Characterization techniques for tobacco and its derivatives: a systematic review.

Biomass and its derivatives have broad applications in the fields of bio-catalysis, energy storage, environmental remediation. The structure and components of biomass, which are vital parameters affecting corresponding performances of derived products, need to be fully understood for further regulating the biomass and its derivatives. Herein, tobacco is taken as an example of biomass to introduce the typical characterization techniques in unraveling the structural information, chemical components, and properties of biomass and its derivatives. Firstly, the structural information, chemical components and application for biomass are summarized. Then the characterization techniques together with the resultant structural information and chemical components are introduced. Finally, to promote a wide and deep study in this field, the perspectives and challenges concerning structure and composition charaterization in biomass and its derivatives are put forward.

Revolutionizing the structural design and determination of covalent-organic frameworks: principles, methods, and techniques.

Covalent organic frameworks (COFs) represent an important class of crystalline porous materials with designable structures and functions. The interconnected organic monomers, featuring pre-designed symmetries and connectivities, dictate the structures of COFs, endowing them with high thermal and chemical stability, large surface area, and tunable micropores. Furthermore, by utilizing pre-functionalization or post-synthetic functionalization strategies, COFs can acquire multifunctionalities, leading to their versatile applications in gas separation/storage, catalysis, and optoelectronic devices. Our review provides a comprehensive account of the latest advancements in the principles, methods, and techniques for structural design and determination of COFs. These cutting-edge approaches enable the rational design and precise elucidation of COF structures, addressing fundamental physicochemical challenges associated with host-guest interactions, topological transformations, network interpenetration, and defect-mediated catalysis.

Use of Ilizarov technique for bilateral knees flexion contracture in Juvenile-onset ankylosing spondylitis: A case report.

BACKGROUND: Ankylosing spondylitis (AS) is a chronic rheumatic disease that primarily affects the spine and the sacroiliac and peripheral joints. Juvenile-onset AS (JoAS) patients will likely present with peripheral joint symptoms. Knee flexion contracture (KFC) and hip flexion contracture (HFC) are common in these patients due to subchondral bone inflammation. The Ilizarov technique is the most commonly used technique for treating KFC. However, its use to treat JoAS-associated KFC has not been reported. CASE SUMMARY: This report presents a case study of a 31-year-old male patient with a squatting gait due to severe bilateral KFC and HFC. The patient had a normal walking pattern until the age of eight, after which he experienced knee and hip pain, leading to the gradual development of KFC and HFC. The patient's primary complaint was an inability to walk upright. The patient was diagnosed with JoAS and underwent hip dissection and release, limited soft tissue release of the hamstring, and gradual traction using the Ilizarov method. Ultimately, the patient was able to walk upright. CONCLUSION: The incidence of squatting gait due to KFC in individuals diagnosed with JoAS was low. Utilizing the Ilizarov technique has proven to be a secure and effective method for managing KFC in JoAS patients. Although the Ilizarov technique cannot substitute for total knee arthroplasty (TKA), its application can delay the need for primary TKA in JoAS patients and alleviate the intricacy and potential complications associated with the procedure.

Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network.

Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target's motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model's error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms.