Objective Evaluation Criteria Research Articles

Leveraging Multivariable Linear Regression Analysis to Identify Patients with Anterior Cruciate Ligament Deficiency Using a Composite Index of the Knee Flexion and Muscle Force

Patients with anterior cruciate ligament (ACL) deficiency (ACLD) tend to have altered lower extremity kinematics and dynamics. Clinical diagnosis of ACLD requires more objective and convenient evaluation criteria. Twenty-five patients with ACLD before ACL reconstruction and nine healthy volunteers were recruited. Five experimental jogging data sets of each participant were collected and calculated using a musculoskeletal model. The resulting knee flexion and muscle force data were analyzed using a t-test for characteristic points, which were the time points in the gait cycle when the most significant difference between the two groups was observed. The data of the characteristic points were processed with principal component analysis to generate a composite index for multivariable linear regression. The accuracy rate of the regression model in diagnosing patients with ACLD was 81.4%. This study demonstrates that the multivariable linear regression model and composite index can be used to diagnose patients with ACLD. The composite index and characteristic points can be clinically objective and can be used to extract effective information quickly and conveniently.

Prediction system of rolling contact fatigue on crossing nose based on support vector regression

It is essential to assess the rolling contact fatigue (RCF) of turnouts and maintain them in advance. It saves a lot of money while protecting the safety of railway operations. In Germany, the damage on rails, especially crossing noses, mainly depends on the subjective judgment of experts. There are no objective and comprehensive evaluation criteria. This paper presents the application of image processing and supervised machine learning algorithms to crossing nose fatigue judgment. The fatigue characteristics of the crossing nose rolling contact surface along the life cycle of the crossing nose are analyzed. The study used crack information from magnetic particle inspection (MPI) images of crossing nose surfaces. It uses basic image processing methods to collect physical information about features of fatigue cracks in images. Existing feature selection methods are used to exclude irrelevant features and retain valuable features. And we select the best feature selection method through the regression results. Statistically significant crack features and combinations that depict the surface fatigue state are found. In this paper, by comparing several usually machine learning regression algorithms, it is found that the supervised learning of support vector machine regression (SVR) has achieved the best results in the regression fitting of the crack feature data in this paper. The regression results form a simple system to evaluate the life cycle of crossing nose. The system finds the location of cracks that can create dangerous defects in the crossing nose surface. The research result consists of the early prediction of rail contact fatigue.

Role of computed tomography findings, complete blood count parameters and systemic inflammatory markers for predicting the severity in interstitial lung diseases.

The present study aimed to find quantitative and semiquantitative methods to detect the development of fibroproliferative processes at an early stage and predict the severity and prognosis of the disease in interstitial lung diseases (ILDs) using High-Resolution Computed Tomography (HRCT), Pulmonary Function Tests (PFTs) and Complete Blood Count (CBC) parameters. A total of 63 patients (26 female and 37 male) who were admitted to our hospital between January 2014 and January 2018, whose follow-ups were regular and who underwent HRCT, PFT, and CBC examinations on the same day, were included in our study. The median age of the patients included was 65 years (range: 47-79). There were significant differences among the mild, moderate, and severe form ILD groups created using the Warrick scoring system for NLR, neutrophil count, and PNR values (p = 0.025, 0.035, 0.006, respectively). Also, there were significant differences among the groups for FVC, FEV1/FVC, PAD, RAA, RV/LV ratio, MLnMD, and MLnC values. Correlation analyses between the parameters revealed significant relationships between Warrick Score, and NLR and neutrophil count, PNR, FVC, FEV1/FVC, PAD, RAA, RV/LV ratio, MLnMD, and MLnC . The results of the present study suggested that NLR, neutrophil count, and PNR values could be used as objective evaluation criteria to determine the severity and prognosis in interstitial lung diseases. Also, usage of Warrick Score, FVC, FEV1/FVC, PAD, RAA, RV/LV ratio, MLnMD, and MLnC values could provide quantitative and semiquantitative data for an objective evaluation. Carrying out multicenter studies and creating a scoring system using these parameters could create standardization in determining the prognosis of patients with ILD.

Thermal and Wet Comfort of Clothing in Different Environments Based on Multidimensional Sensor Data Fusion and Intelligent Detection

With the improvement of quality of life, people pay more and more attention to the comfort performance of clothing, of which thermal and wet comfort is an important part of evaluating the comfort of clothing, referring to the performance of keeping the human body in a reasonable thermal and wet state. When the human body sweats a lot or is in a highly humid environment, the clothing fabric will be soaked to make people feel wet, which seriously affects the comfort performance of clothing wear, and with the rapid development of sensing technology, the comfort of human clothing can be comprehensively evaluated by a variety of sensing data (clothing pressure, temperature, humidity, and heart rate). Therefore, how to analyze and process these data and establish an objective and accurate evaluation criterion for clothing comfort is a difficult problem and has attracted the attention of many researchers. In this paper, an improved kernel function fuzzy kernel c-means clustering algorithm is used to analyze the pressure at specific points in human activities. Unsupervised clustering analysis was performed for five clustering metrics (mean, pressure range, temperature range, humidity range, and heart rate variability). The clustered samples were learned and discriminated by a support vector machine to determine the comfort level of the clothing. The method can be applied to multi-indicator and multiclassification problems, providing smart clothing researchers with an intelligent, objective, and accurate method for evaluating clothing comfort. Experiments show that the method designed in this paper has good performance experience in terms of mean value, pressure range, temperature range, humidity range, and heart rate variability.

An evaluation index system for regional mobile SARS-CoV-2 virus nucleic acid testing capacity in China: a modified Delphi consensus study

BackgroundLarge-scale detection has great potential to bring benefits for containing the COVID-19 epidemic and supporting the government in reopening economic activities. Evaluating the true regional mobile severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus nucleic acid testing capacity is essential to improve the overall fighting performance against this epidemic and maintain economic development. However, such a tool is not available in this issue. We aimed to establish an evaluation index system for assessing the regional mobile SARS-CoV-2 virus nucleic acid testing capacity and provide suggestions for improving the capacity level.MethodsThe initial version of the evaluation index system was identified based on massive literature and expert interviews. The Delphi method questionnaire was designed and 30 experts were consulted in two rounds of questionnaire to select and revise indexes at all three levels. The Analytic Hierarchy Process method was used to calculate the weight of indexes at all three levels.ResultsThe evaluation index system for assessing the regional mobile SARS-CoV-2 virus nucleic acid testing capacity, including 5 first-level indexes, 17 second-level indexes, and 90 third-level indexes. The response rates of questionnaires delivered in the two rounds of consultation were 100 and 96.7%. Furthermore, the authority coefficient of 30 experts was 0.71. Kendall’s coordination coefficient differences were statistically significant (P < 0.001). The weighted values of capacity indexes were established at all levels according to the consistency test, demonstrating that ‘Personnel team construction’ (0.2046) came first amongst the five first-level indexes, followed by ‘Laboratory performance building and maintenance’ (0.2023), ‘Emergency response guarantee’ (0.1989), ‘Information management system for nucleic acid testing resources’ (0.1982) and ‘Regional mobile nucleic acid testing emergency response system construction’ (0.1959).ConclusionThe evaluation system for assessing the regional mobile SARS-CoV-2 virus nucleic acid testing capacity puts forward a specific, objective, and quantifiable evaluation criterion. The evaluation system can act as a tool for diversified subjects to find the weak links and loopholes. It also provides a measurable basis for authorities to improve nucleic acid testing capabilities.

Evaluation and Improvement of Shifting Quality of the Vehicle Gearbox from the Perspective of Sustainable Development in China’s Vehicle Industry

Energy saving and environmental protection are still two important themes in the vehicle industry. The recent rapid transition in energy markets and the technological advances in the demand-side interventions have renewed attention on the driving behavior of the vehicle consumers. The inappropriate gearshift performance of vehicle gearbox would not only increase the fuel consumption, but also interfere with the driver’s driving emotional state, so that the driver is easy to feel uncomfortable and boring, thus further affecting the fuel consumption. Hence, based on the necessity of improving the gearshift performance of vehicle gearbox, this study adopts GSA test technology to study the problems existing in the shifting quality of the commercial vehicle, so as to achieve high shifting efficiency and low fuel consumption. It could establish the subjective and objective evaluation criteria of the gearshift performance of commercial vehicles determined by the test, which provides a reliable theoretical basis for the optimal design of products and the evaluation of gearshift performance. Then, the technicians could formulate the corresponding shift manipulating schemes and optimal matching steps accordingly, to reach the required technical indexes of power, stability and transmission of the whole vehicle system, thus providing a direction for reducing fuel consumption and emissions, and improving the gearshift performance and quality of the whole vehicle.

A combination of multi-objective genetic algorithm and deep learning for music harmony generation

Automatic Music Generation (AMG) has become an interesting research topic for many scientists in artificial intelligence, who are also interested in the music industry. One of the main challenges in Automatic Music Generation is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction. Also, original music contains different elements that should work together, such as melody, harmony, and rhythm; but in the most of previous works, Automatic Music Generation works only for one element (e.g., melody). Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory; and the other two objective functions are modeled scores provided by music experts and ordinary listeners. The scoring of experts and listeners separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks. The proposed music generation system tries to maximize mentioned objective functions to generate a new piece of music, including melody and harmony. The results show that the proposed method can generate pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar.

Frequency and Predictors of Trauma Transfer Futility to a Rural Level I Trauma Center

IntroductionTransfer of trauma patients whose injuries are deemed unsurvivable, often results in early death or transition to comfort care and could be considered misuse of health care resources. This is particularly true where tertiary care resources are limited. Identifying riskfactors for and predicting futile transfers could reduce this impact and help to optimize triage and management. MethodsA retrospective study of interfacility trauma transfers to a single rural Level I rauma center from 2014 to 2019. Futility was defined as death, hospice, or declaration of comfort measures within 48 h of transfer without procedural or radiographic intervention at the accepting center. Multiple logistic regressions identified independent predictors of futile transfers. The predictive power of Mechanism,Glasgow coma scale, Age, and Arterial pressure (MGAP), an injury severity score based on Mechanism, Glasgow coma scale, Age, and systolic blood Pressure, were evaluated. ResultsOf the 3368 trauma transfers, 37 (1.1%) met criteria as futile. Futile transfers occurred among patients who were significantly older with falls as the most common mechanism. Age, Glasgow coma scale, systolic blood Pressure and Injury Severity Score were significant (P < 0.05) independent predictors of futile transfer. MGAP had a high predictive power area under the receiver operating characteristic (AUROC 0.864, 95% confidence interval 0.803-0.925) for futility. ConclusionsA small proportion (1.1%) of transfers to a rural Level I trauma center met criteria for futility. Predictive tools, such as MGAP scoring, can provide objective criteria for evaluation of transfer necessity and prompt care pathways that involve pre-transfer communications, telemedicine, and/or patient centered goals of care discussions. Such tools could be used in conjunction with a more granular assessment regarding potential operational barriers to reduce futile transfers and to enhance optimization of resource utilization in low-resource service areas.

MRDN: A lightweight Multi-stage residual distillation network for image Super-Resolution

For the deep learning based super-resolution (SR) reconstruction method, researchers try to expand the receptive field to improve the reconstruction quality. With the increase of network depth, the consumption of computing resources is impressive. Therefore, SR technology is challenging to apply to small mobile clients. To deal with this defect, we propose a lightweight multi-stage residual distillation network (MRDN) for the SR task in this paper. The model has made the following two main improvements: First, we design a multi-stage residual distillation block (MRDB). It combines the channel separation and the skip connection to reduce the parameter number and guarantee the network’s performance by residual learning. Secondly, we propose efficient pixel attention (EPA) module, which weights different channels according to their importance so that the network can learn more details of pictures. Experiments show that the proposed MRDN is superior to the state-of-the-art models in subjective visual effects and objective evaluation criteria such as PSNR, SSIM, and computational complexity. Taking the basic data set manga109 as an example, the PSNR of this algorithm on scale ×4 reaches 30.66, and the average inference time is 0.021 s. The performance surpasses RFDN, the champion of AIM 2020 Challenge on Efficient Super-Resolution.

Ethical Management of COVID-19 Pandemic at a Neurological Hospital: The Ethicovid Report.

During the first wave of the coronavirus disease-2019 (COVID-19) pandemic, it was necessary to prepare for the possibility of triaging patients who could benefit from access to an intensive care unit (ICU). In our neuroscience institution, the challenge was to continue to manage usual neurological emergencies as well as the influx of COVID-19 patients. We report the experience of an ethical consulting unit to support care clinical decisions during the first wave of the pandemic (March 16 to April 30, 2020). Three objective evaluation criteria were defined: 2 of these criteria, patient's factors and general disease severity (Simplified Acute Physiology Score II), were common to all patients, and the third was the specific severity of the disease (neurological for brain injury, respiratory for COVID-19). Given our scarce resources, we used a high probability of a 3-month modified Rankin Scale ≤3 as the criterion for further resuscitation and management. A total of 295 patients were admitted during the first pandemic wave; 111 with COVID-19 and 184 with neurological emergencies. The ethical unit's expertise was sought for 75 clinical situations in 56 patients (35 COVID-19 and 21 neurological). Decisions were as follows: 11% no limitation on care, 5% expectant care with reassessment (maximum therapy to assess possible progress pending decision), 67% partial limitation (no intensification of care or no transfer to ICU), and 17% limitation of curative care. At no time did a lack of availability of ICU beds require the ethical unit to advise against admission to the ICU. Our ethical consulting unit allowed for collegial ethical decision-making in line with international recommendations. This model could be easily transferred to other triage situations, provided it is adapted to the local context.

Masking and noise reduction processing of music signals in reverberant music

Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction method could reduce the noise in reverberant music more effectively; under an objective evaluation criterion, the signal-to-noise ratio, the de-reverberated music signal processed by the improved spectral subtraction method had a higher signal-to-noise ratio; under a subjective evaluation criterion, mean opinion score (MOS), the de-reverberated music signal processed by the improved spectral subtraction method also had a better evaluation.

Simultaneous Precise Localization And Classification of metal rust defects for robotic-driven maintenance and prefabrication using residual attention U-Net

We present a Simultaneous Precise Localization And Classification architecture employing a U-Net model (called SPLAC U-Net) for an automated simultaneous detection/localization of corrosion and recognition of rust grade from RGB images on metal structures. The proposed architecture is compatible with the innovative idea of automated maintenance and prefabrication since (i) rust detection is carried out at pixel level accuracy in a highly precise manner and (ii) localization/classification is performed simultaneously. Prior works on corrosion identification tend to focus either on localizing defected regions with bounding boxes, with low therefore localization precision, or on only visual inspection or classification and thus they are incompatible with the prefabrication concept. The proposed scheme consists of three layers. In the first layer, we utilize a lightweight deep U-Net model for semantically localizing corroded regions in images. In the second layer, we adopt a novel data projection scheme for refining the contours of the detected objects (i.e., defected regions) of the first layer, enhancing, therefore, localization precision. We find that the network after the boundary refinement process exceeds the typical deep learning methods performance on the accuracy, F1-score, and Intersection over Union (IoU) metrics. In the third and final layer of SPLAC U-Net, we extend the U-Net model by adding residual attention modules to efficiently classify multiple rust grades based on the ISO 8501-1 taxonomy. Experimental results using objective evaluation criteria, such as accuracy, precision, recall, F1-score, IoU, and Receiver Operating Characteristic Curve (ROC) show the outperformance of the proposed SLPAC U-Net in simultaneous detecting/localizing and classifying rust regions compared to other state-of-the-art methods exploiting either color distribution properties or traditional deep learning models. Lastly, a comparison of the computational performance shows that the total required time of the proposed SPLAC U-Net model (segmentation + projection refinement) is less than solely segmenting an image by using traditional deep learning models.

Synthetic Aperture Radar Image Compression Based on a Variational Autoencoder

Given the uniqueness of synthetic aperture radar (SAR) images, traditional optical image compression algorithms cannot fully exploit their redundant information. To improve SAR image compression in terms of rate–distortion performance and visual perception, an end-to-end SAR image compression convolutional neural network (CNN) model based on a variational autoencoder is proposed. The proposed CNN model consists of a main autoencoder and a hyper autoencoder. To reduce dependencies in latent space, a joint transform of linear CNN and nonlinear generalized divisive normalization (GDN) activation is applied in the main autoencoder. Moreover, residual blocks are combined with the transforms to boost the efficiency of feature learning and make use of subpixels to improve the quality of reconstructed images. Instead of a fixed entropy model, a conditioned entropy model that works with a hyperprior network is used to learn the distribution of latents, which helps to further improve the compression quality. During training, the model is optimized by evaluating the rate–distortion performance. The experimental results show that the proposed method can achieve better distortion performance than JPEG, JPEG2000, and the available CNN-based method in terms of objective evaluation criteria and human vision perception quality.

Evaluation of Macular Function after Vitrectomy in Diabetic Macular Edema

Purpose: To document the effects of successful vitrectomy on retinal function and anatomy in diabetic patients. Methods: Three-port pars plana vitrectomy with detachment of posterior vitrous face was performed in 30 eyes of 25 patients with diabetic macular oedema DME (11 male, 14 Female).For each patients, visual acuity (VA) examination, measurement of retinal thickness using optical coherence tomography (OCT), full field electro-retinogram (ERG) and multifocal eletroretinogram (MF-ERG) were performed before and 1week,1month and 3months after vitrectomy. Results: Mean postoperative visual acuity was significantly improved (P&lt;0.05); mean retinal thickness was significantly (P=0.001 ) decreased after 3monthes of surgery (from 450±150 into 220±50 micron ) .b-wave amplitudes of all cone and rod responses of ERG were significantly decreased in all vitrectomized eyes after 1 week. At one month, rod response was still unimproved but improved after 3months. Reduction in foveal Function as well as in para-foveal areas detected in the MF-ERG within 1st month. Then, mean P1 wave amplitude of MF-ERG of central ring increased and mean P1 wave implicit time decreased. These changes of MF-ERG parameters observed 3 months after vitrectomy. Conclusion: Multifocal electroretinogram can be useful to provide objective criteria for functional evaluation before and after vitrectomy in diabetic oedema.

Sustainable improvement and evaluation of the shifting smoothness of vehicle transmission

Energy savings and environmental protection are the only way for the sustainable development of the automotive industry. The poor shifting performance of automobile transmission may reduce the driver's driving pleasure and make the driver feel tired. In addition, improper shifting would also increase fuel consumption. Therefore, in view of the importance of improving the shift performance of vehicle transmission, GSA testing technology was employed for the existing shift quality problems of commercial vehicles to continuously realize high shifting efficiency and low fuel consumption. Through the establishment of subjective and objective evaluation criteria of the experimentally determined shift performance of commercial vehicles, a reliable theoretical basis is provided for product optimization design and shift performance evaluation. As a result, the shift control strategy and optimization matching measures are formulated to ensure that the power, smoothness and transmission of the whole vehicle system meet the technical requirements and finally achieve a rapid and stable gearshift. Thus, this work unveils the high potential of improving the shift performance and quality of the whole vehicle and is expected to have an impact on reducing fuel consumption and emissions in the relevant automotive industry, contributing to the establishment of a more sustainable society.

Objective criteria for MRI evaluation of the effectiveness of treatment of bone metastases in patients with prostate cancer and breast cancer: systematic review and meta-analysis

BACKGROUND: The possibility of a personalized approach to the treatment of metastatic prostate cancer and breast cancer requires objective methods for the evaluation of the response of foci treatment in the skeleton. The proven high efficiency of MRI in detecting bone metastases, in combination with the absence of ionizing radiation, has laid the groundwork for using this method in monitoring the treatment course based on objective criteria for evaluation of the therapeutic outcome.&#x0D; AIM: To assess the possibilities of quantitative and semi-quantitative parameters of MRI-evaluation of treatment efficacy (radiation, chemotherapy, hormone therapy, and targeted therapy) of bone metastases that were used in prostate and breast cancer clinical trials.&#x0D; MATERIALS AND METHODS: We searched the databases Embase, PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), eLibrary until April 1, 2021, using the following keywords: magnetic resonance imaging, MRI, DWI, treatment response, prostate or breast cancer, and bone metastasis. We only included studies related to the MRI-evaluation of treatment efficacy of any type of therapeutic intervention (with the exception of surgery) for metastatic skeletal lesions in this review.&#x0D; RESULTS: We selected and analyzed 11 out of 312 sources found as a result of the search. It allowed us to identify four groups of objective MRI criteria for evaluating the therapeutic effect in metastatic bone lesions in patients with prostate and breast cancer, including the dynamics of sizes, signal intensity on DWI, ADC, and tumor total diffusion volume (tDV). Changes in these quantitative and semi-quantitative indicators, with only one exception, had the same direction, although they differed in numerical values. A random-effects model was used for analysis considering the presence of statistically significant heterogeneity (p 0,1 for 2 test; I2 40%),. The change in ADC as a result of treatment averaged +0.35 [+0.12; +0.49] 103 mm2/s, with average values of ADC before treatment ― 0.83 [0.71; 1.03] 103 mm2/s, after treatment ― 1.18 [0.83; 1.49] 103 mm2/s.&#x0D; CONCLUSION: MRI is an informative technique for the objective evaluation of the response of bone metastases to therapy in patients with prostate cancer and breast cancer based on quantitative and semi-quantitative parameters. It has significant potential as a diagnostic test instrument for monitoring the effectiveness of treatment of metastatic skeletal lesions.

Діагностика та лікування інвагінації кишечника в дітей

Інвагінація кишок є найбільш частою формою набутої непрохідності шлунково-кишкового тракту в дітей. Метою роботи є вивчення особливостей клінічного перебігу та тактики лікування інвагінації кишечника в дітей і проведення аналізу можливостей діагностичних, клінічних та спеціальних методів обстеження. Матеріали та методи дослідження. Проведено аналіз результатів лікування 272 дітей у клініці дитячої хірургії з 2004 по 2015 рік. Розроблені об’єктивні критерії оцінки стадій інвагінації, що корелюють зі ступенем ендотоксикозу, змінами функції дихання та кровообігу, порушеннями перистальтики кишечника, а також даними ультразвукового дослідження органів черевної порожнини. Результати та обговорення. У хворих з І стадією інвагінації (233 дитини) ознаки ендотоксикозу не виявлені або вони були невиражені. У 10 дітей здійснене оперативне лікування, у 4 випадках — лапароскопічно. 32 пацієнти мали ІІ стадію інвагінації, у 8 випадках інвагінація розпрямлена консервативно з першої спроби. Другої спроби в дітей з ІІ стадією інвагінації не робимо. Оперативне розпрямлення здійснене 24 хворим. ІІІ стадія інвагінації кишечника в дітей (7 хворих) мала прояви ендотоксикозу 3-го ступеня. Усім хворим з ІІІ стадією інвагінації виконана серединна лапаротомія. У 5 випадках виявлений некроз інвагінату, цим дітям виконана резекція кишки з подальшим накладанням кінцевої ілеостоми та інтубацією тонкої кишки. У решти (2) хворих інвагінат вдалося розпрямити, і кишка була визнана життєздатною. Накладання первинного анастомозу після резекції кишки в умовах перитоніту вважаємо недопустимим. Висновки: 1. Комплексне обстеження дітей з використанням лабораторних і інструментальних методів стало підставою для виділення 3 стадій інвагінації кишечника, що корелювали зі ступенем ендотоксикозу та порушеннями функції кишечника: I стадія — компенсована; II стадія — субкомпенсована; III стадія — декомпенсована. 2. Об’єктивна оцінка стадій інвагінації дозволяє диференціювати обсяг заходів на етапах лікування залежно від стадії захворювання, що значно спрощує вирішення тактичних завдань, які стоять перед хірургом та анестезіологом, до, під час і після дезінвагінації.

Construction of a human body model for acupuncture and moxibustion treatment by colored Petri nets

Acupuncture and moxibustion treatment has been widely spread all over the world because it has few side effects and is effective for ahead sick and incurable disease. However, the treatment is often performed empirically and clinically, and the mechanism and process of the treatment are not yet scientifically elucidated. Therefore, it is required to establish objective and unified research methods and evaluation criteria that incorporate modern Western medicine and scientific and technological viewpoints. In this paper, a human body model is constructed, which includes the internal organs and the meridians for acupuncture and moxibustion treatment based on traditional Chinese medicine using colored Petri nets. This model aims at expressing the relationship between acupoints and internal organs realistically and accurately in acupuncture and moxibustion treatment, and leading to realization of an objective analysis method of the mechanism and process of acupuncture and moxibustion treatment. Firstly, the calculation of the acupoints’ efficacy on internal organs is discussed, and measurement equations and model construction methods are proposed. Next, an interface is established as a bridge to connect the internal organs and the meridians with acupoints. By Java language, a simulation system is developed based on the proposed Petri net model. Finally, simulations of acupuncture and moxibustion treatment are performed to verify the validity of model.

Construction and application of quality evaluation index system for remote-sensing image fusion

To objectively and justly compare the results of remote-sensing image fusion, evaluate the fusion algorithm, and optimize the fusion process, a quality evaluation index system is constructed by considering both qualitative and quantitative evaluations. The quantitative evaluation indexes are divided into three types based on the analysis of evaluation methods of the fusion quality of primary remote-sensing images. The applicability and effectiveness of six pixel-level fusion methods of optical remote-sensing image fusion were verified based on the constructed quality evaluation index system used to evaluate those methods in offshore and archipelagic areas. The experimental results indicate that the constructed quality evaluation index system evaluates the fusion algorithm objectively, comprehensively, and accurately. The intensity–hue–saturation transformation-based method distorts spectral features of the original image and easily causes spectrum degradation, whereas the wavelet transformation-based method has an advantage in the preservation of spectral features but efficiently produces blockiness and image penumbrae. The principal component analysis transformation-based method preserves more detailed textures and structural characteristics of original images but loses some physical properties. The Brovey transformation-based method results in spectral distortion, the high pass filter-based method shows a clear boundary of surface features after image fusion, and the Gram–Schmidt-based method shows a high ability of spectral information retention, but the latter performs poorly in the preservation of spatial information. The proposed quality evaluation index system of remote-sensing image fusion can be used as the objective effect evaluation criteria for remote-sensing image fusion and offers a reference for future applications of fusion results.

Cephalometric predictors for optimal soft tissue profile outcome in adult Asian class I subjects treated via extraction and non-extraction. A retrospective study

The aim of this retrospective study was to identify cephalometric predictors associated with favourable soft tissue profile outcomes after premolars extraction and non-extraction in class I malocclusion subjects. A total of 80 subjects, treated with non-extraction and premolars extraction (40 subjects each), were equally divided into favourable (FG) and unfavourable (UFG) groups using subjective and objective soft tissue profile outcome assessment methods. An independent t-test was utilized for the comparison of cephalometric measurements between the non-extraction (NE) and premolars extraction (PME) treatment modalities. Cox proportional hazard algorithm regression analysis was performed to identify cephalometric factors associated with favourable soft tissue outcomes. The pre-treatment mean age of the NE group was 20.2±2.3 and PME group was 20.2±2.5 years. After dividing the sample of the NE and PME groups according to subjective and objective soft-tissue outcome assessment criteria, FG and UFG consisted of 20 subjects each. Cox proportional hazard algorithm regression analysis found upper incisor to NA angle (95% CI: 1.033, 1.196) to be associated with FG in NE and upper incisor to SN (95% CI: 1.018, 1.206) and ANB angle (95% CI:1.165, 3.608) in PME. There was a statistically significant strong correlation between subjective and objective evaluation methods (P≤0.001). Cephalometric analysis is a valuable tool to predict soft-tissue outcomes after NE and PME. Increased upper incisors inclinations at the start of NE treatment result in favourable soft tissue profile outcomes. Slightly convex profile and proclined maxillary incisors are the predictors of favourable soft tissue profile outcome after PME. There was a statistically significant association between subjective and objective evaluation criteria of soft tissue outcomes.