Systems Development Research Articles

Enhancing preparedness by developing robust implementation

Abstract In EUVABECO, a goal is defined as a public health objective that motivates a vaccination practice. A vaccination practice encompasses specific methods or approaches used in vaccination efforts, while a tool refers to a tangible resource or software developed to support the implementation of these practices. EUVABECO is undertaking a series of structured activities from January 2024 until mid-2026. These include mapping innovative practices, deriving requirements for tools and validation plans, developing implementation plans, and facilitating their adoption by European MS. The project will also evaluate, validate, and verify these plans to ensure their efficacy and sustainability. EUVABECO’s methodology is informed by the Systems Development Lifecycle (SDLC) framework, utilizing the V-model to ensure a strong linkage between the development and testing phases, thereby guaranteeing a cohesive workflow. Each implementation plan developed will feature comprehensive policy recommendations and a workplan that covers the necessary organizational, technical, legal, and ethical aspects required for the tool’s deployment. Additionally, these plans will include tailored strategies to enhance the tool’s functionality during outbreaks, criteria for verifying implementation effectiveness, and financial considerations critical for supporting adoption. The implementation plans will undergo a rigorous validation process to assess their suitability, transferability, and sustainability across MS. The ultimate goal is to disseminate validated plans whilst ensuring a parallel multistakeholder engagement approach to ensure MS are actively involved in key project phases, thus, facilitating rapid uptake of said plans across EU MS and a transformation of the vaccination ecosystem across Europe.

CollectiveHLS: A Collaborative Approach to High-Level Synthesis Design Optimization

High-Level Synthesis (HLS) has played a pivotal role in making FPGAs accessible to a broader audience by facilitating high-level device programming and rapid microarchitecture customization through the use of directives. However, manually selecting the right directives can be a formidable challenge for programmers lacking a hardware background. This paper presents CollectiveHLS, an ultra-fast, knowledge-driven approach to optimizing HLS designs. It automates the identification and application of optimal directive configurations from the original source code, focusing on minimizing design latency and ensuring synthesizability. This optimization approach is entirely data-driven, offering a generalized HLS tuning solution without reliance on Quality of Result (QoR) models or meta-heuristics. CollectiveHLS is designed, implemented, and evaluated using around 60 applications sourced from well-established benchmark suites and GitHub repositories, all running on a Xilinx UltraScale + MPSoC ZCU104. It achieves an average geometric mean speedup of up to \(23.1\times\) compared to the official source code without directives, while maintaining synthesizability and feasibility rates of 100% and 96.6%, respectively, matching those of Vitis, the industry-standard framework for FPGA acceleration. Comparisons with resource over-provisioning, traditional genetic algorithm-based Design Space Exploration (DSE), and State-of-the-Art (SotA) approaches demonstrate that CollectiveHLS produces designs of comparable quality \(14.6\times\) faster on average. These results underscore the potential of our approach as an ultra-fast and automated solution for HLS optimization.

Mining User Reviews for Key Design Features in Cognitive Behavioral Therapy-Based Mobile Mental Health Apps.

Background: Cognitive behavioral therapy (CBT)-based mobile apps have been shown to improve CBT-based interventions effectiveness. Despite the proliferation of these apps, user-centered guidelines pertaining to their design remain limited. The study aims to identify design features of CBT-based apps using online app reviews. Methods: We used 4- and 5-star reviews, preprocessed the reviews, and represented the reviews using word-level bigrams. Then, we leveraged latent Dirichlet allocation (LDA) and visualization techniques using python library for interactive topic model visualization to analyze the review and identify design features that contribute to the success and effectiveness of the app. Results: A total of 24,902 reviews were analyzed. LDA optimization resulted in 86 topics that were labeled by two independent researchers, with an interrater Cohen's kappa value of 0.86. The labeling and grouping process resulted in a total of six main design features for effective CBT-based mobile apps, namely, mental health management and support, credibility support, self-understanding and personality insights, therapeutic approaches and tools, beneficial rescue sessions, and personal growth and development. Conclusions: The high-level design features identified in this study could evidently serve as the backbone of successful CBT-based mobile apps for mental health.

Evaluating the Patterns of Maize Development in the Hetao Irrigation Region Using the Sentinel-1 GRD SAR Bipolar Descriptor

Assessing maize yield is critical, as it is directly influenced by the crop’s growth conditions. Therefore, real-time monitoring of maize growth is necessary. Regular monitoring of maize growth indicators is essential for optimizing irrigation management and evaluating agricultural yield. However, quantifying the physical aspects of regional crop development using time-series data is a challenging task. This research was conducted at the Dengkou Experimental Station in the Hetao irrigation area, Northwest China, to develop a monitoring tool for regional maize growth parameters. The tool aimed to establish a correlation between satellite-based physical data and actual crop growth on the ground. This study utilized dual-polarization Sentinel-1A GRD SAR data, accessible via the Google Earth Engine (GEE) cloud platform. Three polarization descriptors were introduced: θc (pseudo-scattering type parameter), Hc (pseudo-scattering entropy parameter), and mc (co-polar purity parameter). Using an unsupervised clustering framework, the maize-growing area was classified into several scattering mechanism groups, and the growth characteristics of the maize crop were analyzed. The results showed that throughout the maize development cycle, the parameters θc, Hc, and mc varied within the ranges of 26.82° to 42.13°, 0.48 to 0.89, and 0.32 to 0.85, respectively. During the leaf development stage, approximately 80% of the maize sampling points were concentrated in the low-to-moderate entropy scattering zone. As the plants reached the big trumpet stage, the entire cluster shifted to the high-entropy vegetation scattering zone. Finally, at maturity, over 60% of the sampling points were located in the high-entropy distribution scattering zone. This study presents an advanced analytical tool for crop management and yield estimation by utilizing precise and high-resolution spatial and temporal data on crop growth dynamics. The tool enhances the accuracy of crop growth management across different spatial and temporal conditions.

Unified Platform for Automated Tests of Inverter-Based Resources with Harware-in-the-loop

Testing of inverter-based resources (IBRs) faces challenges in establishing standardized procedures across all product development stages. Most of the testing approaches use Hardware-in-the-Loop (HIL) during the early stages independently from the final product testing, which can lead to discrepant results. To solve these issues, this paper proposes a unified platform for automatically testing IBRs. It can be used during all product development stages, from pure simulation to Controller Hardware-in-the-Loop (CHIL) and finally on the complete commercial product, ensuring uniform test procedures with different test setups. A case study describing compliance tests of grid-connection codes of photovoltaic (PV) inverters are presented to demonstrate the proposed approach. Experimental tests were performed in accordance to Brazilian grid-connection codes in an ISO/IEC 17025 accredited laboratory. The results obtained with the proposed platform in CHIL and full-hardware showed high similarity with the ones obtained with the accredited laboratory, validating the proposed approach. In addition, the platform can be used to perform tests not specified in standards and to verify the performance of IBRs in operating conditions that are often difficult to be tested in laboratory. Ensuring consistent test procedures, the platform facilitates comparable results along all the testing stages, reducing the product development cycle.

Test Case Automation: Transforming Software Testing in the Digital Era

Purpose: This research aims to explore the limitations of manual software testing, the effectiveness of test case automation in addressing these challenges, and the strategies for successful implementation of automation. It also examines the broader impact of adopting test automation on the quality, speed, and efficiency of software delivery. Methodology: The study investigates the challenges of manual testing in agile and DevOps environments and explores the role of automation as a solution. By analyzing various automation tools, frameworks, and best practices, the research provides insights into successful implementation strategies for test case automation. Additionally, it reviews the associated benefits and potential challenges organizations may face when transitioning to automated testing. Findings: Test case automation significantly mitigates the shortcomings of manual testing, particularly in fast-paced development cycles. The findings reveal that automation not only enhances the quality of software by catching defects earlier but also accelerates the testing process, improving overall efficiency. However, the study also identifies challenges, such as the initial investment in tools and skills required for automation, as well as the ongoing need for maintenance of automated test cases. Unique Contribution to Theory, Policy and Practice: This research contributes to the theoretical understanding of software testing (Tom Badgett, Corey Sandler & Glenford J. Myers, 2011) by highlighting the essential role of automation in modern software development methodologies like agile and DevOps. It bridges the gap between traditional manual testing and contemporary automated practices, providing a framework for understanding how automation fits within these rapid development paradigms. In practice, the study encourages organizations to adopt test case automation as a critical policy for improving the quality and speed of software delivery. It offers practical insights into the implementation of automation, including tool selection, training, and process integration, which can guide companies in transitioning to automated testing. Additionally, it highlights the long-term benefits of automation, making it a valuable policy consideration for organizations aiming to optimize their development and testing processes.

Implementing Dynamic Systems Development Method for a Web-Based System to Evaluate Child Health and Growth

The Simpang Gambir UPTD Community Health Center has developed an innovative digital system to monitor the growth of toddlers. Previously, the recording of toddler growth data was done manually, often leading to data loss or damage. This new system is designed to address these issues and provide a more efficient and accurate solution. The system not only facilitates health center staff and posyandu cadres in monitoring toddler development but also assists them in creating digital growth reports. With this system, toddler growth data can be accessed quickly and easily, facilitating decision-making regarding child health management. One of the key features of this system is its ability to track toddler growth based on weight-for-age charts. This feature allows health workers to easily identify toddlers with nutritional problems and promptly provide necessary interventions. Additionally, the system is equipped with a fast data search feature, enabling staff to easily find specific toddler growth data. The development of this system utilizes the Dynamic System Development Method (DSDM), allowing for a structured and efficient development process. With this method, the system can be developed rapidly and in accordance with user needs.

Laboratory assessment of real-time water infiltration measurement in SPV200 bentonite using TDR for high-level radioactive waste disposal design

The water content movement within compacted bentonite holds significance in appraising the thermo-hydro-mechanical (THM) performance of the engineered barrier system (EBS) for the disposal of high-level radioactive waste (HLRW), but it demands considerable time to monitor water infiltration in a representative model. Therefore, employing bentonite with varying water content is favored in the laboratory. A comprehensive 3-D phase diagram was established as a benchmark for dielectric constant, temperature, and volumetric water content (VWC) using Time Domain Reflectometry (TDR). SPV200 bentonite at different densities (1400, 1500, and 1600 kg/m3) and temperatures (25, 40, and 60 °C) were prepared with TDR, achieving real-time VWC measurements within a 6 % relative error. Furthermore, numerical outcomes exhibited trends like the TDR tests. Comparation between the simulation and test data is within a relative error ±10 %, which showcased the potential for efficient and effective estimations at a reduced cost for HLRW disposal design.

STIA-DJANet: Spatial–Temporal Intention-Aware vessel trajectory prediction based on Dual-Joint Attention Network for e-navigation

Harnessing high-precision vessel trajectory prediction holds great promise for enhancing autonomous and safe navigation towards e-navigation. However, many technical researchers and maritime managers realize that only accurate prediction of vessel trajectory is insufficient, extraction and recognition of navigation intentions from vessel around is still crucial issue and gap in current prediction models. In response to solve this shortcoming, we propose an intention-aware model for vessel trajectory prediction, which leverages both of spatial–temporal intentions and dual joint attentions network, namely STIA-DJANet. First, the double intentions of spatial–temporal aware is designed to extract the social relationships between the own vessel trajectory and neighboring trajectories at each timestamp, effectively capturing dynamic changes in their interactions. Second, feature fusion of dual joint attentions for double intentions is proposed to extract navigation features from the intention-aware module, which can be integrated into trajectory prediction. In experiments, numerical results and comparisons on three real-world datasets demonstrate that our framework outperforms state-of-the-art models in terms of the average displacement error (ADE) and final displacement error (FDE) metrics. The average improvements were 42.852% and 44.726%, respectively. Reliable prediction results can significantly advance the development of e-navigation systems development.

Rapid deposition analysis of inhaled aerosols in human airways

A rapid data-driven method for determining regional deposition of inhaled medication aerosols in human airways is presented, which is patient specific. Inhalation patterns, device characteristics, and aerodynamic particle size distribution of medications are considered. The method is developed using dimensional analysis and Buckingham Pi theorem, and provides total, regional, and lobar distributions of aerosol deposition. 34 dimensionless quantities are selected, of which 22 encode features of the airway trees and segmented lobes, 14 pertain to the device and the drug formulation, and 13 the inhalation profile of the subject. The dimensionless correlations are obtained using a large database of computational fluid dynamics results on patient specific airways. The intraclass correlation coefficient between the current method and its training dataset is 0.92. The difference between the predicted average lobar deposition in the six asthma patients and the in-vivo data is 1.3%. The model has the potential to offer insights into the effectiveness of personalized drug delivery in clinical settings and can aid in drug development cycles.

Measures to protect hemp from harmful organisms

Relevance. When cultivating seed hemp, comprehensive crop protection is necessary throughout the entire development cycle “from seed to seed”. A comprehensive system of environmentally oriented protective equipment during pre-sowing and foliar treatment of seeds and plants can provide a favorable phytosanitary environment.Methods. The research work was carried out in accordance with the methodological guidelines for conducting field and vegetation experiments with cannabis and the methodology of field experience with the basics of statistical processing of research results.Results. Etching with the insectofungicide “Celest Top, KS” in pure form and in tank mixtures showed a high degree of protection of seeds from infection during germination. The use of “Celest Top, KS” in its pure form and in combination with the growth regulator “Artafit, VRK” and the liquid fertilizer “Megamix. Seeds” is most effective against hemp fleas. Etching “Artafit, VRK”, “Celest Top, KS” and mixtures with the fertilizer “Megamix. Seeds” and foliar top dressing with the antistressant “Artafit, VRK” contributed to an increase in the indicators of the crop structure. A high yield of seeds and stems was obtained on variants with the “Megamix. Seeds” fertilizer, the increase in seed yield was 0.03 t /ha, stems — 0.16 t /ha relative to the control. The use of the fertilizer “Megamix. Seeds” together with the growth regulator “Artafit, VRK” and the insectofungicide “Celest Top, KS” increased the yield of seeds by 0.28 and 0.17 t/ha, the yield of stems by 2.2 and 3.32 t/ha.

Software-Defined Platform for Global Navigation Satellite System Antenna Array Development and Testing

With the increasing demand for accurate and robust positioning solutions, the use of GNSS antenna arrays has gained significant attention. However, their development and testing are frequently constrained by the inflexibility of traditional hardware platforms, often requiring extensive reconfiguration throughout the development cycle. This paper presents a platform based on a system on chip to develop a highly flexible software-controlled system that is capable of directly sampling up to 16 antenna elements. Multibeam digital beamforming is implemented using the available FPGA resources and the resulting signal is reproduced by the integrated DAC and can be connected to any conventional single antenna GNSS receiver. This paper presents the architecture of the platform, detailing its components and capabilities. Our experimental results demonstrate that the system can phase shift every channel with errors of less than 0.5° and can reconfigure 4 simultaneous beams of a 16-antenna array at speeds of 1.2 kHz, and 20 beams at around 400 Hz. The average delay introduced by each channel of the system is around 381 ns with a maximum deviation of 1.05 ns. The delay was also measured for the implementation using 4 beams, which achieves a slightly bigger average delay of 384.6 ns while keeping the variation to 5 to 6 ns. This system is intended to be used as the backbone for the development of antenna arrays and beamforming algorithms. Given its flexibility, it is not necessary to develop new hardware between development iterations or even for different systems, as only the software layer needs to be modified. Consequently, it is possible to expedite the development stage before producing dedicated solutions for industrial applications.

International Distribution and Development of Rock Mass Classification: A Review

AbstractSince the 1950s, numerous rock mass classification systems (RMCS) have emerged for applications such as tunneling, mining, and slope engineering. The continued proliferation of these systems reflects the complexity of the issue and the absence of a universally accepted approach within the rock engineering community. This review offers an up-to-date examination of the global utilization of RMCS across applications such as tunnels, caverns, mining slope stability, and more. A “family tree of rock mass classification systems” is presented that traces their evolution over the decades. The family tree illustrates a prolific period of system development between 1970 and 2000, followed by a decline in progress and increasing specialization of systems. Based on surveys within the rock engineering community carried out between 2022 and 2024, the dominant systems for underground engineering and slope-related tasks were found to be GSI, RMR, and the Q-system, which have demonstrated their practical efficacy over 5 decades. Based on the surveys, two “world maps of rock mass classification” have been produced that visualize the international RMCS distribution. However, the survey shows that only some new or derived systems developed in the last 25 years have gained global acceptance. The paper closes with an outlook to possible future developments in rock mass classification and characterization.

Effect of Temperature on TiO2/P‐Si‐Based Memristor Devices as Optical/Electrical Synapse

Light‐based modulation on resistive switching has opened a new route for development of the devices for advanced applications like integrated switching with memory functionality, neuromorphic computing, and humanoid robotics. This article presents the wafer‐scalable process to fabricate titanium dioxide (TiO2)/porous silicon (P‐Si)‐based heterostructures for memristor applications. The resistive switching performance of the memristor is analyzed using electrical/optical stimulation. The fabricated devices offer enhanced performance metrics during optical stimulation when compared to electrical stimulation. The memristor device offers the paired pulse facilitation (PPF) index of ≈519% (during optical stimulation), which is 3.4 times of the electrical stimulation. Further, the stability and reliability measurements are performed at different temperatures especially (room temperature to 348 K) under 450 nm illumination. The testing results prove that the developed TiO2/P‐Si‐based memristors are suitable for the next‐generation computation systems development.

A novel hybrid CNN-GRU model for website-based user interface code generator

As software engineering techniques evolve, conventional methods become less efficient due to prolonged development cycles and repetitive tasks. There is a growing need for automated systems that can incorporate User Interface (UI) designs into code. This study proposes a novel Hybrid CNN-GRU model for automatically generate website-based UI codes from UI design images. The proposed method consists of a visual model and a language-decoder part. The visual model employs pretrained CNN models, including EfficientNetV2B0, Xception, and EfficientNetV2M, to extract features from images, whereas a GRU serves as the language model and decoder. An empirical study was conducted to evaluate the effectiveness of various hybrid CNN-GRU approaches for generating UI codes. The EfficientNetV2B0 GRU model demonstrated the fastest training time, whereas the Xception GRU model was the most robust for various data complexities. The results of this study will advance the field of automated code generation, simplify the front-end development process, and shorten the time and effort necessitated for UI code implementations.

Single-cell technology for drug discovery and development

The success rate of drug development today remains low, with long development cycles and high costs, especially in areas such as oncology, neurology, immunology, and infectious diseases. Single-cell omics, encompassing transcriptomics, genomics, epigenomics, proteomics, and metabolomics enable the analysis of gene expression profiles and cellular heterogeneity from the perspective of individual cells, offering a high-resolution view of their functional diversity. These technologies can help reveal disease mechanisms, drug target identification and validation, selection of preclinical models and candidate drugs, and clinical decision-making based on disease response to drugs, all at the single-cell level. The development of deep learning technology has provided a powerful tool for research in drug discovery based on single-cell techniques, which has evolved with the advent of large-scale public databases to predict drug responses and targets. In addition, traditional Chinese medicine (TCMs) research has also entered the era of single-cell technology. Single-cell omics technologies offer an alternative way in deciphering the mechanisms of TCMs in disease treatment, revealing drug targets, screening new drugs, and designing combinations of TCMs. This review aims to explore the application of single-cell omics technologies in drug screening and development comprehensively, highlighting how they accelerate the drug development process and facilitate personalized medicine by precisely identifying therapeutic targets, predicting drug responsiveness, deciphering mechanisms of action. It is also concluded that drug development process and therapeutic efficacy of drugs can be improved by combining single-cell omics and artificial intelligence techniques.

Transcriptome-Optimized Hydrogel Design of a Stem Cell Niche for Enhanced Tendon Regeneration.

Bioactive hydrogels have emerged as promising artificial niches for enhancing stem cell-mediated tendon repair. However, a substantial knowledge gap remains regarding the optimal combination of niche features for targeted cellular responses, which often leads to lengthy development cycles and uncontrolled healing outcomes. To address this critical gap, an innovative, data-driven materiomics strategy is developed. This approach is based on in-house RNA-seq data that integrates bioinformatics and mathematical modeling, which is a significant departure from traditional trial-and-error methods. It aims to provide both mechanistic insights and quantitative assessments and predictions of the tenogenic effects of adipose-derived stem cells induced by systematically modulated features of a tendon-mimetic hydrogel (TenoGel). The knowledge generated has enabled a rational approach for TenoGel design, addressing key considerations, such as tendon extracellular matrix concentration, uniaxial tensile loading, and in vitro pre-conditioning duration. Remarkably, our optimized TenoGel demonstrated robust tenogenesis in vitro and facilitated tendon regeneration while preventing undesired ectopic ossification in a rat tendon injury model. These findings shed light on the importance of tailoring hydrogel features for efficient tendon repair. They also highlight the tremendous potential of the innovative materiomics strategy as a powerful predictive and assessment tool in biomaterial development for regenerative medicine.

Communication as a Strategic Asset of Sustainable Development Planning of Rural Territorial Systems

Successful application of the communicative approach focused on the involvement of local communities in the process of planning the sustainable development of rural territorial systems greatly increases the effectiveness of planning processes. Where state management policy guarantees the participation of local communities in the process of rural territorial systems development, creates conditions for their involvement in the dialogue with local authorities, the chances of successful implementation of sustainable development plans increase significantly. The article makes an attempt to substantiate the necessity of introducing the communicative approach into the management system of rural territorial systems, as it will allow transforming the system of regional planning in Russia and achieving real breakthrough results in the sphere of development of social-ecological-economic components of rural territorial systems.

Online examinations: Factors that impact student experience and perceptions of academic performance

Online examinations are an increasingly common feature of higher education. Research in this area often focuses on academic integrity without exploring student experience in online examinations more broadly. With increasing use of online examinations and associated security measures, there is a plausible risk that the platform, examination conditions and measures to achieve assessment security may have a negative impact on student experience, well-being and performance. This paper reports on a large-scale survey conducted in 2021 and 2022 at a large metropolitan university in Australia. Analysis of 13,751 completed surveys revealed that the majority of students perceived their overall online exam experience positively. However, a thematic analysis of 5,213 optional open-ended responses provided a more detailed understanding of the range of positive and negative influencing factors and their consequences. Many of the issues negatively impacting student experience were related to the assessment design, rather than the assessment platform or exam security. The findings also revealed implications relating to platform functionality, personal technology and exam conditions, including location of exam, and preparation and support. Implications for practice or policy: Academics need to consider the quality and appropriateness of assessment design, as these affect the extent to which students experience online exams positively and are largely independent of the exam and security systems. Institutions can reduce the wide range of negative impacts through improved communication and in-exam support. System developers can iteratively ameliorate negative impacts through investigating student experience and involvement of students in the co-design of assessment platforms.

MobiTest – A Software for Mobile-Based Testing

MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the development of this application, a need arose for manual testing, which unfortunately resulted in the inefficient use of resources. MobiTest was designed to overcome these limitations by providing the ability to generate generic test scripts for any application as needed. It can efficiently and adaptively handle intricate tasks according to predefined parameters. This application thoroughly examines every possible detail, allowing the hacker to exploit the system.