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We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications, including military, environment monitoring, and surveillance and body area sensor networks. A wireless sensor network builds the core part for IoT. Besides this, lifetime maximization is the biggest challenge in the wireless sensor network. Also, In a wireless sensor network, it is difficult to find an optimal node deployment approach that would minimize costs, be robust to node failures, decrease computing overhead and communication, and maintain a high degree of coverage and network connectivity. There is numerous literature addressed this challenge which is discussed in this paper; still there are lot many challenges yet to be addressed. Considering this scenario, in this paper, we propose a scheduled-based node deployment algorithm using Firefly Optimization (FA) to offer a circumstance where we have a group of target points that satisfy p-coverage and sensor nodes that satisfy q-connectivity, with subject to the selection of the optimal number of a sensor node that has the highest energy and minimum distance. The multiple parameters as no. of sensor nodes, distance, survivability factors, coverage, and connectivity of the sensor nodes are considered for designing the fitness function. A comprehensive statistical analysis is done using the simulation results to prove the proposed scheme's efficiency with other existing state-of-the-art methods under various p-coverage and q-connectivity configurations. [ABSTRACT FROM AUTHOR]

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) is steadily revolutionizing people's lives, and accurate location sensing is crucial in achieving this. Global positioning system (GPS) is being widely used outdoors, but its accuracy decreases in indoor environments due to signal attenuation and multipath effect. Simultaneously, Wi-Fi fingerprint-based techniques that use signal strengths from Wi-Fi access points in a building have become more popular for performing indoor positioning. However, location-based services also result in smartphone's battery life consumption because of frequent access point scanning. There are very few studies that focus on the energy conservation of localization systems, despite the fact that it is a significant factor in real-world applications. This paper proposes an intelligent scanning period dilation (ISPD) technique that uses a semi-centralized architecture and schedules Wi-Fi scans by allocating dynamic time intervals for each user. Experimental results show that the proposal saves 7.56% energy while reducing the location accuracy only by 1.35%. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are the substantial parts of any Internet of Things (IoT)-based applications. A WSN is deployed to collect essential data from a specific application field in the IoT. A WSN can fail for various reasons, which can disrupt the operation of numerous IoT applications. The main threat to network failure is topology changes, node failure due to battery drain, or breakdown of the wireless nodes communication module. An effective fault tolerance mechanism for WSN can guarantee reliable data acquisition and transmission in IoT applications. This paper proposes a fault-tolerant cluster-based routing protocol for WSNs using a hybrid algorithm called FAGWO-H, which combines Firefly Optimization (FA) and Grey Wolf Optimization (GWO). FA is employed for optimal clustering, and GWO chooses the best route between the cluster head (CH) and the base station. The proposed method uses two novel fitness functions for FA and GWO. The algorithm considers the energy efficiency and fault-tolerant aspect of the sensor nodes and CHs to improve network performance while meeting QoS criteria. We assessed the effectiveness of the techniques using several WSN scenarios and compared them to the existing state of art methods. [ABSTRACT FROM AUTHOR]

Aim: Vitamin D is suggested to be involved in the pathogenesis of childhood asthma. It is one of the most researched hypotheses but previous reports are inconclusive. The objective of this study was to investigate the association between vitamin D and asthma; and its association with level of control of asthma in children. Materials and Methods: This case control study was conducted in a tertiary care teaching hospital among children of 6-12 years of age. Children with bronchial asthma who were under follow-up in a respiratory clinic were enrolled as the cases. Healthy children with no history of bronchial asthma were enrolled as the controls. Serum 25 hydroxyvitamin D was measured via a chemiluminescence method. Results: There was high prevalence of vitamin D deficiency (<20 ng/mL) among children with asthma compared to control and was associated with occurrence of asthma p=0.000; Univariate analysis of the relationship between asthma and vitamin D showed that decreased vitamin D levels were associated with significantly increased odds of asthmatic state (p<0.001). In multivariate analysis after adjustment for age, body mass index and sex, the relationship between vitamin D and asthma increased; but it was not associated with level of control of asthma. Conclusion: There is high prevalence of vitamin D deficiency among children with asthma. Furthermore, the results did not consistently support that vitamin D levels associate with level of control of asthma. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) not only have become an essential technology in the current scenario but also has been established as a core technology for many applications, like the Internet of Things (IoT). The drawback of limited energy resources in WSN creates the challenge of designing an energy‐efficient network topology and routing path. As these devices are generally not attended for any maintenance, such as battery replacement or charging, these resources need to be intelligently used. The paper represents a methodology for enhancing the lifetime of a network by using a genetic algorithm‐based clusteringand particle swarm optimization‐based routing in WSN. The best cluster head (CH) that collects the data from the remaining nodes is selected using a genetic algorithm, which considers distance and energy parameters. Further, the particle swarm optimization algorithm depends on optimal routing paths that are chosen for all relay nodes to send data to the Base Station (BS). The proposed procedure shows that the relay node supports and facilitates communication among the sink and CH, which enhances energy efficiency. Finally, the quality of service (QoS) of the WSNs is improved, due to which the system would survive longer. The results of simulated experiments show that the proposed approach significantly increases the lifetime of WSN and that energy consumption is reduced when compared with the existing protocols. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Studies to find the relation between asthma severity and Vitamin D yielded controversial reports. The objective of the present study is to find the association between severity of asthma and Vitamin D status in children. MATERIALS AND METHODS: This case-control study was conducted in a tertiary teaching hospital. Details were obtained from children with asthma in the age group of 2-12 years who were under follow-up in the respiratory clinic. They were categorized into two groups according to the Global Initiative for Asthma criteria. Those with moderate-to-severe asthma were grouped as cases, and those with mild asthma were grouped as controls. Vitamin D estimation was done, and levels were analyzed with different levels of asthma severity. RESULTS: Among 140 children with childhood asthma studied, 64 (45.7%) had mild asthma and 76 (54.3%) had moderate-to-severe asthma. Vitamin D was insufficient in 55 (72.4%) cases and 30 (46.9%) controls. There was a significant correlation between severity of asthma and Vitamin D levels (P = 0.001) and peripheral eosinophilia (P = 0.02). Logistic regression analysis showed that Vitamin D insufficiency could increase the risk for severe asthma, which remained after adjustment for potential confounders (odds ratio: 2.81 with 95% confidence interval: 1.36-5.82). CONCLUSION: Screening for Vitamin D insufficiency is suggested for children with severe asthma. Vitamin D supplementation could avoid increasing steroid dose/adding new drugs as controllers. [ABSTRACT FROM AUTHOR]

Localization is the fundamental problem for event detecting and monitoring due to the mobility of nodes UWSNs . In the literature, researchers have examined the localization problem through optimization schemes for evaluating the accurate location of unknown target nodes. However, the existing schemes have not considered the failure of anchor nodes during obstacles, natural disasters and environmental interferences. Due to this, the network leads to high localization error and energy depletion. Therefore, in this paper, we considered anchor node failure and bad distance computational between anchor-target pair for better localization. For this, fault-resilient localization using FL and NSGA I I -based metaheuristic scheme is proposed for UWSNs . In this scheme, first, we refined the correction value of an anchor node to improve the distance between anchor-target pair. Afterward, FL and NSGA II are applied to identify the set of optimal anchor nodes for the fault-resilient localization process. Furthermore, we have also incorporated fitness value with the three parameters such as residual energy, modified anchor-target distance and a number of the connected nodes as the input parameters, whereas probability is the output for the selection of optimal anchor nodes. Performance evaluation of the proposed scheme outperforms the existing scheme in terms of average localization error, better coverage positioning, localized and bad node proportion with a fast convergence rate under different network scenarios. [ABSTRACT FROM AUTHOR]

Fault resilient routing is a typical issue for underwater wireless sensor networks ( UWSNs ) due to contact of underwater creatures, mobility of nodes and natural disaster. In the existing studies, most of the researchers introduced routing scheme for routing the packets towards the base station through a cluster head. However, these schemes use static or mobile nodes for deployment, but nodes move from its original position to another position and get stuck in a particular region due to ocean current, natural disaster and environmental interference. Therefore, this leads to high energy depletion, link failure, disjoint path and overloaded data. To resolve this issue, we have proposed fault resilient routing based on moth flame optimization ( MFO ) scheme for transferring the packets towards the base station through autonomous underwater vehicles ( AUVs ). In this scheme, AUVs is used instead of cluster head to avoid the reclustering, overloading problem and it also feasible for large scale networks. There may be a disjoint path issue, so we have deployed additional mobile nodes with the help of AUVs within the network. Further, a novel fitness function is integrated with the MFO scheme to overcome the link failure problem. The proposed scheme is applied to select the best forwarding node for transmitting the packets towards the nearest AUVs using multi-hop acoustic links. In UWSNs , AUVs is placed to move the packets towards the BS from lower level to upper level. Performance evaluation of proposed scheme shows better result in terms of fault resilient, residual energy, network lifetime, packet delivery ratio and convergence rate than the existing scheme under the different network scenario. [ABSTRACT FROM AUTHOR]

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process. Label free detection of healthy, COVID-19 and recovered saliva using SERS guided Raman fingerprinting. [Display omitted] • Label-free SERS technique has been developed for the discrimination of healthy and COVID-19 infected subjects using saliva • A proof-of-concept model study with saliva spiked different types of corona virus spike proteins was performed • The trained support vector machine classifier achieved a prediction accuracy of 95% for COVID-19 diagnosis • Illustrated a database for different stages of patient recovery with varying Raman signatures. • Higher prediction accuracy could be due to this small sample size employed [ABSTRACT FROM AUTHOR]