Multimedia services are offered to the demanding user by the multimedia cloud. By the fact, the sudden increase of network users has reduced the service receiving response time. Hence, it is ...impossible to achieve service satisfaction. Therefore, user experience based characteristic is a main role in the multimedia content acquisition, such as server distribution imbalance, huge user visits and limited bandwidth. In order to withstand these problems, a new multimedia cloud content distribution method is proposed based upon the integrated user utility and interest discovery. Initially, the interest features of users are extracted through applying an important feature extraction method. Subsequently, the separation of service and non-service users are formed through the development of a group depending on the categorization of same service interest and adjacent region included users. Followed with this, the integrated utility value is adopted to introduce user evaluation strategies. The integrated utility values are computed combining different user experience characteristics such as, user reputation, user selfish behaviours and user physical performance. However, the service user number evaluated by employing the Opposition Grasshopper Optimizer (OGHA) has minimized the content distribution time and user cost. Furthermore, the convergence profile and computational speed of standard GHA is enhanced by introducing the notion of opposition based population initialization in the proposed approach. Simulation outcomes have evidently proved the improvement of multimedia cloud users, minimizing the total cost of multimedia cloud users, and improvement of multimedia content utilization.
Wireless Sensor Networks (WSNs) are used in the design of Internet of Things (IoT) for sensing the environment, collecting the data and to send them to the base station and the locations used for ...analysis. In WSNs for IoT, intelligent routing is an important phenomena that is necessary to enhance the Quality of Service (QoS) in the network. Moreover, the energy required for communication in the IoT based sensor networks is an important challenge to avoid immense packet loss or packet drop, fast energy depletion and unfairness across the network leading to reduction in node performance and increase in delay with respect to packet delivery. Hence, there is an extreme need to check energy usage by the nodes in order to enhance the overall network performance through the application of intelligent machine learning techniques for making effective routing decisions. Many approaches are already available in the literature on energy efficient routing for WSNs. However, they must be enhanced to suite the WSN in IoT environment. Therefore, a new Neuro-Fuzzy Rule Based Cluster Formation and Routing Protocol for performing efficient routing in IoT based WSNs. From the experiments conducted in this research work using the proposed model, it is proved that the proposed routing algorithm provided better network performance in terms of the metrics namely energy utilization, packet delivery ratio, delay and network lifetime.
The intrusion detection systems (IDSs) developed based on classification algorithms for securing wireless sensor networks (WSNs) are unable to attain the required detection accuracy. To handle the ...security issue in WSN, an intelligent IDS is proposed in this work by using a convolution neural network (CNN)-based deep learning approach along with a fuzzy inference model. The proposed IDS keeps track of the network and system activities by using the proposed fuzzy CNN along with spatial and temporal constraints to detect malicious nodes. Moreover, this algorithm has been modelled mathematically by using Feynman Path Integral and Schrodinger equation for handling the spatial and temporal constraints with fuzzy rules. From the experiments conducted in this work, it is proved that the proposed IDS increases the security, detection accuracy and packet delivery ratio, but decreases the delay and false positive rate in WSNs when compared with the existing IDSs.
The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited ...for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Summary
The ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made ...UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s.
In this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods.
Key findings
The AEMDPG‐ARO approach is proposed to solve user association problems in UDN.
Data sample for this study is obtained through the WINNER channel model.
AEMDPG‐ARO approach achieves high data transmission and less latency rate.
It offers better performance with reduced time complexity and better metrics rate.
Respiratory ailments, encompassing a spectrum of disorders, are a leading cause of mortality and morbidity in children, with pneumonia being particularly significant, accounting for 16% of child ...mortality. To ensure timely engagement with healthcare services, it is imperative to instill awareness through Information, Education, and Communication (IEC) initiatives targeting mothers of children under five. The primary objective of this pilot study is to assess the feasibility of a community-based intervention on health-seeking behaviour, knowledge, and practice measures concerning the management and prevention of pneumonia in children.
The pilot study mirrored the main study's procedures in two villages, Bhuvanahalli and Gavanahalli, each randomly assigned as either an experimental or a control group. We selected 12 mothers with children under the age of five who had community-acquired pneumonia, employing a straightforward random technique, with six mothers from each group. These mothers were interviewed using a structured questionnaire focusing on health-seeking behaviour, knowledge, and practices related to the management and prevention of pneumonia. Mothers in the experimental group received a community-based intervention, specifically an educational set focusing on health-seeking behaviour, knowledge, and practice measures concerning the management and prevention of pneumonia in children, while those in the control group continued with their routine practices. We collected post-test data from the mothers in both groups at the 2nd, 4th, and 6th months of the intervention. The data analysis was conducted using the IBM SPSS Statistics for Windows, Version 28 (Released 2021; IBM Corp., Armonk, New York) software. The Mann-Whitney test and Kruskal-Wallis analyses indicated a notable and statistically significant shift in health-seeking behaviour, knowledge, and practices pertaining to the management and prevention of pneumonia in children as a result of the community-based educational intervention implemented in the experimental group (P<0.05).
Community-based intervention is crucial to preventing mortality and morbidity in children. The findings of the pilot study affirm its feasibility and lay a strong foundation for further investigation and implementation.
Childhood pneumonia is a major contributor to illness and death in children under the age of five globally. Despite advancements in medical science, the burden of pediatric community-acquired ...pneumonia (CAP) remains high, particularly in low- and middle-income countries. This systematic review aims to synthesize existing literature on the prevalence, risk factors, and healthcare-seeking behaviors associated with pediatric CAP to inform the development of targeted community-based interventions. An extensive search of various databases such as Medline, EMBASE, Web of Science, Cochrane, PubMed, PubMed Central, Helinet, SpringerLink, Google Scholar, and Biomed Central was performed, resulting in 65 potentially relevant studies. After a thorough evaluation process, 25 studies were selected for the final analysis. These selected studies offered valuable information on the epidemiology, risk factors, and healthcare-seeking behaviors associated with childhood pneumonia. The review revealed that environmental factors such as indoor air pollution, overcrowding, and exposure to tobacco smoke are significant risk factors for pediatric pneumonia. Additionally, socioeconomic factors, including poverty and a lack of access to clean water and sanitation, contribute to the vulnerability of children to this disease. Poor healthcare-seeking behaviors, driven by limited knowledge and awareness of pneumonia symptoms and treatment, further exacerbate the situation. The review also highlighted the critical role of vaccination, particularly against
type b (Hib) and pneumococcus, in preventing pneumonia. However, gaps in vaccination coverage and challenges in accessing healthcare services remain barriers to effective pneumonia control. In light of these findings, the review recommends the implementation of community-based interventions that address the multifaceted determinants of pediatric pneumonia. These interventions should focus on improving environmental conditions, enhancing access to preventive measures such as vaccination, and promoting better healthcare-seeking behaviors through education and awareness campaigns. It is essential for healthcare providers, policymakers, and community members to collaborate in developing and implementing culturally appropriate and sustainable interventions. This cooperation aims to lessen the impact of pneumonia on children and their families.
Abstract
Agriculture exhibitions an important role in the progression and enlargement of the economy of any country. Prediction of crop yield will be useful for farmers, but it is difficult to ...predict crop yield because of the climatic factors such as rainfall, soil factors and so on. To tackle these issues, we are implementing a novel algorithm called Lemuria by applying data mining in agriculture especially for crop yield analysis and prediction. This novel algorithm is the hybridization of classifiers for pre-training, training and testing: deep belief network for feature learning, k-means clustering together with particle swarm optimization (PSO) to get the global solution as well as naïve Bayes clustering with PSO for testing. The performance of the Lemuria algorithm is evaluated in Python, which provides an accuracy of 97.74% for crop prediction by considering the rainfall dataset and also stated that this gives the optimum results in comparison with the existing methodologies.
Summary
Using first‐principles calculations, we investigate a family of doped graphene nanoribbons (GNRs) for their suitability as cathode hosts in lithium‐sulfur batteries. We probe the role played ...by the lone pairs of the dopants in confining the lithium polysulfides (LiPS) to understand the mechanism of binding. Our results show that the Li bond between the polysulfides and the doped GNRs is analogous to a hydrogen bond and also dipole‐dipole interactions play a key role in anchoring the polysulfides. A critical donor‐Li‐acceptor angle of 180° is found to be essential for proper adsorption of LiPS, highlighting the importance of the directionality of lone pairs. The charge lost by the sulfur atom of the polysulfide upon adsorption and shape of the lone pair basins and the value of Electron Localization Function (ELF) at the dopant position can provide a quick estimate of the strength of the bond. Significant contractions in the ELF profiles are also observed upon Li2S adsorption, further providing evidence for the hydrogen bond‐like nature of the Li bond. Our results corroborate the fact that all acceptors capable of forming hydrogen bonds can be employed as suitable dopants for carbon‐based cathode hosts in Li‐S batteries.
The lithium bond between the lithium polysulfides and doped GNRs is found to be analogous to a hydrogen bond with unique directional properties. Shape of the lone pair basins and value of Electron localization function at the position of dopant are identified to reveal the adsorption strength. Employing good hydrogen bond acceptors as suitable dopants for graphene‐based cathode hosts is a promising strategy in the design of lithium sulfur batteries.
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