Deep Learning(DL)is known for its golden standard computing paradigm in the learning community.However,it turns out to be an extensively utilized computing approach in the ML field.Therefore,attaining superior outcome...Deep Learning(DL)is known for its golden standard computing paradigm in the learning community.However,it turns out to be an extensively utilized computing approach in the ML field.Therefore,attaining superior outcomes over cognitive tasks based on human performance.The primary benefit of DL is its competency in learning massive data.The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully.Specifically,various DL approaches outperform the conventional ML approaches in real-time applications.Indeed,various research works are reviewed to understand the significance of the individual DL models and some computational complexity is observed.This may be due to the broader expertise and knowledge required for handling these models during the prediction process.This research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification purposes.This work incorporates a novel fused Squeeze and Excitation(SE)block with the ResNet model for pneumonia prediction and better accuracy.The expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.The experimentation is carried out in Keras,and the model’s superiority is compared with various advanced approaches.The proposed model gives 90%prediction accuracy,93%precision,90%recall and 89%F1-measure.The proposed model shows a better trade-off compared to other approaches.The evaluation is done with the existing standard ResNet model,GoogleNet+ResNet+DenseNet,and different variants of ResNet models.展开更多
Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the la...Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.展开更多
Given the challenges facing most humanitarian operations worldwide, a change of approach is needed to ensure greater sustainability of humanitarian settlements right from the planning stage. Some studies attribute uns...Given the challenges facing most humanitarian operations worldwide, a change of approach is needed to ensure greater sustainability of humanitarian settlements right from the planning stage. Some studies attribute unsustainability to inadequate provision of basic resources and highlight the apparent bottlenecks that prevent access to the meaningful data needed to plan and remedy problems. Most operations have relied on an “ad hoc ism” approach, employing parallel and disconnected data processing methods, resulting in a wide range of data being collected without subsequent prioritization to optimize interconnections that could enhance performance. There have been little efforts to study the trade-offs potentially at stake. This work proposes a new framework enabling all subsystems to operate in a single system and focusing on data processing perspective. To achieve this, this paper proposes a Triple Nexus Framework as an attempt to integrate water, energy, and housing sector data derived from a specific sub-system within the overall system in the application of Model-Based Systems Engineering. Understanding the synergies between water, energy, and housing, Systems Engineering characterizes the triple nexus framework and identifies opportunities for improved decision-making in processing operational data from these sectors. Two scenarios illustrate how an integrated platform could be a gateway to access meaningful operational data in the system and a starting point for modeling integrated human settlement systems. Upon execution, the model is tested for nexus megadata processing, and the optimization simulation yielded 67% satisfactory results, demonstrating that an integrated system could improve sustainability, and that capacity building in service delivery is more than beneficial.展开更多
Usually a buffer layer of cadmium sulphide is used in high efficiency solar cells based on Cu(In,Ga)Se2(CIGS). Because of cadmium toxicity, many in-vestigations have been conducted to use Cd-free buffer layers. Our wo...Usually a buffer layer of cadmium sulphide is used in high efficiency solar cells based on Cu(In,Ga)Se2(CIGS). Because of cadmium toxicity, many in-vestigations have been conducted to use Cd-free buffer layers. Our work focuses on this type of CIGS-based solar cells where CdS is replaced by a ZnS buffer layer. In this contribution, AFORS-HET software is used to simulate n-ZnO: Al/i-ZnO/n-ZnS/p-CIGS/Mo polycrystalline thin-film solar cell where the key parts are p-CIGS absorber layer and n-ZnS buffer layer. The characteristics of these key parts: thickness and Ga-content of the absorber layer, thickness of the buffer layer and doping concentrations of absorber and buffer layers have been investigated to optimize the conversion efficiency. We find a maximum conversion efficiency of 26% with a short-circuit current of 36.9 mA/cm2, an open circuit voltage of 824 mV, and a fill factor of 85.5%.展开更多
文摘Deep Learning(DL)is known for its golden standard computing paradigm in the learning community.However,it turns out to be an extensively utilized computing approach in the ML field.Therefore,attaining superior outcomes over cognitive tasks based on human performance.The primary benefit of DL is its competency in learning massive data.The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully.Specifically,various DL approaches outperform the conventional ML approaches in real-time applications.Indeed,various research works are reviewed to understand the significance of the individual DL models and some computational complexity is observed.This may be due to the broader expertise and knowledge required for handling these models during the prediction process.This research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification purposes.This work incorporates a novel fused Squeeze and Excitation(SE)block with the ResNet model for pneumonia prediction and better accuracy.The expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.The experimentation is carried out in Keras,and the model’s superiority is compared with various advanced approaches.The proposed model gives 90%prediction accuracy,93%precision,90%recall and 89%F1-measure.The proposed model shows a better trade-off compared to other approaches.The evaluation is done with the existing standard ResNet model,GoogleNet+ResNet+DenseNet,and different variants of ResNet models.
文摘Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.
文摘Given the challenges facing most humanitarian operations worldwide, a change of approach is needed to ensure greater sustainability of humanitarian settlements right from the planning stage. Some studies attribute unsustainability to inadequate provision of basic resources and highlight the apparent bottlenecks that prevent access to the meaningful data needed to plan and remedy problems. Most operations have relied on an “ad hoc ism” approach, employing parallel and disconnected data processing methods, resulting in a wide range of data being collected without subsequent prioritization to optimize interconnections that could enhance performance. There have been little efforts to study the trade-offs potentially at stake. This work proposes a new framework enabling all subsystems to operate in a single system and focusing on data processing perspective. To achieve this, this paper proposes a Triple Nexus Framework as an attempt to integrate water, energy, and housing sector data derived from a specific sub-system within the overall system in the application of Model-Based Systems Engineering. Understanding the synergies between water, energy, and housing, Systems Engineering characterizes the triple nexus framework and identifies opportunities for improved decision-making in processing operational data from these sectors. Two scenarios illustrate how an integrated platform could be a gateway to access meaningful operational data in the system and a starting point for modeling integrated human settlement systems. Upon execution, the model is tested for nexus megadata processing, and the optimization simulation yielded 67% satisfactory results, demonstrating that an integrated system could improve sustainability, and that capacity building in service delivery is more than beneficial.
文摘Usually a buffer layer of cadmium sulphide is used in high efficiency solar cells based on Cu(In,Ga)Se2(CIGS). Because of cadmium toxicity, many in-vestigations have been conducted to use Cd-free buffer layers. Our work focuses on this type of CIGS-based solar cells where CdS is replaced by a ZnS buffer layer. In this contribution, AFORS-HET software is used to simulate n-ZnO: Al/i-ZnO/n-ZnS/p-CIGS/Mo polycrystalline thin-film solar cell where the key parts are p-CIGS absorber layer and n-ZnS buffer layer. The characteristics of these key parts: thickness and Ga-content of the absorber layer, thickness of the buffer layer and doping concentrations of absorber and buffer layers have been investigated to optimize the conversion efficiency. We find a maximum conversion efficiency of 26% with a short-circuit current of 36.9 mA/cm2, an open circuit voltage of 824 mV, and a fill factor of 85.5%.
