This study involved the preparation of an all-solid-state ion-selective electrode (ASS-ISE) with copper and a poly(3,4-ethylenedioxythiophene) and polystyrene sulfonate (PEDOT/PSS) conversion layer through electrode deposition. The morphology of the PEDOT/PSS film was characterized, and the performance of the copper ion-selective film was optimized. Additionally, a microfluidic chip for the ASS-ISE with copper was designed and prepared. An integrated microfluidic chip test system with an ASS-ISE was developed using a self-constructed potential detection device. The accuracy of the system was validated through comparison testing with atomic absorption spectrophotometry (AAS). The experimental findings indicate that the relative standard deviation (RSD) of the integrated ASS-ISE with the copper microfluidic chip test system is 4.54%, as compared to the industry standard method. This value complies with the stipulated requirement of an RSD ≤ 5% in DL/T 955-2016.

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation.

Umami is one of five basic tastes, the elucidation of its mechanism by the study of the interaction between umami polypeptides and hT1R1 umami receptors is of great significance. However, research on umami peptides targeting human T1R1 receptors is lacking, and the molecular mechanism remains elusive. Here, we successfully established a system to detect umami peptides targeting human T1R1 receptors by fluorescence spectroscopy, Surface Plasmon Resonance (SPR) and computational simulation. The sensory evaluation, calculated Kd value, and experimental affinity results between the four selected umami peptides (GRVSNCAA, KGDEESLA, KGGGGP, and TGDPEK) and glutamate were tested using this system, and all matched well. The maximum Ka value of GRVSNCAA was 479.55 M-1, and the minimum affinity of TGDPEK was 2.67 M-1. Computational simulations showed that the different peptide binding sites in the hT1R1 binding pocket occupied due to conformational changes are important factors for different taste thresholds, and that peptide hydrophobicity plays an important role in regulating affinity. Thus, our study enables rapid screening of high-intensity umami peptides and the development of T1R1 receptor-based umami detection sensors.

UNASSIGNED: Parkinson\'s disease (PD) is a neurodegenerative disease characterized by the impairment of facial expression, known as hypomimia. Hypomimia has serious impacts on patients\' ability to communicate, and it is difficult to detect at early stages of the disease. Furthermore, due to bradykinesia or other reasons, it is inconvenient for PD patients to visit the hospital. Therefore, it is appealing to develop an auxiliary diagnostic method that remotely detects hypomimia.
UNASSIGNED: We proposed an automatic detection system for Parkinson\'s hypomimia based on facial expressions (DSPH-FE). DSPH-FE provides a convenient remote service for those who potentially suffer from hypomimia and only requires patients to input their facial videos. Specifically, patients can detect hypomimia through two aspects: geometric features and texture features. Geometric features focus on visually representing structures of facial muscles. Facial expression factors (FEFs) are used as the first metric to quantify the current activation state of the facial muscles. Facial expression change factors (FECFs) are subsequently used as the second metric to calculate the moving trajectories of the activation states in the videos. Geometric features primarily concentrate on spatial information, with little involvement of temporal information. Thus, the extended histogram of oriented gradients (HOG) algorithm is introduced. This algorithm can extract texture features within multiple continuous frames and incorporate the temporal information into the features. Finally, these features are applied to four machine learning algorithms to model the relationship between these features and hypomimia.
UNASSIGNED: The DSPH-FE detection system achieved the best performance when concatenating geometric features and texture features, resulting in a F1 score of 0.9997. The best F1 scores achieved with geometric features and texture features were 0.8286 and 0.9446, respectively. This indicated that both geometric features and texture features have an ability to predict hypomimia, and demonstrated that temporal information can boost the model performance. Thus, DSPH-FE is an effective supportive tool in the medical management of PD patients.
UNASSIGNED: Comprehensive experiments demonstrated that proposed features fit well with real-world videos and are beneficial in the clinical diagnosis of hypomimia. In particular, hypomimia had a greater impact on eyes and mouths when patients are smiling.

Sensitive and rapid tests of Escherichia coli drug sensitivity is very important for health of human and animals. An E. coli immunosensor was built based on electrochemical detection and immune detection technologies, through pretreating screen-printed electrodes, and analyzing the optimal reaction concentration of antigen antibody binding with the AC impedance method. Based on the detection system combining the immunosensor and electrochemical workstation, tests were carried out to measure the accuracy of E. coli concentration and drug sensitivity, and error of the detection system was calibrated in accordance with data from the electrochemical workstation. E. coli O157:H7 can be detected in the range of 103 cfu/ml ~ 1012 cfu/ml, and the detection error controlled within 5%. Results from the electrochemical workstation and those from the detection device were consistent, and both demonstrated a greater inhibitory effect of antibiotics on E. coli than on Bacillus subtilis. The electrochemical detection system is highly efficient and accurate, and could be widely applied to E. coli drug sensitivity tests in clinical medicine.

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is -68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.

In view of the shortcomings of the current abnormal data detection system of the protein gene library, such as low detection rate and high error detection rate, the abnormal data detection system of the protein gene library based on data mining technology is designed. The protein gene enters the firewall module of the system, and enters the immune module when it does not match the firewall rules; the memory detector in the immune module presents the protein gene, if the memory detector does not match the protein gene, the mature detector presents the protein gene, if the mature detector does not match the protein gene, it is determined as the normal protein gene data package, if it matches, it is considered that The abnormal data of protein gene was processed by the collaborative stimulation module, and the control module controlled by C8051F060 chip to detect the abnormal data of protein gene library. The immune module generates new protein gene sequences through an immature detector, simulates the immune mechanism of protein gene through a mature detector module, and simulates the secondary response in the abnormal data detection system of protein gene library through memory detector. The system introduces data mining technology into the detection and uses a two-level dynamic optimization algorithm to calculate the ASG similarity value of protein gene secondary structure arrangement. According to this value, the abnormal data detection of the protein gene library is realized by randomly generating protein genes, negative selection, clone selection and copying memory cells through gene expression. The experimental results show that the system can quickly detect abnormal data of the protein gene library, ensure the detection efficiency, and the detection accuracy reaches 97.1%. The system can reduce the error rate of normal protein gene detection as an abnormal protein gene.

Partial discharge (PD) usually reflects failures and potential hazards of equipment, so PD detection is important to protect the power system. The most reliable method now is the pulse current method (PCM), but the device of PCM is large and hard to carry. Ultraviolet (UV) pulse detection is another method to detect PD, which has a high precision, strong anti-interference ability, and a long effective distance. However, the existing detection system does not work well when the PD is weak and can hardly reflect the hidden trouble of equipment. This paper introduces an improved PD detection system, based on the UV Pulse Method, which is of high precision and can reflect early discharge. In this study, a corresponding detection device was also built. This device is handheld, non-contact, easy to use, and of high precision.

In this paper, a magnetoelastic (ME) sensing system for the detection of classical swine fever virus (CSFV) is presented. The detection system comprises a test paper and a measurement circuit. The test paper consists mainly of an ME biosensor to detect the CSFV. Based on the impedance analysis technique, the measurement circuit is designed to measure the resonance frequency of the ME biosensor. The anti-CSFV IgG is immobilized onto the ME sensor surface to form the ME biosensor through a physical absorption approach. The experimental results show that the shift in the resonance frequency of the ME biosensor increases with the augmentation of the CSFV concentration. The effectiveness of the combination between the anti-CSFV IgG and CSFV is confirmed by the scanning electron microscopy (SEM) images, the sandwich-based enzyme-linked immunosorbent assay (ELISA) analysis, the interference study and the reference biosensor test method. The resonance frequency shift is linearly proportional to the concentration in the range from 0 to 2.5μg/ml, and becomes sub-linear at higher concentrations. The ME biosensor for CSFV detection has a sensitivity of about 95Hz/μg·ml(-1), with a detection limit of 0.6μg/ml.