Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from features other than mobile ad hoc networks (MANET), such as aerial mobility in 3D space and frequently changing topology. This paper analyzes the performance of four topology-based routing protocols, dynamic source routing (DSR), ad hoc on-demand distance vector (AODV), geographic routing protocol (GRP), and optimized link state routing (OLSR), by using practical simulation software OPNET 14.5. Performance evaluation carries out various metrics such as throughput, delay, and data drop rate. Moreover, the performance of the OLSR routing protocol is enhanced and named “E-OLSR” by tuning parameters and reducing holding time. The optimized E-OLSR settings provide better performance than the conventional request for comments (RFC 3626) in the experiment, making it suitable for use in UAV ad hoc network (UANET) environments. Simulation results indicate the proposed E-OLSR outperforms the existing OLSR and achieves supremacy over other protocols mentioned in this paper.

ADAPTATIONS TO SUBJECTIVE INSTRUMENTS FOR DYNAMIC VIEW ASSESSMENT EVALUATION

Although daylight changes seen through windows are likely to mediate positive responses in occupants, there are no specialized instruments capturing responses to dynamic attributes in views. This study mapped self-report instruments that could be readily adjusted to measure subjective responses to dynamic views. Through a revision of the literature, 55 papers in view assessment were clustered as a function of prevalent label types and constructs used to investigate responses to static outdoor views. Feature-type was the prevalent label distinction to investigate responses to window views, whilst preference and restoration were the predominant constructs in view out research. Four instruments were deemed appropriate to capture responses to dynamic attributes in views with respect to restoration, provided that semantic adaptations and further validations are set in place. This study highlights the need for more validation studies to increase the robustness of self-report instruments and outlines a theoretical approach to achieve this scope.

Floristic Composition: Dynamic Biodiversity Indicator of Tree Canopy Effect on Dryland and Improved Mediterranean Pastures

Montado is a characteristic ecosystem of the Mediterranean region. The adequate management of this silvo-pastoral ecosystem requires good understanding of the effect of factors such as tree canopy, fertilization and soil amendment on pasture growth. The main objectives of this work were: (1) to evaluate the effect of tree canopy on soil characteristics and pasture productivity and quality; and (2) to test floristic composition assessment as a bio-indicator of soil improvements (amendment and fertilization) in each study area (under and outside tree canopy). Topsoil was characterized at the beginning of the project (October 2015) and at the end of the experiments (spring 2020). Soil parameters obtained by electronic sensors (soil moisture content, soil cone index and surface temperature) were monitored monthly during the 2017/2018 pasture vegetative cycle. Pasture productivity, quality and floristic composition were evaluated every two years (2016, 2018 and 2020) in the spring flowering period. The results of the floristic inventory were submitted to a multilevel pattern analysis (Indicator Species Analysis, ISA). Pasture biodiversity was evaluated based on the calculation of richness indices. This study showed a positive effect of tree canopy on soil fertility and pasture quality (e.g., CP). Pasture productivity, on the other hand, was higher in areas outside tree canopy. The great potential of ISA as a tool for identification of bio-indicator species was also demonstrated. Pasture species were identified as ecological and dynamic attributes characteristic of each study area, before and after soil amendment and fertilization.

Rapid Fault Diagnosis Method of Elevator System Based on Multiattribute Decision Making

Fault tree analysis is often used in elevator fault diagnosis because of its simplicity and reliability. However, the traditional fault tree method has the problems of low efficiency due to ignoring location change of bottom events during troubleshooting. This paper proposes a rapid diagnosis method based on multiattribute decision making to solve the problem. The fault tree of the elevator system is constructed based on expert knowledge and multisource data, and the location-related matrix is constructed according to the complex vertical structure of the elevator. Then, the attributes of bottom events such as the failure probability, search cost, location time cost, and location-related attributes are comprehensively analyzed in this paper. Finally, the TOPSIS method for dynamic attributes is used based on the work above to achieve the optimal troubleshooting sequence of elevator vibration fault. The results show that the proposed method is more efficient when compared to the optimal troubleshooting sequence obtained by the traditional method.

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.

QoS-based semantic web service selection

This thesis discusses the dynamic web service selection in the semantic context with QoS constraints. The goal of this work is to investigate the mechanism of automated QoS-based semantic web service selection. Semantic Web Service (SWS) aims to achieve the automation of web service tasks, such as service discover, selection, composition and invocation. The task of semantic web service selection is further investigated through this thesis. An architecture is proposed to achieve this task by considering QoS parameters. The QoS parameters are classified into dynamic and static attributes in the architecture. The dynamic attributes are evaluated and measured as an overall value by applying utility functions. This overall value can be modeled in the semantic context for the purpose of service selection. Furthermore, the architecture directly models the static QoS attributes in the semantic context for service selection. Finally, an open SWS challenge scenario named hardware purchasing is used in several experiments in order to evaluate the proposed architecture.

QoS-based semantic web service selection

This thesis discusses the dynamic web service selection in the semantic context with QoS constraints. The goal of this work is to investigate the mechanism of automated QoS-based semantic web service selection. Semantic Web Service (SWS) aims to achieve the automation of web service tasks, such as service discover, selection, composition and invocation. The task of semantic web service selection is further investigated through this thesis. An architecture is proposed to achieve this task by considering QoS parameters. The QoS parameters are classified into dynamic and static attributes in the architecture. The dynamic attributes are evaluated and measured as an overall value by applying utility functions. This overall value can be modeled in the semantic context for the purpose of service selection. Furthermore, the architecture directly models the static QoS attributes in the semantic context for service selection. Finally, an open SWS challenge scenario named hardware purchasing is used in several experiments in order to evaluate the proposed architecture.

Effect of soil particle-size distribution (PSD) on soil-subsoiler interactions in the discrete element model

Particle-size distribution (PSD) is a key parameter affecting soil dynamic attributes and cutting forces of tillage tools in the discrete element model in accordance with our preliminary simulations. To accurately determine PSD, significance and mechanism for the effect of PSD under different nominal radii were investigated using the discrete element method (DEM) and validated using the laboratory soil-bin results. Soil disturbance characteristics (soil rupture distance ratio, height of accumulated soil, soil density change rate) and cutting forces (draft and vertical) under different treatments were predicted and measured respectively. The ANOVA outputs showed that PSD significantly affected draft and vertical forces (p

Enhancing the Understanding of Resilience in Health Systems of Low- and Middle-Income Countries: A Qualitative Evidence Synthesis

Background: A country’s health system faces pressure when hit by an unexpected shock, such as what we observe in the midst of the coronavirus disease 2019 (COVID-19) pandemic. The concept of resilience is highly relevant in this context and is a prerequisite for a health system capable of withstanding future shocks. By exploring how the key dimensions of the resilient health system framework are applied, the present systematic review synthesizes the vital features of resilient health systems in low- and middle-income countries. The aim of this review is to ascertain the relevance of health system resilience in the context of a major shock, through better understanding its dimensions, uses and implications. Methods: The review uses the best-fit framework synthesis approach. An a priori conceptual framework was selected and a coding framework created. A systematic search identified 4284 unique citations from electronic databases and reports by non-governmental organisations, 12 of which met the inclusion criteria. Data were extracted and coded against the pre-existing themes. Themes outside of the a priori framework were collated to form a refined list of themes. Then, all twelve studies were revisited using the new list of themes in the context of each study. Results: Ten themes were generated from the analysis. Five confirmed the a priori conceptual framework that capture the dynamic attributes of a resilient system. Five new themes were identified as foundational for achieving resilience: realigned relationships, foresight and motivation as drivers, and emergency preparedness and change management as organisational mechanisms. Conclusion: The refined conceptual model shows how the themes inter-connect. The foundations of resilience appear to be critical especially in resource-constrained settings to unlock the dynamic attributes of resilience. This review prompts countries to consider building the foundations of resilience described here as a priority to better prepare for future shocks.