Application of BIM in sustainability analysis

Building Information Modeling (BIM) is the process of generating and managing building data during its life cycle. Typically it uses three-dimensional, real-time, dynamic building modeling software to increase productivity in building design and construction. The process produces the Building Information Model, which encompasses building geometry, spatial relationships, geographic information, quantities and properties of building components. On the other hand, Green Building Index (GBI) as the localized sustainability building rating system in Malaysia assesses the impact of building on environment based on energy efficiency, indoor environment quality, sustainable site & management, materials & resources, water efficiency and innovation. By integrating GBI assessment criteria with BIM technology, this research proposes a comparative case study analysis of Residential New Construction (RNC) and Non-Residential New Construction (NRNC) based on the Autodesk Ecotect Analysis software (a comprehensive, concept-to-detail sustainable design analysis tool that provides a wide range of simulation and analysis functionality through desktop and web-service platforms) and Autodesk Green Building Studio (A web-based energy analysis service which performs whole building analysis, optimizes energy efficiency, and works toward carbon neutrality earlier in the design process) to investigate the influence of construction material on energy consumption, lifecycle energy cost and carbon emission

On conformally recurrent manifolds of dimension greater than 4

Conformally recurrent pseudo-Riemannian manifolds of dimension n>4 are investigated. The Weyl tensor is represented as a Kulkarni-Nomizu product. If the square of the Weyl tensor is nonzero, a covariantly constant symmetric tensor is constructed, that is quadratic in the Weyl tensor. Then, by Grycak's theorem, the explicit expression of the traceless part of the Ricci tensor is obtained, up to a scalar function. The Ricci tensor has at most two distinct eigenvalues, and the recurrence vector is an eigenvector. Lorentzian conformally recurrent manifolds are then considered. If the square of the Weyl tensor is nonzero, the manifold is decomposable. A null recurrence vector makes the Weyl tensor of algebraic type IId or higher in the Bel - Debever - Ortaggio classification, while a time-like recurrence vector makes the Weyl tensor purely electric.Comment: Title changed and typos corrected. 14 page

A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development

This work pursues two objectives: defining a new concept of risk probability associated with su_ering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Di_erent demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms.This publication has been carried out as part of the Thematic Network for Co-Operative Research in Health (RETICS), reference number RD16/0008/0012, financed by the Carlos III Health Institute-General Subdirection of Networks and Cooperative Investigation Centers (R&D&I National Plan 2013-2016), European Regional Development Funds (FEDER), and the Results Valorization Program financed by the Technical University of Cartagena (PROVALOR-UPCT)

FasL Expression in Articular Discs of Human Temporomandibular Joint and Association with Osteoarthrosis

Background Apoptosis is a programme of cell death which does not induce an inflammatory response. Recent previous research has suggested a correlation between temporomandibular internal derangement and apoptosis. Fas ligand (FasL) is an apoptosis‐inducing factor, known to trigger apoptosis through distinct signal pathways. This study aims to examine, by immunohistochemistry, the expression of FasL in temporomandibular joint (TMJ) articular discs of patients with anterior disc displacement with reduction (ADDwR) and without reduction (ADDwoR) in patients with and without osteoarthrosis (OA). Methods Forty‐two (n = 42) TMJ articular discs were divided into two cut‐offs: (i) 8 control, 17 ADDwR, 17 ADDwoR, and (ii) without OA (n = 25) and with OA (n = 17). The area of immunostaining was compared statistically between groups (P \u3c 0.05). Results Statistically significant differences were found in the expression of FasL in TMJ discs between the three groups (P = 0.001). ADDwR presented significant higher FasL expression when compared with ADDwoR (P \u3c 0.001). Significant higher FasL expression was observed in the group without OA (P = 0.001). All patients without OA presented ADDwR, while all the patients with OA presented ADDwoR. Conclusion A higher area of in situ immunostaining of FasL was found in temporomandibular discs with reduction, which is the less severe condition. Moreover, a reduced expression of FasL in the discs of patients with osteoarthrosis was found, suggesting that some aspects of apoptosis might underlie the progression of TMJ disorders

Mining Discriminative Triplets of Patches for Fine-Grained Classification

Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains a major challenge. We describe a patch-based framework to address this problem. We introduce triplets of patches with geometric constraints to improve the accuracy of patch localization, and automatically mine discriminative geometrically-constrained triplets for classification. The resulting approach only requires object bounding boxes. Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification