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Industrial visual inspection is constrained by scarce labeled defect samples and complex surface patterns in bearings, steel, and ICs, significantly hindering deep learning detection models. This review conducts a systematic comparative analysis of CycleGAN variants tailored for industrial defect data augmentation, focusing on how they address generic CycleGAN limitations (insufficient detail preservation, training instability, poor environmental adaptability). By synthesizing recent relevant studies, it evaluates four representative models: 1D Cycle-GAN and SN_1D CycleGAN for bearing fault diagnosis (1D vibration signals), and AG-CycleGAN and Enhanced-CycleGAN for steel/IC surface defect detection (e.g., scratch, crack, and pit defects). Analysis shows scenario- specific designs drive efficacy: simulation-driven transfer learning (1D Cycle-GAN) mitigates bearing data scarcity, spectral normalization (SN_1D CycleGAN) stabilizes training dynamics, attention mechanisms (AG- CycleGAN) improve defect contrast, and multi-scale mechanisms (Enhanced-CycleGAN) address scale variability problems. The review concludes industrial CycleGAN success depends on aligning innovations with scenario pain points, establishing a practical framework to guide industrial-oriented model design.

This study focuses on the challenges and opportunities faced by developing economies in a globalizing world and explores the path to innovation-driven growth. According to research, globalization presents developing economies with tremendous opportunities for market and technological exchange, but it also brings intense international competition and pressure to upgrade their industries. Case studies, such as the success of South Korea and the challenges of Argentina, demonstrate the importance of innovation- driven growth strategies. To cope with the challenges brought by globalization, it is suggested that developing economies need to enhance their innovation capabilities and economic competitiveness by strengthening international cooperation, increasing education investment, and improving the institutional environment. Through these strategies, these economies can be transformed into more efficient and sustainable development models, thereby occupying a more advantageous position in the global economy.

Student academic prediction aids educators in better understanding and supporting student learning. Educational Data Mining (EDM) and its algorithms are valuable tools for addressing this issue. While numerous models have been applied in EDM, most operate on small and medium-sized datasets. The baseline model Support Vector Machines with Sequential Minimum Optimization (SMO-SVM) excels with such datasets. However, few studies accurately and efficiently process large-scale datasets. In this study, we utilized the educational dataset from the Open University of the United Kingdom to construct prediction models for students' ＂final_result.＂ Leveraging LASSO, XGboost, Deep Neural Networks (DNN), Random Forests, and the Baseline Model Support Vector Classifier, we conducted feature selection and classification phases. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) addressed data imbalance. Experimental results indicate that the proposed Least Absolute Shrinkage and Selection Operator - Random Forest (LASSO-RF) model is effective in predicting student performance on large-scale datasets. Furthermore, it effectively identifies students at risk of failure with an accuracy of nearly 80%, surpassing baseline models such as Support Vector Classifier and LASSO - Deep Neural Network (LASSO-DNN) in both balanced and unbalanced datasets. This demonstrates Random Forest's ability to handle such data, enabling educators to provide accurate guidance to students at risk of dropout or failing to graduate.

Dendrobium officinale has a long history of being consumed as a functional food and medicinal herb for preventing and managing diseases. The phytochemical studies revealed that Dendrobium officinale contained abundant bioactive compounds, such as bibenzyls, polysaccharides, flavonoids, and alkaloids. The experimental studies showed that Dendrobium officinale and its bioactive compounds exerted multiple biological properties like antioxidant, anti-inflammatory, and immune-regulatory activities and showed various health benefits like anticancer, antidiabetes, cardiovascular protective, gastrointestinal modulatory, hepatoprotective, lung protective, and neuroprotective effects. In this review, we summarize the phytochemical studies, bioactivities, and the mechanism of actions of Dendrobium officinale, and the safety and current challenges are also discussed, which might provide new perspectives for its development of drug and functional food as well as clinical applications.

For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.

Patients with primary and bone metastatic breast cancer have significantly reduced survival and life quality. Due to the poor drug delivery efficiency of anti-metastasis therapy and the limited response rate of immunotherapy for breast cancer, effective treatment remains a formidable challenge. In this work, engineered macrophages (Oxa(IV)@ZnPc@M) carrying nanomedicine containing oxaliplatin prodrug and photosensitizer are designed as near-infrared (NIR) light-activated drug vectors, aiming to achieve enhanced chemo/photo/immunotherapy of primary and bone metastatic tumors. Oxa(IV)@ZnPc@M exhibits an anti-tumor M1 phenotype polarization and can efficiently home to primary and bone metastatic tumors. Additionally, therapeutics inside Oxa(IV)@ZnPc@M undergo NIR triggered release, which can kill primary tumors via combined chemo-photodynamic therapy and induce immunogenic cell death simultaneously. Oxa(IV)@ZnPc@M combined with anti-PD-L1 can eliminate primary and bone metastatic tumors, activate tumor-specific antitumor immune response, and improve overall survival with limited systemic toxicity. Therefore, this all-in-one macrophage provides a treatment platform for effective therapy of primary and bone metastatic tumors. Bone metastases are associated with poor prognosis in patients with breast cancer and limited therapeutic options. Here the authors exploit near-infrared light responsive macrophages for the tumor-selective delivery of oxaliplatin prodrug for chemo-photodynamic therapy of primary and bone metastatic breast cancer.

Droplet microfluidics has emerged as a breakthrough technology that is changing our comprehension of single‐cell and their associated research. By separating individual cells within tiny droplets, ranging from nanoliters to picoliters using microfluidic devices, this innovative approach has revolutionized investigations at the single‐cell level. Each of these droplets serves as a distinct experimental reaction vessel, enabling thorough exploration of cellular phenotypic variations, interactions between cells or cell‐microorganisms as well as genomic insights. This review paper presents a comprehensive overview of the current state‐of‐the‐art in droplet microfluidics, which has made single‐cell analysis a practical approach for biological research. The review delves into the technological advancements in single‐cell encapsulation techniques within droplet microfluidics, elucidating their applications in high‐throughput single‐cell screening, intercellular and cell‐microorganism interactions, and genomic analysis. Furthermore, it discusses the advantages and constraints of droplet microfluidic technology, shedding light on critical factors such as throughput and versatile integration. Lastly, the paper outlines the potential avenues for future research in this rapidly evolving field. This paper reviews the transformative impact of droplet microfluidics on single‐cell research. It highlights current advancements in encapsulation techniques, applications in high‐throughput screening, and the study of intercellular and cell‐microorganism interactions. The discussion includes the advantages, limitations, and future research directions of this innovative technology in biological investigations.

Eastern Equine Encephalitis virus (EEEV) is an alphavirus that can cause severe diseases in infected humans. The very low-density lipoprotein receptor (VLDLR) was recently identified as a receptor of EEEV. Herein, we performed cryo-electron microscopy structural and biochemistry studies on the specific interactions between EEEV and VLDLR. Our results show that VLDLR binds EEEV at three different sites A, B and C through its membrane-distal LDLR class A (LA) repeats. Site A is located in the cleft in between the E1-E2 heterodimers. Site B is located near the connecting β ribbon of E2 and is in proximity to site A, while site C is on the domain B of E2. The binding of VLDLR LAs to EEEV is in complex modes, including the LA1-2 and LA3-5 mediated two major modes. Disruption of the LA1-2 mediated binding significantly affect the cell attachment of EEEV. However, the mutation W132G of VLDLR impairs the binding of LA3, drives the switch of the binding modes, and significantly enhances the attachment of EEEV to the cell. The W132G variant of VLDLR could be identified in human genome and SNP sequences, implying that people with similar mutations in VLDLR may be highly susceptible to EEEV infection. Eastern equine encephalitis virus (EEEV) uses the very low-density lipoprotein receptor (VLDLR) to infect cells of different species. This study finds that the ecto LA repeats of VLDLR binds EEEV at three distinct sites, generating multiple different binding modes that facilitate the cross-species transmission of EEEV.

The safe and reliable supply of electric energy is an important foundation for ensuring the rapid development of my country's social economy. In recent years, with the continuous increase in the power demand of the whole society in our country, the scale of investment and construction of power grid projects has continued to expand, strengthening project cost management and improving project investment efficiency has become one of the important goals of investment management of power grid enterprises. The whole process engineering consulting service covers the complete life cycle of the power grid project, from the feasibility study analysis report and feasibility estimation preparation, project preliminary design and preliminary budget preparation, to project decision-making plan, project bidding plan, project whole-process cost management. Completion settlement and final accounts have made important contributions to the investment and construction of power transmission and transformation projects in China.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and the waning of vaccine-elicited neutralizing antibodies suggests that additional coronavirus disease 2019 (COVID-19) vaccine doses may be needed for individuals who initially received CoronaVac. We evaluated the safety and immunogenicity of the recombinant adenovirus type 5 (AD5)-vectored COVID-19 vaccine Convidecia as a heterologous booster versus those of CoronaVac as homologous booster in adults previously vaccinated with CoronaVac in an ongoing, randomized, observer-blinded, parallel-controlled phase 4 trial ( NCT04892459 ). Adults who had received two doses of CoronaVac in the past 3–6 months were vaccinated with Convidecia ( n  = 96) or CoronaVac ( n  = 102). Adults who had received one dose of CoronaVac in the past 1–3 months were also vaccinated with Convidecia ( n  = 51) or CoronaVac ( n  = 50). The co-primary endpoints were the occurrence of adverse reactions within 28 d after vaccination and geometric mean titers (GMTs) of neutralizing antibodies against live wild-type SARS-CoV-2 virus at 14 d after booster vaccination. Adverse reactions after vaccination were significantly more frequent in Convidecia recipients but were generally mild to moderate in all treatment groups. Heterologous boosting with Convidecia elicited significantly increased GMTs of neutralizing antibody against SARS-CoV-2 than homologous boosting with CoronaVac in participants who had previously received one or two doses of CoronaVac. These data suggest that heterologous boosting with Convidecia following initial vaccination with CoronaVac is safe and more immunogenic than homologous boosting. Heterologous vaccination with Convidecia, a recombinant adenovirus type 5-vectored COVID-19 vaccine, after one or two doses of CoronaVac, an inactivated SARS-CoV-2 vaccine, is more reactogenic but elicits significantly higher levels of neutralizing antibodies than homologous vaccination.