Building a binary outranking relation in uncertain, imprecise and multi-experts contexts: The application of evidence theory

AbstractWe consider multicriteria decision problems where the actions are evaluated on a set of ordinal criteria. The evaluation of each alternative with respect to each criterion may be uncertain and/or imprecise and is provided by one or several experts. We model this evaluation as a basic belief assignment (BBA). In order to compare the different pairs of alternatives according to each criterion, the concept of first belief dominance is proposed. Additionally, criteria weights are also expressed by means of a BBA. A model inspired by ELECTRE I is developed and illustrated by a pedagogical example

Preoperative evaluation of patients with ovarian masses using the risk of malignancy index 4 model

Objective: To evaluate the performance of the RMI 4 in discriminating benign from malignant ovarian masses. Study Design: Cross-sectional study. Setting: Assiut Women Health Hospital- Egypt. Materials and methods: This was an observational cross-sectional study involving 91 patients at Women\u27s Health Hospital, Assiut University, Egypt during the period between January, 2016 and January, 2017. Women with ovarian masses planned for surgical management were recruited from the outpatient gynecology clinic of the hospital. Risk of malignancy index (RMI 4) was calculated for all study participants. Biopsies obtained from the ovarian masses after surgical intervention were sent to the pathology lab for histopathological examination. The histopathologic diagnosis of the ovarian masses is considered the gold standard for diagnosis. Results: The mean age of patients in the benign group was 34.83±16.28 years versus 43.43±15.91 in the malignant group. There were 12 postmenopausal patients (15.6%) in the benign group versus 4 postmenopausal patients (28.6%) in the malignant group (p=0.0001). An ultrasound score of 4 was recorded in 85.7% of patients in the malignant group versus only 6.5% in the benign group (p=0.0001). Additionally, tumor size ≥ 7 cm was observed in 85.7% of patients in the malignant group versus 55.8% in the benign group (p=0.0001). The mean value of CA-125 was significantly higher in malignant group than the benign group (142.09±41.50 versus 54.51±32.86 ml, respectively) with p=0.01. RMI 4 had a sensitivity of 75%, specificity of 97.3%, PPV of 85.7%, NPV of 94.8 % and an overall accuracy of 93.4%. Conclusions: RMI 4 is a simple and reliable tool in the primary evaluation of patients with ovarian masses. It can further be used to discriminate benign from malignant ovarian masses with high sensitivity and accuracy

Incorporating spatial and temporal information for microaneurysm detection in retinal images

The retina of the human eye has the potential to reveal crucial information about several diseases such as diabetes. Several signs such as microaneurysms (MA) manifest themselves as early indicators of Diabetic Retinopathy (DR). Detection of these early signs is important from a clinical perspective in order to suggest appropriate treatment for DR patients. This work aims to improve the detection accuracy of MAs in colour fundus images. While it is expected that multiple images per eye are available in a clinical setup, proposed segmentation algorithms in the literature do not make use of these multiple images. This work introduces a novel MA detection algorithm and a framework for combining spatial and temporal images. A new MA detection method has been proposed which uses a Gaussian matched filter and an ensemble classifier with 70 features for the detection of candidates. The proposed method was evaluated on three public datasets (171 images in total) and has shown improvement in performance for two of the sets when compared to a state-of-the-art method. For lesion-based performance, the proposed method has achieved Retinopathy Online Challenge (ROC) scores of 0.3923, 2109 and 0.1523 in the MESSIDOR, DIARETDB1 and ROC datasets respectively. Based on the ensemble algorithm, a framework for the information combination is developed and consists of image alignment, detecting candidates with likelihood scores, matching candidates from aligned images, and finally fusing the scores from the aligned image pairs. This framework is used to combine information both spatially and temporally. A dataset of 320 images that consists of both spatial and temporal pairs was used for the evaluation. An improvement of performance by 2% is shown after combining spatial information. The framework is applied to temporal image pairs and the results of combining temporal information are analyzed and discussed

Assessment of Hypnosis Knowledge Among Dentists: A Cross-Sectional Study

Objective: To assess the attitudes, experiences, training levels, and interest in future education regarding the use of hypnosis by dentists working in Dental Clinic of Monastir, Tunisia. Material and Methods: A cross-sectional study was conducted among 330 dentists working in Dental Clinic of Monastir in November 2019. Students, internship, residents, and professors were randomly selected. This was a survey with a structured questionnaire mailed to all dentists. The questionnaire was pre-fabricated, simple, and close-ended. Data were analyzed using SPSS 24.0 statistical software. Results: A total of 202 dentists respond to this survey. 54% of the participants were interns with a female predominance (66.5%). Findings revealed that beliefs toward hypnosis in the sample were generally positive. Using a visual analogue scale, dentists reported having moderate acknowledge in hypnosis (3.4). 60% of the participants in our study consider that hypnosis is useful in dentistry and, more particularly, in pediatric dentistry. 78.2% of respondents want to follow training courses in hypnosis. Conclusion: Our survey highlights the weak knowledge of participants in hypnosis and reveals certain misconceptions about this procedure. More efforts are required to better educate dentists about hypnosis's benefits in their practice

The role of liver in leptin metabolism in experimental nephrotic syndrome

Leptin is a hormone influencing food intake, energy expenditure and body weight. It is pro-duced by adipocytes, exerts its effects on brain, endocrine pancreas and other organs by acti-vating trans-membrane receptors and is cleared from plasma mainly by the kidneys. Several studies have suggested that leptin's effects on metabolism are mediated by the liver. Our aim was to evaluate the role of the liver in the metabolism of leptin by comparing the serum leptin level in the portal vein with that in inferior vena cava and to study the relationship between leptin and lipoprotein levels in healthy and nephrotic rats. Experimental nephrotic syndrome was conducted in rats by intraperitoneal injection of the supernatant from the kidney suspen-sion obtained by previous unilateral nephrectomy of the same rat and complete Freund's adju-vant. There was a highly significant rise in leptin and lipid profile levels in the nephrotic rats compared with the normal rats. A highly significant increase in leptin in the inferior vena cava was detected compared with the level in the portal veins of nephrotic rats, while insignificant difference was observed in normal rats. This work has stressed the role of liver in leptin and lipid metabolism in nephrotic rats

3DCoMPaT++^{++}: An improved Large-scale 3D Vision Dataset for Compositional Recognition

In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks. 3DCoMPaT$^{++}$ covers 41 shape categories, 275 fine-grained part categories, and 293 fine-grained material classes that can be compositionally applied to parts of 3D objects. We render a subset of one million stylized shapes from four equally spaced views as well as four randomized views, leading to a total of 160 million renderings. Parts are segmented at the instance level, with coarse-grained and fine-grained semantic levels. We introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects. Additionally, we report the outcomes of a data challenge organized at CVPR2023, showcasing the winning method's utilization of a modified PointNet$^{++}$ model trained on 6D inputs, and exploring alternative techniques for GCR enhancement. We hope our work will help ease future research on compositional 3D Vision.Comment: https://3dcompat-dataset.org/v2

Correspondence Between “Stable” Flame Macrostructure and Thermo-acoustic Instability in Premixed Swirl-Stabilized Turbulent Combustion

In this paper, we conduct an experimental investigation to study the link between the flame macroscale structure—or flame brush spatial distribution—and thermo-acoustic instabilities, in a premixed swirl-stabilized dump combustor. We operate the combustor with premixed methane–air in the range of equivalence ratio (φ) from the lean blowout limit to φ=0.75. First, we observe the different dynamic modes in this lean range as φ is raised. We also document the effect of φ on the flame macrostructure. Next, we examine the correspondence between dynamic mode transitions and changes in flame macrostructure. To do so, we modify the combustor length—by downstream truncation—without changing the underlying flow upstream. Thus, the resonant frequencies of the geometry are altered allowing for decoupling the heat release rate fluctuations and the acoustic feedback. Mean flame configurations in the modified combustor and for the same range of equivalence ratio are examined, following the same experimental protocol. It is found that not only the same sequence of flame macrostructures is observed in both combustors but also that the transitions occur at a similar set of equivalence ratio. In particular, the appearance of the flame in the outside recirculation zone (ORZ) in the long combustor—which occurs simultaneously with the onset of instability at the fundamental frequency—happens at similar φ when compared to the short combustor, but without being in latter case accompanied by a transition to thermo-acoustic instability. Then, we interrogate the flow field by analyzing the streamlines, mean, and rms velocities for the nonreacting flow and the different flame types. Finally, we focus on the transition of the flame to the ORZ in the acoustically decoupled case. Our analysis of this transition shows that it occurs gradually with an intermittent appearance of a flame in the ORZ and an increasing probability with φ. The spectral analysis of this phenomenon—we refer to as “ORZ flame flickering”—shows the presence of unsteady events occurring at two distinct low frequency ranges. A broad band at very low frequency in the range ∼(1 Hz–10 Hz) associated with the expansion and contraction of the inner recirculation zone (IRZ) and a narrow band centered around 28 Hz which is the frequency of rotation of the flame as it is advected by the ORZ flow.King Fahd University of Petroleum and Minerals (Grant R12-CE-10)King Abdullah University of Science and Technology (Grant KUS-110-010-01

Improving Resnet-9 Generalization Trained on Small Datasets

This paper presents our proposed approach that won the first prize at the ICLR competition on Hardware Aware Efficient Training. The challenge is to achieve the highest possible accuracy in an image classification task in less than 10 minutes. The training is done on a small dataset of 5000 images picked randomly from CIFAR-10 dataset. The evaluation is performed by the competition organizers on a secret dataset with 1000 images of the same size. Our approach includes applying a series of technique for improving the generalization of ResNet-9 including: sharpness aware optimization, label smoothing, gradient centralization, input patch whitening as well as metalearning based training. Our experiments show that the ResNet-9 can achieve the accuracy of 88% while trained only on a 10% subset of CIFAR-10 dataset in less than 10 minuet

Nano-encapsulation of a novel anti-Ran-GTPase peptide for blockade of regulator of chromosome condensation (RCC1) function in MDA-MB-231 breast cancer cells

Ran is a small ras-related GTPase and is highly expressed in aggressive breast carcinoma. Overexpression induces malignant transformation and drives metastatic growth. We have designed a novel series of anti-Ran-GTPase peptides, which prevents Ran hydrolysis and activation, and although they display effectiveness in silico, peptide activity is suboptimal in vitro due to reduced bioavailability and poor delivery. To overcome this drawback, we delivered an anti-Ran-GTPase peptide using encapsulation in PLGA-based nanoparticles (NP). Formulation variables within a double emulsion solvent evaporation technique were controlled to optimise physicochemical properties. NP were spherical and negatively charged with a mean diameter of 182–277 nm. Peptide integrity and stability were maintained after encapsulation and release kinetics followed a sustained profile. We were interested in the relationship between cellular uptake and poly(ethylene glycol) (PEG) in the NP matrix, with results showing enhanced in vitro uptake with increasing PEG content. Peptide-loaded, pegylated (10% PEG)-PLGA NP induced significant cytotoxic and apoptotic effects in MDA-MB-231 breast cancer cells, with no evidence of similar effects in cells pulsed with free peptide. Western blot analysis showed that encapsulated peptide interfered with the proposed signal transduction pathway of the Ran gene. Our novel blockade peptide prevented Ran activation by blockage of regulator of chromosome condensation 1 (RCC1) following peptide release directly in the cytoplasm once endocytosis of the peptide-loaded nanoparticle has occurred. RCC1 blockage was effective only when a nanoparticulate delivery approach was adopted