Scalability Evaluation Research Articles

SUSTAINABLE AND SCALABLE ENZYMATIC PRODUCTION, STRUCTURAL ELUCIDATION, AND BIOLOGICAL EVALUATION OF NOVEL PHLORIZIN ANALOGUES.

It is not unusual for naturally occurring compounds to be limited for their use in cosmetics due to their low water solubility. Recently, aiming at accessing novel phlorizin analogues, we reported the synthesis of very promising - but low water-soluble - biomass-derived chalcones (CHs) and dihydrochalcones (DHCs) exhibiting antioxidant and anti-tyrosinase activities. Glycosylating bioactive compounds being one of the most common strategies to increase water solubility, herein we report the enzymatic glycosylation of the aforementioned CHs, as well as DHC, using cyclodextrin glycosyltransferases (CGTase), enzymes well-known for catalyzing the selective ⍺(1→4)transglycosylation. Indeed,while most natural glycosides areβ-glycosides (such as phlorizin), the selected enzyme produces selectively newα-glycosides, thus expanding their structural diversity. A first separation using Centrifugal Partition Chromatography (CPC) led to mono-, di- or triglycosides-enriched fractions, which were then submitted to a comprehensive purification strategy for an in-depth chemical profiling of the synthesizedα-glycosides, revealingas major compounds. Surprisingly, among the diglycosides characterized, besides the expected maltoside compounds, nigeroside derivatives were also identified in significant amounts, depending on the starting compound structure. Finally, evaluating the antiradical, anti-tyrosinase and antimicrobial activities of the major glycosides revealed them as potential sustainable alternatives to current petro-sourced cosmetic ingredients.

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization

ABSTRACTKubernetes is an open‐source container orchestration platform, offers a comprehensive suite of features for managing containerized applications effectively. These features encompass horizontal scaling, per‐node‐pool cluster scaling and automated resource request adjustments. This research endeavors to harness these capabilities to address the limitations experienced by fog servers in edge environments, particularly those arising from restricted network connectivity and scalability challenges. In this research paper, the primary focus is on Kubernetes role of enhancing scalability, providing a robust framework for managing containerized applications. The proposed approach involves creating a predefined number of pods and containers within a Kubernetes cluster, specifically designed to efficiently handle incoming requests while optimizing CPU and memory usage. This method implements a microservice architecture for the web tier, with separate pods for the front end, back end and database, ensuring modular and scalable design. All pods communicate and integrate through REST APIs, facilitating seamless interaction and data exchange between the services. When handling web requests, the approach enables and controls both internal and external networks, ensuring secure and efficient communication. The analysis then examines the CPU and memory utilization of the pods, as well as node bandwidth, to provide a comprehensive evaluation of container scalability and performance within the Kubernetes cluster. These findings effectively demonstrate Kubernetes' capability in managing container scalability and optimizing resource utilization, highlighting its efficiency and robustness in a microservice environment.

Implementation of 802.11ax and cell-free massive MIMO scenario for 6G wireless network analysis extending OMNeT++ simulator

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas.

Portability and scalability evaluation of large-scale statistical modeling and prediction software through HPC-ready containers

HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications often require extraordinary efforts to deploy and execute at performance potential on new HPC systems, while the users expert in these applications generally have less expertise in HPC and related technologies. This paper provides a dynamic solution that facilitates containerization for transferring HPC software onto diverse parallel systems. The study relies on the HPC Workflow as a Service (HPCWaaS) paradigm proposed by the EuroHPC eFlows4HPC project. It offers to deploy workflows through containers tailored for any of a number of specific HPC systems. Traditional container image creation tools rely on OS system packages compiled for generic architecture families (x86_64, amd64, ppc64, …) and specific MPI or GPU runtime library versions. The containerization solution proposed in this paper leverages HPC Builders such as Spack or Easybuild and multi-platform builders such as buildx to create a service for automating the creation of container images for the software specific to each hardware architecture, aiming to sustain the overall performance of the software. We assess the efficiency of our proposed solution for porting the geostatistics ExaGeoStat software on various parallel systems while preserving the computational performance. The results show that the performance of the generated images is comparable with the native execution of the software on the same architectures. On the distributed-memory system, the containerized version can scale up to 256 nodes without impacting performance.

Scalable evaluation of Hadamard products with tensor product basis for entropy-stable high-order methods

Scalable evaluation of Hadamard products with tensor product basis for entropy-stable high-order methods

Large Language Model-Based Evaluation of Medical Question Answering Systems: Algorithm Development and Case Study.

Healthcare systems are increasingly resource constrained, leaving less time for important patient-provider interactions. Conversational agents (CAs) could be used to support the provision of information and to answer patients' questions. However, information must be accessible to a variety of patient populations, which requires understanding questions expressed at different language levels. This study describes the use of Large Language Models (LLMs) to evaluate predefined medical content in CAs across patient populations. These simulated populations are characterized by a range of health literacy. The evaluation framework includes both fully automated and semi-automated procedures to assess the performance of a CA. A case study in the domain of mammography shows that LLMs can simulate questions from different patient populations. However, the accuracy of the answers provided varies depending on the level of health literacy. Our scalable evaluation framework enables the simulation of patient populations with different health literacy levels and helps to evaluate domain specific CAs, thus promoting their integration into clinical practice. Future research aims to extend the framework to CAs without predefined content and to apply LLMs to adapt medical information to the specific (health) literacy level of the user.

IJCM_216A: Task force study for evaluation of community level acceptability, scalability and linkage within health system of ICMR pre-validated Labike/ Technologies for screening & diagnosis in rural & urban population–An implementation research

Background: The concept of ABDM- Ayushman Bharat Digital Health Mission and Ayushman Bharat- Pradhan Mantri Jan Arogya Yojna are in root for overall aims of the research and importance of this research proposal. There is a need to study the acceptability & scalability of ICMR pre-validated technologies -Mobile Lab technology & digital technologies, skilled manpower needed for health data updating from individuals given individual health ids. Objectives: To do situational analysis of availability of medical diagnostic, testing facilities in under privileged/ remote areas. Methodology: Study Design – Implementation research, Sample - 1000 households (5000 population) have been equally enrolled from rural and urban areas which are further equally divided into two groups of control and intervention. Base Line Data has been collected on following information- socio-demographic characteristics, blood pressure measurement, health needs, health seeking behavior of community, availability and utilization of existing health care facilities and blood investigations. Study tools: Questionnaire for baseline survey – After informed consent, interview of the family head was done using set of quantitative and open-end qualitative questionnaires. Results: 77.9% and 69.3% belong to Upper Lower class in urban and rural areas. 21% and 15.6 % blood pressure measurements were deranged in urban and rural areas, 11.8 % and 10% population in urban and rural areas showed their willingness for lab investigations at door step through LABIKE. Blood glucose measurements of 5.4 % and 10.6% population in urban and rural areas were deranged. Haemoglobin values of 11.8 % and 17% population in urban and rural areas were low than 10 g/dl. 7 % and 12.7 % population in urban and rural areas utilized existing health care facilities. Conclusion: There is need to generate robust grass root level network by integrating community and health service providers.

Scalable evaluation methods for autonomous vehicles

Effective intelligent driving test and evaluation methods can improve the development and deployment process of autonomous vehicles (AVs). However, due to the extreme complexity and high dimensionality of driving behavior, how to objectively and efficiently evaluate the multi-dimensional performance of AVs in simulation and real-world environments is a long-standing problem. This paper proposes an objective multi-dimensional comprehensive evaluation (OMDCE) method that divides the intelligent driving test into four modules: test scenarios, scenario complexity model, simulation test platform, and automated evaluation system. The scenario complexity model is proposed to bridge the gap between the test scenarios and the automated evaluation system, thus enabling adaptive scaling of the evaluation scale for test scenarios with different difficulty levels. Besides, based on the existing four evaluation metrics for ego performance, altruism performance evaluation is first proposed to comprehensively portray AVs’ intelligence degree. The OMDCE method was evaluated by testing two different intelligent driving algorithms and conducting real vehicle testing. The experimental results demonstrated that the OMDCE method can effectively evaluate AVs in various scenarios and quantitatively measure their multi-dimensional performance. The proposed method reduces the subjectivity of manual evaluation and speeds up the evaluation. Moreover, it improves the universality and scalability of the evaluation metrics system.

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems

Verifying contracts among software components: An ontology-based approach

The goal of Component-Based Software Engineering (CBSE) is the development of software systems in terms of an assembly of pre-fabricated software components. One of the main aims of CBSE is to increase software reuse whereby a software component becomes part of different software systems. Verification is an important task that ensures contract conformance among components. However, current techniques for verification of component matching are poorly used in industry due to the fact that the use of these techniques is complex since they require specialized expertise. Also, the use of such techniques can be time-consuming. In this paper, we present Moctezuma, a framework for verifying the matching of software components that does not require the user possessing highly specialized skills and is able to check contract conformance of functional semantics aspects. Our approach relies on a core ontology of software components, which captures the concepts, properties, relationships, requirements, and software component functionality. We make use of architecture description languages (ADLs) to specify configurations of component interconnections. Interface contracts are specified with a customized version of CORBA-IDL. We employ ontology reasoning engines to check conformance among interface contracts. The accuracy evaluation results have shown that our verifier has a high accuracy for detecting semantics errors. The scalability evaluation shows that our framework exhibits almost a linear behavior. It is concluded that our framework is suitable for verifying the conformance of interface contracts, involving semantics aspects, along a configuration of component interconnections.

Scalability evaluation of forecasting methods applied to bicycle sharing systems

Public Bicycle Sharing Systems (BSS) have spread in many cities for the last decade. The need of analysis tools to predict the behavior or estimate balancing needs has fostered a wide set of approaches that consider many variables. Often, these approaches use a single scenario to evaluate their algorithms, and little is known about the applicability of such algorithms in BSS of different sizes. In this paper, we evaluate the performance of widely known prediction algorithms for three sized scenarios: a small system, with around 20 docking stations, a medium-sized one, with 400+ docking stations, and a large one, with more than 1500 stations. The results show that Prophet and Random Forest are the prediction algorithms with more consistent results, and that small systems often have not enough data for the algorithms to perform a solid work.

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data.

This study introduces a novel methodology designed to assess the accuracy of data processing in the Lambda Architecture (LA), an advanced big-data framework qualified for processing streaming (data in motion) and batch (data at rest) data. Distinct from prior studies that have focused on hardware performance and scalability evaluations, our research uniquely targets the intricate aspects of data-processing accuracy within the various layers of LA. The salient contribution of this study lies in its empirical approach. For the first time, we provide empirical evidence that validates previously theoretical assertions about LA, which have remained largely unexamined due to LA's intricate design. Our methodology encompasses the evaluation of prospective technologies across all levels of LA, the examination of layer-specific design limitations, and the implementation of a uniform software development framework across multiple layers. Specifically, our methodology employs a unique set of metrics, including data latency and processing accuracy under various conditions, which serve as critical indicators of LA's accurate data-processing performance. Our findings compellingly illustrate LA's "eventual consistency". Despite potential transient inconsistencies during real-time processing in the Speed Layer (SL), the system ultimately converges to deliver precise and reliable results, as informed by the comprehensive computations of the Batch Layer (BL). This empirical validation not only confirms but also quantifies the claims posited by previous theoretical discourse, with our results indicating a 100% accuracy rate under various severe data-ingestion scenarios. We applied this methodology in a practical case study involving air/ground surveillance, a domain where data accuracy is paramount. This application demonstrates the effectiveness of the methodology using real-world data-intake scenarios, therefore distinguishing this study from hardware-centric evaluations. This study not only contributes to the existing body of knowledge on LA but also addresses a significant literature gap. By offering a novel, empirically supported methodology for testing LA, a methodology with potential applicability to other big-data architectures, this study sets a precedent for future research in this area, advancing beyond previous work that lacked empirical validation.

Prevention of human exposure to livestock faecal waste in the household: a scoping study of interventions conducted in sub-Saharan Africa

BackgroundPoorly managed animal faecal waste can result in detrimental environmental and public health implications. Limiting human exposure to animal waste through Animal inclusive Water Sanitation and Hygiene (A-WASH) strategies is imperative to improve public health in livestock keeping households but has received little attention to date. A small number of A-WASH interventions have previously been identified through a systematic review by another research team, and published in 2017. To inform intervention design with the most up-to-date information, a scoping study was conducted to map the existing evidence for A-WASH in sub-Saharan Africa (SSA) emerging since the previous review.MethodsThis review followed PRISMA guidelines to identify interventions in SSA published between January 2016 to October 2022. Databases searched included PubMed, PMC Europe, CabDirect and Web of Science. Studies were eligible for inclusion if they were written in English and documented interventions limiting human contact with animal faecal material in the SSA context. Key data extracted included: the intervention itself, its target population, cost, measure of effectiveness, quantification of effect, assessment of success, acceptability and limitations. These data were synthesized into a narrative, structured around the intervention type.FindingsEight eligible articles were identified. Interventions to reduce human exposure to animal faecal matter were conducted in combination with ‘standard’ human-centric WASH practices. Identified interventions included the management of human-animal co-habitation, educational programs and the creation of child-safe spaces. No novel A-WASH interventions were identified in this review, beyond those identified by the review in 2017. Randomised Controlled Trials (RCTs) were used to evaluate six of the eight identified interventions, but as effect was evaluated through various measures, the ability to formally compare efficacy of interventions is lacking.ConclusionThis study indicates that the number of A-WASH studies in SSA is increasing and the use of RCTs suggests a strong desire to create high-quality evidence within this field. There is a need for standardisation of effect measures to enable meta-analyses to be conducted to better understand intervention effectiveness. Evaluation of scalability and sustainability of interventions is still lacking in A – WASH research.

Extension of Computational Co-Design Methods for Modular, Prefabricated Composite Building Components Using Bio-Based Material Systems

Robotic coreless filament winding using alternative material systems based on natural fibers and bio-based resin systems offers possible solutions to the productivity and sustainability challenges of the building and construction sector. Their application in modular, prefabricated structures allows for material-efficient and fast production under tightly controlled conditions leading to high-quality building parts with minimal production waste. Plant fibers made of flax or hemp have high stiffness and strength values and their production consumes less non-renewable energy than glass or carbon fibers. However, the introduction of natural material systems increases uncertainties in structural performance and fabrication parameters. The development process of coreless wound composite parts must thus be approached from the bottom up, treating the material system as an integral part of design and evaluation. Existing design and fabrication methods, as well as equipment, are adjusted to emphasize material aspects throughout the development, increasing the importance of material characterization and scalability evaluation. The reciprocity of material characterization and the fabrication process is highlighted and contributes to a non-linear, cyclical workflow. The implementation of extensions and adaptations are showcased in the development of the livMatS pavilion, a first attempt at coreless filament winding using natural material systems in architecture.

Patient Record Management System using Laravue

This study introduces a Patient Record Management System developed using Laravue, combining Laravel PHP and Vue.js. The system aims to streamline patient record handling, enhance data accessibility, and improve overall healthcare efficiency. Through an analysis of existing systems, the study emphasizes the need for Laravue's adoption. The methodology covers system design, development tools, and user feedback. Results include a comprehensive evaluation of system features, performance, and scalability. The Laravue-based system demonstrates its capability to efficiently manage patient records, ensuring secure and reliable healthcare data management.

Water reuse for vine irrigation: from research to full-scale implementation

Abstract Water scarcity is a worldwide problem, which leads to unprecedented pressure on water supply in arid and semi-arid regions. Treated wastewater is an alternative water resource, therefore, its reuse for agricultural irrigation has been growing worldwide since the beginning of the 21st century. In several regions of wine-producing countries (e.g., Australia, California – USA, Spain), wastewater reuse appears to be the most accessible alternative, both financially and technically, for agricultural uses that notably do not require drinking water. From the summer of 2022, vine irrigation full-scale implementation will start with tertiary treated municipal wastewater in the French Languedoc region. This was made possible thanks to a collaborative research project conducted between 2013 and 2018 to address all potential health and environmental risks associated with this process. This research project was conducted in the south of France, with experimental and control plots both equipped with drip irrigation systems. All the results produced during the research project demonstrated the feasibility of applying this process for vine drip irrigation while effectively managing health and environmental risks and complying with the regulation. A social acceptance and economic study were also performed in order to broaden the scope of the project scalability evaluation.

Dependent tasks offloading in mobile edge computing: A multi-objective evolutionary optimization strategy

Due to the proliferation of applications such as virtual reality and online games with high real-time requirements, Mobile Edge Computing (MEC) has become a promising computing paradigm that can improve user experience and reduce the task offloading latency. The cloud–edge-end collaborative offloading further addresses the problem of insufficient computing resources of edge servers owing to large-scale computing-intensive applications in MEC. However, existing offloading solutions often ignore the important factor of economic cost, making it hard for these solutions to achieve a sustainable cloud–edge-end collaborative computation. To this end, this paper considers a multi-user multi-server, cloud–edge-end collaborative offloading scenario in the presence of dependent offloading tasks for the sake of maximizing rewards and minimizing latency. Each user issues a computing-intensive application consisting of multiple dependent tasks, which are offloaded collaboratively by various computational resources. With the goal of maximizing the yield of offloading for users and server providers, a multi-objective optimization problem of joint task offloading and execution rewards is studied. Technically, a multivariate multi-objective optimization problem with three objectives is modeled. An efficient multi-objective evolutionary optimization algorithm based on MOEA/D is then developed to solve the latency minimization and reward maximization problems. Extensive simulation results verify the effectiveness of the algorithm and illustrate that the proposed algorithm can significantly improve user offloading benefits. In addition, a scalability evaluations of our proposed algorithm is conducted for demonstrating its feasibility in large-scale task offloading scenarios.

Voting System using Blockchain Ethereum (dApp)

The use of E-Voting systems has become popular in these recent years due to the ability of Blockchain environment to provide a more efficient and convenient voting process. Earlier, the security and integrity of e-voting systems has been very concerning, as they are vulnerable to cyber-attacks and manipulation. On the contrary, Blockchain technology provides a decentralized and distributed platform that can ensure the integrity and immutability of data. This research paper proposes an e-voting system based on blockchain technology. The proposed system aims to provide a secure, transparent, and tamper-proof voting process. The system utilizes smart contracts, which automates the voting processes and ensures the accuracy of the results. The system also provides transparency and accuracy, allowing voters to verify their vote and ensuring that the results are accurate and trustworthy. The proposed system will be tested and evaluated to determine its effectiveness and feasibility. The evaluation will focus on the security, scalability, and usability of the system. The security evaluation will test the system's ability to prevent attacks and ensure the confidentiality of the votes. The scalability evaluation will test the system's ability to handle a large number of voters and transactions. The usability evaluation will test the ease of use and accessibility of the system for all types of voters

IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

Robust and Efficient Sorting with Offset-value Coding

Sorting and searching are large parts of database query processing, e.g., in the forms of index creation, index maintenance, and index lookup, and comparing pairs of keys is a substantial part of the effort in sorting and searching. We have worked on simple, efficient implementations of decades-old, neglected, effective techniques for fast comparisons and fast sorting, in particular offset-value coding. In the process, we happened upon its mutually beneficial relationship with prefix truncation in run files as well as the duality of compression techniques in row- and column-format storage structures, namely prefix truncation and run-length encoding of leading key columns. We also found a beneficial relationship with consumers of sorted streams, e.g., merging parallel streams, in-stream aggregation, and merge join. We report on our implementation in the context of Google’s Napa and F1 Query systems as well as an experimental evaluation of performance and scalability.