Actual Size Research Articles

When selection pays: Structured public goods game with a generalized interaction mode.

The public goods game is a broadly used paradigm for studying the evolution of cooperation in structured populations. According to the basic assumption, the interaction graph determines the connections of a player where the focal actor forms a common venture with the nearest neighbors. In reality, however, not all of our partners are involved in every game. To elaborate this observation, we propose a model where individuals choose just some selected neighbors from the complete set to form a group for public goods. We explore the potential consequences by using a pair-approximation approach in a weak selection limit. We theoretically analyze how the number of total neighbors and the actual size of the restricted group influence the critical enhancement factor where cooperation becomes dominant over defection. Furthermore, we systematically compare our model with the traditional setup and show that the critical enhancement factor is lower than in the case when all players are present in the social dilemma. Hence, the suggested restricted interaction mode offers a better condition for the evolution of cooperation. Our theoretical findings are supported by numerical calculations.

Biopsy forceps are useful for measuring esophageal varices in vitro.

To avoid acute variceal bleeding in cirrhosis, current guidelines recommend screening for high-risk esophageal varices (EVs) by determining variceal size and identifying red wale markings. However, visual measurements of EV during routine endoscopy are often inaccurate. To determine whether biopsy forceps (BF) could be used as a reference to improve the accuracy of binary classification of variceal size. An in vitro self-made EV model with sizes ranging from 2 to 12 mm in diameter was constructed. An online image-based survey comprising 11 endoscopic images of simulated EV without BF and 11 endoscopic images of EV with BF was assembled and sent to 84 endoscopists. The endoscopists were blinded to the actual EV size and evaluated the 22 images in random order. The respondents included 48 academic and four private endoscopists. The accuracy of EV size estimation was low in both the visual (13.81%) and BF-based (20.28%) groups. The use of open forceps improved the ability of the endoscopists to correctly classify the varices by size (small ≤ 5 mm, large > 5 mm) from 71.85% to 82.17% (P < 0.001). BF may improve the accuracy of EV size assessment, and its use in clinical practice should be investigated.

Application of Control Algorithm in the Design of Automatic Crimping Device for Connecting Pipe and Ground Wire

Due to the low effectiveness and poor quality of manual crimping of grounding wires, the author proposes the design of an automatic crimping device for connecting tube grounding wires based on intelligent fully automatic technology. The device consists of a microcontroller, an upper computer control interface, an electric push rod, an infrared sensor, a pressure transmitter, and other devices. The staff used the upper computer monitoring interface to set the relevant parameters for grounding wire crimping, and used X-ray digital imaging technology to measure the crimping size of the grounding wire. The size met the set parameter conditions. Through the PID control algorithm in the microcontroller, the stepper motor was controlled to push the clamp to move, completing the automatic crimping of the grounding wire. The X-ray detection method was introduced to detect the quality of the grounding wire after the crimping was completed. The experimental results show that the average deviation between the measured crimping size of the grounding wire and the actual measurement size by the automatic crimping device is only 0.06 mm, indicating that its measurement results are accurate; The success rate of crimping exceeds 95%. The above experimental results verify that the designed crimping device has high stability and reliability, and good quality detection effect.  

Study of real-time parameter measurement of ring rolling pieces based on machine vision.

Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.

Prediction of transmission routes of respiratory infectious diseases in indoor environment: Based on droplet evaporation and sedimentation analysis

Many respiratory infectious diseases are believed to be transmitted from person to person through droplet nuclei in the air or contact with droplet-contaminated surfaces. Most studies related to droplet evaporation only focus on the evaporation process, without dividing the transmission routes of diseases caused by droplet evaporation. In this paper, the actual size of droplet nuclei was analyzed, and droplet evaporation and the changes in component content were predicted. Especially considering the dynamic effect of evaporation on droplets size change, a more realistic sedimentation model was established, and the probability of pathogen transmission through different routes and the corresponding droplet size distribution range were analyzed. The results showed that the size of droplet nuclei and the rate of the evaporation process together determined the time required for droplet evaporation. The number of droplets still suspended in the air after the end of evaporation is much higher than the number of droplets settling on the ground. In addition, the differences in component content during the evaporation process may affect the inactivation of pathogens in droplets. This study provided a reference for the prevention and control strategies of respiratory infectious diseases in indoor environments.

Effect of width and slope of large cross-section tunnel on ceiling smoke temperature under natural ventilation

ABSTRACT When a fire occurs in a large cross-section tunnel with slope, the distribution characteristic of ceiling smoke temperature illustrates special laws under the effect of chimney effect, the thermal radiation feedback, and the air entrance limit from walls. A series of numerical simulations are conducted through FDS simulation software based on the actual size of highway tunnel. The influence of large cross-section tunnel width and slope on ceiling smoke temperature when fire occurs are discussed. The research results show that (1) as the tunnel slope increases from 5° to 20°, the distance between the maximum ceiling smoke temperature point and the fire source is 0.5 m, 1 m, 1.7 m, 2.4 m, respectively, and the distance between the ceiling impact point and the fire source is 1 m, 1.8 m, 2.3 m, 3 m, respectively. As the tunnel width increases from 10 m to 20 m, the distance between maximum ceiling smoke temperature point and fire source increases 0.9 m, 1.1 m, 1.3 m, 1.8 m, 2.5 m, and 2.6 m. (2) The prediction formula of maximum ceiling smoke temperature is modified, and a dimensionless relationship among the maximum ceiling smoke temperature and tunnel slope and tunnel width is established. (3) The formula of longitudinal ceiling smoke temperature distribution is modified, and the relationship among tunnel slope, tunnel width, tunnel height, and longitudinal ceiling smoke temperature distribution is established. The research conclusions can provide a theoretical basis for fire prevention design, smoke control and evacuation of this kind of large cross-section tunnel fire.

Tomography-based digital twin of Nd-Fe-B permanent magnets

Many functional materials have been designed at the multiscale level. To properly simulate their physical properties, large and sophisticated computer models that can replicate microstructural features with nanometer-scale accuracy are required. This is the case for permanent magnets, which exhibit a long-standing problem of a significant offset between the simulated and experimental coercivities. To overcome this problem and resolve the Brown paradox, we propose an approach to construct large-scale finite element models based on the tomographic data from scanning electron microscopy. Our approach reconstructs a polycrystalline microstructure with actual shape, size, and packing of the grains as well as the individual regions of thin intergranular phase separated by triple junctions. Such a micromagnetic model can reproduce the experimental coercivity of ultrafine-grained Nd-Fe-B magnets along with its mechanism according to the angular dependence of coercivity. Furthermore, a remarkable role of thin triple junctions as nucleation centers for magnetization reversal is revealed. The developed digital twins of Nd-Fe-B permanent magnets can assist their optimization toward the ultimate coercivity, while the proposed tomography-based approach can be applied to a wide range of polycrystalline materials.

Immunization Simulation Using Actual-Size Infant Manikins for Medical Students in Indonesia

Introduction Manikins are tools used in simulation training for medical students to develop important skills, one of which is immunization. However, conventional manikins often do not resemble the actual size of an infant's arm or leg. This study aimed to determine the impact of using actual-size manikins on students' knowledge and practical skills, evaluate their confidence in immunization practice, and assess students' responses and feedback regarding the practice using actual-size manikins. Methods This was a quasi-experimental study involving medical students at the Faculty of Medicine, Universitas Indonesia, from October 2020 to April 2021. Students in the intervention group used newly developed actual-size infant arm and leg manikins, while the control group used conventional manikins. All students underwent the objective structured clinical examination (OSCE) and the scores were compared between the 2 groups. Within the intervention group, data on pretest and posttest scores, feedback questionnaires, and self-confidence assessments were also obtained and analyzed. Results A total of 205 students were included. Statistically significant difference was found in the OSCE scores between the intervention and control groups (P &lt; 0.01). Students in the intervention group (n = 108) showed significant improvement in knowledge scores after the workshop (P &lt; 0.01). Most students (81.7%) expressed confidence in administering vaccines to live patients after practicing with manikins. In addition, 98.2% of students (n = 107) acknowledged the benefits of practicing with actual-size manikins in accurately determining the injection sites. Conclusions Simulation with the actual-size manikins significantly improved students' knowledge and practical immunization skills, leading to increased confidence and competence in their immunization skills.

Assessment of Ovbiomu Coal for Industrial Application

This research is to assess the quality of Ovbiomu lignite coal deposited for optimum utilization in metallurgical industries in Ovbiomu. Samples of coal were collected for analysis and sample was collected from five different stationary lots until 10 kilograms of the sample was collected and 500g was taken for characterization. The sample was reduced using jaw crusher and ball milled to a size of 1400μm and 1 kilograms of 1400μm was further reduced to 1100μm and was classified into various sieve sizes using mechanical sieve shaker. CalK2 bomb calorimeter was used to determine the calorific value of the head sample, the economic liberation size and the actual liberation size. The proximate analysis of the head sample and all the sieve sizes was done using Furnace, oven, porcelain crucibles, analytical balance, and desiccator to ascertain the individual carbon content. Ultimate analysis was done using XRD and concentration was done using froth flotation method. Result from sieve analysis shows that at 1000 μm, 710 μm, 500 μm, 355 μm, 250 μm, 180 μm, 25 μm, 90 μm, 63 μm, and -63 μm, the following weight was retained 1.45 g 1.86 g, 3.73 g, 3.5 g, 14.61 g, 48.28 g, 0.15 g, 0.17 g and 0.20 g respectively. The economic liberation size was found out to be 180um where most of the sample is retained and the actual liberation size was found out to be 125um but with a very small quantity retained. The results of the calorific value of the head sample and each of the sieve sizes of 250μm, 180μm, and 125μm, was determined to be 24.51MJ/Kg, 25.86MJ/Kj, 18.57MJ/Kg and 38.07MJ/Kg respectively, the following are result of percentage carbon content, for the Head sample (40.65%), 1000μm (29.55%), 710μm (38.53%), 500μm (42.43%), 335μm (35.42%), 250μm(43.07%), 180μm (30.92),125μm (63.40%) 90μm (0.56%) 63μm (0.62%) and -63μm (9.91%). The result of ultimate analysis of the sample shows Nitrogen (1.15%), Hydrogen (4.80%), Sulphur (0.13%) and Oxygen (29.56%).

An Integrated Model Based Prediction of Machining Accuracy for Milling Machine

The accuracy of machining can be predicted during the design process of CNC machine tools, while the prediction accuracy depends on the prediction model. However, when establishing the prediction model, the coupling effect among the machine tool’s different components is always ignored for modeling efficiency, so this will lead to its reliability cannot be guaranteed. To address this problem, the model reduction theory and the lumped parameter method are used in this study to set the state-space equations of the essential structural parts. Moreover, the electro-mechanical coupling parameter equation of the servo system is transformed into a state-space equation. Then the state-space equations of the components and the servo system are coupled to establish the machine tool dynamic model. Furthermore, a theoretical model of instantaneous undeformed chip thickness is found, and according to the linear relationship between the micro-cutting force and the instantaneous undeformed chip thickness, the instantaneous cutting force model of the cylindrical milling cutter is established. Based on these, the integrated model, which considers the mechanical structure, control system and cutting force, is obtained and further used in Simulink to build the machining accuracy prediction platform, as well as the accuracy of the integrated prediction model is verified. Moreover, the prediction model established by the integrated modeling method can effectively simulate the actual machining conditions of the machine tool, and the superiority of prediction is confirmed by comparing the simulated and measured results in peripheral milling applications. The results show the error in the system response efficiency is 14.8%, while the error in position accuracy and delay characteristics is less than 10%, the error between the predicted size of the prediction model and the actual size is within 28 μm, a minimum prediction error is 7 μm.

TPA: Two-stage progressive attention segmentation framework for hepatocellular carcinoma on multi-modality MRI.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and measurement of hepatocellular carcinoma (HCC). The multi-modality information contained in the multi-phase images of DCE-MRI is important for improving segmentation. However, this remains a challenging task due to the heterogeneity of HCC, which may cause one HCC lesion to have varied imaging appearance in each phase of DCE-MRI. In particular, some phases exhibit inconsistent sizes and boundaries will result in a lack of correlation between modalities, and it may pose inaccurate segmentation results. We aim to design a multi-modality segmentation model that can learn meaningful inter-phase correlation for achieving HCC segmentation. In this study, we propose a two-stage progressive attention segmentation framework (TPA) for HCC based on the transformer and the decision-making process of radiologists. Specifically, the first stage aims to fuse features from multi-phase images to identify HCC and provide localization region. In the second stage, a multi-modality attention transformer module (MAT) is designed to focus on the features that can represent the actual size. We conduct training, validation, and test in a single-center dataset (386 cases), followed by external test on a batch of multi-center datasets (83 cases). Furthermore, we analyze a subgroup of data with weak inter-phase correlation in the test set. The proposed model achieves Dice coefficient of 0.822 and 0.772 in the internal and external test sets, respectively, and 0.829, 0.791 in the subgroup. The experimental results demonstrate that our model outperforms state-of-the-art models, particularly within subgroup. The proposed TPA provides best segmentation results, and utilizing clinical prior knowledge for network design is practical and feasible.

The Role of Magnetic Resonance Imaging in the Preoperative Staging and Treatment of Invasive Lobular Carcinoma

IntroductionInvasive lobular carcinoma (ILC) is known for its diffuse growth pattern and its associated challenges in diagnosing. Magnetic resonance imaging (MRI) is the most accurate imaging modality and might aid in improving preoperative staging compared to full field digital mammography (FFDM) and ultrasound (US), however current literature is inconsistent. The aim of this paper is to evaluate the accuracy of MRI staging compared to FFDM/US and pathology results. MethodsIn this single-centre retrospective study, all patients diagnosed with ILC between 2014 and 2019 who underwent preoperative MRI were included. Specific parameters studied were: (1) the need for second-look targeted biopsies, (2) detection of new tumors (ie, contralateral or multifocal), (3) changes in cTNM-classification, and (4) impact on final treatment plan. Bland-Altman plots were used to compare the tumor sizes measured on MRI and FFDM/US with actual pathological tumor sizes. ResultsNinety-nine patients were included. After performing preoperative MRI, 9 (9.1%) multifocal tumors were diagnosed after additional biopsies. Contralateral tumors were detected twice (2.0%) and cN classification was upgraded in 7 cases (7.1%). Surgical treatment or neoadjuvant treatment plans were changed in 16 patients (16.1%). Compared to histopathological results, FFDM/US underestimated tumor size with a mean of 0.4 cm (Limit of agreement (LoA): -2.8 cm to 2.0 cm) whereas MRI overestimated tumor size with a mean of 0.6 cm (LoA: -1.9 cm to 3.0 cm). ConclusionsIn our study, mean differences in tumor size measurements using FFDM/US and MRI were comparable, with similar random errors. MRI correctly diagnosed multifocal and contralateral tumors more often and provided a better cN staging.

Multi-Objective Optimization for Sustainable Pavement Maintenance Decision Making by Integrating Pavement Image Segmentation and TOPSIS Methods

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.

Corporate Artefacts and Customer Satisfaction in Public Secondary Schools in Nairobi County, Kenya

Purpose: Corporate artefacts are those things that are visible, obvious expressions, manifestations and processes of culture in an organization. They give implicit cues to users about the meaning of the place and norms expectation for behaviour in a place. They are an essential ingredient for superior organizational performance and have been linked to school performance. The purpose of the study was to examine the influence of corporate artefacts on customer satisfaction in public secondary schools in Nairobi County, Kenya. These artefacts such as practices and process, logo, mission and vision etc are important communication elements that customers use to create meanings of messages in an organization like a school.&#x0D; Materials and Methods: This study used a mixed research design with a population study of 3588 students. It included both boys and girls from 12 public secondary schools in Nairobi City County. The students ages ranged from 14 years to 21 years and were in Form 2 to Form 4.  Stratified random sampling was used to sample the population and respondents were then calculated proportionately according to the actual sizes in the schools. A sample size of 369 respondents was selected with 296 respondents responding to the questionnaires and 11 principals or their deputies interviewed. Quantitative data collection was done through questionnaires while qualitative data was collected using interview guides. Descriptive and inferential statistics were used to analyse quantitative data. Analysed data was then presented using graphs, tables and charts. Qualitative data was analysed thematically and presented in a narrative form.  Inferential statistics used were descriptive, correlation, regression and ANOVA analysis. &#x0D; Findings: The findings of the study indicated that students in public secondary schools also referred to as customers were satisfied with the visible structures that existed in their schools that included the logo, the vision and mission. Additionally, the students were satisfied with the processes in the schools that included the teacher students interactions that influenced the learning environment.  The teacher student interaction is a critical process in learning because it helps teachers create a classroom environment that is helpful to learning as well as meeting student’s educational and emotional needs which then impact on the student’s performance, role clarity and behaviour conformity. From the interview findings, the teachers agreed that they have good teacher-student interactions that help to improve learning and students are encouraged to participate in class activities. However, the students were dissatisfied with the technological systems in their schools. This study revealed that technology is a challenge in most schools and has limited access to academic resources which affect their performance and ultimately their satisfaction. This study concludes that corporate artefacts had a positive influence on customer satisfaction in public secondary schools in Nairobi County.&#x0D; Unique Contribution to the theory, practice and Policy: This study was guided by Edgar Schein Model. The study confirmed the critical role that corporate artefacts have on customer satisfaction in public secondary schools. The study recommends that schools management should device a curriculum to educate customers gain an understanding of the meaning of corporate artefacts in organizational settings such as schools. In addition, the findings from this study should provide a guide to policy makers in education and other areas to create policies that will highlight the importance of artefacts in organizations. Successful implementation of such policies should be emphasised.

The defect feature extraction of ultrasonic phased array detection based on adaptive region growth.

An ultrasonic phased array defect extraction method based on adaptive region growth is proposed, aiming at problems such as difficulty in defect identification and extraction caused by noise interference and complex structure of the detected object during ultrasonic phased array detection. First, bilateral filtering and grayscale processing techniques are employed for the purpose of noise reduction and initial data processing. Following this, the maximum sound pressure within the designated focusing region serves as the seed point. An adaptive region iteration method is subsequently employed to execute automatic threshold capture and region growth. In addition, mathematical morphology is applied to extract the processed defect features. In the final stage, two sets of B-scan images depicting hole defects of varying sizes are utilized for experimental validation of the proposed algorithm's effectiveness and applicability. The defect features extracted through this algorithm are then compared and analyzed alongside the histogram threshold method, Otsu method, K-means clustering algorithm, and a modified iterative method. The results reveal that the margin of error between the measured results and the actual defect sizes is less than 13%, representing a significant enhancement in the precision of defect feature extraction. Consequently, this method establishes a dependable foundation of data for subsequent tasks, such as defect localization and quantitative and qualitative analysis.

Gini index based goodness-of-fit test for the Lindley distribution

In survival analysis and reliability studies the Lindley distribution has been widely used. In this article, we introduce a new goodness of fit test for the Lindley distribution based on an estimate of the Gini index. The asymptotic distribution of the new test statistic is investigated. The unknown parameter of the Lindley distribution is estimated by the maximum likelihood estimator. The critical points and the actual sizes of the test are obtained by Monte Carlo simulations. A power comparison study of the existing tests is performed. It is shown that the Gini index-based test that we proposed performs well by comparing its powers with that of other tests. A real-life data analysis is finally presented for illustrative purpose.

Hot Embossing of Micro Grating and Its Replication Accuracy

The Hot Embossing (HE) process was applied in the fabrication of micro grating. High-density polyethene (HDPE) was used as a specimen material for producing microstructure components and parts. In this research, a series of experiments were conducted using an HDPE specimen and embossed the polymer under different parameters such as temperature, embossing time, and embossing force as factors. The experimental setup was based on a response surface method (RSM) that employed a face composite design. The constructed RSM quadratic models assisted in analysing the response parameters, namely accuracy and surface roughness, to determine the relevant linkages between the input variables and the replies. The results defined that hot embossing temperature and hot embossing force will enhance the accuracy and surface quality of the micro grating. Based on RSM, the results revealed that the optimal parameters were as follows: hot embossing temperature is 1650c, embossing force is 10kN/s, and embossing holding time is 150 seconds. Under this condition, the actual deviation of accuracy and surface roughness is 5,75% and -1.32% respectively compared to the actual size of the micro grating mould. Hence, it is highly agreed with the model prediction value. In conclusion, optimization of process parameters on the hot embossing system that was developed is promising to fabricate the HDPE micro grating substrate with optimum dimensional accuracy and surface quality.

Study on the actual particle size, activity concentration, and migration process adsorption behavior of radioactive substances in liquid effluents from nuclear power plants

Radionuclides emitted by nuclear power plants may have effects on the environment and public health. At present, research on radioactive material effluent in the industry mainly focuses on the treatment of radioactive effluent and the particle size distribution of the primary circuit. There is little research on the particle size of radioactive material during the migration process outside the primary circuit system, as well as the flocculation precipitation and other enrichment phenomena during the collection process of effluent. Therefore, this study relies on the sampling of effluent from an in-service nuclear power plant to measure its radioactivity level by particle size range. At the same time, the mixing process of effluent is simulated in the laboratory to simulate the adsorption behavior of effluent during the migration process. It was found that in the activity concentration of detectable radioactive nuclides in the effluent samples, more than 95% of radioactive nuclides exist in the liquid with particle sizes less than 0.1μm, while particle sizes greater than 0.45 μm account for less than 5%. After the sample was filtered by the demineralizer, the radioactive activity decreased. The flocculation precipitation in the waste liquid of the waste water recovery system has a certain contribution to the enrichment of nuclides. With the extension of time, the enrichment of transition elements such as cobalt and manganese is particularly obvious, so that it is distributed in the liquid again with a large particle size. In addition, large particle size substances such as colloids in seawater have a certain adsorption effect on radionuclides, which will lead to its aggregation effect again.

Characterization of Fe-Containing and Pb-Containing Nanoparticles Resulting from Corrosion of Plumbing Materials in Tap Water Using a Hyphenated ATM-DMA-spICP-MS System.

Single-particle inductively coupled plasma mass spectrometry (spICP-MS) has been used to characterize metallic nanoparticles (NPs) assuming that all NPs are spherical and composed of pure element. However, environmental NPs generally do not meet these criteria, suggesting that spICP-MS may underestimate their true sizes. This study employed a system hyphenating the atomizer (ATM), differential mobility analyzer (DMA), and spICP-MS to characterize metallic NPs in tap water. Its performance was validated by using reference Au nanoparticles (AuNPs) and Ag-shelled AuNPs. The hyphenated system can determine the actual size and metal composition of both NPs with additional heating after ATM, while stand-alone spICP-MS misidentified the Ag-shelled AuNPs as smaller individual AgNPs and AuNPs. Dissolved metal ions could introduce artifact NPs after heating but could be eliminated by centrifugation. The hyphenated system was applied to characterize Fe-containing and Pb-containing NPs resulting from the corrosion of plumbing materials in tap water. The mode sizes of Fe-containing and Pb-containing NPs were determined to be 110 and 100 nm and the particle number concentrations were determined to be 4.99 × 107 and 1.40 × 106 #/mL, respectively. Cautions should be paid to potential changes in particle size induced by heating for metallic NPs with a low melting point or a high organic content.

A Super-Resolution Reconstruction Method for Infrared Polarization Images with Sparse Representation of Over-Complete Basis Sets

The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction model based on sparse representation, optimized using Stokes vector images. This model forms the basis for our method aimed at achieving super-resolution reconstruction of infrared polarization images. In this method, we utilize the proposed model to initially reconstruct low-resolution images in blocks. Subsequently, we perform a division by weight, followed by iterative back projection to enhance details and achieve high-resolution reconstruction results. As a supplement, we establish a near-real-time short-wave infrared time-sharing polarization system for data collection. The dataset was acquired to gather prior knowledge of the over-complete basis set and to generate a series of simulated focal plane images. Simulation experimental results demonstrate the superiority of our method over several advanced methods in objective evaluation indexes, exhibiting strong noise robustness in quantitative experiments. Finally, to validate the practical application of our method, we establish a split-focal plane polarization short-wave infrared system for scene testing. Experimental results confirm the effective processing of actual captured data by our method.