Outlier identification and group satisfaction of rating experts: density-based spatial clustering of applications with noise based on multi-objective large-scale group decision-making evaluation

Group satisfaction is a trending issue in large-scale group decision- making (LSGDM) but most existing studies maximize the group satisfaction of LSGDM from the perspective of consensus. However, the clustering algorithm in LSGDM also has an impact on group satisfaction. Hence, this paper proposes a density-based spatial clustering of applications with noise (DBSCAN)-based LSGDM approach in an intuitionistic fuzzy set (IFS) environment. The DBSCAN algorithm is used to identify experts with outlier ratings that can reduce the time consumption and iterations of the LSGDM process and maximize the satisfaction of the group decision. An easy-to-use function is then provided to estimate group satisfaction. Finally, a numerical example of data centre supplier evaluation and comparative analysis is constructed to validate the rationality and feasibility of the proposed DBSCAN-based LSGDM approach in an IFS environment. The results demonstrate that the proposed method can effectively identify outliers in expert ratings and improve group satisfaction in the LSGDM process

A Study of Dance Movement Capture and Posture Recognition Method Based on Vision Sensors

With the development of technology, posture recognition methods have been applied in more and more fields. However, there is relatively little research on posture recognition in dance. Therefore, this paper studied the capture and posture recognition of dance movements to understand the usability of the proposed method in dance posture recognition. Firstly, the Kinect V2 visual sensor was used to capture dance movements and obtain human skeletal joint data. Then, a three-dimensional convolutional neural network (3D CNN) model was designed by fusing joint coordinate features with joint velocity features as general features for recognizing different dance postures. Through experiments on NTU60 and self-built dance datasets, it was found that the 3D CNN performed best with a dropout rate of 0.4, a ReLU activation function, and fusion features. Compared to other posture recognition methods, the recognition rates of the 3D CNN on CS and CV in NTU60 were 88.8% and 95.3%, respectively, while the average recognition rate on the dance dataset reached 98.72%, which was higher than others. The experimental results demonstrate the effectiveness of our proposed method for dance posture recognition, providing a new approach for posture recognition research and making contributions to the inheritance of folk dances. Doi: 10.28991/HIJ-2023-04-02-03 Full Text: PD

Factors associated with distant metastasis in pediatric thyroid cancer: evaluation of the SEER database

Objectives: Controversies regarding factors associated with distant metastasis in pediatric thyroid cancer remain among the scientific community. The aim of this study was to investigate factors influencing distant metastasis in pediatric thyroid cancer. Methods: We reviewed 1376 patients (aged 2 to 18 years) with thyroid cancer treated between 2003 and 2014. Data collected and analyzed included sex, race, age at diagnosis, year of diagnosis, pathological type, number of tumor foci, tumor extension, T-stage, N-stage, surgical procedure and radiation. Univariate and multivariate analyses were conducted to evaluate factors influencing distant metastasis of pediatric thyroid cancer. Results: In the univariate analysis, factors influencing distant metastasis of thyroid cancer were age at diagnosis (P 0.05). Furthermore, according to chi-squared test, younger pediatric thyroid cancer patients with higher T- and N-stages are more likely to have distant metastasis. Conclusion: Age at diagnosis, T-stage and N-stage influence distant metastasis of thyroid cancer patients aged 2 to 18 years; accordingly, more radical treatments may need to be used for patients with those risk elements

Complex Optical Fields Generation Using a Vectorial Optical Field Generator

Owing to their unique properties, optical fields with complex spatial distribution in the cross section have attracted great attention. These complex optical vector fields were discovered to have many interesting applications in particle manipulation and acceleration, nanoscale optical imaging and so on. This thesis is mainly composed of two parts. In the first part, the theory and experimental setup for a Vector Optical Field Generator (VOF-Gen) that is capable of creating an arbitrary beam with independent controls of phase, amplitude and polarization on the pixel level utilizing high resolution reflective phase-only liquid crystal (LC) spatial light modulator (SLM) will be reviewed. Experimental results will be presented in the second part, where various optical fields containing phase, amplitude, polarization and retardation modulations are successfully demonstrated.The capability to modulate each of the individual degree of freedoms will be verified by our experimental results. These demonstrated capabilities lead to a five ring structured complex vectorial optical field with multiple controllable parameters with unique high-numerical-aperture focusing properties that may find important applications in super-resolution imaging, optical nanofabrication, and optical trapping and manipulation

Structured Light from Pupil Plane to Focal Field

It is well known that an optical field consists of phase, amplitude, and the state of polarization (SOP). Spatially customized optical field within the cross section have drawn significant attention recently and expected to lead to new effects and phenomena that can expand the functionality and enhance the capability of optical systems. Under high numerical-aperture (NA) focusing these customized structured lights are expected to exhibit novel phenomena when interact with various type of structured-materials. These interactions may find important applications in super-resolution microscopy, particle trapping and manipulation, materials characterization, as well as three-dimensional high-density optical storage.This dissertation is organized in two parts. In the first part, many different methods to generate complex optical fields using diffractive elements will be reviewed. Among these methods, we will focus on the Vectorial Optical Field Generator (VOF-Gen), which is capable of creating an arbitrary beam with independent controls of phase, amplitude and polarization on the pixel level utilizing high resolution reflective phase-only liquid crystal (LC) spatial light modulator (SLM). Experimental results will be presented, where various optical fields containing phase, amplitude, polarization and retardation modulations are successfully demonstrated.In the second part, focus shaping, three-dimensional (3D) state of polarization and magnetization control, and focusing with spatially variant polarization are investigated and demonstrated. An approach to create diffraction-limited optical focal spots with arbitrarily oriented magnetic dipolar field components in 4Pi microscopy configuration is proposed. This is achieved by focusing two counter-propagating modulated vector beams consisting of complex intensity and polarization distribution. Through combining the magnetic dipole radiation pattern and the Richards-Wolf vectorial diffraction method, the required illuminations at the pupil plane of a 4Pi focusing configuration for the reconstruction of magnetic dipole focal field are found analytically. Furthermore, the orientation of the doughnut shape focal field can be rotated arbitrarily by modulating the pupil field distribution carefully. As an extension, a three-dimensional optical bubble encloses a transversely spinning magnetic field can be obtained by introducing a second magnetic dipole oriented in the orthogonal plane with appropriate amplitude and phase differences

Evaluation of Surgical Effect of Atrial Septal Defect with Tricuspid Regurgitation by Transesophageal 3D Echocardiography Based on MC Image Reconstruction Algorithm

This study was to explore the application and effect of three-dimensional (3D) images of the esophagus in the treatment of atrial septal defect (ASD) combined with tricuspid regurgitation (TR) surgery under the processing of marching cubes (MC) image reconstruction algorithm. The MC image reconstruction algorithm was improved as the optimized MC image reconstruction algorithm. 100 patients who had successfully undergone the ASD combined with TR surgery in the hospital from January 2017 to December 2019 were selected as the research objects and grouped based on size of the defect. The preoperative and postoperative conditions of the patients were analyzed with the MC image reconstruction algorithm. Compared with the traditional MC image algorithm, the optimized MC was advanced with less running time and fewer fixed points (P<0.05). There were significant differences in TR of all ASD patients after the surgery (P<0.05), and the TR of all patients showed obvious declines from the 1st day to 30th day after surgery and gradually stabilized from the 3rd month to the 6th month after surgery. Compared with patients with normal pulmonary artery pressure, the amount of TR in patients with elevated pulmonary artery pressure increased significantly, and the difference was statistically significant (P<0.05). In addition, the improvement of TR after occlusion was correlated with the preoperative ASD of the patient. The optimized MC algorithm had been improved greatly in the number of fixed points and running time. The analysis using the optimized MC algorithm showed that ASD patients generally suffered different degrees of TR, TR increased with the increase of the defect, and good effect could be achieved in surgery of all kinds of ASD patients

Diagnostic accuracy of adrenal imaging for subtype diagnosis in primary aldosteronism: systematic review and meta-analysis

Objectives Accurate subtype classification in primary aldosteronism (PA) is critical in assessing the optimal treatment options. This study aimed to evaluate the diagnostic accuracy of adrenal imaging for unilateral PA classification.Methods Systematic searches of PubMed, EMBASE and the Cochrane databases were performed from 1 January 2000 to 1 February 2020, for all studies that used CT or MRI in determining unilateral PA and validated the results against invasive adrenal vein sampling (AVS). Summary diagnostic accuracies were assessed using a bivariate random-effects model. Subgroup analyses, meta-regression and sensitivity analysis were performed to explore the possible sources of heterogeneity.Result A total of 25 studies, involving a total of 4669 subjects, were identified. The overall analysis revealed a pooled sensitivity of 68% (95% CI: 61% to 74%) and specificity of 57% (95% CI 50% to 65%) for CT/MRI in identifying unilateral PA. Sensitivity was higher in the contrast-enhanced (CT) group versus the traditional CT group (77% (95% CI 66% to 85%) vs 58% (95% CI 50% to 66%). Subgroup analysis stratified by screening test for PA showed that the sensitivity of the aldosterone-to-renin ratio (ARR) group was higher than that of the non-ARR group (78% (95% CI 69% to 84%) vs 66% (95% CI 58% to 72%)). The diagnostic accuracy of PA patients aged ≤40 years was reported in four studies, and the overall sensitivity was 71%, with 79% specificity. Meta-regression revealed a significant impact of sample size on sensitivity and of age and study quality on specificity.Conclusion CT/MRI is not a reliable alternative to invasive AVS without excellent sensitivity or specificity for correctly identifying unilateral PA. Even in young patients (≤40 years), 21% of patients would have undergone unnecessary adrenalectomy based on imaging results alone

Evaluating the Robustness to Instructions of Large Language Models

Recently, Instruction fine-tuning has risen to prominence as a potential method for enhancing the zero-shot capabilities of Large Language Models (LLMs) on novel tasks. This technique has shown an exceptional ability to boost the performance of moderately sized LLMs, sometimes even reaching performance levels comparable to those of much larger model variants. The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks. We conducted an exploration of six models including Alpaca, Vicuna, WizardLM, and Traditional Task-oriented Models(Flan-T5-XL/XXL, T0++) using real-world relation extraction datasets as case studies. We carried out a comprehensive evaluation of these instruction-following LLMs which have been tuned based on open-domain instructions and task-oriented instructions. The main discussion is their performance and robustness towards instructions. We have observed that in most cases, the model's performance in dealing with unfamiliar instructions tends to worsen significantly, and the robustness of the model for RE instructions deteriorates compared to QA. Further, we discovered that up until a certain parameter size threshold (3B), the performance of the FLAN-T5 model improves as the parameter count increases. The robustness of different scales of FLAN-T5 models to RE instruction is worse than the robustness to QA instruction.Comment: In our study, erroneous data analysis inadvertently led to misleading outcomes. Incorrect variables were included, distorting results. This emphasizes the significance of robust data processing and analysis techniques in researc

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve the accuracy of neural networks for HAR. We addressed this problem by proposing a method that combines the improved principal component analysis (PCA) and the improved VGG16 model (a pre-trained 16-layer neural network model) to enhance the accuracy of HAR under small-scale datasets. This method used the improved PCA to enhance features of the extracted components and reduce the dimensionality of the data. The VGG16 model was improved by deleting the complex Fully-Connected layers and adding a Dropout layer between them to prevent the loss of useful information. The experimental results show that the accuracy of our proposed method on HAR is 96.34%, which is 4.27% higher after improvement, and the training time of each round is 10.88 s, which is 12.8% shorter than before

Auditory Brainstem Responses in Senile Presbycusis Patients over 90 years

AbstractObjectiveTo analyze the characteristics of auditory brainstem response (ABR) in presbycusis patients elder than 90 years.MethodsFourteen presbycusis patients elder than 90 years (presbycusis group, 91.1.4± 1.3 years, 26 ears) and 9 normal-hearing young adults (control group, 22.7±1.2 years, 18 ears) participated in the study. Alternative click-evoked ABRs were recorded in both groups. The peak latency (PL) of peak I, III, and V, and the inter-peak latency (IPI) of I-III, III-V, and I-V were compared between groups.ResultsIn elder presbycusis patients, the occurrence rate of peak I and III were both 76.9%, and that of peak V was 84.6%. In presbycusis group, the peak latencies of I, III, V were significantly longer than that of control group (P<0.001). There was no significant difference between groups in the IPI of peak I-I III (P=0.298, peak III-V (P=0.254) and peak I-V (P=0.364).ConclusionsAuditory brainstem responses in presbycusis patients elder than 90 years showed worse wave differentiatio