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The emerging high-bandwidth services of 6G, such as high-definition video transmission and real-time interaction, have promoted the progress of the fiber optic access network industry, driving its development towards the next-generation PON with higher speed and larger system capacity. In response to the future requirements of 200 Gb/s/λ PON for both 20 km and 30 km downlink transmission scenarios, this paper proposes a Simplified Volterra Equalization (SVLE) scheme based on Nyquist PAM4 single-sideband modulation direct detection (SSBM-DD) scheme. In order to verify its advantages, the IQ-modulated Kramers–Kronig reception (KK) scheme is introduced for comparison. Simulation validation platforms for two schemes are conducted, and the performance comparison of the SVLE and KK schemes is carried out. In both, the impact of the carrier signal power ratio (CSPR) on receiver sensitivity, the influence of input optical power on power budget and receiver sensitivity, and the tolerance of receiver sensitivity to the linewidth of the DFB laser are investigated in the simulation. Finally, a comprehensive comparison of the two schemes is presented in terms of system performance, cost, and DSP complexity. In the 20 km downlink transmission scenario, the SVLE scheme outperforms the KK scheme by 4.9 dB in terms of power budget. The total number of multiplications of the SVLE scheme is 37, while that of the KK scheme is 4358. Therefore, the DSP complexity of the SVLE scheme is much lower than that of the KK scheme. The results of the comparison demonstrate that, in downlink transmission scenarios, the SVLE scheme is more suitable than the KK scheme as it exhibits a higher power budget, lower DSP complexity, and lower cost. Consequently, the proposed SVLE scheme could be a highly promising solution for future ultra-high-speed PON downlink transmission.

Phthalate acid esters (PAEs) are one of the most widely used plasticizers globally, extensively employed in various decoration materials. However, studies on the impact of these materials on indoor environmental PAE pollution and their effects on human health are limited. In this study, forty dust samples were collected from four types of stores specializing in decoration materials (flooring, furniture boards, wall coverings, and household articles). The levels, sources, exposure doses, and potential health risks of PAEs in dust from decoration material stores were assessed. The total concentrations of Σ9PAE (the sum of nine PAEs) in dust from all decoration-material stores ranged from 46,100 ng/g to 695,000 ng/g, with a median concentration of 146,000 ng/g. DMP, DEP, DBP, and DEHP were identified as the predominant components. Among all stores, furniture board stores exhibited the highest Σ9PAE (159,000 ng/g, median value), while flooring stores exhibited the lowest (95,300 ng/g). Principal component analysis (PCA) showed that decoration materials are important sources of PAEs in the indoor environment. The estimated daily intakes of PAEs through non-dietary dust ingestion and dermal-absorption pathways among staff in various decoration-material stores were 60.0 and 0.470 ng/kg-bw/day (flooring stores), 113 and 0.780 ng/kg-bw/day (furniture board stores), 102 and 0.510 ng/kg-bw/day (wall covering stores), and 114 and 0.710 ng/kg-bw/day (household article stores). Particularly, staff in wall-covering and furniture-board stores exhibited relatively higher exposure doses of DEHP. Risk assessment indicated that although certain PAEs posed potential health risks, the exposure levels for staff in decoration material stores were within acceptable limits. However, staff in wall covering stores exhibited relatively higher risks, necessitating targeted risk-management strategies. This study provides new insights into understanding the risk associated with PAEs in indoor environments.

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications.

In situ electroporation, a non-invasive technique for enhancing the permeability of cell membranes, has emerged as a powerful tool for intracellular delivery and manipulation. This method allows for the precise introduction of therapeutic agents, such as nucleic acids, drugs, and proteins, directly into target cells within their native tissue environment. Herein, we introduce an innovative electroporation strategy that employs a Janus particle (JP)-based microelectrode to generate a localized and controllable electric field within a microfluidic chip. The microfluidic device is engineered with an indium tin oxide (ITO)-sandwiched microchannel, where the electric field is applied, and suspended JP microelectrodes that induce a stronger localized electric field. The corresponding simulation model is developed to better understand the dynamic electroporation process. Numerical simulations for both single-cell and chain-assembled cell electroporation have been successfully conducted. The effects of various parameters, including pulse voltage, duration medium conductivity, and radius of Janus microelectrode, on cell membrane permeabilization are systematically investigated. Our findings indicate that the enhanced electric intensity near the poles of the JP microelectrode significantly contributes to the electroporation process. In addition, the distribution for both transmembrane voltage and the resultant nanopores can be altered by conveniently adjusting the relative position of the JP microelectrode, demonstrating a selective and in situ electroporation technique for spatial control over the delivery area. Moreover, the obtained differences in the distribution of electroporation between chain cells can offer insightful directives for the electroporation of tissues or cell populations, enabling the precise and targeted modulation of specific cell populations. As a proof of concept, this work can provide a robust alternative technique for the study of complex and personalized cellular processes.

Safe and eco-friendly preservatives are crucial to preventing food spoilage and illnesses, as foodborne diseases caused by pathogens result in approximately 600 million cases of illness and 420,000 deaths annually. ε-Poly-L-lysine (ε-PL) is a novel food preservative widely used in many countries. However, its commercial application has been hindered by high costs and low production. In this study, ε-PL’s biosynthetic capacity was enhanced in Streptomyces albulus WG608 through metabolic engineering guided by multi-omics techniques. Based on transcriptome and metabolome data, differentially expressed genes (fold change >2 or p < 0.05) and differentially expressed metabolites (fold change >1.2 or S. albulus WME10, achieved an ε-PL production rate of 77.16 g/L in a 5 L bioreactor, which is the highest reported ε-PL production to date. These results suggest that the integrative analysis of the transcriptome and metabolome can facilitate the identification of key pathways and genetic elements affecting ε-PL synthesis, guiding further metabolic engineering and thus significantly enhancing ε-PL production. The method presented in this study could be applicable to other valuable natural antibacterial agents.

Dopamine (DA) is an important catecholamine neurotransmitter in the mammalian central nervous system that affects many physiological functions. Hence, a highly sensitive and selective sensing platform is necessary for quantification of DA in the human body. In this study, ternary transition metal tellurides of CoNiTe2 were successfully synthesized using the hydrothermal method. The proposed CoNiTe2 nanomaterials were dispersed well in Nafion to form a well-dispersed suspension and, when dropped on a glassy carbon electrode (GCE) as the working electrode (CoNiTe2/Nafion/GCE) for electrochemical non-enzymatic DA sensing, displayed excellent electrocatalytic activity for dopamine electrooxidation. The morphology and physical/chemical properties of CoNiTe2 nanomaterials were characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). In order to obtain the best electrochemical response to DA from the fabricated CoNiTe2/Nafion/GCE, the experimental conditions of electrochemical sensing, including the CoNiTe2 loading amounts and pH values of the phosphate buffer solution (PBS), were explored to achieve the best electrochemical sensing performance. Under optimal conditions (2 mg of CoNiTe2 and pH 6.0 of PBS), the fabricated CoNiTe2/Nafion/GCE showed excellent electrocatalytic activity of DA electrooxidation. The CoNiTe2/Nafion/GCE sensing platform demonstrated excellent electrochemical performance owing to the optimal structural and electronic characteristics originating from the synergistic interactions of bimetallic Co and Ni, the low electronegativity of Te atoms, and the unique morphology of the CoNiTe2 nanorod. It exhibited a wide linear range from 0.05 to 100 μM, a high sensitivity of 1.2880 µA µM−1 cm−2, and a low limit of detection of 0.0380 µM, as well as acceptable selectivity for DA sensing. Therefore, the proposed CoNiTe2/Nafion/GCE could be considered a promising electrode material for electrochemical non-enzymatic DA sensing.

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model.