Non-Classical Nucleation Phenomena Study And Following Process Monitoring and Optimization in Solution Crystallization Process

Nucleation is a crucial step in the solution crystallization process. Despite their good development, classical nucleation theory and two-step nucleation theory cannot explain all the nucleation phenomena, especially for the non-classical nucleation phenomena which include oiling out, gelation and non-monotonic nucleation. Accordingly, for the non-classical nucleation systems, the crystallization processes are seldom designed based on the nucleation monitoring and supervision. In this thesis, crystallization process optimization was conducted to study the mechanism of non-classical nucleation phenomena and in-line process monitoring technology development. Two kinds of non-classical nucleation phenomena with non-monotonic nucleation rate and gel formation were investigated, and accordingly, two nucleation pathways that self-induced nucleation and jellylike phase mediated nucleation were proposed based on the analysis of in-line spectral monitoring and off-line sample characterizations. Results indicated the agitation level would affect the pre-nucleation clusters’ existence in the non-monotonic nucleation system, and the properties of solvent determined the formation of jellylike phase and the transformation to crystals. Motion-based objects tracking model and the state-of-the-art neural network Mask R-CNN were introduced to monitor the onset of nucleation and following the crystallization process. Combined with a cost-effective camera probe, the developed real-time tracking system can detect the nucleation onset accurately even with ultrasonic irradiation and can extract much more information during the whole crystallization process. Subsequently, ultrasonic irradiation and seeding were used to optimize a non-classical nucleation system that accompanied oiling out phenomenon. Different frequencies and intensities of ultrasonic irradiation and seeds addition time were screened to optimize the nucleation step, which proved their effectiveness of promoting nucleation and narrowing the metastable zone widths of oiling out and nucleation. A fine-tuning of nucleation step was carried out in a mixed suspension mixed product removal (MSMPR)-tubular crystallizer series. The nucleation step was optimized in the MSMPR stage with the aid of principal component analysis, which enabled the growth of crystals in the tubular crystallizer with preferred polymorphism, shape, and size. The study in this thesis provides insights into non-classical nucleation mechanism and nucleation based crystallization process design and optimization

The Resistance of Ship Web Girders in Collision and Grounding

Ship web girders play an important role in ship structure performance during collision and grounding accidents. The behavior of web girders subjected to in-plane concentrated load is investigated by numerical simulation and theoretical analysis in this paper. A numerical simulation based on previous experiment is conducted to give insight to the deformation mechanism of crushing web girders. Some new important deformation characteristics are observed through the simulation results. A new theoretical deformation model is proposed featured with these deformation characteristics, and a simplified analytical method for predicting the instantaneous and mean resistances of crushing web girders is proposed. The proposed method is verified by two previous experiments and a series of numerical simulations. The agreement between the solutions by the proposed method and the experiment results is good. The comparison results between the proposed analytical method and numerical simulation results are satisfactory for most cases. The proposed analytical method will contribute to the establishment of an efficient method for fast and reliable assessment of the outcome of ship accidental collisions and grounding events

Multi-Attention Fusion Drowsy Driving Detection Model

Drowsy driving represents a major contributor to traffic accidents, and the implementation of driver drowsy driving detection systems has been proven to significantly reduce the occurrence of such accidents. Despite the development of numerous drowsy driving detection algorithms, many of them impose specific prerequisites such as the availability of complete facial images, optimal lighting conditions, and the use of RGB images. In our study, we introduce a novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model (MAF). MAF is aimed at significantly enhancing classification performance, especially in scenarios involving partial facial occlusion and low lighting conditions. It accomplishes this by capitalizing on the local feature extraction capabilities provided by multi-attention fusion, thereby enhancing the algorithm's overall robustness. To enhance our dataset, we collected real-world data that includes both occluded and unoccluded faces captured under nighttime and daytime lighting conditions. We conducted a comprehensive series of experiments using both publicly available datasets and our self-built data. The results of these experiments demonstrate that our proposed model achieves an impressive driver drowsiness detection accuracy of 96.8%.Comment: 8 pages, 6 figure

Loss of STAT1 in Bone Marrow-Derived Cells Accelerates Skeletal Muscle Regeneration

BACKGROUND: Skeletal muscle regeneration is a complex process which is not yet completely understood. Evidence suggested that the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway may have a role in myogenesis. In this study, we aim to explore the possible role of STAT1 in muscle regeneration. METHODS: Wild-type and STAT1 knockout mice were used in this study. Tibialis anterior muscle injury was conducted by cardiotoxin (CTX) injection. Bone marrow transplantation and glucocorticoid treatment were performed to manipulate the immune system of the mice. RESULTS: Muscle regeneration was accelerated in STAT1-/- mice after CTX injury. Bone marrow transplantation experiments showed that the regeneration process relied on the type of donor mice rather than on recipient mice. Levels of pro-inflammatory cytokines, TNFα and IL-1β, were significantly higher in STAT1-/- mice at 1 day and/or 2 days post-injury, while levels of anti-inflammatory cytokine, IL-10, were lower in STAT1-/- mice at 2 days and 3 days post-injury. Levels of IGF-1 were significantly higher in the STAT1-/- mice at 1 day and 2 days post-injury. Furthermore, the muscle regeneration process was inhibited in glucocorticoid-treated mice. CONCLUSIONS: Loss of STAT1 in bone marrow-derived cells accelerates skeletal muscle regeneration

SIGIFSDP: A Service Id Guided Intelligent Forwarding Service Discovery Protocol in Pervasive Computing Environments

Service discovery constructs a bridge between the service providers and the service consumers, and is a key point in pervasive computing environments. In group-based service discovery protocols, selective forwarding service requests only based on the service group maybe lead to unnecessary forwarding, which produces large packet redundancy. This paper proposes an efficient service discovery protocol: SIGIFSDP (Service Id Guided Intelligent Forwarding Service Discovery Protocol). In SIGIFSDP, based on GSD, SIGIF (Service Id Guided Intelligent Forwarding) is introduced to select the exact forwarding nodes based on the service id. Theoretical analysis and simulation results using GloMosim verify that SIGIFSDP can save the response time, reduce the service request packets, and improve the efficiency of service discovery

Numerical Simulation of Fragment Separation during Rock Cutting Using a 3D Dynamic Finite Element Analysis Code

To predict fragment separation during rock cutting, previous studies on rock cutting interactions using simulation approaches, experimental tests, and theoretical methods were considered in detail. This study used the numerical code LS-DYNA (3D) to numerically simulate fragment separation. In the simulations, a damage material model and erosion criteria were used for the base rock, and the conical pick was designated a rigid material. The conical pick moved at varying linear speeds to cut the fixed base rock. For a given linear speed of the conical pick, numerical studies were performed for various cutting depths and mechanical properties of rock. The numerical simulation results demonstrated that the cutting forces and sizes of the separated fragments increased significantly with increasing cutting depth, compressive strength, and elastic modulus of the base rock. A strong linear relationship was observed between the mean peak cutting forces obtained from the numerical, theoretical, and experimental studies with correlation coefficients of 0.698, 0.8111, 0.868, and 0.768. The simulation results also showed an exponential relationship between the specific energy and cutting depth and a linear relationship between the specific energy and compressive strength. Overall, LS-DYNA (3D) is effective and reliable for predicting the cutting performance of a conical pick

Experimental Study on the Sloshing of a Rectangular Tank under Pitch Excitations

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Dynamic response of a steel catenary riser at touch-down zone

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Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation

Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention. While most existing methods focus on designing novel architectures, we steer toward a different perspective: performing automated multi-loss adaptation (named Ada-Segment) on the fly to flexibly adjust multiple training losses over the course of training using a controller trained to capture the learning dynamics. This offers a few advantages: it bypasses manual tuning of the sensitive loss combination, a decisive factor for panoptic segmentation; it allows to explicitly model the learning dynamics, and reconcile the learning of multiple objectives (up to ten in our experiments); with an end-to-end architecture, it generalizes to different datasets without the need of re-tuning hyperparameters or re-adjusting the training process laboriously. Our Ada-Segment brings 2.7% panoptic quality (PQ) improvement on COCO val split from the vanilla baseline, achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on ADE20K dataset. The extensive ablation studies reveal the ever-changing dynamics throughout the training process, necessitating the incorporation of an automated and adaptive learning strategy as presented in this paper.Comment: Accepted by AAAI202