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In the wake of the largest-ever recorded outbreak of mpox in terms of magnitude and geographical spread in human history since May 2022, we innovatively developed an automated online sewage virus enrichment and concentration robot for disease tracking. Coupled with an artificial intelligence (AI) model, our research aims to estimate mpox cases based on the concentration of the monkeypox virus (MPXV) in wastewater. Our research has revealed a compelling link between the levels of MPXV in wastewater and the number of clinically confirmed mpox infections, a finding that is reinforced by the ability of our AI prediction model to forecast cases with remarkable precision, capturing 87 % of the data’s variability. However, it is worth noting that this high precision in predictions may be related to the relatively high frequency of data acquisition and the relatively non-mobile isolated environment of the hospital itself. In conclusion, this study represents a significant step forward in our ability to track and respond to mpox outbreaks. It has the potential to revolutionize public health surveillance by utilizing innovative technologies for disease surveillance and prediction.

Financial fraud cases causing serious damage to the interests of investors are not uncommon. As a result, a wide range of intelligent detection techniques are put forth to support financial institutions’ decision-making. Currently, existing methods have problems such as poor detection accuracy, slow inference speed, and weak generalization ability. Therefore, we suggest a distributed knowledge distillation architecture for financial fraud detection based on Transformer. Firstly, the multi-attention mechanism is used to give weights to the features, followed by feed-forward neural networks to extract high-level features that include relevant information, and finally neural networks are used to categorize financial fraud. Secondly, for the problem of inconsistent financial data indicators and unbalanced data distribution focused on different industries, a distributed knowledge distillation algorithm is proposed. This algorithm combines the detection knowledge of the multi-teacher network and migrates the knowledge to the student network, which detects the financial data of different industries. The final experimental results show that the proposed method outperforms other methods in terms of F1 score (92.87%), accuracy (98.98%), precision (81.48%), recall (95.45%), and AUC score (96.73%) when compared to the traditional detection methods.

Abstract Mechanical metamaterials are often designed with particular properties for specific load‐bearing functions. Alternatively, this study aims to create a class of active lattice metamaterials, dubbed self‐activated solids, that can learn desired stiffness tensors from the elastic deformations they experienced, a crucial feature to improve the performance, efficiency, and functionality of materials. Artificial adaptive matters that combine sensory, control, and actuation elements can offer appealing solutions. However, challenges still remain: The designs will rely on accurate off‐line and global computations, as well as intricate coordination among individual elements. Here, a simple online and local learning strategy is initiated based on contrastive Hebbian learning to gradually guide self‐activated solids to possess sought‐after stiffness tensors autonomously and reversibly. During learning, the bond stiffness of the active lattice varies depending only on its local strain. The numerical tests show that the self‐activated solid can not only achieve the desired bulk, shear, and coupling moduli but also manifest uni‐mode and bi‐mode extremal materials by itself after experiencing the corresponding elastic deformations. Further, the self‐activated solid can also achieve the desired time‐varying moduli when exposed to temporally different loads. The design is applicable to any lattice geometries and is resistant to damage and instabilities. The material design approach and the physical learning strategy suggested can benefit the design of autonomous materials, physical learning machines, and adaptive robots.

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments.

Abstract Sensing devices are key nodes for information detection, processing, and conversion and are widely applied in different fields such as industrial production, environmental monitoring, and defense. However, increasing demand of these devices has complicated the application scenarios and diversified the detection targets thereby promoting the continuous development of sensing materials and detection methods. In recent years, Tin+1CnTx (n = 1, 2, 3) MXenes with outstanding optical, electrical, thermal, and mechanical properties have been developed as ideal candidates of sensing materials to apply in physical, chemical, and biological sensing fields. In this review, depending on optical and electrical sensing signals, we systematically summarize the application of Tin+1CnTx in nine categories of sensors such as strain, gas, and fluorescence sensors. The excellent sensing properties of Tin+1CnTx allow its further development in emerging intelligent and bionic devices, including smart flexible devices, bionic E-skin, neural network coding and learning, bionic soft robot, as well as intelligent artificial eardrum, which are all discussed briefly in this review. Finally, we present a positive outlook on the potential future challenges and perspectives of MXene-based sensors. MXenes have shown a vigorous development momentum in sensing applications and can drive the development of an increasing number of new technologies.

Abstract Since the end of 2019, a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has deprived numerous lives worldwide, called COVID-19. Up to date, omicron is the latest variant of concern, and BA.5 is replacing the BA.2 variant to become the main subtype rampaging worldwide. These subtypes harbor an L452R mutation, which increases their transmissibility among vaccinated people. Current methods for identifying SARS-CoV-2 variants are mainly based on polymerase chain reaction (PCR) followed by gene sequencing, making time-consuming processes and expensive instrumentation indispensable. In this study, we developed a rapid and ultrasensitive electrochemical biosensor to achieve the goals of high sensitivity, the ability of distinguishing the variants, and the direct detection of RNAs from viruses simultaneously. We used electrodes made of MXene-AuNP (gold nanoparticle) composites for improved sensitivity and the CRISPR/Cas13a system for high specificity in detecting the single-base L452R mutation in RNAs and clinical samples. Our biosensor will be an excellent supplement to the RT-qPCR method enabling the early diagnosis and quick distinguishment of SARS-CoV-2 Omicron BA.5 and BA.2 variants and more potential variants that might arise in the future.

Abstract In vitro diagnostics (IVD) plays a critical role in healthcare and public health management. Magnetic digital microfluidics (MDM) perform IVD assays by manipulating droplets on an open substrate with magnetic particles. Automated IVD based on MDM could reduce the risk of accidental exposure to contagious pathogens among healthcare workers. However, it remains challenging to create a fully automated IVD platform based on the MDM technology because of a lack of effective feedback control system to ensure the successful execution of various droplet operations required for IVD. In this work, an artificial intelligence (AI)‐empowered MDM platform with image‐based real‐time feedback control is presented. The AI is trained to recognize droplets and magnetic particles, measure their size, and determine their location and relationship in real time; it shows the ability to rectify failed droplet operations based on the feedback information, a function that is unattainable by conventional MDM platforms, thereby ensuring that the entire IVD process is not interrupted due to the failure of liquid handling. We demonstrate fundamental droplet operations, which include droplet transport, particle extraction, droplet merging and droplet mixing, on the MDM platform and show how the AI rectify failed droplet operations by acting upon the feedback information. Protein quantification and antibiotic resistance detection are performed on this AI‐empowered MDM platform, and the results obtained agree well with the benchmarks. We envision that this AI‐based feedback approach will be widely adopted not only by MDM but also by other types of digital microfluidic platforms to offer precise and error‐free droplet operations for a wide range of automated IVD applications.

Abstract Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The gold standard method for the diagnosis of SARS-CoV-2 depends on quantitative reverse transcription-polymerase chain reaction till now, which is time-consuming and requires expensive instrumentation, and the confirmation of variants relies on further sequencing techniques. Herein, we first proposed a robust technique-methodology of electrochemical CRISPR sensing with the advantages of rapid, highly sensitivity and specificity for the detection of SARS-CoV-2 variant. To enhance the sensing capability, gold electrodes are uniformly decorated with electro-deposited gold nanoparticles. Using DNA template identical to SARS-CoV-2 Delta spike gene sequence as model, our biosensor exhibits excellent analytical detection limit (50 fM) and high linearity (R 2 = 0.987) over six orders of magnitude dynamic range from 100 fM to 10 nM without any nucleic-acid-amplification assays. The detection can be completed within 1 h with high stability and specificity which benefits from the CRISPR-Cas system. Furthermore, based on the wireless micro-electrochemical platform, the proposed biosensor reveals promising application ability in point-of-care testing.

Infectious diseases severely threaten public health and global biosafety. In addition to transmission through the air, pathogenic microorganisms have also been detected in environmental liquid samples, such as sewage water. Conventional biochemical detection methodologies are time-consuming and cost-ineffective, and their detection limits hinder early diagnosis. In the present study, ultrafine plasmonic fiber probes with a diameter of 125 μm are fabricated for clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas)-12a-mediated sensing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Single-stranded DNA exposed on the fiber surface is trans-cleaved by the Cas12a enzyme to release gold nanoparticles that are immobilized onto the fiber surface, causing a sharp reduction in the surface plasmon resonance (SPR) wavelength. The proposed fiber probe is virus-specific with the limit of detection of ~2,300 copies/ml, and genomic copy numbers can be reflected as shifts in wavelengths. A total of 21 sewage water samples have been examined, and the data obtained are consistent with those of quantitative polymerase chain reaction (qPCR). In addition, the Omicron variant and its mutation sites have been fast detected using S gene-specific Cas12a. This study provides an accurate and convenient approach for the real-time surveillance of microbial contamination in sewage water.

In precision optical experiments, optical devices are usually suspended by pendulum systems to isolate environmental vibration. However, a traditional pendulum vibration isolator only isolates the horizontal vibration. Here, a new type of pendulum actuator, which bases on giant magnetostrictive composite material, is proposed to achieve environmental vibration isolation. The core element in actuator is a flexible wire, which is made of a thin quartz fiber embedded in epoxy resin-bonded magnetostrictive composite material with Terfenol-D particles. The designed flexible wire is deformable in vertical direction and capable to isolate the vertical vibration. The experiments are performed to verify the performance of the actuator. The response of the actuator shows great linearity with a sensitivity of 0.78 μm/A to the driving current. It also shows a large elongation range up to 5.9 μm in the linear area. Furthermore, performance of actuator on environmental vibration isolation is tested. The peak-to-peak value of vibration along vertical direction can be reduced by 17.5 dB with no negative impact on vibration along horizontal direction, which corroborates the capability of the novel pendulum actuator to isolate the environmental vibration in precision measurement.

This paper presents a wideband low-noise amplifier (LNA) front-end with noise and distortion cancellation for high-frequency ultrasound transducers. The LNA employs a resistive shunt-feedback structure with a feedforward noise-canceling technique to accomplish both wideband impedance matching and low noise performance. A complementary CMOS topology was also developed to cancel out the second-order harmonic distortion and enhance the amplifier linearity. A high-frequency ultrasound (HFUS) and photoacoustic (PA) imaging front-end, including the proposed LNA and a variable gain amplifier (VGA), was designed and fabricated in a 180 nm CMOS process. At 80 MHz, the front-end achieves an input-referred noise density of 1.36 nV/sqrt (Hz), an input return loss (S11) of better than −16 dB, a voltage gain of 37 dB, and a total harmonic distortion (THD) of −55 dBc while dissipating a power of 37 mW, leading to a noise efficiency factor (NEF) of 2.66.

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.

Hand-eye calibration and three-dimensional space target positioning are the keys to realize the automatic grasping of the manipulator. Aiming at the problems of a difficult camera manipulator calibration and poor real-time segmentation and positioning of stacked targets in industrial environment, a rapid target positioning method of self-calibration manipulator is proposed. Firstly, the spatial spherical autonomous path planning is carried out based on the quaternion linear interpolation method to calculate spatially symmetric path trajectory of the calibrator. The RGB-D camera mounted at the end of the manipulator is used to obtain multiple groups of RGB and depth images of the calibration plate. Combined with the position and attitude of the manipulator end, the internal and external parameters of the camera and the hand-eye conversion matrix are automatically calibrated. Then, based on KD tree algorithm, the holes in the point cloud are extracted to plan the shooting pose of the complementary image, and the target object is photographed from multiple symmetric angles. Combined with the iterative shooting pose of the manipulator, the rapid registration of the point cloud is realized and the complete outer surface model of the target object is obtained. Finally, the improved double pyramid feature fusion depth image is used to segment the RGB image through Mask R-CNN and mapped to the point cloud space, to achieve fast target segmentation of end-to-end 3D point cloud. The experimental results show that the eye-in-hand manipulator system can be self-calibrated to greatly simplify the calibration process and achieve the calibration accuracy of the traditional calibration method. The average error in each direction of the calibration result is less than 2 mm, which can achieve the acquisition accuracy of the vision sensor. It can also register and reconstruct point clouds for complex scenes in 1 s. The improved Mask-RCNN increases the segmentation accuracy of stacking objects by 8%. Compared with the physical error of hardware, the positioning error is no more than 0.89% and can meet the requirements of practical applications.

Soil erosion is one of the most serious land degradation problems and the primary environmental issue in the Loess Plateau region of China. To identify the critical sub-basins and assess the impacts of land use change and climatic variability change on soil loss, this study tested the feasibility of the Soil and Water Assessment Tool (SWAT) model on sediment load simulation in the upper Sang-kan (USK) River basin. Based on a land use map of 1986, the SWAT model (Scenario 0) was calibrated at a monthly step using climate data from 1979 to 1985; then it was validated using climate data from 1986 to 1990. The monthly sediment simulation results indicated that the model did not work as well as streamflow simulation, indicating lower NS (Nash-Sutcliffe Efficiency) and r2 values of 0.68, 0.69 and 0.61, 0.59 for the calibration period and validation period, respectively. This model could perform well under relatively low rainfall events, but it underestimated or overestimated the sediment load under high rainfall events. Comparing the results of scenarios with different land use maps (year of 1986 vs. 2012) and climate periods (1979–1990 vs. 2001–2012), it can be concluded that: (i) extreme and severe erosion almost always happened in FRST (forest land) and RNGE (grassland) in the hilly area; (ii) long-term traditional farming weakens the soil anti-erosion capability of land, leading to higher soil erosion, while forest can improve the soil structure, enhance the soil anti-erosion capability, and reduce soil erosion; (iii) both land use change and climatic change have led to the sediment yield decrease in the USK basin. Acting as the major influencing factor, land use change contributed to about 64.9% of the sediment yield reduction in the USK river basin.