Income-carbon footprint relationships for urban and rural households of Iskandar Malaysia

Iskandar Malaysia has a vision to achieve sustainable development and a low carbon society status by decreasing the amount of CO2 emission as much as 60% by 2025. As the case is in other parts of the world, households are suspected to be a major source of carbon emission in Iskandar Malaysia. At the global level, 72% of greenhouse gas emission is a consequence of household activities, which is influenced by lifestyle. Income is the most important indicator of lifestyle and consequently may influence the amount of households' carbon footprint. The main objective of this paper is to illustrate the carbon-income relationships in Iskandar Malaysia's urban and rural areas. Data were gathered through a questionnaire survey of 420 households. The households were classified into six categories based on their residential area status. Both direct and indirect carbon footprints of respondents were calculated using a carbon footprint model. Direct carbon footprint includes domestic energy use, personal travel, flight and public transportation while indirect carbon footprint is the total secondary carbon emission measurement such as housing operations, transportation operations, food, clothes, education, cultural and recreational services. Analysis of the results shows a wide range of carbon footprint values and a significance correlation between income and carbon footprint. The carbon footprints vary in urban and rural areas, and also across different urban areas. These identified carbon footprint values can help the authority target its carbon reduction programs

Features in extractive supervised single-document summarization: case of Persian news

Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either abstractive or extractive methods. Extractive methods are preferable due to their simplicity compared with the more elaborate abstractive methods. In extractive supervised single document approaches, the system will not generate sentences. Instead, via supervised learning, it learns how to score sentences within the document based on some textual features and subsequently selects those with the highest rank. Therefore, the core objective is ranking, which enormously depends on the document structure and context. These dependencies have been unnoticed by many state-of-the-art solutions. In this work, document-related features such as topic and relative length are integrated into the vectors of every sentence to enhance the quality of summaries. Our experiment results show that the system takes contextual and structural patterns into account, which will increase the precision of the learned model. Consequently, our method will produce more comprehensive and concise summaries

Integration of Hi-C with short and long-read genome sequencing reveals the structure of germline rearranged genomes

Here the authors characterize structural variations (SVs) in a cohort of individuals with complex genomic rearrangements, identifying breakpoints by employing short- and long-read genome sequencing and investigate their impact on gene expression and the three-dimensional chromatin architecture. They find breakpoints are enriched in inactive regions and can result in chromatin domain fusions.Structural variants are a common cause of disease and contribute to a large extent to inter-individual variability, but their detection and interpretation remain a challenge. Here, we investigate 11 individuals with complex genomic rearrangements including germline chromothripsis by combining short- and long-read genome sequencing (GS) with Hi-C. Large-scale genomic rearrangements are identified in Hi-C interaction maps, allowing for an independent assessment of breakpoint calls derived from the GS methods, resulting in >300 genomic junctions. Based on a comprehensive breakpoint detection and Hi-C, we achieve a reconstruction of whole rearranged chromosomes. Integrating information on the three-dimensional organization of chromatin, we observe that breakpoints occur more frequently than expected in lamina-associated domains (LADs) and that a majority reshuffle topologically associating domains (TADs). By applying phased RNA-seq, we observe an enrichment of genes showing allelic imbalanced expression (AIG) within 100 kb around the breakpoints. Interestingly, the AIGs hit by a breakpoint (19/22) display both up- and downregulation, thereby suggesting different mechanisms at play, such as gene disruption and rearrangements of regulatory information. However, the majority of interpretable genes located 200 kb around a breakpoint do not show significant expression changes. Thus, there is an overall robustness in the genome towards large-scale chromosome rearrangements

Development of a planar multi-body model of the human knee joint

The aim of this work is to develop a dynamic model for the biological human knee joint. The model is formulated in the framework of multibody systems methodologies, as a system of two bodies, the femur and the tibia. For the purpose of describing the formulation, the relative motion of the tibia with respect to the femur is considered. Due to their higher stiffness compared to that of the articular cartilages, the femur and tibia are considered as rigid bodies. The femur and tibia cartilages are considered to be deformable structures with specific material characteristics. The rotation and gliding motions of the tibia relative to the femur can not be modeled with any conventional kinematic joint, but rather in terms of the action of the knee ligaments and potential contact between the bones. Based on medical imaging techniques, the femur and tibia profiles in the sagittal plane are extracted and used to define the interface geometric conditions for contact. When a contact is detected, a continuous non-linear contact force law is applied which calculates the contact forces developed at the interface as a function of the relative indentation between the two bodies. The four basic cruciate and collateral ligaments present in the knee are also taken into account in the proposed knee joint model, which are modeled as non-linear elastic springs. The forces produced in the ligaments, together with the contact forces, are introduced into the system’s equations of motion as external forces. In addition, an external force is applied on the center of mass of the tibia, in order to actuate the system mimicking a normal gait motion. Finally, numerical results obtained from computational simulations are used to address the assumptions and procedures adopted in this study.Fundação para a Ciência e a Tecnologia (FCT

Modeling of the condyle elements within a biomechanical knee model

The development of a computational multibody knee model able to capture some of the fundamental properties of the human knee articulation is presented. This desideratum is reached by including the kinetics of the real knee articulation. The research question is whether an accurate modeling of the condyle contact in the knee will lead to reproduction of the complex combination of flexion/extension, abduction/adduction and tibial rotation ob-served in the real knee? The model is composed by two anatomic segments, the tibia and the femur, whose characteristics are functions of the geometric and anatomic properties of the real bones. The biomechanical model characterization is developed under the framework of multibody systems methodologies using Cartesian coordinates. The type of approach used in the proposed knee model is the joint surface contact conditions between ellipsoids, represent-ing the two femoral condyles, and points, representing the tibial plateau and the menisci. These elements are closely fitted to the actual knee geometry. This task is undertaken by con-sidering a parameter optimization process to replicate experimental data published in the lit-erature, namely that by Lafortune and his co-workers in 1992. Then, kinematic data in the form of flexion/extension patterns are imposed on the model corresponding to the stance phase of the human gait. From the results obtained, by performing several computational simulations, it can be observed that the knee model approximates the average secondary mo-tion patterns observed in the literature. Because the literature reports considerable inter-individual differences in the secondary motion patterns, the knee model presented here is also used to check whether it is possible to reproduce the observed differences with reasonable variations of bone shape parameters. This task is accomplished by a parameter study, in which the main variables that define the geometry of condyles are taken into account. It was observed that the data reveal a difference in secondary kinematics of the knee in flexion ver-sus extension. The likely explanation for this fact is the elastic component of the secondary motions created by the combination of joint forces and soft tissue deformations. The proposed knee model is, therefore, used to investigate whether this observed behavior can be explained by reasonable elastic deformations of the points representing the menisci in the model.Fundação para a Ciência e a Tecnologia (FCT) - PROPAFE – Design and Development of a Patello-Femoral Prosthesis (PTDC/EME-PME/67687/2006), DACHOR - Multibody Dynamics and Control of Hybrid Active Orthoses MIT-Pt/BSHHMS/0042/2008, BIOJOINTS - Development of advanced biological joint models for human locomotion biomechanics (PTDC/EME-PME/099764/2008)

Features in extractive supervised single-document summarization : case of Persian news

Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either the abstractive or extractive methods. Extractive methods are more popular, due to their simplicity compared with the more elaborate abstractive methods. In extractive approaches, the system will not generate sentences. Instead, it learns how to score sentences within the text by using some textual features and subsequently selecting those with the highest-rank. Therefore, the core objective is ranking and it highly depends on the document. This dependency has been unnoticed by many state-of-the-art solutions. In this work, the features of the document are integrated into vectors of every sentence. In this way, the system becomes informed about the context, increases the precision of the learned model and consequently produces comprehensive and brief summaries