Deployment Method Research Articles

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

Modular shadowgraph imaging for zooplankton ecological studies in diverse field and mesocosm settings

AbstractVarious iterations of shadowgraph imaging have been used to quantify zooplankton in situ with high spatial resolution. Because these systems can image relatively large volumes of water, they are especially useful for resolving less common meso‐ or macrozooplankton taxa (< 50 ind. m−3), such as larval fishes and gelatinous animals. However, larger volume imagers are typically integrated with heavy towed vehicles and deployed from research vessels, which introduces high costs and limits sampling approaches. Here we demonstrate that versatile configurations of shadowgraph imaging, including modular benchtop, handheld, and towed, compact vehicle systems (along with customizable software), allow for tailoring sampling to a variety of marine and freshwater settings (including mesocosms). These systems encompass a suite of possible architectures, designed for adapting the imaging depth of field, acquisition rates, sensor configuration, and deployment method to fit a wide range of sampling protocols, with high vertical resolution (~ 5 cm) and adequate taxonomic capabilities for > 0.5 mm organisms. The benchtop system facilitates an interactive approach to observe and quantify zooplankton behaviors and optical properties. Video footage from the benchtop system generates thousands of regions of interest min−1 for target organisms with variable orientations and swimming postures. When used in conjunction with in situ imaging, the benchtop system can build large machine learning training libraries targeted toward rare or morphologically diverse zooplankton, which often includes the larval stages of economically valuable taxa. These modular hardware and software components increase affordability and versatility while broadening the scope of scientific questions addressed by plankton imaging systems.

Aerial Systems for Releasing Natural Enemy Insects of Purple Loosestrife Using Drones

Lythrum salicaria (purple loosestrife) is an invasive species that displaces native wetland flora in the USA. The detection and manual release of biological control agents for L. salicaria is challenging because L. salicaria inhabits many inaccessible areas. This study was conducted to develop aerial systems for the detection of L. salicaria and the release of its natural enemy, Galerucella calmariensis (Coleoptera: Chrysomelidae). We determined the optimal sensors and flight height for the aerial detection of L. salicaria and designed an aerial deployment method for G. calmariensis. Drone-based aerial surveys were conducted at various flight heights utilizing RGB, multispectral, and thermal sensors. We also developed an insect container (i.e., bug ball) for the aerial deployment of G. calmariensis. Our findings indicated that L. salicaria flowers were detectable with an RGB sensor at flight heights ≤ 15 m above the canopy. The post-release mortality and feeding efficiency of G. calmariensis did not significantly differ from the control group (non-aerial release), indicating the feasibility of the targeted release of G. calmariensis. This innovative study establishes a critical foundation for the future development of sophisticated aerial systems designed for the automated detection of invasive plants and the precise release of biological control agents, significantly advancing ecological management and conservation efforts.

Evaluation method and design of greenhouse pear pollination drones based on grounded theory and integrated theory.

Greenhouse cultivation promotes an efficient and environmentally friendly agricultural production model, significantly enhancing resource sustainability and advancing sustainable agriculture. Traditional greenhouse pollination methods are inefficient and labor-intensive, limiting the economic benefits of greenhouse pear cultivation. To improve pollination efficiency and achieve fully automated mechanized operations, an innovative design method for greenhouse pear pollination drones has been developed. First, design criteria were extracted using Grounded Theory (GT), and the Analytic Hierarchy Process (AHP) was employed to determine the weight of user demand evaluation indicators. Next, the Quality Function Deployment (QFD) method translated user needs into technical requirements, resulting in the final ranking of design element weights. The drone was then designed based on these weighted rankings, yielding an optimal solution. This method quantifies the functional requirements of the product, effectively identifying key needs for the greenhouse pear pollination drones and proposing targeted solutions. Additionally, it provides a design reference for other highly functional agricultural machinery products.

Efficient and secure service function chain deployment method for delay optimization in air traffic information network

The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.

Atmospheric Neutron Single Event Effects Research of Multiple Convolutional Neural Networks based on 28 nm and 16 nm SoC

Abstract The single event effects (SEEs) evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network (CNN) models (Yolov3, MNIST, and ResNet50) in the atmospheric neutron irradiation spectrometer (ANIS) at the China Spallation Neutron Source (CSNS). The Yolov3 and MNIST models were implemented on the XILINX 28 nm system-on-chip (SoC). Meanwhile, the Yolov3 and ResNet50 models were deployed on the XILINX 16 nm FinFET UltraScale+ MPSoC. The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects, including chip type, network architecture, deployment methods, inference time, datasets, and the position of the anchor boxes. The various types of SEE soft errors, SEE cross-sections, and their distribution were analyzed to explore the radiation sensitivities and rules of 28 nm and 16 nm SoC. The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability, long-lifespan domestic artificial intelligence chips.

Pengembangan Makanan Tradisional Apem Kesesi Menggunakan Metode Quality Function Deployment (QFD) dan Konsep Green Product Sebagai Upaya Meningkatkan Kualitas Produk

Apem kesesi is a traditional wet cake food in Pekalongan district. Apem kesesi cake has the problem of not being long-lasting, the packaging and appearance of the product is less attractive, less hygienic, impractical and the taste is not suitable for the younger generation. Therefore, it is necessary to develop apem kesesi products according to needs to improve product quality. Apem kesesi product development uses the quality faction deployment (QFD) method and the green product concept. Based on survey results, consumers want apem kesesi products that are hygienic, practical, unique, have attractive packaging and are durable. The main ingredient for making apem cake is rice flour. Add tapioca flour to give the apem cake a chewy texture, palm sugar to produce a distinctive taste and aroma, then vanilla to strengthen the taste, and sodium benzoate as a preservative. The product packaging design consists of primary packaging and secondary packaging. Based on validation tests and different tests, the results showed that the product design developed met consumer needs and was better than existing products.

Multi-Buoy Deployment Method Based on an Improved Tuna Swarm Optimizer Enhanced with Fractional-Order Calculus Method for Marine Observation

Ocean buoys play a critical role in marine hydrological, water quality, and meteorological monitoring, with applications in navigation, environmental observation, and communication. However, accurately modeling and deploying a multi-buoy system in the complex marine environment presents significant challenges. To address these challenges, this study proposes an enhanced deployment strategy using the tuna swarm optimizer enhanced with the fractional-order calculus method for marine observation. The proposed method first introduces a detailed observation model that precisely captures the performance of buoys in terms of coverage and communication efficiency. By integrating the observation coverage ratio and communication energy consumption, we establish an optimal multi-buoy deployment model. The proposed method leverages tent chaotic mapping to improve the diversity of initial solution generation and incorporates fractional-order calculus to strengthen its search capabilities. Simulation experiments and statistical analysis verify the effectiveness of the proposed deployment model, with the proposed method achieving the best performance in deploying the multi-buoy system, reaching a final fitness value of 0.190052 at iteration 449, outperforming TSA, PSO, GWO, and WOA. These results highlight the potential of the proposed method in optimizing multi-buoy system deployment in marine observation.

A node deployment and resource optimization method for CPDS based on cloud‐fog‐edge collaboration

AbstractWith the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption.

ECOBRICK SOFA DESIGN IS ENVIRONMENTALLY FRIENDLY USING THE HOUSE OF QUALITY METHOD AND THE BOSTON CONSULTING GROUP (BCG) METHOD

Plastic is one of the longest decomposing types of waste with a decomposition time of up to 10 centuries. Plastic waste that is difficult to decompose can be minimized by using ecobrick techniques. This ecobrick technique aims to turn plastic waste that was initially worthless (zero value) into a product that has added value. This study uses four methods, namely the House of Quality (HOQ), Quality Function Deployment (QFD), Slovin, and Boston Consulting Group (BCG) methods. These methods are used to determine product planning and development, consumer desires, product position in market share, and systematic evaluation of its ability to produce products to satisfy consumers. This study involved 100 respondents obtained from the slovin method. Where the respondents are positioned as research subjects. The respondent data was obtained from distributing questionnaires through google forms. The data collected is calculated and analyzed for unique and minimalist sofa House of Quality (HOQ). The results of this study inform that customer feedback on the product or Voice of Customer can affect quality control and improvement by using the statistical method or Statistical Quality Control. Furthermore, unique and minimalist sofa companies each have different product development focuses. Following up on the findings in this study, the company can adjust the needs and desires of these consumers to the design and service standards as well as the obstacles that may occur.

Deployment method of wireless sensor networks based on MaOEA/P-GM algorithm

Deployment method of wireless sensor networks based on MaOEA/P-GM algorithm

Evaluation of Supplier Sustainable Development Programs Using the HFLTS-QFD Method

Objective: This study proposes a decision model for evaluating sustainable supplier development programs (SSDPs) using the HFLTS-QFD technique, which is a combination of Hesitant Fuzzy Linguistic Term Sets (HFLTS) with the Quality Function Deployment (QFD) method. Theoretical Framework: The implementation of supplier development programs is important to improve supplier performance in aspects related to economic, environmental and social aspects. The evaluation of these programs is necessary to decide on their continuation or discontinuation, as well as to direct the allocation of resources. Method: The bibliographical research supported the definition of the requirements and criteria used for the evaluation of SDPs. The implementation of the model was done using MS Excel® software. The application in an illustrative case involving the evaluation of five SDPs allowed us to demonstrate the applicability of the model. Results and Discussion: The most relevant criteria were organization strategy, social responsibility, employee training and environmental management. The programs with the best classification were SDP4 and SDP5. Research Implications: The proposed model supports the selection of criteria considering their relative weights and the degree of difficulty in collecting data from suppliers when evaluating each PDFS. It also has the capacity to assist group decision-making, enabling the assignment of weights to decision-makers. Originality/Value: Unlike previous models for evaluating PDFS, which use economic or environmental criteria, the proposed model considers criteria related to the three dimensions of the Triple Bottom Line, in addition to allowing the use of linguistic expressions to deal with decisions under hesitation and uncertainty.

A Damping Modification Factor Prediction Model for Horizontal Pseudoacceleration Spectrum for Subduction Slab Earthquakes Using Offshore Ground Motions in the Japan Trench Area

ABSTRACT This study develops a damping modification factor (DMF) model for offshore ground motion from subduction slab earthquakes that is a function of the damping ratio, spectral period, moment magnitude, focal depth, and rupture distance. The DMF values for 10 damping ratios between 1% and 30% and 34 spectral periods ranging from 0.03 to 5.0 s are calculated using 2803 offshore ground-motion records obtained from the S-net in Japan. An examination of the residuals from a median model, which considers deployment methods, indicates that DMF is influenced by parameters such as magnitude, depth, and distance. The DMF model includes the following components: a median model considering deployment method, a magnitude linear term, a magnitude square term, a focal depth term, an ln(R) scaling, and a constant term. The influence of damping ratio on model coefficients can be simulated by a quadratic function of the damping ratio. In addition, we established a DMF standard deviation model, for which the influence of damping ratio and spectral period on the standard deviation can be simulated by a quadratic function of the damping ratio and a quartic polynomial of the spectral period, respectively. The uncertainty related to the path and site effects is the main source of the deviation of the DMF model. We have conducted a comparison between the offshore DMF model and onshore model proposed by Rezaeian, Al Atik, et al. (2021), revealing that for lower damping ratios, the offshore DMF is generally lower at periods less than 0.2 s and larger at periods greater than 2.0 s compared to its onshore counterparts. The offshore DMF model adjusts the acceleration spectrum proposed by Tan and Hu (2024) to generate smooth pseudoacceleration spectra for most spectral periods.

Decision tree insights analytics (DTIA) tool: an analytic framework to identify insights from large data records across fields of science

Abstract Supervised machine learning (SML) techniques have been developed since the 1960s. Most of their applications were oriented towards developing models capable of predicting numerical values or categorical output based on a set of input variables (input features). Recently, SML models’ interpretability and explainability were extensively studied to have confidence in the models’ decisions. In this work, we propose a new deployment method named Decision Tree Insights Analytics (DTIA) that shifts the purpose of using decision tree classification from having a model capable of differentiating the different categorical outputs based on the input features to systematically finding the associations between inputs and outputs. DTIA can reveal interesting areas in the feature space, leading to the development of research questions and the discovery of new associations that might have been overlooked earlier. We applied the method to three case studies: (1) nuclear reactor accident propagation, (2) single-cell RNA sequencing of Niemann-Pick disease type C1 in mice, and (3) bulk RNA sequencing for breast cancer staging in humans. The developed method provided insights into the first two. On the other hand, it showed some of the method’s limitations in the third case study. Finally, we presented how the DTIA’s insights are more agreeable with the abstract information gain calculations and provide more in-depth information that can help derive more profound physical meaning compared to the random forest’s feature importance attribute and K-means clustering for feature ranking.

John Williams to Non-Williams

John Williams may have dominion over the Star Wars film scores with eighteen hours of music across nine films, but the mantle of responsibility for the Star Wars musical canon en masse has been undertaken by scores of many different composers, especially video game composers. Scholars of John Williams’s music such as Frank Lehman have analyzed Williams’s nine film scores, but there has been little research into the more than 100 Star Wars video game scores, which raises the question: how has Star Wars music developed without the guidance of its progenitor John Williams? Frank Lehman’s “Complete Catalogue of the Themes of Star Wars: A Guide to John Williams’s Musical Universe”1 cataloged the themes, motifs, source music, and thematic relationships of the nine films and proved musicologically insightful as well as incredibly useful when one seeks an understanding of the intricacies of Williams’s Star Wars film scores. Lehman’s research provides an ideal foundation for the ludomusicological inquisition of the hundred plus Star Wars game scores. The interactive nature of gaming mandates that the scores take player input into consideration leading to many unique methods of musical deployment that have changed from game to game. This research preludes the creation of a Star Wars sound and music anthology, similar to the Norton Anthology of Western Music, where ludomusicality found within scores from video games in the Star Wars universe are analyzed, cataloged, and explored. Each game/series would get its own unique catalog within the greater anthology consisting of a collection of analyses of the motifs, themes, and anything else of particular ludomusical interest. This poster presents a preliminary analysis of the deployment of motifs in the video games Star Wars Chess (1993) and Star Wars: X-Wing (1993), the first steps taken toward cataloging these video game scores. This proposed catalog will include analyses of ludomusicality within the Star Wars video game scores, showing how the player and computer interact to customize the listening experience of original and borrowed themes from the Star Wars movies. Combining Lehman’s method of cataloging with methods of ludomusicological analysis2 will be useful for understanding the plethora of themes, motifs, source music, thematic relationships, lyrics, diegetic music, and ludomusical methods found within the scores of each game. To present an example of this combination of analyses, Lehman’s motifs (denoted by quotation marks) are presented in combination with original analyses and transcription by the author, demonstrating the overlap between the musical material of the Star Wars movies and video games. This series of catalogs would be an excellent resource for all future Star Wars ludomusicological research as well as further expanding the literature in the field.

Research on Design and Staged Deployment of LEO Navigation Constellation for MEO Navigation Satellite Failure

Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium Earth orbit (MEO) navigation satellites, this paper designs the LEO navigation constellation and considers the task requirements of different stages of constellation deployment. Firstly, the LEO navigation constellation is designed by a non-dominated sorting genetic algorithm II (NSGA-II). The average position dilution of precision (PDOP) is 1.676, which is an improvement compared to the average PDOP offered by the four traditional GNSS. Secondly, the staged deployment of constellation takes into account the degradation of constellation performance caused by the failure of MEO navigation satellites, and the Monte Carlo method is used to analyze the case of three simultaneous satellite failures. The results show that a single satellite failure within each orbital plane and adjacent satellites with close phase separation has a great impact on the performance of the MEO navigation constellation. On this basis, a staged deployment strategy was adopted in order to balance cost, risk, and performance. The three phases deploy 66, 156, and 288 satellites, respectively; as a make-up constellation under contingencies, a navigation enhancement constellation, and an independent navigation constellation, the deployment of the staged sub-constellations meets the mission requirements. The constellation design and staged deployment method proposed in this paper can provide reference for the future study of LEO navigation constellations.

Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest

To address the limitation of traditional machine learning models in explaining the rockburst intensity prediction process, this study proposes an interpretable rockburst intensity prediction model. The model was developed using 350 sets of actual rockburst sample data to explore the impact of input metrics on the final rockburst intensity level. The collected data underwent pre-processing using the isolation forest algorithm and synthetic minority oversampling technique. The random forest model was optimized through 5-fold cross-validation and the Optuna framework, resulting in the establishment of an Optuna-random forest (Op-RF) model that generates decision rules through its internal decision tree, utilizing the properties of the random forest model. The model was further interpreted using the Shapley additive explanations algorithm, both locally and globally. The results demonstrate that the proposed model achieved an area under curve score of 0.984. In comparison to eight other machine learning models, the proposed Op-RF model demonstrated superior accuracy, precision, recall, and F1 score. The model provides a transparent explanation of the prediction process, linking impact characteristics to the final output. Additionally, a cloud deployment method for the rockburst intensity prediction model is provided and its effectiveness is demonstrated through engineering verification. The proposed model offers a new approach to the application of machine learning in rockburst intensity prediction.

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, however, often leads to highly nontrivial nonconvex optimizations. Although convex-approximation-based solutions are considered in the literature, their approximation to the actual throughput may be loose and sometimes lead to unsatisfactory performance. With this consideration, in this article, we propose a novel graph neural network (GNN) method for the network node deployment problem. Specifically, we fit a GNN to the network throughput and use the gradients of this GNN to iteratively update the locations of the network nodes. Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method. To further improve the throughput, we also study a hybrid node deployment method based on this approach. To train the desired GNN, we adopt a policy gradient algorithm to create datasets containing good training samples. Numerical experiments show that the proposed methods produce competitive results compared with the baselines.

An Improved Quantum Particle Swarm Optimization Algorithm for Target Tracking Deployment in Spatial Sensor Networks

This paper presents a comprehensive review of the current research on spatial sensor networks and the node deployment methods employed for mobile target tracking. The study introduces particle swarm optimization (PSO) and its quantum behavior extension, detailing concepts such as the quantum state wave function and particle position representation. Subsequently, an improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. This enhanced algorithm increases population diversity by incorporating quantum rotation gates and quantum mutation mechanisms, expands the search space through the superposition state and interference principles of quantum mechanics, and dynamically adjusts algorithm parameters to balance global exploration and local search. These modifications aim to improve both the convergence speed and accuracy of the algorithm. Simulation results demonstrate that the improved QPSO algorithm surpasses traditional mobile tracking deployment algorithms and the standard quantum behavior particle swarm optimization algorithm in terms of target tracking deployment within spatial sensor networks. Notably, it significantly enhances the tracking success rate and reduces tracking errors.

The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment.

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.