Though in most of the previously published literature endogenous endophthalmitis has been a metastatic ocular infection, the present study describes a series of endogenous bacterial endophthalmitis de novo in onset, without any identifiable predisposing factors. The overall age of presentation was in a younger population than in previously published series. The overall visual outcome was poor, probably due to the serious nature of disease itself and the relatively late presentation.
As the exact pain measurement is an important prerequisite for pain management, this work aims to measure Pain objectively. As the physiological parameters have significant relationship with pain, we intend to measure ECG, HR, BP and GSR with the equal amount of pain being induced on all the subjects. For this purpose, we have designed an apparatus called Pain Inducer which consists of five discs mounted on a central slot to induce the pain. The experiments (Pre Pain, During Pain and Post pain experiments) were conducted inside the controlled laboratory environment. The parameters have been acquired and analyzed by an instrument called physiograph (RMS Polyrite-D). We have chosen 50 male volunteers of age between 20 and 22 years, for this purpose. The subjects were with normal appetite (food habits) and residing in a confined locality. It is found that both the cardiac parameters and somatic resistance showed significant changes between the 'pain' and 'no pain' conditions. However more experimental studies with additional physiological parameters on large number of subjects in actual clinical setup are required to validate this method. Also, a generalized pain measurement system for all kinds of pain is another challenge to be addressed. Further work on this area may improve the utility of this method in clinical practices for measuring pain objectively. It leads to reduced administration of painkillers.
Internet of Things (IoT) is a technological revolution that redefined communication and computation of modern era. IoT generally refers to a network of gadgets linked via wireless network and communicates via internet. Resource management, especially energy management, is a critical issue when designing IoT devices. Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment. In this point of view, the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e., EECBRM in IoT environment. The proposed EECBRM model has three stages namely, fuzzy logic-based clustering, Lion Whale Optimization with Tumbling (LWOT)-based routing and cluster maintenance phase. The proposed EECBRM model was validated through a series of experiments and the results were verified under several aspects. EECBRM model was compared with existing methods in terms of energy efficiency, delay, number of data transmission, and network lifetime. When simulated, in comparison with other methods, EECBRM model yielded excellent results in a significant manner. Thus, the efficiency of the proposed model is established.
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