A Novel Chain Formation Scheme for Balanced Energy Consumption in WSN-based IoT Network

In the Internet of Things (IoT) technologies, wireless sensor networks (WSNs) are one essential part. The IoT network commonly consists of WSNs, where hundreds or even thousands of small sensors are capable of sensing, processing, and sending environmental phenomena in the targeted region. The energy consumption imbalance of sensors becomes the cause of the network performance decrement, as sensor nodes have limited energy available for operation after being randomly deployed. Therefore, more research is necessary for the design of energy-efficient routing algorithms in energy-constrained WSNs. This paper focuses on the chain-based routing algorithm, which is a popular algorithm for achieving energy efficiency in WSN-based IoT network. Chain-based routing algorithms offer numerous advantages for WSNs, such as energy conservation and extended lifetime of WSNs. However, they face challenges due to the issue of internal communication imbalance. The objective of our study is to design a novel chain formation scheme that improves the energy consumption imbalance caused by internal communication in WSN-based IoT network. The proposed scheme is categorized in three phases (initial communication phase, chain formation phase, and data collection phase). In the first phase, the sink acquires their location information from sensors deployed in the sensing region. Then the sensing region is separated into sub-regions and with the number of sensor nodes is balanced employing the concept of the k-dimensional binary tree (K-D-B-tree). The sub-regions are organized into a binary tree structure, which is then formed into a chain. Lastly, data is collected along the chain, and the selected representative sensor transmits the collected data to the sink. We utilized the OMNET++ simulator and demonstrated effective simulation results in terms of network lifetime and average residual energy. In the simulation results, a novel chain formation scheme outperforms the power-efficient gathering in sensor information systems (PEGASIS) and the concentric clustering scheme for efficient energy consumption in the PEGASIS (CCS)

Complications of nephrotic syndrome

Nephrotic syndrome (NS) is one of the most common glomerular diseases that affect children. Renal histology reveals the presence of minimal change nephrotic syndrome (MCNS) in more than 80% of these patients. Most patients with MCNS have favorable outcomes without complications. However, a few of these children have lesions of focal segmental glomerulosclerosis, suffer from severe and prolonged proteinuria, and are at high risk for complications. Complications of NS are divided into two categories: disease-associated and drug-related complications. Disease-associated complications include infections (e.g., peritonitis, sepsis, cellulitis, and chicken pox), thromboembolism (e.g., venous thromboembolism and pulmonary embolism), hypovolemic crisis (e.g., abdominal pain, tachycardia, and hypotension), cardiovascular problems (e.g., hyperlipidemia), acute renal failure, anemia, and others (e.g., hypothyroidism, hypocalcemia, bone disease, and intussusception). The main pathomechanism of disease-associated complications originates from the large loss of plasma proteins in the urine of nephrotic children. The majority of children with MCNS who respond to treatment with corticosteroids or cytotoxic agents have smaller and milder complications than those with steroid-resistant NS. Corticosteroids, alkylating agents, cyclosporin A, and mycophenolate mofetil have often been used to treat NS, and these drugs have treatment-related complications. Early detection and appropriate treatment of these complications will improve outcomes for patients with NS

Anti-Photoagaing and Photo-Protective Compounds from Marine Organisms

This Special Issue Book ""Anti-Photoagaing and Photo-Protective Compounds from Marine Organisms"" is aimed at collecting literature on the below-mentioned keyword topics, which can significantly increase our basic understanding of marine-derived compounds in cosmeceutical product development and increases the value of marine products at the industrial level

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page