Systems Development Research Articles

A model-driven, metrics-based approach to assessing support for quality aspects in MLOps system architectures

In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.

Mobile Application System For Point of Sales in Coffee Sales at Langit Coffee Space

The coffee shop industry in Indonesia is experiencing rapid growth, leading to intense competition among businesses. Langit Coffee Space faces challenges in managing stock and financial reporting, which are still handled manually, resulting in frequent errors and difficulties in storing and retrieving increasing amounts of data. This study aims to develop a mobile-based Point of Sale (POS) application tailored for Langit Coffee Space to improve operational efficiency. Using the Extreme Programming methodology, the application integrates features for real-time stock management, digital ordering, automated financial reporting, and support for multiple payment methods, including digital payments. Testing results indicate that the application successfully reduced stock recording errors by 30% and shortened the time required to prepare financial reports by up to 50%, significantly enhancing efficiency. Compared to other solutions, this application provides flexibility for future feature enhancements, such as loyalty programs, which may not be available in general POS applications.

BCL11b interacts with RNA and proteins involved in RNA processing and developmental diseases

BCL11b is a transcription regulator and a tumor suppressor involved in lymphomagenesis, central nervous system (CNS) and immune system developments. BCL11b favors persistence of HIV latency and contributes to control cell cycle, differentiation and apoptosis in multiple organisms and cell models. Although BCL11b recruits the non-coding RNA 7SK and epigenetic enzymes to regulate gene expression, BCL11b-associated ribonucleoprotein complexes are unknown. Thanks to CLIP-seq and quantitative LC-MS/MS mass spectrometry approaches complemented with systems biology validations, we show that BCL11b interacts with RNA splicing and non-sense-mediated decay proteins, including FUS, SMN1, UPF1 and Drosha, which may contribute in isoform selection of protein-coding RNA isoforms from noncoding-RNAs isoforms (retained introns or nonsense mediated RNA). Interestingly, BCL11b binds to RNA transcripts and proteins encoded by the same genes (FUS, ESWR1, CHD and Tubulin). Our study highlights that BCL11b targets RNA processing and splicing proteins, and RNAs that implicate cell cycle, development, neurodegenerative, and cancer pathways. These findings will help future mechanistic understanding of developmental disorders. ImportanceBCL11b-protein and RNA interactomes reveal BLC11b association with specific nucleoprotein complexes involved in the regulation of genes expression.BCL11b interacts with RNA processing and splicing proteins.

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.

Lake responses and mechanisms to El Niño on the Tibetan Plateau using deep learning-based semantic segmentation

Numerous lakes across the Tibetan Plateau (TP) serve as crucial indicators of climate change and are significantly influenced by El Niño events. Previous studies of lake response to El Niño events have focused on a limited number of lakes. Despite advances in remote sensing technology, there have been few comprehensive and large-scale studies using deep learning, and there are still gaps in understanding the response mechanisms on a larger scale. This study leverages advanced deep-learning techniques to map lake responses, offering unprecedented insights into the large-scale hydrological impacts of El Niño. Our results show that lakes shrink significantly during El Niño events on the TP. Lakes located in the central and southern parts of the TP and small lakes with areas ranging from 1 to 50 km2 (over 60 % of them) exhibited strong responses. The range of lake response to El Niño events varies with their intensity, with stronger El Niño events causing an expansion of the response range along the latitudinal direction. We propose four possible mechanisms for lake response patterns to El Niño from the perspective of lake water sources. Strong shrinkage is primarily caused by decreased precipitation and increased evaporation, with a possible contribution from reduced meltwater. Strong expansion is due to increased precipitation, more glacier and frozen soil meltwater, and reduced evaporation. For slight shrinkage and expansion patterns, the balance of meltwater may offset or even counteract the El Niño signal. The study’s results could improve predictions of extreme weather events like droughts and floods in the Third Pole region, enhance water resource management and responsiveness, and offer valuable insights for ecological monitoring and early warning systems development.

Identifying insect predators using camera traps reveal unexpected predator communities in oilseed rape fields

Cameras are common tools for ecologists studying species abundance, richness, and interactions. They are mainly used to study large animals but are increasingly used to study invertebrates. Camera traps could be a powerful tool to identify and quantify the natural enemies, such as predators, of specific agricultural pests. This knowledge can then be applied to adapt farming practices to favour the predators identified and improve conservation biocontrol.Here, cameras were used to identify predators of two insect pests of oilseed rape (Brassica napus): the pollen beetle (Brassicogethes aeneus) and the brassica pod midge (Dasineura brassicae). These insects are spring pests of oilseed rape and are both susceptible to predation by ground-dwelling arthropods during their development cycle. The objectives of this study were to 1) identify the predators of the pest species, and 2) investigate the hypothesis that the diel activity periods of the predators match the timing of pest larval presence on the ground. Field observations were conducted in two locations over two consecutive years in the UK.In contrast to previous reports, our data collected using cameras showed that larval stages of predatory beetles are responsible for most of the predation events and that adult stages were not frequently observed feeding on the prey. Our data also showed that the traditional method, using pitfall traps, failed to detect the presence of large numbers of beetle larvae. Interestingly, the pests dropped to the ground and were therefore most susceptible to predation at the time when predators were most active.These results give new insight on the predators of the two most important spring pests of oilseed rape in Europe. This knowledge can be used by practitioners to develop framing practices targeted on enhancing the abundance of the larval stages of these beneficial arthropods to better control the pests in a more sustainable way.

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.

Фенологические особенности развития сортов вишни в меняющихся погодно-климатических условиях

Annotation. Cherry is a stone fruit crop cultivated in almost all regions of Russia. Currently, there is a significant change in weather and climatic conditions, their impact leads to a shift in the phenological phases of plant development and to disruption of the stages of organogenesis. In this regard, there is a need to study the passage of phenological phases of cherry development in the changing weather and climatic conditions of the southern region. The research was carried out on the basis of the collective use center (CUC) “Genetic collection of fruit crops” in the period 2020-2023 in the Kuban horticulture zone of Krasnodar region. The objects of research were 11 varieties of sour cherries of various ecological and geographical origins. Planting scheme is 5x3 m, rootstock is antipka seedlings. During the research, the sum of active temperatures during the cherry growing season was determined, which determines the occurrence of the main phenological phases of development of cherry varieties in the annual development cycle. It was established that over the years of research, on average, the sum of active temperatures exceeded the long-term average by 551,7 °C. It was determined that during the research period the phenological phase “beginning of growing season” of cherries took place at a sum of temperatures of 32.8-54.2 °C. The phenological phase of “beginning of flowering” for most varieties in 2020 and 2023 occurred earlier than the long-term average date by 2-8 days, in 2023 – by 2-6 days, and in 2021-2022 – later by 2-14 days. It was revealed that at the phenophase "beginning of flowering", early cherry varieties (Krasnodarskaya sladkaya, Chudo vishnya), medium (Timati, Feya, Assol’, Svatlaya) are strongly affected by stress. Tamaris and Hodosa cherry varieties of late ripening showed relatively high resistance to stress during the flowering period. Key words: cherry, variety, phenological characteristics, flowering, climate.

Preliminary Studies to Bridge the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation

This study addresses the prevalent gap between structured models and informal architectural methodologies in software engineering. Recognizing the potential of informal architecture artifacts in analytical processes, we introduce a methodology that efficiently transforms these informal components into structured models. This method facilitates understanding and utilizing informal diagrams and enhances analytical capabilities through graph analysis techniques. By leveraging user-friendly tools such as Draw.io, the methodology democratizes the modeling process, making sophisticated architectural analyses accessible to a broader spectrum of professionals without requiring deep expertise in formal methods. The innovative aspects of this methodology lie in its ability to streamline the transformation process, significantly improving both the efficiency and effectiveness of model creation and analysis. These enhancements are demonstrated through a practical application involving a sample architecture diagram, where the resulting model is thoroughly analyzed using advanced graph analysis tools. This approach bridges the theoretical and practical divides in software architecture.

Design and development of an eHealth intervention to support self-management in people with musculoskeletal disorders: ‘eHealth: it's TIME’

Objective This study aimed to co-design and develop a user-centred, theory-based eHealth-mediated self-management support follow-up prototype for adults with musculoskeletal disorders. Design A three-step system development cycle was employed. Step 1 involved creating intervention features and content, with two focus groups reviewing prioritised eHealth intervention elements based on earlier research. Step 2 involved heuristic testing using Nielsen's 10 heuristic principles. Step 3 incorporated qualitative think-aloud interviews and the System Usability Scale. Setting Republic of Ireland. Participants Step 1 included adults with musculoskeletal disorders (n = 12). Step 2 involved five reviewers. Step 3 included people with musculoskeletal disorders (n = 5) and musculoskeletal physiotherapists (n = 5). Results Participants in step 1 approved four main intervention components, which map to recognised theoretical frameworks, and suggested increased use of visual and interactive elements. Heuristic testing in step 2 identified design and navigation issues. In Step 3, usability testing, additional navigation, content and design recommendations were identified. The overall median system usability score (interquartile range) was 75 (0) out of 100 for adults with musculoskeletal disorders and 77.5 (2.5) out of 100 for musculoskeletal physiotherapists, indicating good usability. Conclusion A theory-based, user-centred eHealth-mediated follow-up self-management support prototype has been developed for people with musculoskeletal disorders, with the next steps focusing on feasibility testing in clinical practice, with a more diverse population.