Resource Usage Research Articles

Predictive Value of Machine Learning Models in Mortality of Coronavirus Disease 2019 (COVID-19) Pneumonia

The coronavirus disease 2019 (COVID-19) became the most spread and lethal disease in the last 3 years. Early predictions could optimize the decision-making process, healthcare outcomes, and effective usage of healthcare resources during peaks. This study set out to predict the mortality risk of COVID-19 patients by investigating 14 machine learning (ML) models using extensive clinical, laboratory, and image-based features. Additionally, feature importances in each model and the influences of features in the mortality prediction of ML models have been evaluated in this study. Data from 252 patients during the 5th peak of the COVID-19 pandemic (July 2021-September 2021) with 42 features were used for the training of ML models. Fourteen ML models were created using the fivefold cross-validation method. Each model was trained using a training-validation dataset with its own optimized parameters. The performance of models has been evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. The highest values of accuracy (87.30%), precision (100%), sensitivity (77.27%), specificity (100%), AUC (91.90%), and F1 score (77.99%) were observed for the linear discriminant analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), KNN, Passive Aggressive Classifier (PAC), and LDA models, respectively, when training was performed with all 42 features. By using feature selection techniques, the support vector classifier (SVC) model with 10 features showed the most AUC of 93.40%. The features of mechanical ventilation, consolidation, fatigue, malignancy, dry cough, level of consciousness (LOC), gender, diarrhea, O2 therapy, and SpO2 are potential predictors of mortality rates in COVID-19 patients.

Softwarized and containerized microservices-based network management analysis with MSN

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

Analysis of the Development Trends of Preschool Population and the Demand for Preschool Education Resources under the New Population Situation: Taking Beijing as an example

The development of preschool education has become an important task in the development of education in China. However, under the trend of fewer children and changes in new population policies, the development trend of preschool education population and the demand for semester education resources in China are not clear, which brings many challenges to the allocation of future education resources. This study uses a Population Development Environment Analysis (PDE) model to predict the demand for educational resources among the preschool population in Beijing from 2020 to 2040. The research results show that during the prediction period, the scale of education eligible young children, the scale of preschool children in the first three years, the scale of kindergarten buildings, and the demand for full-time teachers and caregivers all show a trend of first increasing and then decreasing. The demand for education funds and per capita funds is gradually increasing. The future allocation of preschool education resources needs to be adjusted reasonably based on the actual needs and usage of resources. After investing heavily in the allocation of preschool education resources, attention should be paid to the relative surplus of resources caused by the subsequent decrease in resource demand.

The Transition in Economic Theory from Linear to Circular for the Sustainability - A Case Study

The ability of our society to endure and expand while preserving enough natural resources for future generations to live is known as sustainability. Sustainable development, which combines structures, procedures, and local, national, and international support, supports its long-term goal. It is the high time, when the world is looking for the environmental conservation, conservation of the planet's biodiversity, social progress and better life style for the environment means a transition for the sustainability. The economy needs to undergo a profound transition because it was built on the principles of "take, make, consume, and discard" and the so-called "linear model," which was based on the idea that resources were infinite and easily accessible from the start of capital accumulation and the industrial revolution. According to this study, a product's value can only be increased by staying within the circularity loop, which means moving from the current linear model paradigm to a circular model. Study also reveals that the businesses and production units that want to generate a competitive edge while using fewer resources need to redesign and restructure their current processes. And it is highly required that the very last resort should be resource disposal and incineration. It is therefore highlighted that the program for the usage of material resources in respect to the global 2030 plan will not be implemented effectively unless the concept of a "circular economy" is adopted in place of the "linear economy." The author of this article examines how the "linear" to "circular" economy changes using a few chosen cases, offering a comprehensive grasp of the topic and the mechanisms of global transformation.

Research on the impact of leadership on improving urban water efficiency and water conservation policies

AbstractThe increasing urbanization and growing urban population present significant challenges for urban water resources. Inadequate management has led to a decline in water quality and a mismatch between the availability and usage of city water resources. In this period of significant growth, improving urban water efficiency and reinforcing water resource protection are especially important. Leadership is essential in developing and carrying out urban water resource policies. This research investigates how leadership influences the operational efficiency of water resources protection policies. The study seeks to fill the gap in understanding the relationship between leadership, member trust, and policy effectiveness in water resource management by constructing a theoretical model and conducting empirical analysis through questionnaire surveys. The findings of this study are expected to provide valuable insights into the significance of leadership in driving improvements in water conservation policies. By establishing a link between leadership, member trust, and operational efficiency, the research contributes to both theoretical knowledge and practical implications for enhancing water resource management strategies.

Beyond Docker: Enhancing vantage6 with Kubernetes for Federated Learning.

Vantage6, a powerful platform for privacy-preserving analysis in the life-sciences domain, employs Docker in its data nodes when performing tasks like federated learning. This tight bond to Docker poses challenges on infrastructure security, efficient computing resources usage, and integration of alternative container technologies. To overcome these challenges, we explored the integration of Kubernetes into vantage6 through a Proof-of-Concept (PoC) approach. The PoC designed and implemented a Kubernetes-based architecture for vantage6, which can be easily deployed on a single machine or a cluster. This PoC serves as the reference to accelerate the adoption of Kubernetes in vantage6.

The analysis of the models performance indicators by cloud and post-cloud computing paradigms

The work describes cloud and post-cloud computing paradigms, on the basis of which it is possible to model and build intelligent video surveillance systems. Based on the analysis of literary sources, it was concluded that performance indicators largely depend on the paradigm of the computational model, but the degree of importance of each performance indicator is different for different paradigms. Taking into account the peculiarities of different paradigms, the performance indicators characteristic of each of them are described. It has been found that the common indicators for evaluating all models developed on the basis of cloud and post-cloud computing paradigms (with varying degrees of importance) are: Throughput, Network Congestion, SLA Violation, Fault Tolerance, Statistical Analysis Measurements, The Number of Orchestration Decisions, The number of Contradictory Decisions, Time to Adaptation, Competition Ratio, Cache Hit Ratio, The number of Contradictory Actions, Oscillation Mitigation, Cost, Technique’s Overhead, Privacy. Based on the analysis of cloud and post-cloud computing simulators, it is established that the metrics of resource usage, energy, response time, and latency are those that can be used by researchers when conducting a comparative analysis of models built on the basis of the following paradigms: fog, edge, fog, and cloud computing. The practical significance of the conducted research is that, depending on the tasks that will be set before the future video surveillance system, researchers will be able to evaluate the effectiveness of using different architectures (Cloud, FOG, Mist, etc.) with the help of certain simulators. In the future, it is advisable to conduct a study of the degree of importance of common metrics for evaluating all models developed on the basis of cloud and post-cloud computing paradigms, and conduct their normalization for the possibility of conducting an adequate comparative analysis. It is also relevant in future research that it is advisable to conduct research using different types of sensor devices (surveillance cameras with different technical characteristics, unmanned aerial vehicles, etc.).

Assessment of timber engineering needs and practices in Bhutan: A comprehensive study

A survey was conducted to explore the intensity of timber usage and evaluate the knowledge and experiences amongst working professional timber handlers in Bhutan. Subsequently, a need assessment survey was carried out to understand the need and demand for certification courses in timber engineering in the country. Despite having an abundant timber resource and massive usage, Bhutan still needs to gain expertise in timber engineering. This study involved 90 respondents from timber processing, construction and manufacturing sectors across 12 districts in the country. Twenty-one respondents are timber technocrats who have significantly contributed to timber associate infrastructure, while 61 are from the Wood-based Industries, specifically from the timber manufacturing sectors. The key findings from this survey observed several barriers to timber use that include limited knowledge amongst designers, clients and construction companies, alongside concerns about durability and fire resistance. As per the findings, these challenges could be addressed through a dedicated timber engineering course, an idea supported by participants, with 80% of manufacturing professionals and 55–60% of others. The respondents also highlighted the demand for timber testing facilities in the country. This research underscores the need for timber engineering expertise in Bhutan's construction industry.

On-chip topological beamformer for multi-link terahertz 6G to XG wireless.

Terahertz (THz) wireless communication holds immense potential to revolutionize future 6G to XG networks with high capacity, low latency and extensive connectivity. Efficient THz beamformers are essential for energy-efficient connections, compensating path loss, optimizing resource usage and enhancing spectral efficiency. However, current beamformers face several challenges, including notable loss, limited bandwidth, constrained spatial coverage and poor integration with on-chip THz circuits. Here we present an on-chip broadband THz topological beamformer using valley vortices for waveguiding, splitting and perfect isolation in waveguide phased arrays, featuring 184 densely packed valley-locked waveguides, 54 power splitters and 136 sharp bends. Leveraging neural-network-assisted inverse design, the beamformer achieves complete 360° azimuthal beamforming with gains of up to 20 dBi, radiating THz signals into free space with customizable user-defined beams. Photoexciting the all-silicon beamformer enables reconfigurable control of THz beams. The low-loss and broadband beamformer enables a 72-Gbps chip-to-chip wireless link over 300 mm and eight simultaneous 40-Gbps wireless links. Using four of these links, we demonstrate point-to-4-point real-time HD video streaming. Our work provides a complementary metal-oxide-semiconductor-compatible THz topological photonic integrated circuit for efficient large-scale beamforming, enabling massive single-input multiple-output and multiple-input and multiple-output systems for terabit-per-second 6G to XG wireless communications.

Unveiling a water-resilient service economy: A model-based approach for enhanced service excellence in GVCs

Urbanization, climate change, and irresponsible resource management exacerbate the global water crisis. The necessity for water resilience, the capacity of systems and communities to adjust and flourish in the face of water shocks and pressures, has been brought to light by these critical issues. Water resilience enables Global Value Chains (GVCs) to survive scarcity, pollution, and flooding, ensuring sustainability and service delivery. Current service excellence models focus on stakeholder satisfaction, punctuality, and reliability over water resilience. This oversight may limit GVC growth and flexibility, reducing sector services. As recommended service excellence models focus on satisfaction, punctuality and reliability among stakeholders but water resilience is not considered. This can create issues for the growth and flexibility of GVCs which could cut back on services sector. This research is examined a complex relationship between service quality and water resilience to improve the GVCs in China regions especially Guangdong Province, Shanghai Municipality, and Beijing Municipality. By using multiple regression, GVCs service quality and water resilience is analyzed in the existence of Service Excellence Model. The study used 15 years (2009–2023) secondary data to measure how water resilience and GVCs services quality affect each other in Chinese regions. The results show that water resilience strategies can strengthen global production networks, optimize resource usage, and enhance service excellence. Chinese GVCs can produce a water-resilient service economy, enlightening service quality and preserving competitiveness in rapidly changing global markets.

Activating sustainability in the design process

Abstract Design decisions have an impact on societies and the environment. Making the impact of design tangible facilitates the mitigation of its negative consequences. The purpose of this paper is to outline what methods and actions a User Experience (UX) practitioner could use in order to support sustainable development. At Ergosign GmbH, an agency creating digital experiences, we conducted a review of existing guidelines and principles to determine the relevance and usefulness of those frameworks in the User Experience Design (UXD) context. The analysis resulted in the creation of six guiding principles to introduce sustainability concerns into the design process. Additionally, we mapped related UX methods and heuristics to each principle. Each of the six principles, as well as an exemplary method, are described in detail in this article. A first practical evaluation of the principles suggests that assessing impact and identifying preventive actions is possible at all stages of the design process. Furthermore, there is no strict correlation of a principle with a particular project phase. While an early analysis generates a more holistic action plan, a later assessment provides more concrete advice. Both approaches can deliver notable improvements in the reduction of resource usage.

The Impact of Educational Informatics on School Management Decision-Making in the Context of Big Data

In the domain of private education, particularly vocational education, school management encounters a complex and variable environment with diverse demands. As the advancement of educational informatics progresses, schools have accumulated a substantial amount of data, including student learning behavior, teaching data, and classroom utilization statistics. However, the efficient utilization of these data for scientific management decisions to enhance the quality of education and resource efficiency presents an urgent challenge. The study of the impacts of educational informatics on school management decision-making is of significant importance. It aids administrators in comprehensively understanding and analyzing the allocation and usage of educational resources, thereby enhancing management efficiency and teaching quality and further promoting the development of educational informatics. Despite some progress in data analysis and decision-making methods within educational informatics, deficiencies remain. Traditional methods often overlook comprehensive considerations of spatiotemporal data, failing to accurately reflect the dynamic changes in educational resources. Existing decision-making methods, predominantly based on single-agent models, lack studies on multi-agent collaborative decision-making, resulting in suboptimal decision outcomes. This study comprises three main components: first, the data modeling of educational informatics based on a spatiotemporal data model; second, an intelligent decision-making framework for school management under a reinforcement learning mechanism; and third, the implementation of an intelligent school management decision-making method based on multi-agent reinforcement learning. Through these investigations, the scientific nature and efficiency of school management are enhanced, providing new ideas and methods for management decision-making in other educational domains.

On the Optimization of Kubernetes toward the Enhancement of Cloud Computing

With the vigorous development of big data and cloud computing, containers are becoming the main platform for running applications due to their flexible and lightweight features. Using a container cluster management system can more effectively manage multiocean containers on multiple machine nodes, and Kubernetes has become a leader in container cluster management systems, with its powerful container orchestration capabilities. However, the current default Kubernetes components and settings have appeared to have a performance bottleneck and are not adaptable to complex usage environments. In particular, the issues are data distribution latency, inefficient cluster backup and restore leading to poor disaster recovery, poor rolling update leading to downtime, inefficiency in load balancing and handling requests, poor autoscaling and scheduling strategy leading to quality of service (QoS) violations and insufficient resource usage, and many others. Aiming at the insufficient performance of the default Kubernetes platform, this paper focuses on reducing the data distribution latency, improving the cluster backup and restore strategies toward better disaster recovery, optimizing zero-downtime rolling updates, incorporating better strategies for load balancing and handling requests, optimizing autoscaling, introducing better scheduling strategy, and so on. At the same time, the relevant experimental analysis is carried out. The experiment results show that compared with the default settings, the optimized Kubernetes platform can handle more than 2000 concurrent requests, reduce the CPU overhead by more than 1.5%, reduce the memory by more than 0.6%, reduce the average request time by an average of 7.6%, and reduce the number of request failures by at least 32.4%, achieving the expected effect.

Client-Server Code Mobility at Runtime

Application logic is inherently distributed between client and server due to the fundamental Client/Server architecture of the Web. The individual distribution is specified at design time and remains unchanged stable, preventing individual load distribution between clients and server at runtime. Dynamic code mobility at runtime, in contrast, allows to balance the needs of users, through increased responsiveness, and software providers, through better resource usage and cost reductions. Enabled by WebAssembly, the Web ecosystem recently provides the technological foundation for relocating code units during runtime. However, leveraging these capabilities to enhance web applications with dynamic code migration presents challenges for web engineers. In response, we propose an innovative distributed Client/Server software architecture for web applications. This architecture facilitates the dynamic migration of code at runtime, and addresses the technical challenges like dependency management, control and data flow distribution, communication, and interfaces. This novel software architecture serves as a reference for web engineers aiming to enrich their web applications with dynamic code mobility. Additionally, it contributes to the ongoing reevaluation of the Web ecosystem in light of the widespread adoption and standardization of WebAssembly across major browsers. Through experimentation in four scenarios, we demonstrate the feasibility of implementing this architecture, its negligible impact on performance and the optimization potential for individual code distributions across client and server.

Challenges and Strategies: Usage of Multimedia Resources in Teaching Social Studies Concepts in the Junior High Schools of Ghana

Challenges and Strategies: Usage of Multimedia Resources in Teaching Social Studies Concepts in the Junior High Schools of Ghana

Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency

Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency

Complete Mechanization of Oil Seed Crop Cultivation to Augment the Potentiality of Yellow Revolution in India: A Review

This study explores how mechanization could support oil seed production in India in line with the self-sufficiency objectives of the yellow revolution. The text highlights the limitations, such as low yields and reliance on imports, and highlights how mechanization can increase productivity, profitability, and resource usage. Particular mechanized techniques for different stages such as field preparation, sowing, weed management, and harvesting are explored with an emphasis on groundnut, Castor, Sunflower, Rapeseed, Soybean and sesame cultivation. This review paper promotes broader adoption of mechanization as a crucial tactic to accomplish sustainable growth in oil seed production and reduce dependence on imports.

Design and Implementation of Deduplication on F2FS

Data deduplication technology has gained popularity in modern file systems due to its ability to eliminate redundant writes and improve storage space efficiency. In recent years, the flash-friendly file system (F2FS) has been widely adopted in flash memory-based storage devices, including smartphones, fast-speed servers, and Internet of Things. In this article, we propose F2DFS (deduplication-based F2FS), which introduces three main design contributions. First, F2DFS integrates inline and offline hybrid deduplication. Inline deduplication eliminates redundant writes and enhances flash device endurance, while offline deduplication mitigates the negative I/O performance impact and saves more storage space. Second, F2DFS follows the file system coupling design principle, effectively leveraging the potentials and benefits of both deduplication and native F2FS. Also, with the aid of this principle, F2DFS achieves high-performance and space-efficient incremental deduplication. Third, F2DFS adopts virtual indexing to mitigate deduplication-induced many-to-one mapping updates during the segment cleaning. We conducted comprehensive experimental comparisons between F2DFS, native F2FS, and other state-of-the-art deduplication schemes, using both synthetic and real-world workloads. For inline deduplication, F2DFS outperforms SmartDedup, Dmdedup, and ZFS, in terms of both I/O bandwidth performance and deduplication rates. And for offline deduplication, compared to SmartDedup, XFS, and BtrFS, F2DFS shows higher execution efficiency, lower resource usage, and greater storage space savings. Moreover, F2DFS demonstrates more efficient segment cleanings than native F2FS.

Performance of laser metal deposition on hot-rolled stainless steel for hybrid steel structures

The construction sector is increasingly recognising the potential of metal additive manufacturing (AM) for fabricating complex steel details with enhanced performance and reduced resource usage. In the coming years, the application of AM technologies is expected to advance, leading to the development of new structural elements and improved performance of existing ones. This progression involves the integration of conventional construction steel with AM techniques, such as laser metal deposition (LMD), to create hybrid systems that incorporate efficient details. LMD offers the advantage of overcoming size limitations associated with laser powder bed fusion (LPBF) while leveraging its geometric capabilities. In this study, the mechanical behavior of hybrid steel components, fabricated using LMD on AISI316L stainless steel plates, was investigated through an extensive experimental campaign involving 40 samples. Axial tests, fatigue tests, and material characterisation were conducted to evaluate strength criteria and long-term behavior. Results demonstrate that LMD-produced AISI316L exhibits mechanical properties compliant with EN1993's "Design of steel structures" encompassing elastic and plastic behavior. Furthermore, both LMD samples and hybrid specimens exhibit promising fatigue performance, aligning with the highest fatigue detail category (detail category 160) for conventional steel details.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.