Comparative efficacy and safety of all kinds of intraocular lenses in presbyopia-correcting cataract surgery: a systematic review and meta-analysis.

PURPOSE: To assess the efficacy and safety of various intraocular lenses (IOLs), including standard monofocal, bifocal, trifocal, extended depth of focus (EDOF), and enhanced monofocal IOLs, post-cataract surgery through a network meta-analysis. METHODS: A systematic search of PubMed, Cochrane Library, and Web of Science was conducted to identify relevant studies from the past 5 years. Parameters such as binocular visual acuities, spectacle independence, contrast sensitivity (CS), and optical quality were used to evaluate efficacy and safety. Data from the selected studies were analyzed using Review Manager 5.4 and STATA 17.0 software. RESULTS: Twenty-eight Randomized Controlled Trials (RCTs) comprising 2465 subjects were included. Trifocal IOLs exhibited superior uncorrected near visual acuity (UNVA) compared to monofocal IOLs (MD: -0.35; 95% CI: -0.48, -0.22). Both trifocal (AcrySof IQ PanOptix IOLs group MD: -0.13; 95% CI: -0.21, -0.06) and EDOF IOLs (MD: -0.13; 95% CI: -0.17, -0.09) showed better uncorrected intermediate visual acuity (UIVA) than monofocal IOLs. Trifocal IOLs ranked highest in spectacle independence at various distances (AT LISAtri 839MP group: SUCRA 97.5% for distance, 80.7% for intermediate; AcrySof IQ PanOptix group: SUCRA 83.0% for near). CONCLUSIONS: For cataract patients who want to treat presbyopia, trifocal IOLs demonstrated better visual acuity and spectacle independence at near distances. Different types of trifocal IOL characteristics differ. EDOF and enhanced monofocal IOLs have improved visual quality at intermediate distances.Therefore, It is very important to select the appropriate IOLs based on the lens characteristics and patient needs.

A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization.

In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time-frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time-frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm.

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

Diagnosis of Compound Fault Using Sparsity Promoted-Based Sparse Component Analysis.

Compound faults often occur in rotating machinery, which increases the difficulty of fault diagnosis. In this case, blind source separation, which usually includes independent component analysis (ICA) and sparse component analysis (SCA), was proposed to separate mixed signals. SCA, which is based on the sparsity of target signals, was developed to sever the compound faults and effectively diagnose the fault due to its advantage over ICA in underdetermined conditions. However, there is an issue regarding the vibration signals, which are inadequately sparse, and it is difficult to represent them in a sparse way. Accordingly, to overcome the above-mentioned problem, a sparsity-promoted approach named wavelet modulus maxima is applied to obtain the sparse observation signal. Then, the potential function is utilized to estimate the number of source signals and the mixed matrix based on the sparse signal. Finally, the separation of the source signals can be achieved according to the shortest path method. To validate the effectiveness of the proposed method, the simulated signals and vibration signals measured from faulty roller bearings are used. The faults that occur in a roller bearing are the outer-race flaw, the inner-race flaw and the rolling element flaw. The results show that the fault features acquired using the proposed approach are evidently close to the theoretical values. For instance, the inner-race feature frequency 101.3 Hz is very similar to the theoretical calculation 101 Hz. Therefore, it is effective to achieve the separation of compound faults utilizing the suggest method, even in underdetermined cases. In addition, a comparison is applied to prove that the proposed method outperforms the traditional SCA method when the vibration signals are inadequate.

Exploring the effects of graphene and temperature in reducing electron beam damage: A TEM and electron diffraction-based quantitative study on Lead Phthalocyanine (PbPc) crystals.

High-resolution transmission electron microscopy (TEM) of organic crystals, such as Lead Phthalocyanine (PbPc), is very challenging since these materials are prone to electron beam damage leading to the breakdown of the crystal structure during investigation. Quantification of the damage is imperative to enable high-resolution imaging of PbPc crystals with minimum structural changes. In this work, we performed a detailed electron diffraction study to quantitatively measure degradation of PbPc crystals upon electron beam irradiation. Our study is based on the quantification of the fading intensity of the spots in the electron diffraction patterns. At various incident dose rates (e/Å2/s) and acceleration voltages, we experimentally extracted the decay rate (1/s), which directly correlates with the rate of beam damage. In this manner, a value for the critical dose (e/Å2) could be determined, which can be used as a measure to quantify beam damage. Using the same methodology, we explored the influence of cryogenic temperatures, graphene TEM substrates, and graphene encapsulation in prolonging the lifetime of the PbPc crystal structure during TEM investigation. The knowledge obtained by diffraction experiments is then translated to real space high-resolution TEM imaging of PbPc.

Fluorescent MoS2 Quantum Dots: Ultrasonic Preparation, Up-Conversion and Down-Conversion Bioimaging, and Photodynamic Therapy.

Small size molybdenum disulfide (MoS2) quantum dots (QDs) with desired optical properties were controllably synthesized by using tetrabutylammonium-assisted ultrasonication of multilayered MoS2 powder via OH-mediated chain-like Mo-S bond cleavage mode. The tunable up-bottom approach of precise fabrication of MoS2 QDs finally enables detailed experimental investigations of their optical properties. The synthesized MoS2 QDs present good down-conversion photoluminescence behaviors and exhibit remarkable up-conversion photoluminescence for bioimaging. The mechanism of the emerging photoluminescence was investigated. Furthermore, superior (1)O2 production ability of MoS2 QDs to commercial photosensitizer PpIX was demonstrated, which has great potential application for photodynamic therapy. These early affording results of tunable synthesis of MoS2 QDs with desired photo properties can lead to application in fields of biomedical and optoelectronics.