• Steel and Composite Structures
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• v.47 no.2
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• pp.253-267
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• 2023

The bearing capacities resisted by the two-bay beams of multi-story planar frames with unequal spans under column removal scenarios differ considerably owing to the asymmetric stress on the left and right beams connected to the failed column and cause the potential for beams with larger span-to-depth ratios to be unable to exert effectively, which is disadvantageous for resisting the vertical load in unequal-span frame structures. To address this problem, the structural measure of adding braces to the weak bays of multi-story unequal-span frames was proposed, with the objective of achieving a coordinated stress state in two-bay beams with unequal spans, thereby improving the collapse resistance of unequal-span frame structures. Before conducting the numerical simulation, the modeling methods were verified by previous experimental results of two multi-story planar frames with and without steel braces. Thereafter, the effects of the tensile and compressive braces on the collapse behavior of the frame structures were elucidated. Then, based on the mechanical action laws of the braces throughout the collapse process, a detailed design method for improving the collapse resistance of unequal-span frame structures was proposed. Finally, the proposed design method was verified by using sufficient example models, and the results demonstrated that the design method has good application prospects and high practical value.

• Mycobiology
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• v.51 no.1
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• pp.49-59
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• 2023

Antrodia cinnamomea, an edible and medicinal fungus with significant economic value and application prospects, is rich in terpenoids, benzenoids, lignans, polysaccharides, and benzoquinone, succinic and maleic derivatives. In this study, the transcriptome of A. cinnamomea cultured on the wood substrates of Cinnamomum glanduliferum (YZM), C. camphora (XZM), and C. kanehirae (NZM) was sequenced using the high-throughput sequencing technology Illumina HiSeq 2000, and the data were assembled by de novo strategy to obtain 78,729 Unigenes with an N50 of 4,463 bp. Compared with public databases, about 11,435, 6,947, and 5,994 Unigenes were annotated to the Non-Redundant (NR), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genome (KEGG), respectively. The comprehensive analysis of the mycelium terpene biosynthesis-related genes in A. cinnamomea revealed that the expression of acetyl-CoA acetyltransferase (AACT), acyl-CoA dehydrogenase (MCAD), 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA), mevalonate pyrophosphate decarboxylase (MVD), and isopentenyl diphosphate isomerase (IDI) was significantly higher on NZM compared to the other two wood substrates. Similarly, the expression of geranylgeranyltransferase (GGT) was significantly higher on YZM compared to NZM and XZM, and the expression of farnesyl transferase (FTase) was significantly higher on XZM. Furthermore, the expressions of 2,3-oxidized squalene cyclase (OCS), squalene synthase (SQS), and squalene epoxidase (SE) were significantly higher on NZM. Overall, this study provides a potential approach to explore the molecular regulation mechanism of terpenoid biosynthesis in A. cinnamomea.

• Composites Research
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• v.36 no.2
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• pp.69-79
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• 2023

Oil-water separation is a critical process for several industrial applications, including oil production, wastewater treatment, food processing, and environmental area such as marine oil spills. The separation efficiency of oil-water mixtures can be influenced by various factors such as mixture composition, oil and water conditions, and the separation technology used. Over the years, various technologies have been developed to separate water and oil by physical, chemical and biological methods. This paper presents an overview of the various methods and technologies available for oil-water separation, including gravity separation, centrifugal separation, and separation using adsorbents, filters. The strengths and limitations of each method are discussed, along with recent research trends and future prospects. Furthermore, this paper aims to provide direction for future research and industrial application of sustainable and environmentally friendly oil-water separation technologies. In conclusion, we provide a comprehensive overview of recent oil-water separation technologies that will be beneficial to researchers and industrialists in the field of oil-water separation.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.625-633
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• 2023

The global pet culture-related industry is currently experiencing a trend of expansion. Within the pet industry, pet food holds a significant share, occupying a substantial portion. Presently, the domestic pet food market exhibits a high dependency on imported products, underscoring the critical importance of prioritizing the acquisition of intellectual property rights to ensure competitiveness and facilitate technological development within the relevant industry. we have undertaken an assessment of the current status and prospects of domestic pet food patents. Specifically, we have conducted temporal and applicant-specific statistical analyses, as well as IPC technology analyses, to examine the stages of technological advancement, corporate technological development and innovation capabilities, patent application distribution, and the technological landscape of key enterprises and research institutions. The research findings indicate that domestic research activities related to pet food have entered a mature phase, and the trends in patent applications for domestic pet food indicate a notable participation of multinational corporations alongside domestic enterprises.

• Smart Structures and Systems
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• v.32 no.6
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• pp.349-358
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• 2023

Real-Time Hybrid Testing (RTHT) requires the numerical substructure calculations to be completed within the defined integration time interval due to its real-time loading demands. For solving the problem, A Real-Time Hybrid Testing based on Restart-Loading Technology (RTHT-RLT) is proposed in this paper. In the proposed method, in case of the numerical substructure calculations cannot be completed within the defined integration time interval, the experimental substructure was returned back to the initial state statically. When the newest loading commands were calculated by the numerical substructure, the experimental substructure was restarted loading from the initial state to the newest loading commands so as to precisely disclosing the dynamic performance of the experimental substructure. Firstly, the methodology of the RTHT-RLT is proposed. Furthermore, the numerical simulations and experimental tests on one frame structure with a viscous damper are conducted for evaluating the feasibility and effectiveness of the proposed RTHT-RLT. It is shown that the proposed RTHT-RLT innovatively renders the nonreal-time refined calculation of the numerical substructure feasible for the RTHT. The numerical and experimental results show that the proposed RTHT-RLT exhibits excellent performance in terms of stability and accuracy. The proposed RTHT-RLT may have broad application prospects for precisely investigating the dynamic behavior of large and complex engineering structures with specific experimental substructure where a restarting procedure does not affect the relevant hysteretic response.

• Current Optics and Photonics
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• v.7 no.6
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• pp.608-637
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• 2023

The initial motivation in colloid science and engineering was driven by the fact that colloids can serve as excellent models to study atomic and molecular behavior at the mesoscale or microscale. The thermal behaviors of actual atoms and molecules are similar to those of colloids at the mesoscale or microscale, with the primary distinction being the slower dynamics of the latter. While atoms and molecules are challenging to observe directly in situ, colloidal motions can be easily monitored in situ using simple and versatile optical microscopic imaging. This foundational approach in colloid research persisted until the 1980s, and began to be extensively implemented in optics and photonics research in the 1990s. This shift in research direction was brought by an interplay of several factors. In 1987, Yablonovitch and John modernized the concept of photonic crystals (initially conceptualized by Lord Rayleigh in 1887). Around this time, mesoscale dielectric colloids, which were predominantly in a suspended state, began to be self-assembled into three-dimensional (3D) crystals. For photonic crystals operating at optical frequencies (visible to near-infrared), mesoscale crystal units are needed. At that time, no manufacturing process could achieve this, except through colloidal self-assembly. This convergence of the thirst for advances in optics and photonics and the interest in the expanding field of colloids led to a significant shift in the research paradigm of colloids. Initially limited to polymers and ceramics, colloidal elements subsequently expanded to include semiconductors, metals, and DNA after the year 2000. As a result, the application of colloids extended beyond dielectric-based photonic crystals to encompass plasmonics, metamaterials, and metasurfaces, shaping the present field of colloidal optics and photonics. In this review we aim to introduce the research trajectory of colloidal optics and photonics over the past three decades; To elucidate the utility of colloids in photonic crystals, plasmonics, and metamaterials; And to present the challenges that must be overcome and potential research prospects for the future.

• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Structural Engineering and Mechanics
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• v.90 no.4
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• pp.325-343
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• 2024

Triple-tower double-cable suspension bridges have increased confinement stiffness imposed by the main cable on the middle tower, which has bright application prospects. However, vertical bending and torsional vibrations of the double-cable and the girder are coupled in such bridges due to the hangers. In particular, the bending vibration of the towers in the longitudinal direction and torsional vibrations about the vertical axis influence the vertical bending and torsional vibrations of the stiffening girders, respectively. The conventional analytical algorithm for assessing the dynamic features of the suspension bridge is not directly applicable to this type of bridge. This study attempts to mitigate this problem by introducing an analytical algorithm for solving the triple-tower double-cable suspension bridge's natural frequencies and mode shapes. D'Alembert's principle is employed to construct the differential equations of the vertical bending and torsional vibrations of the stiffening girder continuum in each span. Vibrations of stiffening girders in each span are interrelated via the vibrations of the main cables and the bridge towers. On this basis, the natural frequencies and mode shapes are derived by separating variables. The proposed algorithm is then applied to an engineering example. The natural frequencies and mode shapes of vertical bending and torsional vibrations derived by the analytical algorithm agreed well with calculations via the finite element method. The fundamental frequency of vertical bending and first- and second-order torsion frequencies of double-cable suspension bridges are much higher than those of single-cable suspension bridges. The analytical algorithm has high computational efficiency and calculation accuracy, which can provide a reference for selecting appropriate structural parameters to meet the requirements of dynamics during the preliminary design.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Applied Chemistry for Engineering
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• v.35 no.3
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• pp.155-181
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• 2024

In recent years, significant research has been conducted to enhance the performance of existing membranes for efficient CO₂ capture, aiming to expand their application in carbon capture processes. Membrane technology has emerged as a promising carbon capture approach to addressing the net-zero challenge due to its cost and energy efficiency, continuous operation, and compact process size. Among the various types of membranes studied, mixed-matrix membranes (MMMs) have been proposed as an alternative to conventional membranes to enhance the efficiency of gas separation processes. Various common 2D nanomaterials, characterized by their ease of modification, functionalization, and compatibility with other materials, have been used to create efficient MMMs for gas separation. This article comprehensively reviews the recent developments in MMMs using 2D nanomaterials. It also discusses the current challenges and prospects of 2D nanomaterial-based membranes for CO₂ separation and capture.