Performance evaluation of handover triggering condition estimation using mobility models in heterogeneous mobile networks

Heterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi-Fi, Zigbee, and Z-wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi-Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model

Global genetic diversity and evolutionary patterns among Potato leafroll virus populations

Potato leafroll virus (PLRV) is a widespread and one of the most damaging viral pathogens causing significant quantitative and qualitative losses in potato worldwide. The current knowledge of the geographical distribution, standing genetic diversity and the evolutionary patterns existing among global PLRV populations is limited. Here, we employed several bioinformatics tools and comprehensively analyzed the diversity, genomic variability, and the dynamics of key evolutionary factors governing the global spread of this viral pathogen. To date, a total of 84 full-genomic sequences of PLRV isolates have been reported from 22 countries with most genomes documented from Kenya. Among all PLRV-encoded major proteins, RTD and P0 displayed the highest level of nucleotide variability. The highest percentage of mutations were associated with RTD (38.81%) and P1 (31.66%) in the coding sequences. We detected a total of 10 significantly supported recombination events while the most frequently detected ones were associated with PLRV genome sequences reported from Kenya. Notably, the distribution patterns of recombination breakpoints across different genomic regions of PLRV isolates remained variable. Further analysis revealed that with exception of a few positively selected codons, a major part of the PLRV genome is evolving under strong purifying selection. Protein disorder prediction analysis revealed that CP-RTD had the highest percentage (48%) of disordered amino acids and the majority (27%) of disordered residues were positioned at the C-terminus. These findings will extend our current knowledge of the PLRV geographical prevalence, genetic diversity, and evolutionary factors that are presumably shaping the global spread and successful adaptation of PLRV as a destructive potato pathogen to geographically isolated regions of the world

Peripartum Cardiomyopathy: Facts and Figures

Peripartum cardiomyopathy (PPCM) is a rare clinical entity during pregnancy. PPCM is a diagnosis of exclusion. These patients do not have prior history of heart disease, and there are no other known possible causes of heart failure. It is more common in African countries, may be related to the consumption of kanwa, in the postpartum period. The multiparity, African descent and pregnancy-induced hypertension are a few risk factors for PPCM. The exact etiology of PPCM is not known; possible theories range from myocarditis to the maladaptation to the changes of pregnancy. The clinical manifestation varies from shortness of breath to thromboembolic phenomenon. Echocardiography is essential for diagnosis as well as differential diagnosis of PPCM. These patients preferably are managed in tertiary healthcare facilities. Anticoagulation and antiarrhythmic medications are pillars for the management of PPCM patients. If required, mechanical devices should be used temporarily. PPCM patients may need heart transplant. The beneficial role of bromocriptine and immunosuppression is not clear in PPCM patients. Subsequent pregnancies should be avoided to prevent the PPCM occurrence

Modernizing Energy Efficiency Improvement With q-Rung Orthopair Fuzzy MULTIMOORA Approach

Low quality home energy appliances and cables are responsible for huge electricity wastage in low-income households. Absence of energy efficiency appliances standards and labeling and a policy for low-income households are among the main causes of electricity bleeding in developing countries. Energy efficiency standards and labeling for appliances and cables initiative can save significant amount of energy in Pakistan. In this paper, we used four alternatives for energy efficiency improvement for low income households at national level in Pakistan. For this we developed an extended “multi-criteria decision-making” (MCDM) approach to choose best alternative as a solution that should be able to evaluate and select the best scenario of energy efficiency with respect to eight criteria. We also developed the q-ROF Einstein aggregation operators based MULTIMOORA approach. This approach is a new hybrid technique that is more efficient to capture uncertain and incomplete assessments of stakeholders in terms of q-ROFNs. MCDM approaches are effective tools for solving energy decision-making challenges. Our results show that adoption of standards and labeling criteria for home appliances can improve living standards of low income households. This adoption has great potential to conserve electricity, environment and cost with introducing low income energy policy at government level

Inheritance pattern of earliness in cotton (gossypium hirsutum L.)

Fifty one genotypes of Gossypium hirsutum L. were evaluated for variation based on earliness characteristics and seed cotton yield. Seven divergent genotypes including four early and three late maturing genotypes were crossed in a full diallel mating system. Preliminary ANOVA showed significant differences for all the traits.. Additive-dominance model was employed for the interpretation of inheritance pattern controlling earliness and seed cotton yield. The scaling tests fully met the pre-requisites of additive-dominance model and all the traits in F 1 generation showed complete adequacy. The data for seed cotton yield in F 1 and days take to 1 st boll opening in F 2 generation were partially adequate for additive-dominance model and for boll maturation period was inadequate in F 2 generation. Additive component (D) and dominance components (H 1&H 2) were found significant for all the traits thus confirming the contribution of both additive and non-additive effects in both generations except for days to 1 st flower in F 2 generation where the dominance components were non-significant. Formal ANOVA results revealed the contribution of both additive \u27a\u27 and non additive effects \u27b\u27 for all the traits in both generations except for the trait nodes for 1 st fruiting branch. Inheritance of incase of nodes to 1 st branch was controlled by partial dominance; earliness index by over dominance; and days taken to 1 st boll opening and seed cotton yield by complete dominance in both generations. In F1 generation, days to 1 st flower was controlled by over dominance while in F 2 it was inherited additively. Boll maturation period was controlled by over dominance. Heritability estimates in both generations remained moderate to high except for days to 1 st flower where it was very low. Hybrid vigour can be exploited in the case of traits controlled by complete dominance and over dominance while in cases additive control pedigree selection might be fruitful for improvement of the crop

QoS-Oriented Optimal Relay Selection in Cognitive Radio Networks

A cognitive radio network can be employed in any wireless communication systems, including military communications, public safety, emergency networks, aeronautical communications, and wireless-based Internet of Things, to enhance spectral efficiency. The performance of a cognitive radio network (CRN) can be enhanced through the use of cooperative relays with buffers; however, this incurs additional delays which can be reduced by using virtual duplex relaying that requires selection of a suitable relay pair. In a virtual duplex mode, we mimic full-duplex links by using simultaneous two half-duplex links, one transmitting and the other one receiving, in such a way that the overall effect of duplex mode is achieved. The relays are generally selected based on signal-to-interference-plus-noise ratio (SINR). However, other factors such as power consumption and buffer capacity can also have a significant impact on relay selection. In this work, a multiobjective relay selection scheme is proposed that simultaneously takes into account throughput, delay performance, battery power, and buffer status (i.e., both occupied and available) at the relay nodes while maintaining the required SINR. The proposed scheme involves the formulation of four objective functions to, respectively, maximize throughput and buffer space availability while minimizing the delay and battery power consumption. The weighted sum approach is then used to combine these objective functions to form the multiobjective optimization problem and an optimal solution is obtained. The assignments of weights to objectives have been done using the rank sum (RS) method, and several quality-of-service (QoS) profiles have been considered by varying the assignment of weights. The results gathered through simulations demonstrate that the proposed scheme efficiently determines the optimal solution for each application scenario and selects the best relay for the respective QoS profile. The results are further verified by using the genetic algorithm (GA) and particle swarm optimization (PSO) techniques. Both techniques gave identical solutions, thus validating our claim

A Dynamic Multi-Mobile Agent Itinerary Planning Approach in Wireless Sensor Networks via Intuitionistic Fuzzy Set

In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with “Directed Acyclic Graph” (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time)

An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy Consumption

The prediction of energy consumption plays a significant role in energy conservation and reducing the cost of power generation, to improve energy sustainability and economic stability. Current studies show an increased interest in the application of Machine Learning algorithms to forecast energy utilisation in smart homes. The performance of these Machine Learning algorithms is evaluated using accuracy algorithms. The process of manually selecting best-performing Machine Learning algorithms is still very challenging for data analysts and decision makers because the algorithms might not work well in a different use case or data-set. To address this, we propose a decision algorithm model using machine learning based data mining and picture fuzzy operators. First, Machine Learning algorithms are trained and tested to predict energy consumption of smart home appliances with respect to the weather information. Second, score values of Lasso Regression are used to understand the patterns and features of weather information for smart house micro-climate. We then propose a decision matrix using fuzzy operators to aggregate Machine Learning algorithms, prior to ranking using a score function. Finally, the electricity consumption of appliances as well as total energy consumed in the smart home is provided in Kilowatts (KW)