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Abstract Background This retrospective study aimed to evaluate the precision and safety outcomes of image‐guided lung percutaneous thermal ablation (LPTA) methods, focusing on radiofrequency ablation (RFA) and microwave ablation (MWA). The study utilized an innovative angle reference guide to facilitate these techniques in the treatment of lung tumors. Methods This study included individuals undergoing LPTA with the assistance of laser angle guide assembly (LAGA) at our hospital between April 2011 and March 2021. We analyzed patient demographics, tumor characteristics, procedure details, and complications. Logistic regressions were employed to assess risk factors associated with complications. Results A total of 202 patients underwent ablation for 375 lung tumors across 275 sessions involving 495 ablations. Most procedures used RFA, especially in the right upper lobe, and the majority of ablations were performed in the prone position (49.7%). Target lesions were at a median depth of 39.3 mm from the pleura surface, and remarkably, 91.9% required only a single puncture. Complications occurred in 31.0% of ablations, with pneumothorax being the most prevalent (18.3%), followed by pain (12.5%), sweating (6.5%), fever (5.0%), cough (4.8%), hemothorax (1.6%), hemoptysis (1.2%), pleural effusion (2.0%), skin burn (0.6%), and air emboli (0.2%). The median procedure time was 21 min. Notably, smoking/chronic obstructive pulmonary disease emerged as a significant risk factor for complications. Conclusion The LAGA‐assisted LPTA enhanced safety by improving accuracy and reducing risks. Overall, this investigation contributes to the ongoing efforts to refine and improve the clinical application of these thermal ablation techniques in the treatment of lung tumors.

ABSTRACT Aims/Introduction Cardiovascular mortality risk is elevated among patients with diabetes and concurrent chronic kidney disease. However, controversy surrounds the use of aspirin for primary prevention within this population. This study aims to assess the effectiveness and safety of low‐dose aspirin for primary prevention in patients with diabetes and pre‐end‐stage renal disease. Materials and Methods This was a retrospective population‐based cohort study using the National Health Insurance Research Database in Taiwan. The study included adults with type 2 diabetes who were enrolled in the pre‐end‐stage renal disease pay‐for‐performance program and had no atherosclerotic cardiovascular disease. We used propensity score analysis to control baseline characteristics between the two groups. Clinical outcomes including cardiovascular mortality, all‐cause mortality, major bleeding, and renal disease progression were compared between patients who first received aspirin and those who did not. Results Between January 2012 and December 2015, a total of 2,155 low‐dose aspirin users and 6,737 nonaspirin users were identified. Following propensity score adjustment, aspirin use exhibited a comparable risk of cardiovascular death compared with nonaspirin users (adjusted hazard ratio [aHR] 1.12; 95% confidence interval [CI] 0.65–1.95; P = 0.681). The risk of all‐cause mortality was similar between the two groups (aHR 1.07; 95% CI 0.92–1.24; P = 0.385). Similar risks were observed in terms of major bleeding and renal disease progression. Conclusions In patients with diabetes and pre‐end‐stage renal disease who lacked atherosclerotic cardiovascular disease, low‐dose aspirin did not demonstrate a reduction in mortality. These findings do not support the use of aspirin for primary prevention in this high‐risk population.

Key Clinical Message One Kirsten Ras (KRAS) G12C mutated non‐small cell lung cancer (NSCLC) patient had improved poor performance status and obtained mixed response with the first‐line KRAS‐targeted treatment of sotorasib. After disease progression, partial response was achieved with chemotherapy plus immunotherapy. KRAS G12C mutated immunoenvironment in NSCLC may favor the immunotherapy. Abstract KRAS is one of the most commonly mutated genes, which used to be untargetable. The phase II CodeBreak 100 trial revealed 6.8‐month median progress‐free survival (PFS) and 12.5‐month overall survival (OS) in previously treated KRAS G12C‐mutant NSCLC patients treated with KRAS inhibitor, sotorasib. The specimens of the brain, lymph node (LN), and blood from the patient were analyzed by next‐generation sequencing. Hematoxylin and eosin staining and immunohistochemistry were performed for pathological characterization. Computed tomography (CT) and magnetic resonance imaging (MRI) scan were used for treatment response evaluation. The patient was diagnosed in a bad Eastern Cooperative Oncology Group performance status (ECOG‐PS) with metastatic KRAS G12C‐mutated lung adenocarcinoma who had achieved mixed response to sotorasib as the first‐line treatment. Although 5‐month PFS of the treatment with sotorasib was not surprising, the patient achieved significantly improved ECOG‐PS score from 4 to 1. Subsequently, partial response (PR) was achieved with the treatment of pemetrexed plus pembrolizumab. This case highlights superior efficacy of first‐line treatment with sotorasib for the advance untreated KRAS G12C‐mutant patients. The high efficacy of the treatment with chemotherapy plus immunotherapy revealed that immunoenvironment of KRAS G12C‐mutated patient may favor the immunotherapy.

Abstract Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions. Linear self‐organising map (SOM) introduces lateral interaction in a general form in which signals of any modality can be used. Some approaches directly incorporate SOM learning rules into neural networks, but incur complex operations and poor extendibility. The efficient way to implement lateral interaction in deep neural networks is not well established. The use of Laplacian Matrix‐based Smoothing (LS) regularisation is proposed for implementing lateral interaction in a concise form. The authors’ derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means, and they both show the topology‐preserving capability. The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders. Additionally, the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated. Furthermore, the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks. Overall, LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

Abstract Endobronchial ultrasound bronchoscopy (EBUS) and needle confocal laser endomicroscopy (nCLE) are techniques for screening benign and malignant lesions of the hilar and mediastinal lymph node (HMLN). This study investigated the diagnostic potential of EBUS, nCLE, and combined EBUS and nCLE in HMLN lesions. We recruited 107 patients with HMLN lesions who were examined by EBUS and nCLE. A pathological examination was performed, and the diagnostic potential of EBUS, nCLE, and combined EBUS‐nCLE approach was analyzed according to the results. Among the 107 cases of HMLN lesions, 43 cases were benign and 64 cases were malignant on pathological examination, 41 cases were benign and 66 cases were malignant on EBUS examination; 42 cases were benign and 65 cases were malignant on nCLE examination; 43 cases were benign and 64 cases were malignant on combined EBUS‐nCLE examination. The combination approach had 93.8% sensitivity, 90.7% specificity, and 0.922 area under the curve, which was higher than those of EBUS (84.4%, 72.1%, and 0.782, respectively) and nCLE diagnosis (90.6%, 83.7%, and 0.872, respectively). The combination approach had a higher positive predictive value (0.908), negative predictive value (0.881), and positive likelihood ratio (10.09) than that of EBUS (0.813, 0.721, and 3.03, respectively) and nCLE (0.892, 0.857, and 5.56, respectively), whereas, the negative likelihood ratio was lower than that for EBUS (0.22) and nCLE (0.11). No serious complications occurred in patients with HMLN lesions. To summarize, the diagnostic efficacy of nCLE was better than EBUS. The EBUS‐nCLE combination is a suitable approach for diagnosing HMLN lesions.

Abstract A wide variety of primary and secondary lymphoma types involves the skin. However, reports with comparisons between both groups are limited in Taiwan. We retrospectively enrolled all cutaneous lymphomas and evaluated their clinicopathologic features. There were 221 cases of lymphoma: 182 (82.3%) primary and 39 (17.7%) secondary. Mycosis fungoides was the most common primary T‐cell lymphoma, 92 (41.7%) cases, followed by CD30‐positive T‐cell lymphoproliferative disorders including lymphomatoid papulosis (n = 33, 14.9%) and cutaneous anaplastic large cell lymphoma (n = 12, 5.4%). The most frequent primary B‐cell lymphomas were marginal zone lymphoma (n = 8, 3.6%) and diffuse large B‐cell lymphoma (DLBCL), leg type (n = 8, 3.6%). DLBCL including variants was the most common secondary lymphoma involving skin. Most primary lymphomas presented at low‐stage (T‐cell, 86%; B‐cell, 75%), whereas the majority of secondary lymphomas presented at high‐stage (T‐cell, 94%; B‐cell, 100%). Patients with secondary lymphomas had an older mean age, more frequent B symptoms, lower serum albumin and hemoglobin, and a higher frequency of atypical lymphocytes in blood than those with primary lymphomas. In primary lymphomas, older age, lymphoma types, decreased lymphocyte counts and atypical lymphocytes in blood were poorer prognostic factors. In secondary lymphoma patients, lymphoma types, high serum lactate dehydrogenase and low hemoglobin levels predicted poorer survival. We found that the distribution of primary cutaneous lymphomas in Taiwan mirrors that of other Asian countries but shows some differences as compared with Western countries. Primary cutaneous lymphomas have a better prognosis than secondary lymphomas. Histologic classification of lymphomas highly correlated with disease presentation and prognosis.

Abstract Although vacuum‐deposited metal halide perovskite light‐emitting diodes (PeLEDs) have great promise for use in large‐area high‐color‐gamut displays, the efficiency of vacuum‐sublimed PeLEDs currently lags that of solution‐processed counterparts. In this study, highly efficient vacuum‐deposited PeLEDs are prepared through a process of optimizing the stoichiometric ratio of the sublimed precursors under high vacuum and incorporating ultrathin under‐ and upper‐layers for the perovskite emission layer (EML). In contrast to the situation in most vacuum‐deposited organic light‐emitting devices, the properties of these perovskite EMLs are highly influenced by the presence and nature of the upper‐ and presublimed materials, thereby allowing us to enhance the performance of the resulting devices. By eliminating Pb° formation and passivating defects in the perovskite EMLs, the PeLEDs achieve an outstanding external quantum efficiency (EQE) of 10.9% when applying a very smooth and flat geometry; it reaches an extraordinarily high value of 21.1% when integrating a light out‐coupling structure, breaking through the 10% EQE milestone of vacuum‐deposited PeLEDs.

Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.

For a bus transit system with peak and normal periods, we derived an approximated formulated solution, to provide a patchwork for a published paper. Based on the same numerical example of the original model, our approximated solution attains the minimum cost within 0.3% to indicate that our approximated formulated solution is a good supplementary tool to estimate the optimal solution.

Abstract Lithium plating seriously threatens the life of lithium‐ion batteries at low temperatures charging conditions, but the onboard detection and quantification of lithium plating are severely hampered by the limited available signals and volatile operating conditions in real scenarios. Herein, we propose a detection method to predict the occurrence and quantification of lithium plating under uncertain conditions by only using constant‐current curves during charging based on deep learning. A deep neural network (DNN) is developed to extract data‐driven features induced by lithium plating from the charge curves, avoiding the challenge of manual feature selection. Only using the most common voltage and current signals as inputs, the network exhibits superior adaptability and accuracy. The detection accuracy of the proposed method is 98.64%, while the quantity of the lithium plating can be accurately predicted with a root‐mean‐square error

Abstract Introduction Heat shock protein 40 (HSP40) is a vaccine adjuvant candidate for Streptococcus pneumoniae. The mechanism by which HSP40 activates the human dendritic cells (DCs) is unclear. Methods DCs were isolated from human peripheral blood and their markers (HLA‐DR, CD86, CD83, and CD80) were detected by flow cytometry. The messenger RNA (mRNA) and secretion levels of inflammary cytokines were measured after DCs were stimulated with recombinant HSP40 (rHSP40). Short hairpin RNAs were used to knock down toll‐like receptor 2 (TLR2) and TLR4. The TLR2‐ or TLR4‐deficient DCs were treated with lipopolysaccharides, rHSP40, or peptidoglycan, and then the secretion levels of tumor necrosis factor‐α (TNF‐α) and interleukin‐6 (IL‐6) were measured. Moreover, the secretion levels of TNF‐α and IL‐6 were measured after DCs were treated with mitogen‐activated protein kinase (MAPK) inhibitors including SB203580, SP600125, and U0126. In addition, the phosphorylation levels of p38 MAPK and Jun N‐terminal kinase (JNK) in DC cells were determined using western blot analysis after treatment with rHSP40 for different times. Results DCs were successfully isolated and cultured. rHSP40 treatment significantly increased cytokine levels in a concentration‐dependent manner. TLR4 deficiency, but not TLR2 deficiency, significantly suppressed the rHSP40‐induced secretion of tumor necrosis factor‐α (TNF‐α) and interleukin‐6 (IL‐6). SB203580 and SP600125 significantly inhibited the rHSP40‐induced secretion of TNF‐α and IL‐6. rHSP40 significantly enhanced the phosphorylation of p38 MAPK and JNK. Conclusion HPS40 stimulates the immune response of DCs via the p38 MAPK and JNK signaling pathways, which depend on TLR4.

Abstract Segmenting the region of interest (ROI) from medical images is a fundamental but challenging task due to the illumination change and imaging devices. Although many models based on the local region‐based active contour model (LR‐ACM) are proposed to deal with intensity inhomogeneity, it is still difficult for the global energy‐based ACM with local image information to accurately extract the ROI from medical images. To solve this problem, this study proposes a novel localised ACM by constructing the gradient information based on the probability scores from the fuzzy k‐nearest neighbour classifier. Different from the traditional LR‐ACMs, our model is based on local rather than global image statistics, where a probability score‐based edge detector is directly used for gradient information. The energy functional consists of localised region energy and an edge energy. By introducing a local characteristics function, the localised region energy with the probability‐score‐based edge information is formulated, which can make the evolution curve stop on the exact boundaries of ROI. The edge energy including the regularisation and the penalty terms is used to avoid the reinitialisation process and smooth the evolution curve during evolution. To maintain the signed distance property of the evolution curve, a novel potential function in the penalty term is designed, which can consistently control the diffusion direction of the evolution curve. Experiments on the medical images including the cardiac magnetic resonance imaging and the 3DIRCADb databases demonstrate that the proposed model is more robust and accurate to extract the ROI than the popular localised and region‐based ACMs. The code is available at: https://github.com/HuaxiangLiu/Localized_ERAC/.

Building information modeling (BIM) has recently gained popularity in the architecture, engineering, and construction industry. Specifically, BIM has been applied in facilities management (FM). However, FM-integrated BIM and MM-integrated BIM are likely to fail when BIM-FM models are not effectively updated or maintained. Thus, it is critical to focus on the management of BIM-FM models during the operation stage of buildings. While several researchers have examined BIM applications and system developments in the context of FM, there is a dearth of research on BIM management, particularly in the operation stage of sustainable buildings. Thus, an approach for BIM-FM models management is herein proposed for building projects. A BIM-FM models management (BFMM) system for owners during the operation stage was developed to ensure effective implementation for the management of BIM-FM models. Using a building in Taiwan as a case study, this study discussed and evaluated the effectiveness of the proposed BFMM system. The results reveal that the BFMM system significantly increases the efficiency of BIM-FM model management. The results of this study can provide a useful reference for those interested in adopting BIM to manage building project facilities. This study concludes by presenting the advantages and limitations of BIM-FM models as well as suggestions for future applications.

Considering the influence of confining pressure in the actual service environment of concrete in underground projects, the damage characteristics of fiber-reinforced concrete under the action of confining pressure are evaluated according to the variation of measured permeability values by means of a triaxial ultralow permeability damage test system. The tests were carried out according to the stress loading and unloading paths of 1⟶5⟶10⟶15⟶20⟶10⟶5⟶1 MPa for an axial pressure when the confining pressure value was 1 MPa and kept constant. Firstly, the damage permeability of the fiber-reinforced concrete specimens was verified to be significantly lower than that of the ordinary concrete under the same confining pressure. Secondly, by conducting single loading and unloading tests at different loading rates (0.5, 1.0, 1.5, and 3.0 MPa/s), it was confirmed that the loading rate had a significant effect on the damage cracking of the specimens, with the faster the loading rate, the more pronounced the damage characteristics. Finally, a triaxial cyclic dynamic load test with a confining pressure value of 1 MPa and an axial loading speed of 1.5 MPa/s was carried out, revealing that the damage characteristics of the test blocks changed from elastic deformation to obvious plastic deformation damage as the number of loading times increased.

The parallel seismic method is an effective method to detect the length of pile. In the paper, based on the ray theory of Snell’s law, the two methods of determining the pile length by the first transmission P-wave in the side hole are discussed and modified. The error influencing factors of both two methods are then analysed. Finally, the finite element software ABAQUS was used to simulate the inspection of pile foundations by the parallel seismic method. The numerical simulation results were compared with the results of the error calculation theory to verify the applicability of the error calculation theory. The error calculation theory was then tested and applied in an engineering example. The results show that (1) in order to reduce errors, unreasonable data points need to be removed when fitting the inspection data points and (2) the larger the pile slenderness ratio, the smaller the distance between the side holes and the pile, and the more accurate the pile length results obtained.

In order to study the effect of mineral composition on the physical properties of lateritic clays, the mineral compositions of four kinds of lateritic clay from Guangxi province in China were quantitatively analyzed by means of DTA, XRD, SEM, XRF and total chemical element analysis. On this basis, the variation law of macroscopic physical properties of lateritic clay was analyzed from the perspective of mineralogy. The results show that kaolinite and goethite are the main minerals of lateritic clays from Guangxi in China. The mineral compositions of these four samples are quantitatively and accurately analyzed by Bogue’s method. According to the study on mineral compositions of soil samples, the physical properties of lateritic clay are related to its clay minerals and cemented substances. The liquid limit (LL) or plastic limit (PL) increases with the increase of the ability and content of clay minerals to adsorb water. The lateritic clay is dominated by kaolinite minerals with low adsorption capacity, so the observed boundary water content (LL and PL) of high value of lateritic clays can be explained by the contribution of “inert water” in soil pores. The iron-bonded cement minerals (the form of existence is goethite) in lateritic clay have great influence on the liquid and plastic limits, which decrease linearly with the increase of goethite, whereas the goethite has relatively smaller effect on shrinkage characteristics. It is believed that the shrinkage characteristics of lateritic clay may also be affected by other factors.

Vehicle mass is one of the most critical parameters in the vehicle control system, based on the discrete vehicle longitudinal dynamic equation after the forward Euler approximation, non‐linear particle filter is introduced to estimate the vehicle mass intelligently, and it gains a competitive advantage that statistical characteristics of noise and uncertainties in the system are not necessary to be known or supposed in advance. As a sort of recursive, Bayesian state estimator, vehicle mass is regarded as a constant state variable to constitute the discrete state‐space equation, motor torque is selected as input signal, and the measurable vehicle speed is selected to constitute the observation equation, parameters such as rolling resistance coefficient, air drag coefficient and road slop are considered as high‐power noise and uncertainties. The performance of the proposed vehicle mass estimator is tested by several groups of load and the results demonstrate that the output of the particle filter based vehicle mass estimator can converge to the real value and keep steady.

It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

For mass rapid transit (MRT) projects, the characteristics of MRT engineering include large scopes, long durations, complex interfaces, significant requirements for management, and complicated coordination and cooperation. Owing to the complexity of these projects, the use of building information modeling (BIM) technology will be necessary and helpful to improve construction management. The increasing application of BIM in the architecture, engineering, and construction (AEC) industries has strengthened the need for effective construction management for general contractors (GC) for BIM implementation during the construction phase. Collaboration-based BIM model development for GCs typically involves relevant participants from different fields during the construction phase of MRT projects. However, collaboration-based BIM model development tasks often create numerous practical problems (e.g., reworks and errors regarding BIM model development). As the BIM manager and engineers for the general contractor who handle BIM model development are located in different departments and different office locations in MRT construction projects, a centralized platform is needed to share the latest real-time BIM model developments for related participants. Effective collaboration-based BIM model development management (CBMDM) is therefore essential for GCs to obtain accurate BIM models before the implementation of BIM-related applications. While the latest information for collaboration-based BIM model development should be updated and tracked effectively for CBMDM for related participants during the development process, in practice, GCs lack systematic approaches and information management platforms for use in CBMDM tasks. Thus, this study proposes a novel approach and develops an information system to manage and track all CBMDM tasks for GCs during the BIM development process. The purpose of this study is to develop a CBMDM system to improve the efficiency of sharing and tracking information for CBMDM activities. The research scope of this study is to propose an approach and system for CBMDM work in large-scale construction and infrastructure projects. The CBMDM system assists users by providing workflow management of CBMDM tasks for BIM engineers and managers so users can manage all BIM model development tasks effectively. The proposed system can also be utilized for other types of large-scale construction projects for CBMDM tasks. Furthermore, to understand its effectiveness, the proposed CBMDM system is applied to a case study of an MRT project in Taiwan. Finally, the major practical advantages, disadvantages, and limitations of the system are identified for infrastructure projects.

Educational management and social psychology researchers have frequently suggested that job burnout and even turnover intention of college teachers can be induced by stress, which is an inherent part of fast-changing environments and advanced educational technology. However, studies about the contingency effect remain limited. We articulate the effect of role stress and technostress by integrating organizational behaviour and educational management literature. Particularly, this study tries to investigate the moderating effect of teacher agility and leader-member exchange differentiation on suppressing burnout. According to the job demands-resources model, we proposed that the negative effect of stress on burnout depends on the degree of agility and leader-member change quality (LMXD). A study of 271 samples supports the propositions. Specifically, the adverse effect of role stress on job burnout is strengthened by both employee agility and LMXD. We further elaborate theoretical implications on educational management, social psychology, and job demands-resources model.

Purpose. To analyze the distribution of the offset between the pupil center and the coaxially sighted corneal light reflex (P-Dist), the effects of 50% and 100% angle kappa adjustments on refractive and visual quality in patients with moderate myopia were investigated. Methods. A randomly selected 254 patients (254 eyes) with moderate myopia who underwent femtosecond laser-combined LASIK were examined. During the operation, the P-Dist of the patients was recorded by the x- and y-axis eyeball-tracking adjustment program of the WaveLight Eagle Vision EX500 excimer laser system. Preoperatively and 3 months postoperatively, the WaveLight® ALLEGRO Topolyzer was used to measure the pupil size and center position, and the wavefront sensor was used to measure the wavefront aberrations. The visual function tester (OPTEC 6500) measured contrast sensitivity. Results. The average P-Dist was 0.220 ± 0.102 mm. When the P-Dist >0.220 mm, the postoperative residual cylinder was 0.29 ± 0.34 D in the group with the 50% adjustment and 0.40 ± 0.32 D in the 100% group, which was significantly higher than the 50% group P=0.036. The coma was 0.21 ± 0.17 μm in the 50% adjusted group and 0.34 ± 0.25 μm in the 100% group, which was significantly higher than that in the 50% group P=0.021. At the 1.5 c/d spatial frequency, contrast sensitivity in the adjusted 100% group was significantly lower than that in the 50% group under visual glare conditions P=0.039. Conclusion. The postoperative visual acuity and spherical equivalent were not affected in the two groups. However, when P-Dist >0.220 mm, the residual astigmatism and coma were lower in the 50% group. Individualized operations for those with moderate myopia and large-angle kappa in which 100% adjustment is chosen may not result in a better visual quality effect than 50%.

Traditional overload protection methods usually use either breakers or converters, focused on the side of power supply. However, these schemes may suffer from a slow response time or load dependence. Particularly, the facility may not be able to remain as a regular working condition when an overload occurs. To resolve this problem, the proposed feedback‐controlling resonant switching algorithm aims to provide an expected load constant current to protect the load from overload without sacrifice for a normal load operation. On the basis of a negative feedback‐control mechanism, the proposed model can detect the load current and thus generate an appropriate switch signal fast and accurately. The switch open period is decided by the model parameters and load current, and it can be set in advance by the timer. On the other hand, the switch closed period is determined by the expected load current that is independent on the load size. The switching acts at the resonant zero‐voltage point, so that no power is consumed during the switching action. The performance simulation with DC 28 V supply confirms that the proposed model can maintain a predefined load constant current for an overload protection effectively.