, No. 4 ( 2022-04-06), p. 105-
    Abstract: In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor. The attitude and position information of the quadrotor is directly mapped to the PWM signals of the four rotors through neural network control. To constrain the size of policy updates, a PPO algorithm based on Monte Carlo approximations is proposed to achieve the optimal penalty coefficient. A policy optimization method with a penalized point probability distance can provide the diversity of policy by performing each policy update. The new proxy objective function is introduced into the actor–critic network, which solves the problem of PPO falling into local optimization. Moreover, a compound reward function is presented to accelerate the gradient algorithm along the policy update direction by analyzing various states that the quadrotor may encounter in the flight, which improves the learning efficiency of the network. The simulation tests the generalization ability of the offline policy by changing the wing length and payload of the quadrotor. Compared with the PPO method, the proposed method has higher learning efficiency and better robustness.
    Type of Medium: Online Resource
    ISSN: 2076-0825
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2682469-3
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  • 6
    In: Energies, MDPI AG, Vol. 17, No. 9 ( 2024-04-30), p. 2154-
    Abstract: Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2437446-5
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  • 7
    Online Resource
    Online Resource
    MDPI AG ; 2024
    In:  Information Vol. 15, No. 5 ( 2024-05-09), p. 266-
    In: Information, MDPI AG, Vol. 15, No. 5 ( 2024-05-09), p. 266-
    Abstract: The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy.
    Type of Medium: Online Resource
    ISSN: 2078-2489
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2599790-7
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  • 8
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Actuators Vol. 12, No. 1 ( 2023-01-10), p. 35-
    In: Actuators, MDPI AG, Vol. 12, No. 1 ( 2023-01-10), p. 35-
    Abstract: Soft actuators have a high potential for the creative design of flexible robots and safe human–robot interaction. So far, significant progress has been made in soft actuators’ flexibility, deformation amplitude, and variable stiffness. However, there are still deficiencies in output force and force retention. This paper presents a new negative pressure-driven folding flexible actuator inspired by origami. First, we establish a theoretical model to predict such an actuator’s output force and displacement under given pressures. Next, five actuators are fabricated using three different materials and evaluated on a test platform. The test results reveal that one actuator generates a maximum pull force of 1125.9 N and the maximum push force of 818.2 N, and another outputs a full force reaching 600 times its weight. Finally, demonstrative experiments are conducted extensively, including stretching, contracting, clamping, single-arm power assistance, and underwater movement. They show our actuators’ performance and feature coupling hardness with softness, e.g., large force output, strong force retention, two-way working, and even muscle-like explosive strength gaining. The existing soft actuators desire these valuable properties.
    Type of Medium: Online Resource
    ISSN: 2076-0825
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2682469-3
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  International Journal of Molecular Sciences Vol. 23, No. 22 ( 2022-11-09), p. 13793-
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 23, No. 22 ( 2022-11-09), p. 13793-
    Abstract: Soybean (Glycine max) is an important oil crop, but the regulatory mechanisms underlying seed oil accumulation remain unclear. We identified a member of the GmWRI1s transcription factor family, GmWRI1c, that is involved in regulating soybean oil content and nodulation. Overexpression of GmWRI1c in soybean hairy roots increased the expression of genes involved in glycolysis and de novo lipogenesis, the proportion of palmitic acid (16:0), and the number of root nodules. The effect of GmWRI1c in increasing the number of root nodules via regulating the proportion of palmitic acid was confirmed in a recombinant inbred line (RIL) population. GmWRI1c shows abundant sequence diversity and has likely undergone artificial selection during domestication. An association analysis revealed a correlation between seed oil content and five linked natural variations (Hap1/Hap2) in the GmWRI1c promoter region. Natural variations in the GmWRI1c promoter were strongly associated with the GmWRI1c transcript level, with higher GmWRI1c transcript levels in lines carrying GmWRI1cHap1 than in those carrying GmWRI1cHap2. The effects of GmWRI1c alleles on seed oil content were confirmed in natural and RIL populations. We identified a favourable GmWRI1c allele that can be used to breed new varieties with increased seed oil content and nodulation.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 10
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 22 ( 2022-11-16), p. 15107-
    Abstract: For more than 20 years, disaster dynamic monitoring and early warning have achieved orderly and sustainable development in China, forming a systematic academic research system and top-down policy design, which are inseparable from the research of China’s scientific community and the promotion of government departments. In the past, most of the research on dynamic disaster monitoring and early warning focused on specific research in a certain field, scene, and discipline, while a few studies focused on research review or policy analysis, and few studies combined macro and meso research reviews in academia with national policy analysis for comparative analysis. It is necessary and urgent to explore the interaction between scholars’ research and policy deployment, which can bring theoretical contributions and policy references to the top-down design, implementation promotion, and academic research of China’s dynamic disaster monitoring and early warning. Based on 608 international research articles on dynamic disaster monitoring and early warning published by Chinese scholars from 2000–2021 and 187 national policy documents published during this period, this paper conducts a comparative analysis between the knowledge maps of international research hotspots and the co-occurrence maps of policy keywords on dynamic disaster monitoring and early warning. The research shows that in the stage of initial development (2000–2007), international research articles are few and focused, and research hotspots are somewhat alienated from policy keywords. In the stage of rising development (2008–2015), after the Wenchuan earthquake, research hotspots are closely related to policy keywords, mainly in the fields of geology, engineering disasters, meteorological disasters, natural disasters, etc. Meanwhile, research hotspots also focus on cutting-edge technologies and theories, while national-level policy keywords focus more on overall governance and macro promotion, but the two are gradually closely integrated. In the stage of rapid development (2016–2021), with the continuous attention and policy promotion of the national government, the establishment of the Ministry of Emergency Management, and the gradual establishment and improvement of the disaster early warning and monitoring system, research hotspots and policy keywords are integrated and overlapped with each other, realizing the organic linkage and mutual promotion between academic research and political deployment. The motivation, innovation, integration, and transformation of dynamic disaster monitoring and early warning are promoted by both policy and academic research. The institutions that issue policies at the national level include the State Council and relevant departments, the Ministry of Emergency Management, the Ministry of Water Resources, and other national ministries and commissions. The leading affiliated institutions of scholars’ international research include China University of Mining and Technology, Chinese Academy of Sciences, Wuhan University, Shandong University of Science and Technology, and other institutions. The disciplines involved are mainly multidisciplinary geosciences, environmental sciences, electrical and electronic engineering, remote sensing, etc. It is worth noting that in the past two to three years, research and policies focusing on COVID-19, public health, epidemic prevention, environmental governance, and emergency management have gradually increased.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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