Design and Development of an Airblast Atomiser for the KAVERI engine and the sectoral combustor tests

This report deals with the design and development of an airblast atomiser for application in the KAVERI engine. Five atomisers of the chosen design were fabricated and tested at ambient conditions to determine the fuel spray SMD, patternation, cone angle and atomiser flow number. The atomiser performance parameters specified were achieved and hot tests carried out in the 90° combustor sector. The combustor pressure loss, exit temperature distribution, ignition and stability limits were evaluate

Overview of Android for User Applications

This paper presents the requirements to develop the applications on the Android Operating System. Mobiles are the hot cakes in now-a-days market. Features of mobile depend on software. As mobiles are the low-powered devices that uses battery to run and it is a rechargeable one, so the operating system for mobiles has played a crucial role. Necessity for developing applications that could run on mobiles increases at current days. In order to fulfill that features Google introduced Android. Dalvik Virtual Machine facilitates run time environment. Android components are necessary to develop the applications. Android operating system first developed by Google later Open Handset Alliance. Android Operating system provides flexible environment for writing applications in java language. This operating system is free, robust and user friendly

Identification of the initial molecular changes in response to circulating angiogenic cells-mediated therapy in critical limb ischemia

BackgroundCritical limb ischemia (CLI) constitutes the most aggressive form of peripheral arterial occlusive disease, characterized by the blockade of arteries supplying blood to the lower extremities, significantly diminishing oxygen and nutrient supply. CLI patients usually undergo amputation of fingers, feet, or extremities, with a high risk of mortality due to associated comorbidities.Circulating angiogenic cells (CACs), also known as early endothelial progenitor cells, constitute promising candidates for cell therapy in CLI due to their assigned vascular regenerative properties. Preclinical and clinical assays with CACs have shown promising results. A better understanding of how these cells participate in vascular regeneration would significantly help to potentiate their role in revascularization.Herein, we analyzed the initial molecular mechanisms triggered by human CACs after being administered to a murine model of CLI, in order to understand how these cells promote angiogenesis within the ischemic tissues.MethodsBalb-c nude mice (n:24) were distributed in four different groups: healthy controls (C, n:4), shams (SH, n:4), and ischemic mice (after femoral ligation) that received either 50 mu l physiological serum (SC, n:8) or 5x10(5) human CACs (SE, n:8). Ischemic mice were sacrificed on days 2 and 4 (n:4/group/day), and immunohistochemistry assays and qPCR amplification of Alu-human-specific sequences were carried out for cell detection and vascular density measurements. Additionally, a label-free MS-based quantitative approach was performed to identify protein changes related.ResultsAdministration of CACs induced in the ischemic tissues an increase in the number of blood vessels as well as the diameter size compared to ischemic, non-treated mice, although the number of CACs decreased within time. The initial protein changes taking place in response to ischemia and more importantly, right after administration of CACs to CLI mice, are shown.ConclusionsOur results indicate that CACs migrate to the injured area; moreover, they trigger protein changes correlated with cell migration, cell death, angiogenesis, and arteriogenesis in the host. These changes indicate that CACs promote from the beginning an increase in the number of vessels as well as the development of an appropriate vascular network.Institute of Health Carlos III, ISCIII; Junta de Andaluci

Detection of Eye Diseases (Glaucoma & ARMD)

: As population aging has become a major demographic trend around the world, patients suffering from eye diseases, such as Glaucoma, ARMD are expected to increase. Early detection and appropriate treatment of eye diseases are of great significance to prevent vision loss and promote living quality. Conventional diagnosis methods are tremendously dependent on physicians, professional experience and knowledge, which lead to high misdiagnosis rate and huge waste of medical data. In this project, a deep learning model-based method which is inspired by the diagnostic process of human ophthalmologists is proposed to automatically classify the fundus photographs into 2 types with or without ARMD categories also, with or without Glaucoma. The project consists of two different neural network models developed to recognize the diseases, Glaucoma and ARMD.Better accuracy is obtained as we use deep learning. This project will be an aid to eye specialists in giving an efficient treatment. Eyesight is one of the most important senses, the developed project can help people all over to maintain eye care. This project uses Kaggle Glaucoma and ARMD datasets. This model predicts Glaucoma with 90% accuracy and ARMD with more than 70% accuracy

Hyperbolic hyperbolic-by-cyclic groups are cubulable

We show that the mapping torus of a hyperbolic group by a hyperbolic automorphism is cubulable. Along the way, we (i) give an alternate proof of Hagen and Wise's theorem that hyperbolic free-by-cyclic groups are cubulable, and (ii) extend to the case with torsion Brinkmann's thesis that a torsion-free hyperbolic-by-cyclic group is hyperbolic if and only if it does not contain $\mathbb{Z}^2$-subgroups.Comment: 11 page

Ecological Impact Assessment in Business Operations: A Framework Combining Zoological Insights and AI Algorithms

In response to the expanding industrial footprint in environmentally delicate regions, there arises a critical demand for holistic frameworks that assess and mitigate the ecological impact of business operations. This research introduces an innovative methodology that combines classical zoological insights with advanced artificial intelligence (AI) algorithms to comprehensively analyze and address the environmental ramifications of industrial activities. Conducted in a hypothetical locale, the study centers on the identification of pivotal species, mapping of ecological hotspots, and forecasting biodiversity shifts. Findings reveal the susceptibility of specific species, such as the Red-crowned Crane and Amur Tiger, while uncovering distinct ecological hotspots marked by habitat disruption, pollution dispersion, and noise impact. Predictive models delineate taxonomic disparities in biodiversity alterations, underscoring the imperative for precisely targeted conservation initiatives. Proposed mitigation strategies, tailored to recognized hotspots, advocate for habitat restoration, pollution management, and operational adjustments. The amalgamation of zoological insights and AI not only enriches the depth of ecological comprehension but also furnishes pragmatic solutions for businesses to curtail their environmental impact. This research adds to the ongoing discourse on sustainable business practices, advocating for a symbiotic equilibrium between economic progress and environmental preservation. Acknowledging constraints and suggesting paths for future investigation, the paper lays the groundwork for a transformative approach to corporate environmental responsibility, encouraging proactive engagement in sustainable practices for the preservation of ecosystems and global biodiversity

Enhancing Patient Outcomes in Intra-Aortic Balloon Pump Therapy: The Critical Role of Nurse-Led Interventions

Intra-aortic balloon pump (IABP) therapy is a vital intervention for patients with severe cardiac conditions, offering haemodynamic support in life-threatening situations. However, the success of this therapy depends significantly on the knowledge, skills, and care provided by nursing professionals. This comprehensive review explores the essential role of nurses in optimising IABP therapy, focusing on their key responsibilities, patient monitoring, and the delivery of compassionate, patient-centred care.The review outlines the evolution of IABP technology, its mechanism of action, and the common complications associated with its use, including limb ischaemia, infection, and bleeding. A key focus is the impact of nurse-led interventions in minimising these risks, ultimately improving patient recovery, reducing hospital stays, and enhancing survival rates. Evidence suggests that proactive nursing care, including vigilant monitoring and early detection of complications, plays a critical role in improving patient outcomes.Despite technological advancements, challenges persist in the effective use of IABP therapy, such as gaps in training, variations in nursing practice, and limited resources. To address these issues, the review emphasises the need for evidence-based guidelines, continuous professional education, and multidisciplinary collaboration. Integrating these elements ensures that nurses can provide high-quality care, enhancing the safety and effectiveness of IABP therapy.Additionally, this article highlights areas for further research into innovative technologies and nurse-led solutions that could enhance IABP management. It also underscores the need for policy reforms to support the implementation of best practices in healthcare systems, ultimately improving patient outcomes. Core Insight: Nurses are at the heart of optimising intra-aortic balloon pump (IABP) therapy, bridging technology with compassionate care. Their expertise in monitoring, early intervention, and patient-centred support significantly improves outcomes. Strengthening nurse-led interventions through evidence-based training and collaboration is essential to maximising IABP’s potential and transforming cardiac care

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

In the 21st century, modern technology is playing an important role in providing innovative on traditional challenges across various domains or sectors. One such challenging task is of daily attendance marking and tracking. Manual attendance requires efforts and it is time-consuming. Sometimes attendance cannot be mark due to human errors. Relying on voice, iris, or fingerprint recognition, increases the complexity and the hardware infrastructure of the system and also increases the cost. To effectively address such issues, we have developed a “Camera based Attendance System”. This system encompasses several crucial stages, including data entry, dataset of multiple people. It is an image-based face recognition system for marking attendance on the SQL database. It excels in detecting and recognizing multiple individuals faces from image and comparing it with the dataset for accurately marking the attendance. This makes the attendance marking process fully automatic. Remarkably, our proposed system attains an impressive recognition and provides the accuracy of approximately 95%. With this solution, daily attendance marking and recording becomes effortless and the stored attendance record can be also used in future if require, eliminating the risk of attendance not getting marked due to human error.&nbsp

Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach

© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments

Epstein-Barr virus-encoded microRNA BART1 induces tumour metastasis by regulating PTEN-dependent pathways in nasopharyngeal carcinoma.

Epstein-Barr virus (EBV), aetiologically linked to nasopharyngeal carcinoma (NPC), is the first human virus found to encode many miRNAs. However, how these viral miRNAs precisely regulate the tumour metastasis in NPC remains obscure. Here we report that EBV-miR-BART1 is highly expressed in NPC and closely associated with pathological and advanced clinical stages of NPC. Alteration of EBV-miR-BART1 expression results in an increase in migration and invasion of NPC cells in vitro and causes tumour metastasis in vivo. Mechanistically, EBV-miR-BART1 directly targets the cellular tumour suppressor PTEN. Reduction of PTEN dosage by EBV-miR-BART1 activates PTEN-dependent pathways including PI3K-Akt, FAK-p130(Cas) and Shc-MAPK/ERK1/2 signalling, drives EMT, and consequently increases migration, invasion and metastasis of NPC cells. Reconstitution of PTEN rescues all phenotypes generated by EBV-miR-BART1, highlighting the role of PTEN in EBV-miR-BART-driven metastasis in NPC. Our findings provide new insights into the metastasis of NPC regulated by EBV and advocate for developing clinical intervention strategies against NPC