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  • MDPI AG  (11)
  • 1
    Online Resource
    Online Resource
    Hierarchical Spatially Varying Coefficient Process Regression for Modeling Net Anthropogenic Nitrogen Inputs (NANI) from the Watershed of the Yangtze River, China
    MDPI AG ; 2023
    In:  Sustainability Vol. 15, No. 16 ( 2023-08-18), p. 12567-
    Details
    In: Sustainability, MDPI AG, Vol. 15, No. 16 ( 2023-08-18), p. 12567-
    Abstract: The increasing discharge of nitrogen nutrients into watersheds calls for assessing and predicting nitrogen inputs, as an important basis for formulating management strategies. The traditional net anthropogenic nitrogen inputs (NANI) budgeting model relies on 45 predictor variables, for which data are sourced from local or national statistical yearbooks. The large number of predictor variables involved makes NANI accounting difficult, and the missingness of data reduces its accuracy. This study aimed to build a prediction model for NANI based on as few predictor variables as possible. We built a prediction model based on the last 30 years of NANI data from the watershed of the Yangtze River in China, with readily available and complete socio-economic predictor variables (per gross domestic product, population density) through a hierarchical spatially varying coefficient process model (HSVC), which exploits underlying spatial associations within 11 sub-basins and the spatially varying impacts of predictor variables to improve the accuracy of NANI prediction. The results showed that the hierarchical spatially varying coefficient model performed better than the Gaussian process model (GP) and the spatio-temporal dynamic linear model (DLM). The predicted NANIs within the entire catchment of the Yangtze River in 2025 and in 2030 were 11,522.87 kg N km−2 to 12,760.65 kg N km−2, respectively, showing an obvious increasing trend. Nitrogen fertilizer application was predicted to be 5755.1 kg N km−2 in 2025, which was the most significant source of NANI. In addition, the point prediction and 95% interval prediction of NANI in the watershed of the Yangtze River for 2025 and 2030 were also provided. Our approach provides a simple and easy-to-use method for NANI prediction.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su151612567
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2518383-7
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  • 2
    Online Resource
    Online Resource
    Experimental Investigation of Flow-Induced Motion and Energy Conversion of a T-Section Prism
    MDPI AG ; 2018
    In:  Energies Vol. 11, No. 8 ( 2018-08-06), p. 2035-
    Details
    In: Energies, MDPI AG, Vol. 11, No. 8 ( 2018-08-06), p. 2035-
    Abstract: Flow-induced motion (FIM) performs well in energy conversion but has been barely investigated, particularly for prisms with sharp sections. Previous studies have proven that T-section prisms that undergo galloping branches with high amplitude are beneficial to energy conversions. The FIM experimental setup designed by Tianjin University (TJU) was improved to conduct a series of FIM responses and energy conversion tests on a T-section prism. Experimental results are presented and discussed, to reveal the complete FIM responses and power generation characteristics of the T-section prism under different load resistances and section aspect ratios. The main findings are summarized as follows. (1) Hard galloping (HG), soft galloping (SG), and critical galloping (CG) can be observed by varying load resistances. When the load resistances are low, HG occurs; otherwise, SG occurs. (2) In the galloping branch, the highest amplitude and the most stable oscillation cause high-quality electrical energy production by the generator. Therefore, the galloping branch is the best branch for harvesting energy. (3) In the galloping branch, as the load resistances decrease, the active power continually increases until the prism is suppressed from galloping to a vortex-induced vibration (VIV) lower branch with a maximum active power Pharn of 21.23 W and a maximum ηout of 20.2%. (4) Different section aspect ratios (α) can significantly influence the FIM responses and energy conversions of the T-section prism. For small aspect ratios, galloping is hardly observed in the complete responses, but the power generation efficiency (ηout,0.8 = 27.44%) becomes larger in the galloping branch.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en11082035
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2437446-5
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  • 3
    Online Resource
    Online Resource
    Personal Goals, Barriers to Self-Management and Desired mHealth Application Features to Improve Self-Care in Multi-Ethnic Asian Patients with Type 2 Diabetes: A Qualitative Study
    MDPI AG ; 2022
    In:  International Journal of Environmental Research and Public Health Vol. 19, No. 22 ( 2022-11-21), p. 15415-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 22 ( 2022-11-21), p. 15415-
    Abstract: (1) Introduction: The ubiquity of mobile phones suggests the potential of mobile health applications to reach patients with type 2 diabetes and engage them to improve self-care. This study aimed to explore personal goals, barriers to self-management and desired mobile health application features to improve self-care among multi-ethnic Asian patients with type 2 diabetes. (2) Methods: We conducted semi-structured interviews with patients with type 2 diabetes (n = 29). Patients were recruited from a multi-disciplinary center for diabetes and metabolism in Singapore, using a purposive sampling strategy. Various visual materials, collated from existing mobile health application features, were used to facilitate the discussion. Interviews were transcribed verbatim and thematically analyzed. (3) Results: A total of 29 patients participated in 11 focus group discussions or one-on-one interviews. Personal goals for self-management were centered around short-term outcome expectancy, such as better glucose control and a reduced number of medications. Self-management was hampered by competing priorities and limited healthy food options when at work, while a lack of tailored advice from healthcare providers further diminished competence. The desired mobile health app features to improve self-care behaviors included quantifiable goal-setting, personalized nudges based on tracked data, built-in resources from credible sources, in-app social support through virtual interaction with peers and healthcare providers, technology-driven novel data logging and user-defined nudges. (4) Conclusions: We identified a set of app features that may foster motivation to engage in lifestyle modification for patients with T2DM. The findings serve to inform the design of artificial intelligence-enabled mobile health application intervention aimed at improving diabetes self-care.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph192215415
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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  • 4
    Online Resource
    Online Resource
    Effect of Water Content on Strength of Alluvial Silt in The Lower Yellow River
    MDPI AG ; 2022
    In:  Water Vol. 14, No. 20 ( 2022-10-13), p. 3231-
    Details
    In: Water, MDPI AG, Vol. 14, No. 20 ( 2022-10-13), p. 3231-
    Abstract: In order to study the strength characteristics of alluvial silt in the lower Yellow River Channel at Luokou, Jinan, by varying the moisture content five times, direct shear tests were conducted and soil–water characteristics curves were obtained to explore the relationships between moisture content and matric suction as well as the bishop coefficient and shear strength. The soil–water characteristics curve test shows that the water retention curve of silt samples in the lower Yellow River can be fitted by the VG (Van Genuchten) model with the appropriate fitting coefficients. The direct shear test reveals that the relationship between shear stress and shear displacement alters from the shear softening type to the hardening type with the increase in moisture content and normal stress. The cohesion has a nonlinear inverse relationship with moisture content while a small variation is reported in the internal friction angle. Finally, a simple shear strength equation for silt in the lower Yellow River is proposed in relation to moisture content, to define the relationship between the effective stress parameter and the matric suction for future engineering purposes.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w14203231
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2521238-2
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  • 5
    Online Resource
    Online Resource
    α-Viniferin and ε-Viniferin Inhibited TGF-β1-Induced Epithelial-Mesenchymal Transition, Migration and Invasion in Lung Cancer Cells through Downregulation of Vimentin Expression
    MDPI AG ; 2022
    In:  Nutrients Vol. 14, No. 11 ( 2022-05-30), p. 2294-
    Details
    In: Nutrients, MDPI AG, Vol. 14, No. 11 ( 2022-05-30), p. 2294-
    Abstract: Resveratrol has well-known anticancer properties; however, its oligomers, including α-viniferin, ε-viniferin, and kobophenol A, have not yet been well investigated. This is the first study examining the anti-epithelial-mesenchymal transition (EMT) effects of α-viniferin and ε-viniferin on A549, NCI-H460, NCI-H520, MCF-7, HOS, and U2OS cells. The results showed that α-viniferin and ε-viniferin significantly inhibited EMT, invasion and migration in TGF-β1- or IL-1β-induced non-small cell lung cancer. α-Viniferin and ε-viniferin also reversed TGF-β1-induced reactive oxygen species (ROS), MMP2, vimentin, Zeb1, Snail, p-SMAD2, p-SMAD3, and ABCG2 expression in A549 cells. Furthermore, ε-viniferin was found to significantly inhibit lung metastasis in A549 cell xenograft metastatic mouse models. In view of these findings, α-viniferin and ε-viniferin may play an important role in the prevention of EMT and cancer metastasis in lung cancer.
    Type of Medium: Online Resource
    ISSN: 2072-6643
    URL: Article
    DOI: 10.3390/nu14112294
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518386-2
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  • 6
    Online Resource
    Online Resource
    Dual-Core Fiber-Based Interferometer for Detection of Gas Refractive Index
    MDPI AG ; 2020
    In:  Photonics Vol. 7, No. 4 ( 2020-11-15), p. 111-
    Details
    In: Photonics, MDPI AG, Vol. 7, No. 4 ( 2020-11-15), p. 111-
    Abstract: We demonstrate a dual-core fiber-based Mach–Zehnder interferometer that could be used for precise detection of variations in refractive indices of gaseous samples. The fiber used here have a solid germanium-doped silica core and an air core that allows gases to flow through. Coherent laser beams are coupled to the two cores, respectively, and thus excite guiding modes thereby. Interferogram would be produced as the light transmitted from the dual cores interferes. Variations in refractive index of the hollow core lead to variations in phase difference between the modes in the two cores, thus shifting the interference fringes. The fringe shifts can be then interrogated by a photodiode together with a narrow slit in front. The resolution of the sensor was found to be ~1 × 10−8 RIU, that is comparable to the highest resolution obtained by other fiber sensors reported in previous literatures. Other advantages of our sensor include very low cost, high sensitivity, straightforward sensing mechanism, and ease of fabrication.
    Type of Medium: Online Resource
    ISSN: 2304-6732
    URL: Article
    DOI: 10.3390/photonics7040111
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2770002-1
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  • 7
    Online Resource
    Online Resource
    Anandins A and B, Two Rare Steroidal Alkaloids from a Marine Streptomyces anandii H41-59
    MDPI AG ; 2017
    In:  Marine Drugs Vol. 15, No. 11 ( 2017-11-10), p. 355-
    Details
    In: Marine Drugs, MDPI AG, Vol. 15, No. 11 ( 2017-11-10), p. 355-
    Type of Medium: Online Resource
    ISSN: 1660-3397
    URL: Article
    DOI: 10.3390/md15110355
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2175190-0
    SSG: 15,3
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  • 8
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    Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 12 ( 2023-06-08), p. 2598-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 12 ( 2023-06-08), p. 2598-
    Abstract: Over the past ten years, breakthroughs in battery technology have dramatically propelled the evolution of electric vehicle (EV) technologies. For EV applications, accurately estimating the state-of-charge (SOC) is critical for ensuring safe operation and prolonging the lifespan of batteries, particularly under complex loading scenarios. Despite progress in this area, modeling and forecasting the evaluation of multiphysics and multiscale electrochemical systems under realistic conditions using first-principles and atomistic calculations remains challenging. This study proposes a solution by designing a specialized Transformer-based network architecture, called Bidirectional Encoder Representations from Transformers for Batteries (BERTtery), which only uses time-resolved battery data (i.e., current, voltage, and temperature) as an input to estimate SOC. To enhance the Transformer model’s generalization, it was trained and tested under a wide range of working conditions, including diverse aging conditions (ranging from 100% to 80% of the nominal capacity) and varying temperature windows (from 35 °C to −5 °C). To ensure the model’s effectiveness, a rigorous test of its performance was conducted at the pack level, which allows for the translation of cell-level predictions into real-life problems with hundreds of cells in-series conditions possible. The best models achieve a root mean square error (RMSE) of less than 0.5 test error and approximately 0.1% average percentage error (APE), with maximum absolute errors (MAE) of 2% on the test dataset, accurately estimating SOC under dynamic operating and aging conditions with widely varying operational profiles. These results demonstrate the power of the self-attention Transformer-based model to predict the behavior of complex multiphysics and multiscale battery systems.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12122598
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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  • 9
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    Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health
    MDPI AG ; 2023
    In:  Energies Vol. 16, No. 9 ( 2023-04-30), p. 3855-
    Details
    In: Energies, MDPI AG, Vol. 16, No. 9 ( 2023-04-30), p. 3855-
    Abstract: Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en16093855
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2437446-5
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  • 10
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    Pd-Modified LaFeO3 as a High-Efficiency Gas-Sensing Material for H2S Gas Detection
    MDPI AG ; 2022
    In:  Nanomaterials Vol. 12, No. 14 ( 2022-07-18), p. 2460-
    Details
    In: Nanomaterials, MDPI AG, Vol. 12, No. 14 ( 2022-07-18), p. 2460-
    Abstract: As a typical p-type semiconductor gas-sensing material, LaFeO3 has good response stability to H2S, but its responsiveness is low, and the detection limit is not low enough for large-scale use in the field of gas sensors. To obtain better performance, we synthesized Pd modified LaFeO3 using the sol–gel method. A total of 3 wt% of Pd–LaFeO3 with a high specific surface area had the highest response to H2S (36.29–1 ppm) at 120 °C, with relatively fast response–recovery times (19.62/15.22 s), and it had higher selectivity to H2S with other gases. Finally, we detected the H2S concentrations in the air around the shrimps, and the H2S concentrations that we obtained by the 3 wt% Pd–LaFeO3 in this study were within 10% of those obtained by GC–MS. According to the experimental results, noble-metal surface modification improves the performance of gas-sensing materials, and Pd–LaFeO3 has considerable potential in H2S detection.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    URL: Article
    DOI: 10.3390/nano12142460
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662255-5
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