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We present CFRCTop, a MATLAB implementation for topology optimization of continuous fiber-reinforced composite structures. The implementation includes density and fiber-orientation filtering, finite element analysis, sensitivity analysis, design variable updating, verification of optimality of fiber orientations, and visualization of results. This code is built upon the well-known topology optimization code top88. The template stiffness matrices (TSMs)-based method is employed for efficient finite element analysis and sensitivity analysis. The density and fiber-orientation variables are updated separately. Visualization of spatially varying fiber orientations is provided. Extensions to solving various problems are also discussed. Computational performance and scalability are studied to showcase the high efficiency of this implementation. CFRCTop is intended for students and newcomers in the field of topology optimization.

Dispersity or molar mass distribution ( Ð M ) profoundly influences polymer properties, but its control remains a challenge, especially for ring-opening polymerization (ROP). We report here a simple approach to tailor Ð M of aliphatic polyethers, e.g., poly(ethylene oxide) and poly(propylene oxide), by introducing an editable chain-transfer reaction in organocatalyzed ROP of epoxides. Trifluoroacetate, acting as the chain-transfer agent (CTA), brings about non-uniform growth of polyether chains while ensuring complete initiation efficiency and readily removable CTA residue. Triple control over Ð M (1.05–2.00), molar mass (3.6–17.7 kg mol −1 ), and end group is achieved simply by regulating the feed ratio, conforming well with the theoretical modeling. Experimental and calculation results reveal the orthogonality between chain growth and transesterification which is key to the three-modular ROP for independent Ð M control. Molar mass distribution profoundly influences polymer properties, but its control remains challenging. Here, the authors report a simple approach to tailor the dispersity of aliphatic polyethers by introducing an editable chain-transfer reaction in organocatalyzed ring opening polymerization of epoxides.

The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing in a G2P robotic mobile fulfillment system with multiple picking stations. To model this complex problem, we develop a mathematical formulation and propose a two-phase heuristic algorithm that combines simulated annealing, genetic algorithms, and beam search for efficient solution. In addition, we explore and compare two order allocation strategies—order similarity and order association—across a range of operational scenarios. Extensive computational experiments and sensitivity analyses demonstrate the effectiveness of the proposed approach and provide insights into how strategic order allocation can significantly improve picking efficiency. Computational experiments on small-scale instances show that our algorithm achieves near-optimal solutions with up to 93.3% reduction in computation time compared to exact optimization for small cases. In large-scale scenarios, the order similarity strategy reduces rack movements by up to 44.8% and the order association strategy by up to 33.5% relative to a first-come, first-served baseline. Sensitivity analysis reveals that the association strategy performs best with fewer picking stations and lower rack capacity, whereas the similarity strategy is superior in systems with more stations or higher rack capacity. The findings offer practical guidance for the design and operation of intelligent warehousing systems.

A QuEChERS-UPLC-MS/MS method was developed and validated for simultaneous determination of six pesticides (chlorpyrifos, malathion, carbofuran, thiodicarb, deltamethrin, and lambda-cyhalothrin) in dairy products. Samples (n=50), collected from a supermarket in Chongqing, China, were extracted with 1% acetic acid–acetonitrile, purified using PSA, and quantified by external-standard UPLC-MS/MS. All pesticides showed excellent linearity (0.001–0.50 mg/L, R² > 0.99), with LOQs of 0.002 mg/kg. Average recoveries at four spiking levels (0.002, 0.01, 0.1, 1.0 mg/kg) ranged from 89% to 110% (RSD 0.5–9.5%, n=5). Analysis of real samples revealed residues below China’s MRLs (GB 2763–2021), confirming regulatory compliance. The method meets analytical requirements for dairy matrices and offers a reliable framework adaptable to other international standards.

In this study, we investigate the seroprevalence of SARS-CoV-2 antibodies among blood donors in the cities of Wuhan, Shenzhen, and Shijiazhuang in China. From January to April 2020, 38,144 healthy blood donors in the three cities were tested for total antibody against SARS-CoV-2 followed by pseudotype SARS-CoV-2 neutralization tests, IgG, and IgM antibody testing. Finally, a total of 398 donors were confirmed positive. The age- and sex-standardized SARS-CoV-2 seroprevalence among 18–60 year-old adults (18–65 year-old in Shenzhen) was 2.66% (95% CI: 2.24%–3.07%) in Wuhan, 0.033% (95% CI: 0.0029%–0.267%) in Shenzhen, and 0.0028% (95% CI: 0.0001%–0.158%) in Shijiazhuang, respectively. Female sex and older-age were identified to be independent risk factors for SARS-CoV-2 seropositivity among blood donors in Wuhan. As most of the population of China remained uninfected during the early wave of the COVID-19 pandemic, effective public health measures are still certainly required to block viral spread before a vaccine is widely available. Here, the authors determine seroprevalence of antibodies to SARS-CoV-2 in healthy blood donors in the cities of Wuhan, Shenzhen, and Shijiazhuang in China between January and April 2020. The age- and sex-standardized SARS-CoV-2 seroprevalence among 18–60 year-old adults is, with 2.66%, the highest in Wuhan.

Significant advances have been made in the molecular assays used for the detection of human immunodeficiency virus (HIV), which are crucial in preventing HIV transmission and monitoring disease progression. Molecular assays for HIV diagnosis have now reached a high degree of specificity, sensitivity and reproducibility, and have less operator involvement to minimize risk of contamination. Furthermore, analyses have been developed for the characterization of host gene polymorphisms and host responses to better identify and monitor HIV-1 infections in the clinic. Currently, molecular technologies including HIV quantitative and qualitative assays are mainly based on the polymerase chain reaction (PCR), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), and branched chain (b) DNA methods and widely used for HIV detection and characterization, such as blood screening, point-of-care testing (POCT), pediatric diagnosis, acute HIV infection (AHI), HIV drug resistance testing, antiretroviral (AR) susceptibility testing, host genome polymorphism testing, and host response analysis. This review summarizes the development and the potential utility of molecular assays used to detect and characterize HIV infections.

One challenge of solving topology optimization problems under harmonic excitation is that usually a large number of displacement and adjoint displacement vectors need to be computed at each iteration step. This work thus proposes an adaptive hybrid expansion method (AHEM) for efficient frequency response analysis even when a large number of excitation frequencies are involved. Assuming Rayleigh damping, a hybrid expansion for the displacement vector is developed, where the contributions of the lower-order modes and higher-order modes are given by the modal superposition and Neumann expansion, respectively. In addition, a simple (yet accurate) expression is derived for the residual error of the approximate displacement vector provided by the truncated hybrid expansion. The key factors affecting the convergence rate of the truncated hybrid expansion series are uncovered. Based on the Strum sequence, the AHEM can adaptively determine the number of lower-order eigenfrequencies and eigenmodes that need to be computed, while the number of terms that need to be preserved in the truncated Neumann expansion can be determined according to the given error tolerance. The performance of the proposed AHEM and its effectiveness for solving topology optimization problems under harmonic excitation are demonstrated by examining several 2D and 3D numerical examples. The non-symmetry of the optimum topologies for frequency response problems is also presented and discussed.

Electrical heating elements, which are widely used to heat the vapor cell of ultrasensitive atomic magnetometers, inevitably produce a magnetic field interference. In this paper, we propose a novel measurement method of the amplitude of electrical-heating-induced magnetic field for an atomic magnetometer. In contrast to conventional methods, this method can be implemented in the atomic magnetometer itself without the need for extra magnetometers. It can distinguish between different sources of magnetic fields sensed by the atomic magnetometer, and measure the three-axis components of the magnetic field generated by the electrical heater and the temperature sensor. The experimental results demonstrate that the measurement uncertainty of the heater’s magnetic field is less than 0.2 nT along the x-axis, 1.0 nT along the y-axis, and 0.4 nT along the z-axis. The measurement uncertainty of the temperature sensor’s magnetic field is less than 0.02 nT along all three axes. This method has the advantage of measuring the in-situ magnetic field, so it is especially suitable for miniaturized and chip-scale atomic magnetometers, where the cell is extremely small and in close proximity to the heater and the temperature sensor.

Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and illness outbreaks. Here, an unsupervised learning–assisted and surface acoustic wave–interdigital transducer‐driven nano‐lens holography biosensing platform is developed for the ultrasensitive and amplification‐free detection of viable bacteria. The monitoring device integrated with the nano‐lens effect can achieve the holographic imaging of polystyrene microsphere probes in an ultra‐wide field of view (∽28.28 mm2), with a sensitivity limit of as low as 99 nm. A lightweight unsupervised learning hologram processing algorithm considerably reduces training time and computing hardware requirements, without requiring datasets with manual labels. By combining phage–mediated viable bacterial DNA extraction and enhanced CRISPR–Cas12a systems, this strategy successfully achieves the ultrasensitive detection of viable Salmonella in various real samples, demonstrating enhanced accuracy validated with the qPCR benchmark method. This approach has a low cost (∽$500) and is rapid (∽1 h) and highly sensitive (∽38 CFU mL−1), allowing for the amplification‐free detection of viable bacteria and emerging as a powerful tool for food safety inspection and clinical diagnosis. This study presents an unsupervised‐learning‐assisted, surface acoustic wave (SAW)–interdigital transducer (IDT)‐driven nano‐lens holography biosensing platform. By combining phage‐mediated viable bacterial DNA extraction and the enhanced CRISPR–Cas12a system, this approach demonstrates great potential for the sensitive and accurate quantification of viable pathogenic bacteria in complex sample matrices, offering a powerful tool for food safety inspection and clinical diagnosis.

The objectives are to explore the effect of a random forest algorithm on the state prediction and fault classification of smart meters, so that the smart meters can run more stably. Based on the principle of the random forest algorithm and Light Gradient Boosting Machine (LightGBM) algorithm, its theoretical basis and application are deeply analyzed and improved. An improved fault classification and state prediction model of smart meters is designed based on a random forest-improved LightGBM algorithm. The built model algorithm is evaluated by utilizing public data sets. The results show that, by preprocessing the fault data set of smart meters, 8 fault feature types including fault type, working time, and fault month are obtained. When the improved LightGBM algorithm is trained based on random forest, the average accuracy of the algorithm is 67.65%, the average recall rate is 64.11%, and the average F1 value is 65.73%. Meanwhile, the difference between the algorithm and the random forest algorithm and the Correlation-based Feature Selection (CFS) algorithm is studied. Therefore, the prediction accuracy and fault classification of the constructed model algorithm for smart meters are higher than those of the other two algorithms. It indicates that the algorithm has a good application effect and high practical application value and can provide a scientific and useful reference for the follow-up research of smart meters.