Validation of an Ultrashort Persian Version of Oral Health Impact Profile (OHIP-5) Questionnaire

Objective: To validate the ultrashort (5-item) Persian version of OHIP by investigating its psychometric properties. Material and Methods: Construct validity was assessed by examining the correlation between OHIP-5 scores and self-reported oral health status, judgment for dental treatment needs and the number of natural teeth. Reliability was calculated using Cronbach’s alpha and corrected item-total correlation. Effect size (ES) and Standardized Response Mean (SRM) were calculated for the responsiveness of the scale and factor analysis was done by measuring Kaiser-Meyer-Olkin (KMO), Bartlett’s sphericity test and scree plot. Results: In 430 subjects (mean age 41.56+/-11.35 years, 56% female) the correlations between OHIP-5 scores and mentioned items were significant (p<0.01) indicating sufficient construct validity. The reliability coefficient (Cronbach's alpha) of the OHIP-5 was above the recommended 0.7 thresholds (0.809) and considered well. For evaluation of responsiveness, the ES was measured to be 5.604 and the SRM was 1.5. Moreover, in the confirmatory factor analysis, the unidimensional model for OHIP5 approved by indices (KMO=0.81, p<0.001 for Bartlett sphericity). Conclusion: The Persian version of OHIP-5 is a precise, valid, reliable and unidimensional instrument for assessing oral health-related quality of life among the general adult population

A Study on Learning Social Robot Navigation with Multimodal Perception

Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to navigate human-inhabited public spaces, such a navigation task becomes more than only obstacle avoidance, but also requires considering surrounding humans and their intentions to somewhat change the navigation behavior in response to the underlying social norms, i.e., being socially compliant. Machine learning methods are shown to be effective in capturing those complex and subtle social interactions in a data-driven manner, without explicitly hand-crafting simplified models or cost functions. Considering multiple available sensor modalities and the efficiency of learning methods, this paper presents a comprehensive study on learning social robot navigation with multimodal perception using a large-scale real-world dataset. The study investigates social robot navigation decision making on both the global and local planning levels and contrasts unimodal and multimodal learning against a set of classical navigation approaches in different social scenarios, while also analyzing the training and generalizability performance from the learning perspective. We also conduct a human study on how learning with multimodal perception affects the perceived social compliance. The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies. We open-source our code for the community's future use to study multimodal perception for learning social robot navigation

The Study of Fiber Fines and Its Effects On Optical and Physical Propertie of Newsprint Paper from CMP Pulp

The present study deals with effects of CMP  fines on optical and physical properties of newsprint papers, for which, different batches for CMP fines (0%, 10%. 20%. 30%) of totally 80% pulp  is taken, Where the remaining 20% of imported long fiber pulp was taken as fixed amount . The study focused on surving the effects of CMP fines on optical and physical properties of newsprint paper ,after mixing the pulps and making the handsheets . Generally, the results show the increase in CMP fine amount lead to increase ,Air Resistance and Opacity and decrease Caliper and Roughness, Also that changes in CMP fines has no effect on Brightness

Uso de farinha de minhoca com vermi‑húmus em dieta para codornas de postura

The objective of this work was to evaluate the effect of dietary earthworm (Eisenia fetida) meal (EW), associated with vermi-humus (VH), on the performance, egg characteristics, immunity, and blood constituents of laying quails. A total of 336 female quails (163.94±1.5 g), with 30 days of age, was distributed in 7 treatments and 4 replicates of 12 birds during 42 days. The following treatments were evaluated: control diet without the inclusion of VH and EW; diet with the inclusion of only 0.8% VH; and diets with 0.8% VH supplemented with different EW levels (0.5, 1.0, 1.5, 2.0, and 2.5%). A greater body weight, weight gain, and feed intake were obtained with 1.5% EW; a higher egg productivity, with 0.5–1.0% EW; and a higher egg mass, with 0.5% EW. The inclusion of 2.5% EW reduced eggshell weight and thickness. Levels of 1.0–2.0% EW decreased malondialdehyde in the eggs, whereas 2.0% EW reduced cholesterol content. Higher blood cell volume and antibody titer were obtained with 1.0% EW, whereas higher total protein, globulin, and calcium were obtained with 0.5% EW. Levels of 1.0–1.5% EW + 0.8% VH improve egg production and characteristics, as well as the humoral response of quails, whose performance is not affected.O objetivo deste trabalho foi avaliar o efeito de farinha de minhoca (EW) (Eisenia fetida) dietética, associada ao vermi-húmus (VH), sobre o desempenho, as características dos ovos, a imunidade e os constituintes do sangue de codornas poedeiras. Um total de 336 codornas fêmeas (163,94±1,5 g), com 30 dias de idade, foi distribuído em 7 tratamentos e 4 repetições de 12 aves, por 42 dias. Foram avaliados os seguintes tratamentos: dieta controle sem a inclusão de VH e EW; dieta com inclusão somente de 0,8% VH; e dieta com 0,8% VH suplementada com diferentes níveis de EW (0,5, 1,0, 1,5, 2,0 e 2,5%). Foram obtidos maiores peso vivo, ganho de peso e consumo de ração com 1,5% de EW; maior produção de ovos com 0,5–1,0% de EW; e maior massa de ovos com 0,5% de EW. A inclusão de 2,5% EW reduziu o peso e a espessura da casca do ovo. Níveis de 1,0–2,0% de EW diminuíram o malonaldeído nos ovos, enquanto o de 2,0% de EW reduziu o conteúdo de colesterol. Foram obtidos maiores volume de células sanguíneas e título de anticorpos com 1,0% de EW, enquanto maiores proteína total, globulina e cálcio foram obtidos com 0,5% de EW. Níveis de 1,0–1,5% + 0,8% de VH melhoram a produção e as características dos ovos, bem como a resposta humoral das codornas, cujo desempenho não é afetado

Design, development, and evaluation of a registry system for hyperbaric oxygen therapy: A methodological study

Abstract Background and Aims Hyperbaric oxygen therapy (HBOT), utilizes 100% oxygen at pressures greater than sea‐level atmospheric pressure, for the treatment of conditions in which the tissues starve for oxygen. The Undersea and Hyperbaric Medical Society (UHMS) has granted HBOT approval for the treatment of various conditions. On the other hand, applying informatics registry systems can improve care delivery, ameliorate outcomes, and reduce the costs and medical errors for the patients receiving HBOT treatment. Therefore, we aimed to design, develop, and evaluate a registry system for patients undergoing HBOT. Methods In the first phase, the conceptual and logical models were designed after conducting symposiums with experts and having other experts review the models. In the second phase, the system was developed on the web using ASP.NET  and C# programming languages frameworks. The last phase involved Nielsen's heuristic evaluation method for the system's usability. Five experts evaluated the system, including three health information management specialists and two medical informatics specialists. Results The hyperbaric patient information registry system (HPIRS) interacts with three types of users—a specialist physician, a nurse, and a system administrator. A scenario for each predefined activity was designed, and all the information was stored in the SQL servers. The five experts independently found 152 issues, of which 84 were duplicates. The 68 distinct issues of the system were then resolved. Conclusions The design and development of such registry systems can make data available and stored carefully to improve clinical care and medical research and decrease costs and errors. These registries can provide the healthcare systems with E‐health applications, improved data management, more secure data transfer, and support for statistical reporting. The implemented heuristic evaluation method can also provide a low‐cost and readily available system to fix the issues of the designed systems

Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis

International audienceNeuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cogni-tive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS