446494 research outputs found
Sort by
Artificial Intelligence for Automated Grading and Treatment Planning in Periodontitis
No full textPeriodontitis is a widespread chronic inflammatory disease that continues to threaten oral health and contributes to systemic complications. Its diagnosis and grading largely depend on probing and radiographic assessment, yet these approaches vary across clinicians and lack precision in detecting early bone alterations. Deep learning has been introduced to address these shortcomings by automatically analysing dental images and extracting both global bone patterns and site-specific features relevant to disease severity. Encoder-decoder networks can delineate alveolar bone contours and periodontal pockets, while classification models combine these representations to generate reproducible grading outcomes. Compared with conventional methods, such systems offer more consistent evaluation of complex regions, reduce observer variability, and shorten the time required for clinical interpretation. Integration with structured reporting further facilitates incorporation into electronic health records, enabling routine use in follow-up and treatment planning. Remaining barriers include annotation inconsistency, equipment-related variability, and limited validation across centers. This review aims to synthesize current progress in deep learning-based grading of periodontitis, clarify unresolved challenges, and outline requirements for clinical adoption
The tumor microenvironment and its immunosuppressive characteristics-IFN-γ immunotherapy approaches
No full textAdvancements in cancer research and technology are driving a significant paradigm shift from traditional, one-size-fits-all treatments toward a more personalized and precise medical approach. This modern strategy, often termed precision medicine, offers tailored therapeutic care by addressing the specific genetic, molecular, and epidemiologic factors unique to each patient's disease. Among the most promising emerging strategies is immunotherapy, which represents a fundamental departure from conventional therapies like chemotherapy. Immunotherapy leverages detailed knowledge of the tumor's genetic makeup and its surrounding microenvironment to target key mutations. The primary goal is to enhance the body's own immune system, empowering it to effectively detect and eradicate cancer cells. However, tumors are notoriously adept at developing resistance; they dynamically reshape their local microenvironment into an immunosuppressive fortress to evade immune detection. This paper specifically analyzes the complex mechanisms of this immunosuppressive tumor microenvironment and investigates how scientists are employing various immunomodulators to overcome these defensive strategies, thereby restoring the immune system's ability to fight the cancer
Chemical Modification of Bioactive Peptides and Their Advances in Drug Innovation
No full textPeptides are an important class of molecules in contemporary drug research. They show very high binding specificity toward biological targets. They also participate in many essential biological processes. At the same time, natural peptides encounter major difficulties in medical applications. They often display poor chemical and biological stability. They usually have a very short half-life in circulation. They are also absorbed poorly when taken orally. These limitations restrict their direct clinical value as medicines. This study focuses on the chemical modification of bioactive peptides. Several common strategies are widely investigated. Cyclization of the peptide backbone produces a more rigid and stable structure. Lipidation of the peptide chain allows longer retention in the bloodstream and strengthens interactions with cell membranes. PEGylation and glycosylation both increase hydrophilicity and reduce rapid clearance from the body. Substitution of natural amino acids with D-amino acids improves resistance to enzymatic degradation and extends biological activity. Other methods also provide important support. Solid-phase peptide synthesis offers a practical and efficient route for preparing modified peptides on a large scale. Nanocarrier systems can protect peptide drugs during administration and assist in targeted delivery. In addition, artificial intelligence is being applied to predict structural features and to design new peptide molecules with higher efficiency
Comparative Analysis of Immunostimulatory Activation Mechanisms and Design Strategies for Two Novel Vaccine Platforms: SLP+CpG and mRNA–LNP
No full textWith the continuous expansion of the spectrum of immune-related diseases, traditional vaccines and therapies are far from meeting the urgent needs with the required precision and durability. Therefore, there is a requirement for mechanistically programmable immune platforms. Because of their advantages in antigen design and innate immune regulation, synthetic long peptides combined with CpG and messenger RNA-lipid nanoparticles have become research hotspots over the last few years. This review focuses on core elements and the latest development of these two platforms: at the molecular and process level, the review outlines SLP multi-epitope concatenation and auxiliary epitope design, CpG regulation of the TLR9-MyD88 axis, mRNA nucleoside modifications, UTR/ORF optimization, and LNP delivery strategies; at the level of immunological mechanism, it makes a comparison between exogenous "pulsed" antigen presentation and endogenous short-term continuous expression. Generally speaking, SLP+CpG displays epitope-level precision and programmable personalization, while mRNA-LNP has potential in multi-antigen parallel processing and rapid iterative scaling. However, bottlenecks like cross-presentation, "pulsed" antigen availability, expression-innate balance, formulation immunogenicity, and cold chain consistency are still problems to be overcome. Future directions shall focus on high-precision antigen prediction and selection, cDC1-biased and organ-targeted delivery, heterologous sequential and dose-interval optimization, and biomarker-driven personalized combination therapies to further guarantee more reproducible and scalable clinical translation
Modeling and simulation of current control for permanent magnet synchronous motors based on multilayer perceptron neural networks
No full textIn the traditional finite control set model predictive current control(FCS-MPCC) for a permanent magnetic synchronous motor (PMSM), periodic delay caused by computational latency and hardware register update mechanisms leads to control commands lagging behind actual motor states, thereby impairing dynamic response and control stability of the PMSM. To address the issue, this paper introduces a two-step finite control set model predictive current control(FCS-MPCC) method. By predicting two-step current states simultaneously and generating control commands for the current time step in the previous cycle, the introduced method effectively reduces the impact of control delay and improves prediction accuracy. However, while the two-step FCS-MPCC method enhances control performance, the more complex computational logic increases the computational burden, limiting its real-time applicability. To overcome the limitation, the paper proposes a method based on the multilayer perceptron(MLP) neural network, which replaces traditional model predictive control strategies with a data-driven method. By learning the optimization rules of the two-step FCS-MPCC, the MLP neural network can replicate its control performance without requiring online computational efforts. The simulation results demonstrate that the proposed method exhibits strong robustness under secondary load disturbances, further validating its potentials for application in motor control
On the effects of Sun's gravity and solar radiation pressure to the Earth-Moon distant retrograde orbits
No full textThe distant retrograde orbits(DROs) form a family of planar periodic orbits that are retrograde around the Moon in the Earth-Moon circular restricted three-body problem(CR3BP). Known for their "low-energy insertion, long-term stability, and global accessibility", DROs are regarded as ideal candidates for future cis-lunar exploration. Sun's gravity and solar radiation pressure(SRP), as primary perturbations in the Earth-Moon system, could significantly alter the orbit dynamics of spacecraft. Based on the above considerations, the effect of the Sun's gravity and SRP on the dynamics and geometry of Sun-resonant DROs within a quasi-bicircular problem(QBCP) is investigated. Each resonant DRO in the CR3BP bifurcates into at least two branches in the QBCP. Furthermore, the Sun's gravity can qualitatively alter the phase-space structure of most resonant DROs, transforming them from stable to unstable, which opens the possibility of low-energy transfer. Regarding SRP, it induces more complex changes in the phase-space structures and leads to more bifurcation types, such as tangent bifurcation, period-doubling bifurcation and second-class Hopf bifurcation, thus deriving richer families of orbits. It is worthily noted that SRP may help stabilize orbits, making it beneficial for station-keeping. Additionally, SRP can significantly alter the geometry of DROs. Especially when the spacecraft which has a relatively large lightness number, is pitched at certain angles, the planar DROs evolve into spacial orbits to make them more suitable for cis-lunar space missions
Study on method of eddy-current-loss suppression and power-factor enhancement of integrated magnetic-field-modulated machine
No full textIntegrated magnetic-field-modulated machine is a single-air-gap magnetic-field-modulated machine, which is highly integrated by using the magnetic gear and high-speed permanent-magnet machine. It has the advantages of compact structure, high torque density, high reliability, which has potential application prospects in aerospace area. However, this kind of machine uses the multiple modulation harmonics with small amplitude for energy conversation, resulting in the large harmonic loss and low power factor. To solve the above mentioned problems, the eddy current loss suppression and power factor enhancement methods of the integrated magnetic-field-modulated machine are studied. Firstly, the modulated magnetic-field composition of machine is analyzed to determine the contribution of permanent-magnet modulated harmonics and armature modulated harmonics to electromagnetic torque. Secondly, the loss production mechanism is studied. As the eddy current loss of permanent magnet is the main loss, the influence of the permanent magnet segmentation on the eddy current loss is studied. In order to optimize the air gap performance, two improved configurations of the modulated rotor are proposed. Thirdly, the expression of the power factor under different control strategy is deduced. The power factor enhancement method is studied in aspect of the control strategy and the machine design. Finally, a prototype of the integrated magnetic-field-modulated machine is manufactured. The test bench is built up to carry out the experiment. The experimental results verify the correctness and feasibility of the theoretical analysis. The results show that the circumferential segmentation scheme of the permanent magnet can reduce the eddy current loss by at least 75%. The sinusoidal-surface configuration of the modulated rotor can reduce the eddy current loss to 73% of the conventional configuration. By adjusting the input phase angle of the machine, the power factor can be increased by 25%, the eddy current loss can be reduced by 50%, and the efficiency can be increased by 10% without the sacrificing torque
Remaining useful life prediction method of PEMFC based on joint feature extraction of LSTM and improved Transformer
No full textProton exchange membrane fuel cells (PEMFC) are a crucial component of modern sustainable clean energy generation technology. Accurate prediction of performance degradation is key to enhancing the performance of PEMFC systems and is also an important step in promoting this clean energy technology for broader applications. Traditional methods for predicting performance degradation typically achieve their aims through mechanistic models and forecasting algorithms, refining model parameters and algorithm structures to improve accuracy. However, these methods often fall short in fully considering the detailed characteristics implied by aging data over long-time scales and the phenomenon of voltage recovery. Therefore, this paper proposes a predictive model that combines long short-term memory (LSTM) networks with an enhanced Transformer for joint feature extraction to achieve precise predictions of PEMFC output voltage. Initially, based on traditional Transformer architecture, an optimized design is performed to build an improved Transformer model suitable for PEMFC remaining useful life (RUL) prediction. Secondly, the improved Transformer model is embedded into the conventional LSTM framework, constructing a combined LSTM and improved Transformer joint feature extraction model. Under steady-state, dynamic and pseudo-dynamic datasets, finally, the LSTM, convolutional neural networks (CNN), CNN-LSTM, the improved Transformer, and the joint feature extraction model were evaluated for output voltage prediction and quantitatively compared. The results indicate that the improved Transformer and the joint prediction model show significant improvements over other comparative models in evaluation metrics such as RMSE, MAE, and R2. This confirms that the proposed prediction model can enhance the RUL prediction accuracy of PEMFC to some extent
A Short Review On Automated and Semi-Automated Cleaning Systems for Photovoltaic Panels to Enhance Power Efficiency
No full textThe efficiency of solar panels depends on both photovoltaic technology and operating conditions, including cell structure, material properties, and system design. Monocrystalline PV modules are currently the most widely used due to technological advances and their high photoelectric conversion efficiency. However, environmental soiling from dust and debris degrades performance by increasing surface contamination and inducing localized hotspots, while even limited partial shading can cause significant energy losses due to electrical mismatch within the module. Consequently, current research and industrial efforts focus on reducing soiling-related losses, improving long-term system reliability, and advancing photovoltaic technologies. Promising solutions include anti-soiling surface coatings and automated cleaning systems based on robotic and electrostatic methods, particularly suited to arid and dusty environments. In parallel, emerging technologies such as bifacial and perovskite PV modules offer opportunities for higher energy yield and enhanced scalability of solar power systems
The aerodynamics of a Savonius blade
No full textFaced with the energy crisis and global warming, renewable energies are emerging as a key response, particularly wind power. Among these various technologies, vertical axis wind turbines (VAWTs) present a promising energy source in rural areas. The Savonius rotor, a drag-driven device, one of the famous vertical axis wind turbine designs, offers many advantages such as easy installation and fast automatic start-up. However, its efficiency is limited by negative torque on the return blade. Research aims to improve this rotor by optimizing many factors as the overlap ratio and blade shape, while integrating technologies such as end plates and deflectors to minimise the negative torque and increase energy production. The power factor of a conventional Savonius rotor extends from 0.12 to 0.18, but improvements could increase it to 0.52. This article provides an in-depth analysis of performance and power enhancement technologies for Savonius rotors, suggesting future prospects. In addition to examining proven solutions, the article highlights new avenues of research that could strengthen the role of this rotor in the future energy mix