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Abstract The segmentation and extraction on color features can provide useful information for many different application domains. However, most of the existing image processing algorithms on feature extraction are gray image-based and consider only one-dimensional parameters. In order to carry out a fast and accurate color feature extraction, this paper proposes a color extraction algorithm by segmentation that is called a color extraction algorithm This algorithm is compared under different color distribution situations, and the extraction effect on color is also shown by the combination of the segmentation and feature extraction algorithms. Experimental results show that such segmentation algorithm has some advantages for color segmentation. In the fuzzy color image preprocessing, this paper gives the location method of region of interest. Moreover, compared with other existing extraction algorithms, the presented segmentation extraction algorithm in this paper not only has higher accuracy, shorter extraction time and stronger anti-interference ability, but also has better effect on more divergent color edge. Experimental evaluation of the proposed color extraction algorithm demonstrates dominance over existing algorithms for feature extraction. These researches in this paper provide a new way of thinking for color feature extraction by segmentation, which has an important theoretical references and practical significance.

Abstract Introduction This study aimed to understand the impact of the coronavirus disease 2019 (COVID-19) epidemic on the distribution and antibiotic resistance of pathogenic bacteria isolated from the lower respiratory tract of children in our hospital. Methods Antimicrobial susceptibility tests were performed on bacteria isolated clinically from the lower respiratory tracts of children in our hospital from 2018 to 2021 by the Kirby–Bauer method and automated systems. Results From 2018 to 2021, the top three lower respiratory tract clinical isolates in our hospital were Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae. These three species showed obvious seasonal epidemic patterns, and their numbers decreased significantly during the COVID-19 epidemic, from 4559 in 2019 to 1938 in 2020. Bacterial resistance to antibiotics also changed before and after the COVID-19 epidemic. The annual proportions of methicillin-resistant S. aureus (MRSA) were 41%, 37.4%, 26.2%, and 29.8%. The resistance rates of Klebsiella pneumoniae to ceftriaxone were 40.5%, 51.9%, 35.3%, and 53.3%, and the detection rates of carbapenem-resistant K. pneumoniae (CRKP) were 2.7%, 11.1%, 5.9%, and 4.4%. The detection rates of β-lactamase-producing H. influenzae were 51.9%, 59.2%, 48.9%, and 55.3%. The rate of MRSA, ceftriaxone-resistant K. pneumoniae, CRKP, and β-lactamase-producing H. influenzae decreased significantly in 2020 compared with 2019, whereas that of carbapenem-resistant P. aeruginosa and carbapenem-resistant A. baumannii increased. The detection rates of β-lactamase-negative ampicillin-resistant H. influenzae (BLNAR) gradually increased over the 4 years. Conclusions Protective measures against COVID-19, including reduced movement of people, hand hygiene, and surgical masks, may block the transmission of S. pneumoniae, H. influenzae, and M. catarrhalis and reduce the detection rate of MRSA, ceftriaxone-resistant K. pneumoniae, CRKP, and β-lactamase-producing H. influenzae.

Abstract A brushless DC (BLDC) motor is synchronous motor with trapezoidal/square wave counter-electromotive force, which is a typical example of highly coupled nonlinear systems. In industrial control, BLDC motor drive usually uses proportional–integral (PI) controller to control the speed, but it is very difficult to adjust the scale factors. In this study, we present a particle swarm algorithm-tuned fuzzy logic-PI (PF-PI) controller applied to the speed control system. The objective of this paper is to optimally tune the PI controller parameters to obtain the best drive response. The scale factors are optimized using particle swarm optimized-PI (P-PI) controller and PF-PI controller. The three performance indicators integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are used to measure the effectiveness of PF-PI controller optimization. The results show that the optimal torque ripple and speed response curves are obtained by using ITAE as the performance indicator. The conclusions demonstrate that the proposed method provides superior dynamic performance for BLDC motor. Highlights (1) In terms of research content, we propose a new PF-PI controller driven control system based on the traditional BLDC speed control system, and the applicability of three performance indicators on the controller is discussed. (2) In terms of research method, we compare the no-load start, variable speed and sudden addition disturbance load start capabilities of P-PI controller and PF-PI controller, and verify the fast and robustness of PF-PI controller. (3) In the research significance, the PI controller structure is improved and the dynamic performance of BLDC speed control system is enhanced.

Abstract Wafer dicing saw is one of the core equipment in the manufacturing process of semiconductor integrated circuit components. The cutting accuracy of dicing saw directly affects the overall quality of processed chips. This paper systematically investigates the relationship between the main cutting process parameters and the cutting quality of the dicing saw. The orthogonal experimental design method and genetic algorithm are used to optimize the cutting process parameters, solving the high cost and low efficiency problems caused by the traditional way of selecting parameters by trial and error. After optimization, the average maximum chipping width is only 38.54 μm, which is 8.23% better than the traditional way of cutting quality. Based on the blade thickness of 35 μm, the maximum chipping width reached the industry-recognized best standard of 1.1 times the blade thickness, further proving the effectiveness of the method. Article Highlights The first joint application of orthogonal regression design method and evoluti-onary algorithm for parameter optimiza-tion of dicing saw. A set of optimal cutting parameters are found and verified by experiments. Compare and contrast the optimal cutting parameters cutting’s performance.

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening immune-mediated disease that affects patients with known genetic defects and is increasingly found among those with autoimmune diseases and persistent infections. Talaromyces marneffei (TM) is a human opportunistic fungus that commonly infects immunodeficient or immunosuppressed individuals. Few TM-associated secondary HLH cases resulting from autoimmune deficiency have been reported previously. The current case study describes a pediatric patient hospitalized with recurrent fever and lymphadenopathy. The child had abnormal blood cell classification, and microscopy revealed mature granulocytes that phagocytized fungal spores. It was speculated that the patient was infected with TM. The pathogen was detected earlier than the blood culture and confirmed by metagenomic next-generation sequencing. Whole-exome sequencing revealed that the patient had complex mutations associated with immunodeficiency. This included a mutation in exon 3 of the CD40LG gene, c.346G>A, which may be linked to hyper-IgM syndrome, a primary immunodeficiency disease with immunoglobulin conversion recombination defects that could explain the patient’s increased susceptibility to serious opportunistic infections. In addition, a heterozygous frameshift variant, c.820dup (p.Asp274GlyfsTer61), was detected in exon 6 of CARD9, a key gene associated with fungal immune surveillance. After 4 days of fungal treatment, the abnormal blood cell clusters disappeared, but other infections occurred in succession for 6 months after rehabilitation. The patient was followed with the aim of providing subsequent immunotherapy. This study found that infection can trigger HLH in HIV-negative individuals, highlighting the importance of early definitive identification of the causative agent and investigation of potential immunodeficiency.

Crack identification plays a vital role in preventive maintenance strategies during highway pavement maintenance. Therefore, accurate identification of cracks in highway pavement images is the key to highway maintenance work. In this paper, an improved U-Net network adopting multi-scale feature prediction fusion and the improved parallel attention module was put forward to better identify concrete cracks. Multiscale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus using global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, which can give more weight to the target region in the image and further capture the global contextual information of the image to improve the recognition accuracy. Improving the bottleneck layer is used to improve the robustness of the model and prevent overfitting. Experiments show that the improved U-Net network in this paper has a significant improvement over the original U-Net network. The performance of the proposed method in this paper was investigated on two publicly available datasets (Crack500 and CFD) and compared with competing methods proposed in the literature. Using the Crack500 dataset, the method in this paper achieved the highest score in precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). Similarly, for the CFD dataset, the method in this paper achieved high values for precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%). Thus, the method has several advantages for identifying cracks in highway pavements and is an ideal tool for practical work. In future work, identifying more crack types and model light-weighting are the key objectives. Meanwhile, this paper provides a new idea for road crack identification.

Path planning is an important aspect and component in the research of mobile-robot-related technologies. Many path planning algorithms are only applicable to static environments, while in practical tasks, the uncertainty in dynamic environments increases the difficulty of path planning and obstacle avoidance compared with static environments. To address this problem, this paper proposes an RRT*-FDWA algorithm. RRT* first generates a global optimal path, and then, when obstacles exist nearby, an FDWA algorithm fixes the local path in real time. Compared with other path planning algorithms, RRT*-FDWA can avoid local minima, rapidly perform path replanning, generate a smooth optimal route, and improve the robot’s maneuvering amplitude. In this paper, the effectiveness of the algorithm is verified through experiments in dynamic environments.

Abstract Background Salmonella is one of the main causative agents of diarrhea which results in substantial disease burden. To determine the prevalence, serotype distribution, and antimicrobial resistance profiles of clinical Salmonella isolates in Shenzhen, a 6-year surveillance study was conducted. Results A total of 297 (5.7%) Salmonella strains were isolated from stool samples from 5239 patients. Among the 42 serotypes identified, serotype Typhimurium was the most common one which represented 39.7% of the isolates (118), followed by serotype Enteritidis (71, 23.9%), London (12, 4.0%), 4, 5, 12: i: - (11, 3.7%), and Senftenberg (8, 2.7%). A high frequency of resistance was found in ampicillin (70.6%), piperacillin (64.5%), tetracycline (63.5%), and streptomycin (54.3%). Resistance to ampicillin and tetracycline was observed in 95.3% of S. Typhimurium isolates; and nalidixic acid in 93.1% of S. Enteritidis isolates. Resistance to 5 or more antimicrobial agents was found in 78.8% of S. Typhimurium and 69.0% of S. Enteritidis isolates. A decreased susceptibility to ciprofloxacin and levofloxacin was associated with amino acid alteration in gyrA gene. Point mutations without amino acid changes were seen in gyrB, parC, and parE genes. Conclusions A broad range of serotypes are responsible for Salmonellosis in Shenzhen, with Enteritidis and Typhimurium being the most common serotypes. The high level of antibiotic resistance is of public health significance and ongoing monitoring combined with rational use of antibiotics are recommended. Point mutations in gyrA gene might play an important role in the resistance to fluoroquinolones.

Abstract Background Acute meningitis and encephalitis syndromes (AMES) is a severe neurological infection which causes high case fatality and severe sequelae in children. To determine the etiology of childhood AMES in Shenzhen, a hospital-based study was undertaken. Methods A total of 240 cerebrospinal fluid (CSF) samples from 171 children meeting the case definition were included and screened for 12 common causative organisms. The clinical data and conventional testing results were collected and analyzed. Whole genome sequencing was performed on a Neisseria meningitidis isolate. Results A pathogen was found in 85 (49.7%) cases; Group B Streptococcus (GBS) was detected in 17 cases, Escherichia coli in 15, Streptococcus pneumoniae in 14, enterovirus (EV) in 13, herpes simplex virus (HSV) in 3, N. meningitidis in 1, Haemophilus influenzae in 1, and others in 23. Notably, HSV was found after 43 days of treatment. Twelve GBS and 6 E. coli meningitis were found in neonates aged less than 1 month; 13 pneumococcal meningitis in children aged > 3 months; and 12 EV infections in children aged > 1 year old. The multilocus sequence typing of serogroup B N. meningitidis isolate was ST-3200/CC4821. High resistance rate to tetracycline (75%), penicillin (75%), and trimethoprim/sulfamethoxazole (75%) was found in 4 of S. pneumoniae isolates; clindamycin (100%) and tetracycline (100%) in 9 of GBS; and ampicillin (75%) and trimethoprim/sulfamethoxazole (67%) in 12 of E. coli. Conclusions The prevalence of N. meningitidis and JEV was very low and the cases of childhood AMES were mainly caused by other pathogens. GBS and E. coli were the main causative organisms in neonates, while S. pneumoniae and EV were mainly found in older children. HSV could be persistently found in the CSF samples despite of the treatment. A better prevention strategy for GBS, the introduction of pneumococcal vaccine, and incorporation of PCR methods were recommended.

High thermal conductivity polymer matrix composites have become an urgent need for the thermal management of modern electronic devices. However, increasing the thermal conductivity of polymer-based composites typically results in loss of lightweight, flexibility and electrical insulation. Herein, the polyvinyl alcohol (PVA)/PVA-chitosan-adsorbed multi-walled carbon nanotubes/PVA (PVA/CS@MWCNTs) composite films with a sandwich structure were designed and fabricated by a self-construction strategy inspired by the surface film formation of milk. The obtained film simultaneously possesses high thermal conductivity, electrical insulation, and excellent flexibility. In this particular structure, the uniform intermediate layer of PVA-CS@MWCNTs contributed to improving the thermal conductivity of composite films, and the PVA distributed on both sides of the sandwich structure maintains the electrical insulation of the films (superior electrical resistivity above 1012 Ω·cm). It has been demonstrated that the fillers could be arranged in a horizontal direction during the scraping process. Thus, the obtained composite film exhibited high in-plane thermal conductivity of 5.312 W·m−1·K−1 at fairly low MWCNTs loading of 5 wt%, which increased by about 1190% compared with pure PVA (0.412 W·m−1·K−1). This work effectively realizes the combination of high thermal conductivity and excellent electrical insulation, which could greatly expand the application of polymer-based composite films in the area of thermal management.

Polarizer is a key component of the liquid crystal display panel, and the optical properties mainly include transmittance, degree of polarization and chromaticity. Polarizer is made of a multilayer optical film, in which the PVA polarizing film is the core structure for realizing the polarization of the whole polarizer. PVA polarizing film is commonly manufactured through a multi-step craft, including rinsing, dyeing, stretching and correcting. The correction process has a significant impact on the final apparent color and optical properties of the polarizer. In this study, the KI concentration in the correcting tank, ranging from 1% to 3%, was systematically investigated. With the increase in KI concentration, the Raman vibration peak at 160 cm−1 representing I5− ions gradually weakened, while the Raman vibration peak at 110 cm−1 representing I3− ions gradually increased, indicating that the KI in the correcting tank changed the chemical equilibrium of polyiodide ions in PVA. Then abundant chromophore I5− ions were consumed and I3− ions generated, so that the apparent color of PVA polarizing film gradually changed from dark blue to dark gray, and the chromaticity a-value and b-value gradually increased. The change in the concentration of dichroic species (I5− and I3−) in PVA polarizing film had directly affected its transmittance in the visible range. From the UV-Vis transmittance spectrum of PVA polarizing film, when the I5− ions were consumed and I3− ions generated, the transmittance of PVA polarizing film in the region of 675–525 nm wavelength increased gradually while the polarization degree also increased.