Search Results (5)

The resource-constrained nature of wireless sensor networks (WSNs) creates a number of difficulties in their operation and design that lower their performance. However, distinct applications with unique constraints in their nature make it more difficult for such resource-constrained networks to meet application objectives. These issues can be observed at various WSN layers, from the physical layer up to the application layer. Routing protocols are primarily focused on WSN functioning at the routing layer. These obstacles make routing protocols perform worse, which lowers the performance of WSNs as a whole. This study’s objective is to pinpoint WSN performance issues and examine how they affect routing protocol performance. To this end, a detailed literature review was conducted to determine the problems influencing the performance of the routing protocols. Then, an actual investigation was carried out by simulating various routing protocols, taking into consideration these issues, in order to validate the impact of the discovered challenges from the literature. The findings are shown. On the basis of the findings from the empirical study and the literature review, suggestions are offered for a better protocol choice in light of the application nature and the problems that need to be addressed. Full article

The rapid advancement of medical imaging technologies has propelled the development of automated systems for the identification and classification of lung diseases. This study presents the design and implementation of an innovative image retrieval system utilizing artificial neural networks (ANNs) to enhance the accuracy and efficiency of diagnosing lung diseases. The proposed system focuses on addressing the challenges associated with the accurate recognition and classification of lung diseases from medical images, such as X-rays and CT scans. Leveraging the capabilities of ANNs, specifically convolutional neural networks (CNNs), the system aims to capture intricate patterns and features within images that are often imperceptible to human observers. This enables the system to learn discriminative representations of normal lung anatomy and various disease manifestations. The design of the system involves multiple stages. Initially, a robust dataset of annotated lung images is curated, encompassing a diverse range of lung diseases and their corresponding healthy states. Subsequently, a pre-processing pipeline is implemented to standardize the images, ensuring consistent quality and facilitating feature extraction. The CNN architecture is then meticulously constructed, with attention to layer configurations, activation functions, and optimization algorithms to facilitate effective learning and classification. The system also incorporates image retrieval techniques, enabling the efficient searching and retrieval of relevant medical images from the database based on query inputs. This retrieval functionality assists medical practitioners in accessing similar cases for comparative analysis and reference, ultimately supporting accurate diagnosis and treatment planning. To evaluate the system’s performance, comprehensive experiments are conducted using benchmark datasets, and performance metrics such as accuracy, precision, recall, and F1-score are measured. The experimental results demonstrate the system’s capability to distinguish between various lung diseases and healthy states with a high degree of accuracy and reliability. The proposed system exhibits substantial potential in revolutionizing lung disease diagnosis by assisting medical professionals in making informed decisions and improving patient outcomes. This study presents a novel image retrieval system empowered by artificial neural networks for the identification and classification of lung diseases. By leveraging advanced deep learning techniques, the system showcases promising results in automating the diagnosis process, facilitating the efficient retrieval of relevant medical images, and thereby contributing to the advancement of pulmonary healthcare practices. Full article

This paper assesses the performance of waste heat recovery from commercial kitchen wastewater in practice. A pilot study of heat recovery from the kitchen at Penrhyn Castle, a tourist attraction in North Wales (UK), is outlined. The pilot heat recovery site was designed and installed, comprising a heat exchanger, recirculation pumps, buffer tank and an extensive temperature/flow monitoring system for performance monitoring of the waste heat recovery system. Continuous monitoring was conducted for a period of 8 months, covering the 2022 tourist season. The recovered heat from the kitchen wastewater preheats the incoming cold freshwater supply and consequently reduces the amount of energy consumed for subsequent water heating. Retrofitting the pilot heat recovery system to the kitchen drains resulted in a heat saving of 240 kWh per month on average, a reduction of 928.8 kg CO₂e per year, and a payback period for the investment costs of approximately two years, depending on the cost of energy supply. The presented results illustrate the potential of this form of renewable heat in reducing the carbon footprint of water heating activities in buildings and the hospitality sector. Full article

The present research focuses on heat recovery from hot kitchen wastewater to fulfil the dual objective of reducing energy consumption and CO₂ emissions, while simultaneously improving the fat, oil and grease (FOG) removal efficiency of the grease trap (GT). A GT was retrofitted with a novel heat exchanger design (termed as a hybrid GT device) to enhance wastewater thermal recovery and FOG removal capabilities. Hot wastewater containing FOG was assessed in a full-scale experimental GT. The governing parameters of temperature, mass flow rate and FOG content were monitored. Results indicate that the hybrid GT improves FOG removal performance by lowering the temperature of GT hot wastewater by approximately 25%. The hybrid GT enables improvement in energy efficiency and cost savings for commercial kitchens/wastewater generators, lowering the carbon footprint and cost of food preparation. Full article

Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement. Full article