Online estimating weight of white Pekin duck carcass by computer vision.

The increasing consumption of ducks and chickens in China demands characterizing carcasses of domestic birds efficiently. Most existing methods, however, were developed for characterizing carcasses of pigs or cattle. Here, we developed a noncontact and automated weighing method for duck carcasses hanging on a production line. A 2D camera with its facilitating parts recorded the moving duck carcasses on the production line. To estimate the weight of carcasses, the images in the acquired dataset were modeled by a convolution neuron network (CNN). This model was trained and evaluated using 10-fold cross-validation. The model estimated the weight of duck carcasses precisely with a mean abstract deviation (MAD) of 58.8 grams and a mean relative error (MRE) of 2.15% in the testing dataset. Compared with 2 widely used methods, pixel area linear regression and the artificial neural network (ANN) model, our model decreases the estimation error MAD by 64.7 grams (52.4%) and 48.2 grams (45.0%). We release the dataset and code at https://github.com/RuoyuChen10/Image_weighing.

A Hydrogel-Based Self-Sensing Underwater Actuator.

Soft robots made of hydrogels are suited for underwater exploration due to their biocompatibility and compliancy. Yet, reaching high dexterity and actuation force for hydrogel-based actuators is challenging. Meanwhile, real-time proprioception is critical for feedback control. Moreover, sensor integration to mimic living organisms remains problematic. To address these challenges, we introduce a hydrogel actuator driven by hydraulic force with a fast response (time constant 0.83 s). The highly stretchable and conductive hydrogel (1400% strain) is molded into the PneuNet shape, and two of them are further assembled symmetrically to actuate bi-directionally. Then, we demonstrate its bionic application for underwater swimming, showing 2 cm/s (0.19 BL/s) speed. Inspired by biological neuromuscular systems' sensory motion, which unifies the sensing and actuation in a single unit, we explore the hydrogel actuator's self-sensing capacity utilizing strain-induced resistance change. The results show that the soft actuator's proprioception can monitor the undulation in real-time with a sensitivity of 0.2%/degree. Furthermore, we take a finite-element method and first-order differential equations to model the actuator's bending in response to pressure. We show that such a model can precisely predict the robot's bending response over a range of pressures. With the self-sensing actuator and the proposed model, we expect the new approach can lead to future soft robots for underwater exploration with feedback control, and the underlying mechanism of the undulation control might offer significant insights for biomimetic research.

A Review on Rail Defect Detection Systems Based on Wireless Sensors.

Small defects on the rails develop fast under the continuous load of passing trains, and this may lead to train derailment and other disasters. In recent years, many types of wireless sensor systems have been developed for rail defect detection. However, there has been a lack of comprehensive reviews on the working principles, functions, and trade-offs of these wireless sensor systems. Therefore, we provide in this paper a systematic review of recent studies on wireless sensor-based rail defect detection systems from three different perspectives: sensing principles, wireless networks, and power supply. We analyzed and compared six sensing methods to discuss their detection accuracy, detectable types of defects, and their detection efficiency. For wireless networks, we analyzed and compared their application scenarios, the advantages and disadvantages of different network topologies, and the capabilities of different transmission media. From the perspective of power supply, we analyzed and compared different power supply modules in terms of installation and energy harvesting methods, and the amount of energy they can supply. Finally, we offered three suggestions that may inspire the future development of wireless sensor-based rail defect detection systems.

Physical Cytometry: Detecting Mass-Related Properties of Single Cells.

The physical properties of a single cell, such as mass, volume, and density, are important indications of the cell's metabolic characteristics and homeostasis. Precise measurement of a single cell's mass has long been a challenge due to its minute size. It is only in the past 10 years that a variety of instruments for measuring living cellular mass have emerged with the development of MEMS, microfluidics, and optics technologies. In this review, we discuss the current developments of physical cytometry for quantifying mass-related physical properties of single cells, highlighting the working principle, applications, and unique merits. The review mainly covers these measurement methods: single-cell mass cytometry, levitation image cytometry, suspended microchannel resonator, phase-shifting interferometry, and opto-electrokinetics cell manipulation. Comparisons are made between these methods in terms of throughput, content, invasiveness, compatibility, and precision. Some typical applications of these methods in pathological diagnosis, drug efficacy evaluation, disease treatment, and other related fields are also discussed in this work.

Measurement methods of single cell drug response.

In the last decades, a wide multitude of research activity has been focused on the development of new drugs, and devoted to overcome the challenges of high cost and low efficiency in drug evaluation. The measurement of drug response at the single cell level is a quicker, more direct and more accurate way to reflect drug efficacy, which can shorten the drug development period and reduce research costs. Therefore, the single cell drug response (SCDR) measurement technology has aroused extensive attention from researchers, and has become a hot topic in the fields of drug research and cell biology. Recent years have seen the emergence of various SCDR measurement technologies that feature different working principles and different levels of measurement performance. To better examine, compare and summarize the characteristics and functions of these technologies, we select signal-to-noise ratio, throughput, content, invasion, and device complexity as the criteria to evaluate them from the drug efficacy perspective. This review aims to highlight sixteen kinds of SCDR measurement technologies, including patch-clamp technique, live-cell interferometry, capillary electrophoresis, secondary ion mass spectrometry, and more, and report widespread representative examples of SCDR measurement the recent approaches for over the past forty years. Based on their reaction principles, these technologies are classified into four categories: electrical, optical, electrochemical, and mass spectrometry, and a detailed comparison is made between them. After in-depth understanding of these technologies, it is expected to improve or integrate these technologies to propose better SCDR measurement strategies, and explore methods in new drug development and screening, as well as disease diagnosis and treatment.

AI powered electrochemical multi-component detection of insulin and glucose in serum.

Multi-component detection of insulin and glucose in serum is of great importance and urgently needed in clinical diagnosis and treatment due to its economy and practicability. However, insulin and glucose can hardly be determined by traditional electrochemical detection methods. Their mixed oxidation currents and rare involvement in the reaction process make it difficult to decouple them. In this study, AI algorithms are introduced to power the electrochemical method to conquer this problem. First, the current curves of insulin, glucose, and their mixed solution are obtained using cyclic voltammetry. Then, seven features of the cyclic voltammetry curve are extracted as characteristic values for detecting the concentrations of insulin and glucose. Finally, after training using machine learning algorithms, insulin and glucose concentrations are decoupled and regressed accurately. The entire detection process only takes three minutes. It can detect insulin at the pmol level and glucose at the mmol level, which meets the basic clinical requirements. The average relative error in predicting insulin concentrations is around 6.515%, and that in predicting glucose concentrations is around 4.36%. To verify the performance and effectiveness of the proposed method, it is used to determine the concentrations of insulin and glucose in fetal bovine serum and real clinical serum samples. The results are satisfactory, demonstrating that the method can meet basic clinical needs. This multi-component testing system delivers acceptable detect limit and accuracy and has the merits of low cost and high efficiency, holding great potential for use in clinical diagnosis.

Microliter Sample Insulin Detection Using a Screen-Printed Electrode Modified by Nickel Hydroxide.

The monitoring of insulin, which is the only hormone that helps regulate blood glucose levels in the body, plays a key role in the diagnosis and treatment of diabetes. However, most techniques today involve complicated electrode fabrication and testing processes, which are time-consuming and costly, and require a relatively large volume of sample. To overcome these drawbacks, we present here a low-cost insulin detection method based on a screen-printed electrode (SPE) modified by nickel hydroxide (Ni(OH)2). This novel method only requires 300 µL of insulin sample, and the time it takes for electrode preparation is about 12 times shorter than traditional electrode fabrication methods such as coating and sol-gel methods. The electrochemical behaviors of the Ni(OH)2-coated SPE (NSPE) sensing area in insulin aqueous solutions are studied using cyclic voltammetry, amperometric i-t curves, and electrochemical impedance spectroscopy. The results demonstrate that the NSPE sensing surface has excellent detection properties, such as a high sensitivity of 15.3 µA·µM-1 and a low detection limit of 138 nM. It takes a short time (∼10 min) to prepare the NSPE sensing surface, and only two drops (∼300 µL) of insulin samples are required in the detection process. Moreover, the selectivity of this method for insulin detection is verified by detecting mixtures of insulin and ascorbic acid or bovine hemoglobin. Finally, we discuss the potential clinical applications of this method by detecting various concentrations of insulin in human serum.

Nanocalorimeters for biomolecular analysis and cell metabolism monitoring.

Nanocalorimeters, or microfabricated calorimeters, provide a promising way to characterize the thermal process of biological processes, such as biomolecule interactions and cellular metabolic activities. They enabled miniaturized heat measurement onto a chip device with potential benefits including low sample consumption, low cost, portability, and high throughput. Over the past few decades, researchers have tried to improve nanocalorimeters' performance, in terms of sensitivity, accuracy, and detection resolution, by exploring different sensing methods, thermal insulation techniques, and liquid handling methods. The enhanced devices resulted in new applications in recent years, and here we have summarized the performance parameters and applications based on categories. Finally, we have listed the current technical difficulties in nanocalorimeter research and hope for future solutions to overcome them.

A Review on Microscopic Visual Servoing for Micromanipulation Systems: Applications in Micromanufacturing, Biological Injection, and Nanosensor Assembly.

Micromanipulation is an interdisciplinary technology that integrates advanced knowledge of microscale/nanoscale science, mechanical engineering, electronic engineering, and control engineering. Over the past two decades, it has been widely applied in the fields of MEMS (microelectromechanical systems), bioengineering, and microdevice integration and manufacturing. Microvision servoing is the basic tool for enabling the automatic and precise micromanipulation of microscale/nanoscale entities. However, there are still many problems surrounding microvision servoing in theory and the application of this technology's micromanipulation processes. This paper summarizes the research, development status, and practical applications of critical components of microvision servoing for micromanipulation, including geometric calibration, autofocus techniques, depth information, and visual servoing control. Suggestions for guiding future innovation and development in this field are also provided in this review.

Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration.

With its simple theory and strong implementation, extreme learning machine (ELM) becomes a competitive single hidden layer feed forward networks for nonlinear multivariate calibration in chemometrics. To improve the generalization and robustness of ELM further, stacked generalization is introduced into ELM to construct a modified ELM model called stacked ensemble ELM (SE-ELM). The SE-ELM is to create a set of sub-models by applying ELM repeatedly to different sub-regions of the spectra and then combine the predictions of those sub-models according to a weighting strategy. Three different weighting strategies are explored to implement the proposed SE-ELM, such as the Winner-takes-all (WTA) weighting strategy, the constraint non-negative least squares (CNNLS) weighing strategy and the partial least squares (PLS) weighting strategy. Furthermore, PLS is suggested to be selected as the optimal weighting method that can handle the multi-colinearity among the predictions yielded by all the sub-models. The experimental assessment of the three SE-ELM models with different weighting strategies is carried out on six real spectroscopic datasets and compared with ELM, back-propagation neural network (BPNN) and Radial basis function neural network (RBFNN), statistically tested by the Wilcoxon signed rank test. The obtained experimental results suggest that, in general, all the SE-ELM models are more robust and more accurate than traditional ELM. In particular, the proposed PLS-based weighting strategy is at least statistically not worse than, and frequently better than the other two weighting strategies, BPNN, and RBFNN.

A Review of Automated Microinjection of Zebrafish Embryos.

Cell microinjection is a technique of precise delivery of substances into cells and is widely used for studying cell transfection, signaling pathways, and organelle functions. Microinjection of the embryos of zebrafish, the third most important animal model, has become a very useful technique in bioscience. However, factors such as the small cell size, high cell deformation tendency, and transparent zebrafish embryo membrane make the microinjection process difficult. Furthermore, this process has strict, specific requirements, such as chorion softening, avoiding contacting the first polar body, and high-precision detection. Therefore, highly accurate control and detection platforms are critical for achieving the automated microinjection of zebrafish embryos. This article reviews the latest technologies and methods used in the automated microinjection of zebrafish embryos and provides a detailed description of the current developments and applications of robotic microinjection systems. The review covers key areas related to automated embryo injection, including cell searching and location, cell position and posture adjustment, microscopic visual servoing control, sensors, actuators, puncturing mechanisms, and microinjection.

Determination of the Three-Dimensional Rate of Cancer Cell Rotation in an Optically-Induced Electrokinetics Chip Using an Optical Flow Algorithm.

Our group has reported that Melan-A cells and lymphocytes undergo self-rotation in a homogeneous AC electric field, and found that the rotation velocity of these cells is a key indicator to characterize their physical properties. However, the determination of the rotation properties of a cell by human eyes is both gruesome and time consuming, and not always accurate. In this paper, a method is presented to more accurately determine the 3D cell rotation velocity and axis from a 2D image sequence captured by a single camera. Using the optical flow method, we obtained the 2D motion field data from the image sequence and back-project it onto a 3D sphere model, and then the rotation axis and velocity of the cell were calculated. After testing the algorithm on animated image sequences, experiments were also performed on image sequences of real rotating cells. All of these results indicate that this method is accurate, practical, and useful. Furthermore, the method presented there can also be used to determine the 3D rotation velocity of other types of spherical objects that are commonly used in microfluidic applications, such as beads and microparticles.

A fast autofocus sharpness function of microvision system based on the Robert function and Gauss fitting.

For the microvision system, a new autofocus evaluation function based on the Robert function is proposed by increasing the threshold value. Compared with the traditional evaluation function, the new focus function reduces the local extreme value and increases the steepness of the focusing curve. According to the characteristics of the focusing evaluation function, the focus curve can be divided into two stages: the gentle area and the steep area. In the gentle area, there will be set a large step-length to realize the fast search. In the steep area, the data will be fitted by Gauss method, and on the basis of the fitting results, the motor of microvision system was directly driven to achieve the focal plane and this method has been improved in real-time and accuracy.

[A Surface Plasmon Micro-Ring Sensor Suitable for Humidity Sensing].

Temperature is a very important parameter in scientific research, production and life. Almost all the properties of materials are related to temperature. The precise measurement of the temperature is a very important task, so the temperature sensor is widely used as a core part in the temperature measuring instrument. A novel surface plasmon micro-ring sensor suitable for humidity sensing is presented in this paper. The sensor uses a multi-layered surface plasmon waveguide structure and choosing Polyimide (Polyimide, PI) as the moisture material. We get the transfer function of surface plasmon micro-ring sensor by using transfer matrix method. Refractive indexes of Polyimide and the multilayer waveguide structure change as environment relative humidity changes, thus leading to an obvious peak drift of output spectrum. The paper mainly discusses the influence of the changes of the refractive index of humidity-sensing parts on the output spectrum, and the transmission characteristics of multilayer waveguide structure. Through the finite element method and the theoretical simulation of Matlab, We can draw: When the length between the two coupling points of the U-shaped waveguide is an integer multiple of circumference of the micro-ring, an obvious drift in the horizontal direction appears, the free spectral range (FSR) doubled and the sensitivity is 0.0005 µm/%RH; When the external environment relative humidity RH changes from 10% to 100% RH, scatter is change between including (including 0.005 m to 0.005 m, compared to other humidity sensor, the Sensitivity of sensor improves 10~50 times and the transmission is very stable. Results show that the design of surface plasma micro ring sensors has better sensitivity, stable performance and can be used in the humidity measurement, achieving a high sensitivity in the sense of humidity when the wide range of filter frequency selection is taken into account, and providing a theoretical basis for the preparation of micro-optics.