User profiling for big social media data using standing ovation model

Multimedia Tools and Applications - Online Social Networks (OSNs) have recently been the subject of numerous studies that have attempted to develop effective methods for classifying and analyzing...     

Effect of cassava varieties on the sorption isotherm of tapioca grits

Cassava (Manihot esculenta Cranzt) deteriorates rapidly. Hence it is processed into various products within 2 to 3 days after harvesting. For this work, tapioca was produced from three different varieties of cassava. They are Odongbo, Okoiyawo and TMS30572. The sorption isotherm for the tapioca grits from three different varieties of cassava at temperatures 25, 32 and 45 °C were determined experimentally and modelled using five different models. Using analysis of variance at 0.01 level, it was shown that the experimental sorption isotherms of the tapioca from the various cassava varieties are not significantly different from each other. However, the Guggenheim (1966) , Anderson (1946) & de Boer (1953) (GAB) model gave the best fit for sorption isotherm of tapioca from either the Odongbo or Okoiyawo varieties, while the exponential model was the best for that from TMS30572 variety.     

Surface Characterization of Mild Steel During Atmospheric Corrosion After Being Treated by Sodium Dihydrogen Orthophosphate

Protection of Metals and Physical Chemistry of Surfaces - The objective of this study was to characterize the surfaces of the steel products produced locally during their exposure to the industrial...     

Analysis of energy-tax for multipath routing in wireless sensor networks

Recently, multipath routing in wireless sensor networks (WSN) has got immense research interest due to its capability of providing increased robustness, reliability, throughput, and security. However, a theoretical analysis on the energy consumption behavior of multipath routing has not yet been studied. In this paper, we present a general framework for analyzing the energy consumption overhead (i.e., energy tax) resulting from multipath routing protocol in WSN. The framework includes a baseline routing model, a network model, and two energy consumption schemes for sensor nodes, namely, periodic listening and selective wake-up schemes. It exploits the influence of node density, link failure rates, number of multiple paths, and transmission environment on the energy consumption. Scaling laws of energy-tax due to routing and data traffic are derived through analysis, which provide energy profiles of single-path and multipath routing and serve as a guideline for designing energy-efficient protocols for WSN. The crossover points of relative energy taxes, paid by single-path and multipath routing, reception, and transmission, are obtained. Finally, the scaling laws are validated and performance comparisons are depicted for a reference network via numerical results.     

Study of the Enhancement of the Efficiency of the Monocrystalline Silicon Solar Cell by Optimizing Effective Parameters Using PC1D Simulation

Silicon - In this paper, simulation of a monocrystalline silicon solar cell was done using PC1D software. The impact of different solar cell parameters, with their effects on power and efficiency,...     

Prioritized Medium Access Control in Cognitive Radio Ad Hoc Networks: Protocol and Analysis

Cognitive radio (CR) technology enables opportunistic exploration of unused licensed channels. By giving secondary users (SUs) the capability to utilize the licensed channels (LCs) when there are no primary users (PUs) present, the CR increases spectrum utilization and ameliorates the problem of spectrum shortage. However, the absence of a central controller in CR ad hoc network (CRAHN) introduces many challenges in the efficient selection of appropriate data and backup channels. Maintenance of the backup channels as well as managing the sudden appearance of PUs are critical issues for effective operation of CR. In this paper, a prioritized medium access control protocol for CRAHN, PCR-MAC, is developed which opportunistically selects the optimal data and backup channels from a list of available channels. We also design a scheme for reliable switching of a SU from the data channel to the backup channel and vice-versa. Thus, PCR-MAC increases network throughput and decreases SUs’ blocking rate. We also develop a Markov chain-based performance analysis model for the proposed PCR-MAC protocol. Our simulations, carried out in \(NS-3\) , show that the proposed PCR-MAC outperforms other state-of-the-art opportunistic medium access control protocols for CRAHNs.     

Rapid Quantification of Live Cell Receptors Using Bioluminescence in a Flow‐Based Microfluidic Device

The number of receptors expressed by cells plays an important role in controlling cell signaling events, thus determining its behaviour, state and fate. Current methods of quantifying receptors on cells are either laborious or do not maintain the cells in their native form. Here, a method integrating highly sensitive bioluminescence, high precision microfluidics and small footprint of lensfree optics is developed to quantify cell surface receptors. This method is safe to use, less laborious, and faster than the conventional radiolabelling and near field scanning methods. It is also more sensitive than fluorescence based assays and is ideal for high throughput screening. In quantifying β₁ adrenergic receptors expressed on the surface of H9c2 cardiomyocytes, this method yields receptor numbers from 3.12 × 10⁵ to 9.36 × 10⁵ receptors/cell which are comparable with current methods. This can serve as a very good platform for rapid quantification of receptor numbers in ligand/drug binding and receptor characterization studies, which is an important part of pharmaceutical and biological research.     

Effective Traffic Grooming Algorithms in SONET/WDM Ring Networks

Much work has focused on traffic grooming in SONET/WDM ring networks. Previous work has considered many aspects of traffic grooming, including minimizing the number of ADMs, minimizing the number of wavelengths, considering different traffic models, using different network architectures, incorporating switching capability and so on. In this work, we study traffic grooming in unidirectional ring networks with no switching capability under both uniform traffic and non-uniform traffic models to reduce electronic multiplexing costs. Based on the clustering notion, we derive a general and tighter lower bound for the number of ADMs required in traffic grooming under the uniform all-to-all traffic model. This bound reduces to special cases obtained in previous work. We also derive general, tighter, and closed form lower bounds for the number of ADMs required under two non-uniform traffic models: the distance-dependent traffic model and the non-uniform symmetric traffic model. Cost-effective multi-phase algorithms that exploit traffic characteristics are then designed and studied to efficiently groom traffic streams under different traffic models. Our numerical and simulation results show that the proposed multi-phase algorithms outperform existing traffic grooming algorithms by using a fewer number of ADMs. Our algorithms in several cases also achieve the lower bounds derived.     

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.     

Climate change and the UN-2030 agenda: Do mitigation technologies represent a driving factor? New evidence from OECD economies

Clean Technologies and Environmental Policy - The United Nations 2030 agenda states various sustainable development goals (SDGs) to counter climate change affecting individuals' health and...