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AIM: To investigate Omicron's impact on clinical presentation of acute primary angle closure (APAC) in China. METHODS: A consecutive case series with historical controls was conducted at Shenzhen Eye Hospital, the largest specialized hospital in Shenzhen, China. Medical records from a two-month period during the Omicron pandemic (December 1, 2022, to January 31, 2023) were compared with records from two control groups (12/2018–1/2019 and 12/2021–1/2022) before pandemic. Patients with APAC were included, and the prevalence of APAC and demographic characteristics in Omicron-infected and non-infected patients were compared. RESULTS: Seventy-one (23.43%) out of 303 patients were diagnosed with APAC in the pandemic cohort, which was 2.98 and 2.61 times higher than that in control cohorts (7.87% in 2019, 8.96% in 2022, P

AIM: To address the challenges of data labeling difficulties, data privacy, and necessary large amount of labeled data for deep learning methods in diabetic retinopathy (DR) identification, the aim of this study is to develop a source-free domain adaptation (SFDA) method for efficient and effective DR identification from unlabeled data. METHODS: A multi-SFDA method was proposed for DR identification. This method integrates multiple source models, which are trained from the same source domain, to generate synthetic pseudo labels for the unlabeled target domain. Besides, a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances. Validation is performed using three color fundus photograph datasets (APTOS2019, DDR, and EyePACS). RESULTS: The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks. It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains. CONCLUSION: The multi-SFDA method provides an effective approach to overcome the challenges in DR identification. The method not only addresses difficulties in data labeling and privacy issues, but also reduces the need for large amounts of labeled data required by deep learning methods, making it a practical tool for early detection and preservation of vision in diabetic patients.

AIM: To gain insights into the global research hotspots and trends of myopia. METHODS: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS: A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION: Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

AIM: To analyze research hotspots and trends of artificial intelligence in diabetic retinopathy(DR)based on bibliometrics and high-impact papers.METHODS: Papers on artificial intelligence in DR research published in the Web of Science Core Collection(WoSCC)from January 1, 2012, to December 31, 2022 were retrieved. The data was analyzed by CiteSpace software to examine annual publication number, countries, institutions, source journal, research categories, keywords, and to perform an in-depth analysis of high-impact papers.RESULTS: A total of 1 009 papers on artificial intelligence in DR from 79 countries were included in the study, with 272 papers published in 2022. Notably, China and India contributed 287 and 234 papers, respectively. The United Kingdom exhibited a centrality score of 0.31, while the United States boasted an impressive H-index of 48. Three prominent institutions in the United Kingdom(University of London, Moorfields Eye Hospital, and University College London)and one institution in Egypt(Egyptian Knowledge Bank)all achieved a notable H-index of 14. The primary academic disciplines associated with this research field encompassed ophthalmology, computer science, and artificial intelligence. Burst keywords in the years 2021～2022 included transfer learning, vessel segmentation, and convolutional neural networks.CONCLUSION: China emerged as the leading contributor in terms of publication number in this field, while the United States stood out as a key player. Notably, Egyptian Knowledge Bank and University of London assumed leading roles among research institutions. Additionally, IEEE Access was identified as the most active journal within this domain. The research focus in the field of artificial intelligence in DR has transitioned from AI applications in disease detection and grading to a more concentrated exploration of AI-assisted diagnostic systems. Transfer learning, vessel segmentation, and convolutional neural networks hold substantial promise for widespread applications in this field.

AIM: To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend. METHODS: On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms. RESULTS: Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification. CONCLUSION: The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.

AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification. METHODS: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established. Each algorithm was combined with four models for experiments on the HMM dataset. Each experiment consisted of 20 active learning rounds, with 100 images selected per round. The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+ outperformed other algorithms. Finally, this study employed six models, including EfficientFormer, to classify HMM. The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+ algorithm to achieve satisfactory classification results with a small dataset. RESULTS: ALFA-Mix+ outperforms other algorithms with an average superiority of 16.6, 14.75, 16.8, and 16.7 rounds in terms of accuracy, sensitivity, specificity, and Kappa value, respectively. This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images. The EfficientFormer achieved the best results with an accuracy, sensitivity, specificity, and Kappa value of 0.8821, 0.8334, 0.9693, and 0.8339, respectively. Therefore, by combining ALFA-Mix+ with EfficientFormer, this study achieved results with an accuracy, sensitivity, specificity, and Kappa value of 0.8964, 0.8643, 0.9721, and 0.8537, respectively. CONCLUSION: The ALFA-Mix+ algorithm reduces the required samples without compromising accuracy. Compared to other algorithms, ALFA-Mix+ outperforms in more rounds of experiments. It effectively selects valuable samples compared to other algorithms. In HMM classification, combining ALFA-Mix+ with EfficientFormer enhances model performance, further demonstrating the effectiveness of ALFA-Mix+.

In this paper, we investigated the spin transport properties of binuclear manganese phthalocyanine (M n 2 P c 2) spintronic devices sandwiched between two nickel electrodes using the non-equilibrium Green's function method in combination with density functional theory. Based on the calculation results, the M n 2 P c 2 device exhibited excellent spin-filtering capabilities, demonstrating an exceptionally high spin filter efficiency (SFE). Irrespective of the parallel or antiparallel orientation of magnetization in the electrodes, we observed that when both manganese atoms were in a spin-up state, the SFE of spin-resolved currents under finite bias and the thermoelectric currents induced by temperature gradients at fixed temperatures were both close to 100%. The large spin Seebeck polarization of the M n 2 P c 2 device was also obtained at low reference temperatures. This study explores the potential for developing multifunctional spintronic single-molecule devices using Ni − M n 2 P c 2. [ABSTRACT FROM AUTHOR]

Based on the wear resistance application scenarios of the heat surface tubes, fans and other components of popwer station boiler, coatings were prepared on the substrate 20G steel by high velocity arc - spraying technology with the application of two kinds of FeCr - based coating materials containing different carbon and rare earth element contents. Subsequently, the microscopic morphology and phase composition of the coatings were characterized through optical microscope (OM), scanning electron microscope (SEM), X - ray diffractometer (XRD) and white light interferometer system, and the mechanical and wear resistance properties of the coatings were tested through Vickers microhardness, tensile bonding strength, ball - disk friction and wear tests. Results showed that the microstructure of these two coatings was a typical lamellar structure. As B, C and other hard phase elements were distributed in FeCr solid solution, the microhardness of the coatings was much higher than that of the substrate. Furthermore, the high carbon content and the addition of rare earth elements contributed to a decrease of the porosity of the coatings from 2.22±0.47% to 0.92±0.13%, an increase of the microhardness from 8.05±0.53 GPa to 10.47±0.68 GPa, and a reduction of the wear weight loss by 40%. Judging from the wear morphology of the coatings, high microhardness and low porosity made the wear mechanism changed from adhesive wear to abrasive wear. In general, applying FeCr - based coatings to protect the wear surfaces could effectively solve the wear problem of the boiler parts of the power station, and thus ensure the safe and stable operation of the boiler units.

AIM: To delineate the different imaging characteristics of uveal schwannoma from melanoma and discuss the optimal treatment strategy for intraocular schwannoma. METHODS: Case series of three patients diagnosed with intraocular schwannoma was collected at Zhongshan Ophthalmic Center, Guangzhou, China from July 2014 to December 2020. All the study patients underwent ultrasonography and magnetic resonance imaging (MRI). The clinical features, therapeutic strategies, and prognoses of all patients were reviewed. RESULTS: Ultrasonography of all three patients (all females, mean age, 39y, age range, 23-54y) showed low to medium reflectivity with a homogeneous internal structure. MRI of all three patients demonstrated isointensity on T1-weighted imaging spin-echo (T1WI SE) images and hypointense on fast spin-echo T2-weighted images (FSE T2WI) images with respect to the brain. Minimally invasive pars plana vitrectomy (PPV) and local resection of the tumor was performed for all patients, and the diagnosis of schwannoma was confirmed by histopathological examination. CONCLUSION: The present study indicates that ultrasonography and MRI features of uveal schwannoma may contribute to the differentiation of uveal schwannoma from melanoma, and the optimal therapy for intraocular schwannoma is minimally invasive PPV and local resection.

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential. [ABSTRACT FROM AUTHOR]

High-energy irradiation significantly increases the optical losses and noise coefficients of laser materials, leading to a substantial decrease in the slope efficiency or gain performance of laser output. To address this issue, we propose a strategy to enhance the radiation resistance of glasses/fibers by introducing phase interfaces. Based on the sol–gel method, through phase-separation techniques and high-temperature annealing treatments, silica-rich and phosphorus-rich phases were formed in erbium-ytterbium co-doped high-phosphorus silica glass, and nanoscale phase interfaces with specific densities, stability levels, and homogeneous distributions of doped elements were constructed between the phases. Using high-resolution transmission electron microscopy, nuclear magnetic resonance, and spectroscopic analyses, we tracked the evolution of the internal microstructure of the glasses at the atomic level. The findings confirmed that annealing effectively controlled the density of the phase interfaces formed. Under 1 kGy X-ray irradiation, glasses with effective phase interfaces exhibited significant reduction in radiation-induced attenuation (RIA) and improvement in photoluminescence intensity compared to pristine glasses. This indicated that effective phase interfaces could act as complex centers for irradiation-induced point defects, absorbing radiant energy and trapping these defects, thus mitigating high-energy radiation-induced damages. Furthermore, online irradiation tests on the Er3+/Yb3+ co-doped silica fibers supported this result. Compared to pristine fiber, fibers annealed for 3 h and annealed for 20 h with different phase interfacial densities showed 45% and 73% lower RIA at 1080 nm, respectively. Highlights: Erbium-ytterbium co-doped high-phosphorus silica glasses/fibers were synthesized via a modified sol–gel method, incorporating nanoscale phase-interface structures. Annealing treatment was utilized to increase the density of phase interfaces in the glasses/fibers. Strengthening the phase interfaces resulted in reduced radiation-induced attenuation and improved radiation resistance of glasses/fibers. [ABSTRACT FROM AUTHOR]

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge ( S O C ) constraint of the battery, partition the S O C into different states. Using S O C as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery S O C to prevent S O C from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station. [ABSTRACT FROM AUTHOR]

Magnesium-based amorphous alloys have aroused broad interest in being applied in marine use due to their merits of lightweight and high strength. Yet, the poor corrosion resistance to chloride-containing seawater has hindered their practical applications. Herein, we propose a new strategy to improve the chloride corrosion resistance of amorphous Mg65Cu15Ag10Gd10 alloys by engineering atomic-to-nano scale structural homogeneity, which is implemented by heating the material to the critical temperature of the liquid–liquid transition. By using various electrochemical, microscopic, and spectroscopic characterization methods, we reveal that the liquid–liquid transition can rearrange the local structural units in the amorphous structure, slightly decreasing the alloy structure’s homogeneity, accelerate the formation of protective passivation film, and, therefore, increase the corrosion resistance. Our study has demonstrated the strong coupling between an amorphous structure and corrosion behavior, which is available for optimizing corrosion-resistant alloys.

AIM: To investigate microvascular changes in eyes with central retinal vein occlusion (CRVO) complicated by macular edema before and after intravitreal conbercept injection and evaluate correlations between these changes and best-corrected visual acuity (BCVA) and retinal thickness. METHODS: Twenty-eight eyes of 28 patients with macular edema caused by CRVO were included in this retrospective study. All patients received a single intravitreal conbercept injection to treat macular edema. BCVA and the results of optical coherence tomography angiography (OCTA) automatic measurements of the vessel density in the superficial (SCP) and deep retinal capillary plexus (DCP), the foveal avascular zone (FAZ) area, the FAZ perimeter (PERIM), the vessel density within a 300-μm wide ring surrounding the FAZ (FD-300), the acircularity index (AI), the choriocapillaris flow area, and retinal thickness were recorded before and at one month after treatment and compared with the results observed in age- and sex- matched healthy subjects. RESULTS: The vessel density in the SCP and DCP, the FD-300, and the flow area of the choriocapillaris were all significantly lower in CRVO eyes than in healthy eyes, while the AI and retinal thickness were significantly higher (all P

AIM: To compare visual quality in cataract patients with low corneal astigmatism who underwent intraocular lens (IOL) implantation, and evaluate effects of low levels of astigmatism on visual outcomes in multifocal pseudophakic eyes. METHODS: This retrospective review of clinical records comprised patients with preoperative regular corneal astigmatism of 0.75-1.0 diopters (D) with-the-rule or 0.5-0.75 D against-the-rule who had uneventful cataract surgery and AcrySof IQ ReSTOR Toric-2 IOL (ART2) or AcrySof IQ ReSTOR IOL (ReSTOR) implantation. Retrospective data collection included postoperative ART2 axis rotation, uncorrected astigmatism, uncorrected entire visual acuities, distance corrected entire visual acuities, average modulation transfer function (aMTF), Strehl ratio (SR), spectacle independence, and patient satisfaction between groups. RESULTS: Mean ART2 axis rotation was 3.12°±0.70°. No secondary surgery was required to realign IOL axis. Residual astigmatism values were -0.18±0.07 D and -0.91±0.25 D in groups ART2 and ReSTOR (P

AIM: To determine the prevalence and characteristics of peripheral myopic retinopathy among a sample of Guangzhou office workers. METHODS: A cross-sectional study of Guangzhou Chinese office works in different departments and units of the Guangzhou Power Supply Bureau, China, in 2016. Myopic retinopathy was recorded and analyzed with a scanning laser ophthalmoscope and by slit-lamp microscopy combined with a three-mirror contact lens. RESULTS: In total, 1910 eyes of 955 subjects (508 females and 447 males) aged 21-59y were included; 69.6% of these eyes were myopic. The myopia group had a younger age and worse uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA) when compared with hyperopia and emmetropia groups (P

Rare-earth-doped silica-based composite glasses (Re-SCGs) are widely used as high-quality laser gain media in defense, aerospace, energy, power, and medical applications. The variable regional chemical environments of Re-SCGs can induce new photoluminescence properties of rare-earth ions but can cause the selective aggregation of rare-earth ions, limiting the application of Re-SCGs in the field of high-power lasers. Here, topological engineering is proposed to adjust the degree of cross-linking of phase-separation network chains in Re-SCGs. A combination of experimental and theoretical characterization techniques suggested that the selective aggregation of rare-earth ions originates from the formation of phase-separated structures in glasses. The decomposition of nanoscale phase separation structures to the sub-nanometer scale, enabled by incorporating Al3+ ions, not only maintains the high luminescence efficiency of rare earth ions but also increases light transmittance and reduces light scattering. Furthermore, our investigation encompassed the exploration of the inhibitory mechanism of Al3+ ions on phase-separation structures, as well as their influence on the spectral characteristics of Re-SCGs. This work provides a new design concept for composite glass materials doped with rare-earth ions and could broaden their application in the field of high-power lasers. [ABSTRACT FROM AUTHOR]