The role of perspective in refereeing: Insights from a video-based decision-making experiment.

Decision accuracy is a crucial factor in the evaluation of refereeing performance. In sports research, officials' decision-making is frequently assessed outside real games through video-based decision experiments, where they evaluate recorded game situations from a third-person perspective. This study examines whether the inclusion of the first-person perspective influences decision accuracy and certainty. Twenty-four professional officials from the first and second German basketball leagues participated in the study. The officials assessed 50 game situations from both first-person and third-person perspectives, indicating their decisions and certainty levels. The statistical analysis utilises signal detection theory to evaluate the efficacy of the first-person perspective compared to the third-person perspective in identifying rule violations and no-calls in video recordings. The findings indicate that the first-person perspective does not yield superior accuracy in identifying foul calls. However, scenes from the first-person perspective exhibit a significant 9% increase in correctly identifying no-calls. Furthermore, officials report significantly higher levels of decision certainty and comfort when using the first-person perspective. The study suggests that sports officials may benefit from incorporating additional scenes from the first-person perspective into video-based decision training. Future studies should explore whether this additional perspective improves the training effect and translates into enhanced in-game performance.

Construction of "De-Capacity Reduction" financial performance index for Chinese coal industry enterprises --based on Fisher discriminant analysis.

In February 2016, the Chinese government focused on removing excess capacity in coal industry enterprises, and the research goal of the paper was to determine how much impact this will have on the financial performance of coal industry enterprises. The paper collected the financial performance indicators of Chinese state-owned coal industry enterprises from 2011 to 2021, and discriminant analysis was used to calculate the financial performance index evaluation system. The conclusions are: (1) From 2011 to 2016, the financial performance index of Chinese state-owned coal industry enterprises before De-Capacity continued to decline, from 2.062 in 2011 to 1.639 in 2016; In 2017-2021, the financial performance index of Chinese state-owned coal industry enterprises after De-Capacity continued to rise, from 1.482 in 2017 to 1.515 in 2021. (2) From 2011 to 2020, the cumulative financial performance index for the whole trade of state-owned coal industry in the past decade was 18.340, with state-owned large coal industry enterprises having the best financial performance, with a 10-year cumulative index of 20.618, followed by state-owned medium-sized coal industry enterprises, with a 10-year cumulative index of 17.944, and the worst among state-owned small coal industry enterprises, with a 10-year cumulative index of 17.271. (3) If the market adjustment started in 2012 is also considered as a component of "De-Capacity", two pressures from the market and the government have prompted the transformation of state-owned coal industry enterprises. The industry wide financial performance index has increased from 1.554 in 2012 to 1.559 in 2020, with an average annual increase of 0.04 %.

Evaluation of High-Dimensional Data Classification for Skin Malignancy Detection Using DL-Based Techniques.

Skin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data's underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data's complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer.

Impact of Rainfall on the Detection Performance of Non-Contact Safety Sensors for UAVs/UGVs.

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios.

A benchmark for evaluation of structure-based online tools for antibody-antigen binding affinity.

The prediction of binding affinity changes caused by missense mutations can elucidate antigen-antibody interactions. A few accessible structure-based online computational tools have been proposed. However, selecting suitable software for particular research is challenging, especially research on the SARS-CoV-2 spike protein with antibodies. Therefore, benchmarking of the mutation-diverse SARS-CoV-2 datasets is critical. Here, we collected the datasets including 1216 variants about the changes in binding affinity of antigens from 22 complexes for SARS-CoV-2 S proteins and 22 monoclonal antibodies as well as applied them to evaluate the performance of seven binding affinity prediction tools. The tested tools' Pearson correlations between predicted and measured changes in binding affinity were between -0.158 and 0.657, while accuracy in classification tasks on predicting increasing or decreasing affinity ranged from 0.444 to 0.834. These tools performed relatively better on predicting single mutations, especially at epitope sites, whereas poor performance on extremely decreasing affinity. The tested tools were relatively insensitive to the experimental techniques used to obtain structures of complexes. In summary, we constructed a list of datasets and evaluated a range of structure-based online prediction tools that will explicate relevant processes of antigen-antibody interactions and enhance the computational design of therapeutic monoclonal antibodies.

Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device.

Electrochromic devices, capable of modulating light transmittance under the influence of an electric field, have garnered significant interest in the field of smart windows and car rearview mirrors. However, the development of high-performance electrochromic devices via large-scale explorations under miscellaneous experimental settings remains challenging and is still an urgent problem to be solved. In this study, we employed a two-step machine learning approach, combining machine learning algorithms such as KNN and XGBoost with the reality of electrochromic devices, to construct a comprehensive evaluation system for electrochromic materials. Utilizing our predictive evaluation system, we successfully screened the preparation conditions for the best-performing device, which was experimentally verified to have a high transmittance modulation amplitude (62.6%) and fast response time (5.7 s/7.1 s) at 70 A/m2. To test its stability, experiments over a long cycle time (1000 cycles) are performed. In this study, we develop an innovative framework for assessing the performance of electrochromic material devices. Our approach effectively filters experimental samples based on their distinct properties, substantially minimizing the expenditure of human and material resources in electrochromic research. Our approach to a mathematical machine learning evaluation framework for device performance has effectively propelled and informed research in electrochromic devices.

Technical and health governance aspects of the external quality assessment system for classical and new psychoactive substances analysis testing in blood.

New psychoactive substances (NPS) are uncontrolled analogues of existing drugs or newly synthesized chemicals that exhibit psychopharmacological effects. Due to their diverse nature, composition, and increasing prevalence, they present significant challenges to the healthcare system and drug control policies. In response, healthcare system laboratories have developed analytical methods to detect NPS in biological samples. As a Regional Reference Centre, the Sicilian CRQ Laboratory (Regional Laboratory for Quality Control) developed and conducted an External Quality Assessment (EQA) study to assess, in collaboration with the Istituto Superiore di Sanità (ISS), the ability of different Italian laboratories to identify NPS and traditional drugs of abuse (DOA) in biological matrices. Two blood samples were spiked with substances from various drug classes, including synthetic cannabinoids, cathinones, synthetic opiates, and benzodiazepines, at concentrations ranging from 2 to 10 ng/mL. The blood samples were freeze-dried to ensure the stability of DOA and NPS. Twenty-two laboratories from the Italian healthcare system participated in this assessment. The information provided by the laboratories during the registration in an in-house platform included a general description of the laboratory, analytical technique, and the chosen panels of analytes. The same platform was employed to collect and statistically analyze the data and record laboratory feedback and comments. The evaluation of the results revealed that the participating laboratories employed three different techniques for analyzing the samples: GC-MS, LC-MS, and immunoenzymatic methods. Approximately 90 % of the laboratories utilized LC-MS techniques. Around 40 % of false negative results were obtained, with the worst results in the identification of 5-chloro AB PINACA. The results showed that laboratories that used LC-MS methods obtained better specificity and sensitivity compared to the laboratories using other techniques. The results obtained from this first assessment underscore the importance of external quality control schemes in identifying the most effective analytical techniques for detecting trace molecules in biological matrices. Since the judicial authorities have not yet established cut-off values for NPS, this EQA will enable participating laboratories to share their analytical methods and expertise, aiming to establish common criteria for NPS identification.

Performance evaluation of a newly developed 2019-nCoV nucleic acid detection kit based on Ion Proton sequencing platform and its comparison with the MGI Tech (DNBSEQ-G99) platform.

PURPOSE: To evaluate the performance of a newly developed 2019-nCoV nucleic acid detection kit based on Ion Proton sequencing platform and make comparation with MGI Tech (DNBSEQ-G99) platform. METHODS: References and clinical samples were used to evaluate the precision, agreement rate, limit of detection (LOD), anti-interference ability and analytical specificity. Twenty-seven clinical specimens were used to make comparison between two platforms. RESULTS: The kit showed good intra-assay, inter-assay, inter-day precision between different operators and laboratories, fine agreement rate with references, a relatively low LOD of 1 × 103 copies/ml, anti-interference capability of 5 % whole blood and 1mg/ml mucin and no cross reaction with twenty-nine common clinical pathogens. Consistency of variant classification was observed between two platforms. The WGS from Ion Proton tended to have higher coverage and less missing data. CONCLUSIONS: The newly developed kit has shown satisfactory performances and excellent consistency with DNBSEQ-G99, making it a good alternative choice clinically.

Adapting multilingual vision language transformers for low-resource Urdu optical character recognition (OCR).

In the realm of digitizing written content, the challenges posed by low-resource languages are noteworthy. These languages, often lacking in comprehensive linguistic resources, require specialized attention to develop robust systems for accurate optical character recognition (OCR). This article addresses the significance of focusing on such languages and introduces ViLanOCR, an innovative bilingual OCR system tailored for Urdu and English. Unlike existing systems, which struggle with the intricacies of low-resource languages, ViLanOCR leverages advanced multilingual transformer-based language models to achieve superior performances. The proposed approach is evaluated using the character error rate (CER) metric and achieves state-of-the-art results on the Urdu UHWR dataset, with a CER of 1.1%. The experimental results demonstrate the effectiveness of the proposed approach, surpassing state of the-art baselines in Urdu handwriting digitization.

Hospital performance evaluation indicators: a scoping review.

BACKGROUND: Hospitals are the biggest consumers of health system budgets and hence measuring hospital performance by quantitative or qualitative accessible and reliable indicators is crucial. This review aimed to categorize and present a set of indicators for evaluating overall hospital performance. METHODS: We conducted a literature search across three databases, i.e., PubMed, Scopus, and Web of Science, using possible keyword combinations. We included studies that explored hospital performance evaluation indicators from different dimensions. RESULTS: We included 91 English language studies published in the past 10 years. In total, 1161 indicators were extracted from the included studies. We classified the extracted indicators into 3 categories, 14 subcategories, 21 performance dimensions, and 110 main indicators. Finally, we presented a comprehensive set of indicators with regard to different performance dimensions and classified them based on what they indicate in the production process, i.e., input, process, output, outcome and impact. CONCLUSION: The findings provide a comprehensive set of indicators at different levels that can be used for hospital performance evaluation. Future studies can be conducted to validate and apply these indicators in different contexts. It seems that, depending on the specific conditions of each country, an appropriate set of indicators can be selected from this comprehensive list of indicators for use in the performance evaluation of hospitals in different settings.

Statistical mirroring: A robust method for statistical dispersion estimation.

This study introduces statistical mirroring as an innovative approach to statistical dispersion estimation, drawing inspiration from the Kabirian-based isomorphic optinalysis model, aimed at enhancing robustness and mitigating biases in estimation methods. Beyond scale-invariant characteristics, the proposed estimators emphasize scaloc-invariant robustness, thereby addressing a critical gap in dispersion estimation. By highlighting statistical meanic mirroring, alongside other forms of proposed statistical mirroring, the study underscores the adaptability and customization potential. Through extensive Monte Carlo simulations and real-life applications, in comparison with classical estimators, the results of the performance evaluation of the proposed estimators demonstrate robustness, efficiency, and transformations-invariance. The research offers a paradigm shift in addressing longstanding challenges in dispersion estimation, offering a new category of dispersion estimation and increased resistance to outliers. Notable limitations include selecting and evaluating the proposed statistical meanic mirroring under Gaussian and Gaussian mixture model distributions. This research paper significantly contributes to statistical methodologies, offering avenues for expanding knowledge in dispersion estimation. It recommends further exploration of proposed estimators across various statistical mirroring types and encourages comparative studies to establish their effectiveness, thereby advancing statistical knowledge and tools for precise data analysis.•The proposed methodology involves preprocessing transformations, statistical mirror design, and optimization to transform a univariate set into a bivariate one, facilitating the fitting of an isomorphic optinalysis model.•Estimators rely on a foundational bijective mapping of isoreflective pairs, deducing the probability of proximity or deviation from any defined center. This contrasts with classical estimators that utilize average or median deviations from a mean or median center.

MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis.

Purpose: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. Approach: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. Results: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. Conclusions: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.

Performance Evaluation of Noninvasive Prenatal Testing in Screening Chromosome Disorders: A Single-Center Observational Study of 15,304 Consecutive Cases in China.

Objective: This study was to evaluate the performance of noninvasive prenatal testing (NIPT) in detecting fetal chromosome disorders in pregnant women. Methods: From October 1st, 2017, to December 31th, 2022, a total of 15,304 plasma cell free DNA-NIPT samples were collected for fetal chromosome disorders screening. The results of NIPT were validated by confirmatory invasive testing or clinical outcome follow-up. Further, NIPT performance between low-risk and high-risk groups, as well as singleton pregnancy and twin pregnancy groups was compared. Besides, analysis of 111 false-positive cases was performed. Results: Totally, NIPT was performed on 15,086 eligible venous blood samples, of which 179 (1.19%) showed positive NIPT results and 68 were further validated to be true positive samples via confirmatory invasive testing or follow-up of clinical outcomes. For common chromosome aneuploidies, sex chromosome abnormalities (SCA) and other chromosomal aneuploidies, the detection sensitivities of NIPT were all 100%, the specificities were 99.87%, 99.70%, and 99.68% and the positive predictive values (PPVs) were 65.45%, 31.82%, and 10.91%, respectively. No statistically significant variance in detection performance was observed among 2987 high-risk and 12,099 low-risk subjects, as well as singleton and twin pregnancy subjects. The concentration of cell-free fetal DNA of 111 false-positive cases ranged from 5.5% to 33.7%, which was higher than the minimum requirement of NIPT. Conclusion: With stringent protocol, NIPT shows high sensitivity and specificity for detecting fetal chromosome disorders in a large-scale clinical service, helping improving overall pregnancy management.

Evaluating the performance of selected military hospitals in Tehran in response to Covid-19 pandemic: A cross-sectional study.

Background and Aims: The rapid spread of coronavirus disease 2019 (Covid-19) led the need to admit a large number of infected people to hospitals in a short period of time, turning them into one of the most important responsive organizations. This study aims to evaluate the performance of selected military hospitals because they carried out a military operation in Tehran in response to the recent pandemic. Methods: This is a descriptive-analytical study. The statistical population of this study consisted of military hospitals responding to Covid-19 pandemic in Tehran. A checklist to evaluate the performance of hospitals in response to Covid-19 pandemic (six areas, 23 sub-areas and 152 items) was used as a data collection tool in this study. This tool had six domains, including risk management and planning, coordination and communication, infection prevention and control, diagnosis and treatment, education and training, and resource management. Results: The overall performance of selected hospitals was 63%, which indicated a good performance. The domain of coordination and communication obtained the lowest score. Conclusion: The investigated hospitals had good performance because they had a desirable access to resources. Periodic self-assessment and accreditation is recommended to improve the performance of these hospitals.

Performance and application of the total-body PET/CT scanner: a literature review.

BACKGROUND: The total-body positron emission tomography/computed tomography (PET/CT) system, with a long axial field of view, represents the state-of-the-art PET imaging technique. Recently, the total-body PET/CT system has been commercially available. The total-body PET/CT system enables high-resolution whole-body imaging, even under extreme conditions such as ultra-low dose, extremely fast imaging speed, delayed imaging more than 10 h after tracer injection, and total-body dynamic scan. The total-body PET/CT system provides a real-time picture of the tracers of all organs across the body, which not only helps to explain normal human physiological process, but also facilitates the comprehensive assessment of systemic diseases. In addition, the total-body PET/CT system may play critical roles in other medical fields, including cancer imaging, drug development and immunology. MAIN BODY: Therefore, it is of significance to summarize the existing studies of the total-body PET/CT systems and point out its future direction. This review collected research literatures from the PubMed database since the advent of commercially available total-body PET/CT systems to the present, and was divided into the following sections: Firstly, a brief introduction to the total-body PET/CT system was presented, followed by a summary of the literature on the performance evaluation of the total-body PET/CT. Then, the research and clinical applications of the total-body PET/CT were discussed. Fourthly, deep learning studies based on total-body PET imaging was reviewed. At last, the shortcomings of existing research and future directions for the total-body PET/CT were discussed. CONCLUSION: Due to its technical advantages, the total-body PET/CT system is bound to play a greater role in clinical practice in the future.

A novel IFN-CSM-CoCoSo approach for multiple-attribute group decision-making with intuitionistic fuzzy sets: An application in assessing corporate social responsibility performance.

With the rapid growth of the economy, enterprises have encountered a series of problems while pursuing economic benefits, such as food safety and environmental pollution issues, resource shortages and energy consumption issues, which affect the sustainable development of enterprises. Establishing a corporate performance evaluation system from the perspective of social responsibility, based on stakeholder theory and the importance of overall goals reflected in the weight of social responsibility indicators, is a very effective measure to achieve corporate social responsibility (CSR) goals through CSR motivation and stakeholders. The performance evaluation of CSR from the perspective of environmental accounting is a MAGDM. Recently, the CoCoSo technique and cosine similarity measure (CSM) technique was utilized to conduct the MAGDM. The intuitionistic fuzzy sets (IFSs) are utilized as a technique for conducting uncertain information during the performance evaluation of CSR from the perspective of environmental accounting. In this study, the intuitionistic fuzzy CoCoSo based on the CSM (IFN-CSM-CoCoSo) technique is built for MAGDM with IFSs. Finally, a numerical example for performance evaluation of CSR from the perspective of environmental accounting is conducted to verify the IFN-CSM-CoCoSo technique.

Design and performance evaluation of an off-grid hybrid solar system in a remote location: A case study of Legundi Island.

Legundi Island, located in Lampung Bay, Indonesia, has high solar energy potential but still relies on Diesel Power Plants. This research aims to design a hybrid Solar Power Plant and Diesel Power plant system that is environmentally friendly and reliable. Analysis was conducted using the Homer software. •This research designs a hybrid Solar Power Plant system with a capacity of 170.2 kWp and a battery of 1,036 kWh, integrated with three existing Diesel Power Plants units with a capacity of 200 kW.•This system can generate electricity of 296,391 kWh/year with a renewable energy fraction reaching 79.9% and an electricity surplus of 0.988%.•Fuel consumption decreased by 79.3% compared to the existing Diesel Power Plant system.

ACTOR: Adaptive Control of Transmission Power in RPL.

RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

Visualizing pursed lips breathing of patients with chronic obstructive pulmonary disease through evaluation of global and regional ventilation using electrical impedance tomography.

Objective. This study aims to explore the possibility of using electrical impedance tomography (EIT) to assess pursed lips breathing (PLB) performance of patients with chronic obstructive pulmonary disease (COPD).Methods. 32 patients with COPD were assigned equally to either the conventional group or the EIT guided group. All patients were taught to perform PLB by a physiotherapist without EIT in the conventional group or with EIT in the EIT guided group for 10 min. The ventilation of all patients in the final test were continuously monitored using EIT and the PLB performances were rated by another physiotherapist before and after reviewing EIT. The global and regional ventilation between two groups as well as between quite breathing (QB) and PLB were compared and rating scores with and without EIT were also compared.Results.For global ventilation, the inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB was significantly larger than those during QB for both group (P< 0.001). The inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB in the EIT guided group were higher compared to those in the conventional group (P< 0.001), as well as expiratory flow expiratory uniformity and respiratory stability were better (P< 0.001). For regional ventilation, center of ventilation significantly decreased during PLB (P< 0.05). The expiratory time constant during PLB in the EIT guided group was greater than that in the conventional group (P< 0.001). Additionally, Bland-Altman plots analysis suggested a high concordance between subjective rating and rating with the help of EIT, but the score rated after EIT observation significantly lower than that rated subjectively in both groups (score drop of -2.68 ± 1.1 in the conventional group and -1.19 ± 0.72 in the EIT guided group,P< 0.01).Conclusion.EIT could capture the details of PLB maneuver, which might be a potential tool to quantitatively evaluate PLB performance and thus assist physiotherapists to teach PLB maneuver to patients.