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Introduction: Public awareness of the COVID-19 pandemic has resulted in a reduction in healthcare utilisation for other diseases. Understanding how the COVID-19 pandemic affects hospital admissions and the disease patterns is crucial for proper triaging and planning of health resources in future pandemics. Aim: To compare the clinical spectrum of paediatric inpatients in the post-COVID-19, pre-COVID-19 and during COVID-19 pandemic periods in a tertiary level teaching centre. Materials and Methods: This ambispective study was conducted at the Paediatrics Department of SAT, Govt. Medical College, Thiruvananthapuram, Kerala, India. The analysis included all cases hospitalised from April 2019 to March 2022, with the data divided into three time periods. The discharge diagnoses of all cases admitted during the study period were coded according to the ICD-10 criteria and included in the analysis. Differences in admissions, disease patterns, and ICU admissions during the three time periods (pre-COVID-19, post-COVID-19, and during COVID-19) were compared using oneway ANOVA and Tukey's post-hoc test. Results: In the present study, ward admissions (p-value <0.001), respiratory infections (p-value 0.031), asthma (p-value 0.009), and chronic systemic diseases (p-value 0.018) decreased in the post-COVID-19 period compared to the pre-COVID-19 period, whereas ICU admissions (p-value 0.010) and Diabetic Ketoacidosis (DKA) (p-value 0.002) increased. When comparing COVID-19 and post-COVID-19 periods, there was an increase in ward admissions (p-value 0.01), respiratory infections (p-value 0.018), and diarrhoea (p-value 0.029), but a decrease in the proportion of ICU admissions (p-value 0.01). There was no significant difference in mortality among the three time periods. Conclusion: There was a significant difference in the clinical spectrum of paediatric inpatients. The significant decrease in admissions of chronic systemic diseases, which require regular follow-up for management changes, raises concerns about the potential impact on patient care. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy‐efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c‐means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO‐based clustering (GWO‐C) and Cat Swarm Optimization based Energy‐Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria. [ABSTRACT FROM AUTHOR]

Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an NP-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on k-coverage hole restoration and m-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate. [ABSTRACT FROM AUTHOR]