Assessment method to identify the potential of rooftop pv systems in the residential districts

The installation of rooftop PV systems in residential buildings and dwellings has increased rapidly in the past decade, and these systems have become a major source of renewable energy in many countries. This paper presents a new method of estimating the potential of rooftop PV systems to meet energy demands in residential districts by introducing a roof suitability factor. The method of assessment is based on an online tool called SunSPot, which uses a solar radiation heat map layer of building roofs and the PVSYST solar performance software. A sample of 400 houses from four suburbs considered in the Sydney City Council 2030 sustainability plan was selected to conduct the performance analysis of rooftop PV systems and develop a formula that can estimate the suburban annual energy production. The results show that if the dwelling roofs in residential suburbs could be covered by PV arrays it would produce enough electricity to exceed the local electricity demand and, in some suburbs, a surplus of more than 87%

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph-matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7% while using only a ResNet-50.Comment: Update Appendi

Mapping for engagement: setting up a community based participatory research project to reach underserved communities at risk for Hepatitis C in Ho Chi Minh City, Vietnam

Background: Approximately 1. 07 million people in Vietnam are infected with hepatitis C virus (HCV). To address this epidemic, the South East Asian Research Collaborative in Hepatitis (SEARCH) launched a 600-patient cohort study and two clinical trials, both investigating shortened treatment strategies for chronic HCV infection with direct-acting antiviral drugs. We conducted ethnographic research with a subset of trial participants and found that the majority were aware of HCV infection and its implications and were motivated to seek treatment. However, people who inject drugs (PWID), and other groups at risk for HCV were under-represented, although injecting drug use is associated with high rates of HCV. Material and Methods: We designed a community-based participatory research (CBPR) study to engage in dialogues surrounding HCV and other community-prioritized health issues with underserved groups at risk for HCV in Ho Chi Minh City. The project consists of three phases: situation analysis, CBPR implementation, and dissemination. In this paper, we describe the results of the first phase (i.e., the situation analysis) in which we conducted desk research and organized stakeholder mapping meetings with representatives from local non-government and community-based organizations where we used participatory research methods to identify and analyze key stakeholders working with underserved populations. Results: Twenty six institutions or groups working with the key underserved populations were identified. Insights about the challenges and dynamics of underserved communities were also gathered. Two working groups made up of representatives from the NGO and CBO level were formed. Discussion: Using the information provided by local key stakeholders to shape the project has helped us to build solid relationships, give the groups a sense of ownership from the early stages, and made the project more context specific. These steps are not only important preliminary steps for participatory studies but also for other research that takes place within the communities

Cyclic peptide-poly(HPMA) nanotubes as drug delivery vectors : in vitro assessment, pharmacokinetics and biodistribution

Size and shape have progressively appeared as some of the key factors influencing the properties of nanosized drug delivery systems. In particular, elongated materials are thought to interact differently with cells and therefore may allow alterations of in vivo fate without changes in chemical composition. A challenge, however, remains the creation of stable self-assembled materials with anisotropic shape for delivery applications that still feature the ability to disassemble, avoiding organ accumulation and facilitating clearance from the system. In this context, we report on cyclic peptide-polymer conjugates that self-assemble into supramolecular nanotubes, as confirmed by SANS and SLS. Their behaviour ex and in vivo was studied: the nanostructures are non-toxic up to a concentration of 0.5 g L and cell uptake studies revealed that the pathway of entry was energy-dependent. Pharmacokinetic studies following intravenous injection of the peptide-polymer conjugates and a control polymer to rats showed that the larger size of the nanotubes formed by the conjugates reduced renal clearance and elongated systemic circulation. Importantly, the ability to slowly disassemble into small units allowed effective clearance of the conjugates and reduced organ accumulation, making these materials interesting candidates in the search for effective drug carriers

Artificially Introduced Aneuploid Chromosomes Assume a Conserved Position in Colon Cancer Cells

BACKGROUND: Chromosomal aneuploidy is a defining feature of carcinomas. For instance, in colon cancer, an additional copy of Chromosome 7 is not only observed in early pre-malignant polyps, but is faithfully maintained throughout progression to metastasis. These copy number changes show a positive correlation with average transcript levels of resident genes. An independent line of research has also established that specific chromosomes occupy a well conserved 3D position within the interphase nucleus. METHODOLOGY/PRINCIPAL FINDINGS: We investigated whether cancer-specific aneuploid chromosomes assume a 3D-position similar to that of its endogenous homologues, which would suggest a possible correlation with transcriptional activity. Using 3D-FISH and confocal laser scanning microscopy, we show that Chromosomes 7, 18, or 19 introduced via microcell-mediated chromosome transfer into the parental diploid colon cancer cell line DLD-1 maintain their conserved position in the interphase nucleus. CONCLUSIONS: Our data is therefore consistent with the model that each chromosome has an associated zip code (possibly gene density) that determines its nuclear localization. Whether the nuclear localization determines or is determined by the transcriptional activity of resident genes has yet to be ascertained

SOME REMARKS ON THE LOGARITHMIC SIGNATURES OF FINITE ABELIAN GROUPS

In the paper about the cryptosystem MST3, Svaba and Trung pro- posed a way to build a cryptosystem based on the concept of logarithmic signa- tures, and they choose Suzuki\u27s group, which is not abelian for implementing. Recently, to reason why these methods cannot be applied to abelian groups; Sv- aba, Trung and Wolf developed some algorithms to factorize the fused transver- sal logarithmic signatures (FTLS). Their attacks can be avoided by some mod- ications, which is the aim of this paper, where we will use the weakness of the discrete logarithm problem (DLP) to propose two cryptosystems. The rst one is based on the new concept about quasi-logarithmic signature of nite solvable groups, which is the generalization of logarithmic signatures. The second is built on the logarithmic signatures of nite cyclic 2-groups, which include two interesting examples on Pell\u27s curves and elliptic curves over nite elds

A novel ontology framework supporting model-based tourism recommender

In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework. © 2021, Institute of Advanced Engineering and Science. All rights reserved