Systems Development Research Articles

Occupational exposure to arsenic and leukopenia risk: Toxicological alert.

Arsenic and its inorganic compounds affect numerous organs and systemic functions, such as the nervous and hematopoietic systems, liver, kidneys, and skin. Despite a large number of studies on arsenic toxicity, rare reports have investigated the leukopenia incidence in workers exposed to arsenic. In workplaces, the main source of workers' exposure is the contaminated air by the inorganic arsenic in mines, arsenic or copper smelter industries, and chemical factories. Erythropoiesis inhibition is one of the arsenic effects and it is related to regulatory factor GATA-1. This factor is necessary for the normal differentiation of early erythroid progenitors. JAK-STAT is an important intracellular signal transduction pathway responsible for the mediating normal functions of several cytokines related to cell proliferation and hematopoietic systems development and regulation. Arsenic inactivates JAK-STAT by inhibiting JAK tyrosine kinase and using the IFNγ pathway. The intravascular hemolysis starts after the absorption phase when arsenic binds to the globin of hemoglobin in erythrocytes and is transported into the body, which increases the oxidation of sulfhydryl groups in hemoglobin. So, this article intends to highlight the potential leukopenia risk via inhalation for workers exposed to arsenic and suggests a possible mechanism for this leukopenia through the JAK-signal transducer and activator of transcription (STAT) pathway inhibition.

Changing focus: making sustainability a major theme in existing university modules

In this article we report some practical experiences of integrating education for sustainable development into established university teaching modules. The 2021 guidance on Education for Sustainable Development from the UK’s standards bodies QAA and Advance HE is an important and urgent motivation for introducing sustainability into university courses. We take as our context a first-year introductory module in Chemical Products and Process in the School of Chemistry and Chemical Engineering and a second-year module in Software Engineering and Systems Development in the School of Electronics, Electrical Engineering and Computer Science, both at Queen’s University Belfast. We outline some of the challenges of adding new themes to existing courses. We comment on ways of presenting themes of sustainable development alongside existing module content, and we indicate the type of work students produced. We identify approaches that resulted in good outcomes, and outline changes we have made with a view to improved future outcomes.

Heat release efficiency Betterment inside a novel-designed latent heat exchanger featuring arc-shaped fins and a rotational mechanism via numerical model and artificial neural network

Thermal energy storage (TES) units featuring phase change materials (PCMs) have a crucial impact on efficient energy management. Since energy demands keep changing, and the world increasingly relies on sustainable energy sources, innovation is continuously required. New technologies, materials, and methods for energy storage must be developed to meet these shifting needs effectively. In this study, a triplex-tube heat exchanger (TTHE) was employed as a TES medium, utilizing PCM within the middle tube. Because of its high thermal energy storage capacity, RT50 was selected as the PCM. Four innovative arc-shaped fins were strategically integrated within the PCM space to accelerate heat release. The system was subjected to rotational speeds of 0.1, 0.3, 0.5, 1, and 1.5 rpm. The investigation was carried out in two stages: initially, the impact of varying rotational speeds on the discharging process of the PCM in a finless TTHE was explored; subsequently, the influence of the same speeds on the solidification behavior of the finned TTHE was analyzed. Natural convection and PCM’s solidification process were examined through the enthalpy-porosity method. The findings represented that in the absence of fins, the device’s rotation had a more noticeable impact on the solidification behavior; however, incorporating fins was much more influential. Up to 2850 s, all the finned systems had solidified entirely in the presence or absence of the rotational mechanism, while in the finless system with a rotational speed of 1.5, 30.52 % of the material was still unsolidified until this time. Finally, the influence of three key structural parameters of the fins: α (outer fins arc angle), β (inner fins arc angle), and S (space between fins) on the discharging time of the PCM was analyzed using an artificial neural network (ANN) model. The study offered critical insights for optimizing fin configuration by systematically varying these parameters within defined ranges. An ANN predictive model was also proposed to assist TES system manufacturers and developers in future design and optimization efforts. The results demonstrated that the optimal setting of the TTHE (with the parameters of α = 60°, β = 60°, S = 10 mm) achieved the liquid fraction of 0.1, 80.33 % faster than the finless system.

Risk Assessment of The Latifiya Expressway Segment Using a Proactive Approach

Road safety assessment is an essential step in the management of transportation projects. The proactive approach in the safety assessment considers the pre-construction stage to reduce the likelihood of road accidents. This research aims to assess the risk of road infrastructure in Iraq by applying the most recent methodology based on road features and supported traffic data. The selected methodology significantly contributes to enhancing a proactive approach to road safety and incorporating risk assessment in the planning and design stages of road projects. A segment of 15km from the beginning of the Latifiya expressway was selected for this study and assessed using the international road assessment program methodology. Data on road features and traffic were collected for this goal; where data on 38 aspects of road features is collected. The traffic data includes traffic volume and spot speed. Then these data were coded and processed using VIDA online web. The assessment results were presented for three road user groups: vehicle riders, pedestrians, and motorcyclists. The results showed that the assessed segment has a high-risk level; 1 to 3 stars for vehicle riders and 1 star for motorcyclists and pedestrians. Countermeasures were presented, and their efficiency was tested to raise the star rating for all road user groups to five stars.

More Automation for Design, Data Preparation & CAM Programming

Leading CAD/CAM system developer Tebis has released Tebis 4.1 Version Release 8. The company says the upgrades give new features and extensions offering even more automation for design, data preparation and CAM programming.

Assessing the transformative impact of cloud computing on software deployment and management

Cloud computing has fundamentally transformed the landscape of software deployment and management, offering significant benefits and reshaping traditional approaches. This review explores the transformative impact of cloud computing on these domains, highlighting key changes and advantages. Firstly, cloud computing has revolutionized software deployment by introducing scalable and flexible infrastructure solutions. Unlike traditional onpremises systems that require significant upfront investment and ongoing maintenance, cloud platforms offer ondemand resources and payasyougo models. This shift enables organizations to deploy software rapidly, adapt to changing needs, and scale resources efficiently without the constraints of physical hardware. Additionally, cloud computing enhances software management through centralized control and automation. Cloud environments provide integrated management tools that streamline the deployment, monitoring, and maintenance of applications. These tools facilitate automated updates, patch management, and system backups, reducing the burden on IT teams and minimizing downtime. Furthermore, cloudbased management systems offer realtime visibility and analytics, allowing for proactive performance monitoring and troubleshooting. The collaborative nature of cloud computing also fosters improved development and deployment practices. Cloud platforms support DevOps methodologies by enabling continuous integration and continuous delivery (CI/CD) pipelines. This integration accelerates software development cycles, enhances collaboration among distributed teams, and ensures consistent and reliable deployments. Moreover, the cloud's global reach and accessibility break down geographical barriers, allowing organizations to deploy software across multiple regions effortlessly. This geographic flexibility enhances the user experience and ensures high availability and performance, regardless of the user's location. Despite these advancements, the transition to cloud computing presents challenges, including data security and compliance concerns. Organizations must implement robust security measures and adhere to regulatory requirements to protect sensitive information and maintain trust. In conclusion, cloud computing has had a profound impact on software deployment and management, offering scalable, flexible, and efficient solutions. Its transformative effects include streamlined operations, improved collaboration, and global accessibility. As cloud technology continues to evolve, organizations must navigate associated challenges while leveraging its benefits to drive innovation and efficiency in software management. Keywords: Management, Impact, Cloud Computing, Software Deployment, Assessing

Developing crossplatform software applications to enhance compatibility across devices and systems

In an increasingly interconnected world, the need for software applications that function seamlessly across diverse devices and operating systems is paramount. Developing crossplatform software applications addresses this need by providing a unified user experience and operational efficiency regardless of the hardware or system being used. This approach eliminates the need for multiple versions of the same application, streamlining development and reducing costs while improving accessibility and consistency. Crossplatform development involves creating software that is compatible with various operating systems such as Windows, macOS, iOS, and Android, as well as different device types including desktops, tablets, and smartphones. Key methodologies in this domain include the use of frameworks and tools such as React Native, Flutter, and Xamarin, which allow developers to write code once and deploy it across multiple platforms. These frameworks offer a range of features to enhance user interfaces, manage system resources efficiently, and ensure robust performance across devices. The benefits of crossplatform applications are manifold. They provide a consistent user experience, as the same application behaves similarly across different devices, enhancing usability and customer satisfaction. Additionally, they simplify maintenance and updates, as changes need only be implemented once rather than across multiple codebases. This approach also accelerates timetomarket by leveraging shared codebases, thereby enabling faster development cycles and quicker deployment. However, developing crossplatform applications also presents challenges. Ensuring consistent performance and functionality across diverse systems can be complex, requiring careful design and testing. Developers must also navigate varying hardware capabilities and user interface guidelines for different platforms. Despite these challenges, advances in development frameworks and tools continue to improve the efficiency and effectiveness of crossplatform solutions. In conclusion, crossplatform software development represents a strategic approach to enhancing compatibility and accessibility across devices and systems. By leveraging modern frameworks and tools, organizations can deliver cohesive, highquality applications that meet the needs of a diverse user base while optimizing development resources and costs. Keywords: : Developing, CrossPlatform, Software Applications, Compatibility, Devices.

Modeling contextual goals and detecting context conflicts from BDD-based user stories

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development.

Tree density by development stage in the “El Carrizal” wathershed of Tapalpa, Jalisco

The Tapalpa forest presents an abundance of pine trees in the juvenile stage ofregeneration and sapling, but this amount progressively decreases in thesuccessive stages, when the trees show larger dimensions and take up more spaceto grow. However, it was unknown how much the loss was and how much itreached the maximum size. The objective was to quantify the grove, the individualspace, the separation between trees and their decrease. The stages were classifiedaccording to the normal diameter and the sites were classified as excessive,recommendable, sufficient and poor density. Frequency distribution, loss, spacing,and distance between trees were calculated using the Hart-Becking formula.During the development cycle, the loss was close to 90 % with a rate of 0.2 to 3%, presenting a cumulative of 60 % in the monte bravo and vardazcal stages. Therecommended class began with 1 300 to 2 700 trees ha⁻¹ in the juvenile phase,with their respective spacing of 2.07 to 2.98 m, while the mature stage culminatedwith 150 trees ha⁻¹ separated at 8.77 m. The distance between trees is consideredan auxiliary tool in thinning operations.

Application and Evaluation of a Multimodal Training on the Second Victim Phenomenon at the European Researchers' Network Working on Second Victims Training School: Mixed Methods Study.

Health care workers (HCWs) are often impacted by distressing situations during patient care and can experience the second victim phenomenon (SVP). Addressing an adequate response, training, and increasing awareness of the SVP can increase HCWs' well-being and ultimately improve the quality of care and patient safety. This study aims to describe and evaluate a multimodal training organized by the European Researchers' Network Working on Second Victims to increase knowledge and overall awareness of SVP and second victim programs. We implemented a multimodal training program, following an iterative approach based on a continuous quality improvement process, to enhance the methodology and materials of the training program over the duration of 2 years. We conducted web-based surveys and group interviews to evaluate the scope and design of the training, self-directed learning materials, and face-to-face activities. Out of 42 accepted candidates, 38 (90%) participants attended the 2 editions of the Training School program. In the second edition, the level of participants' satisfaction increased, particularly when adjusting the allocated time for the case studies' discussion (P<.001). After the multimodal training, participants stated that they had a better awareness and understanding of the SVP, support interventions, and its impact on health care. The main strengths of this Training School were the interdisciplinary approach as well as the contact with multiple cultures, the diversity of learning materials, and the commitment of the trainers and organizing team. This multimodal training is suitable for different stakeholders of the health care community, including HCWs, clinical managers, patient safety and quality-of-care teams, academicians, researchers, and postgraduate students, regardless of their prior experience with SVP. Furthermore, this study represents a pioneering effort in elucidating the materials and methodology essential for extending this training approach to similar contexts.

AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.

Architectural Approaches to Migrating Key Features in Android Apps

The rapid evolution of Android platforms and user expectations necessitates continual enhancement and migration of key features within Android applications. This paper explores various architectural approaches to effectively migrate essential functionalities in Android apps, ensuring scalability, maintainability, and optimal user experience. Initially, the study delineates the challenges inherent in feature migration, including compatibility issues, performance constraints, and the preservation of existing user interfaces. It then examines prominent architectural paradigms such as Model-View-View Model (MVVM), Clean Architecture, and modular architectures, evaluating their suitability for facilitating seamless feature transitions. The research further investigates the role of microservices and component-based designs in enabling incremental migration, allowing developers to update or replace specific modules without overhauling the entire application. Additionally, the paper discusses best practices for managing dependencies, ensuring backward compatibility, and employing automated testing to mitigate risks during the migration process. Case studies of successful Android app migrations are presented to illustrate the practical application of these architectural strategies. The findings highlight that adopting a modular and flexible architectural framework significantly enhances the ability to migrate key features efficiently while minimizing disruptions. Moreover, the integration of robust architectural patterns fosters better code maintainability and accelerates the development cycle for future updates. In conclusion, this study provides a comprehensive guide for Android developers aiming to navigate the complexities of feature migration, advocating for architectural approaches that balance innovation with stability. The insights garnered offer valuable implications for the design and evolution of resilient Android applications in a dynamic technological landscape.

Applicable International Standardized Basis in Defining Data for Security Systems Development

Creating an intelligent security environment is one of the main trends in the activity of scientific organizations, where security technologies are a significant part of the product range. Organizations using these technologies, including critical infrastructure with built-in security systems, provide increased operational efficiency, guaranteeing readiness for action in emergency situations. For this purpose, standardized approaches are applied to determine the requirements for the development of security systems, according to the specifics of the organization, ensuring the prevention of any level of potential consequences of possible risk events, both in the structural objects of the organization and for the population in the adjacent territories. The author's intention is through this publication to present his experience in the application of modern standardized approaches ensuring the construction and operation of critical infrastructure security systems developed by scientific organizations in the conditions of key partnerships of the Competence Center.

Developing and Gathering Validity Evidence for an Instrument to Measure How High School Students Identify as Researchers

AbstractThe authentic research experience, which provides students with meaningful collaborative research opportunities designed to promote discovery and innovation under the guidance of mentors, is increasing as a way to attract and engage students in STEM fields. However, despite the increase in authentic research experiences offered to students, there has been little research, particularly at the high school level, investigating students’ attitudes about themselves as researchers. To address this need, we developed a theory (or construct) for how high school age students self-identify as researchers and a companion survey to measure their identity. After three iterative development cycles, 823 high school students from diverse backgrounds were administered the 12-item survey, the Researcher Identity Survey—Form G (RISG). The partial credit Rasch model (1960/1980) was used to analyze the survey data. The results indicate that the survey identifies and locates high school age students as researchers validly and reliably along an easy to use and interpret scale. The survey holds promise as an important element for use in programs designed to broaden the entryway for students into the STEM disciplines.

An integrated single-cell reference atlas of the human endometrium

The complex and dynamic cellular composition of the human endometrium remains poorly understood. Previous endometrial single-cell atlases profiled few donors and lacked consensus in defining cell types. We introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas (313,527 cells) combining published and new endometrial single-cell transcriptomics datasets of 63 women with and without endometriosis. HECA assigns consensus and identifies previously unreported cell types, mapped in situ using spatial transcriptomics and validated using a new independent single-nuclei dataset (312,246 nuclei, 63 donors). In the functionalis, we identify intricate stromal–epithelial cell coordination via transforming growth factor beta (TGFβ) signaling. In the basalis, we define signaling between fibroblasts and an epithelial population expressing progenitor markers. Integration of HECA with large-scale endometriosis genome-wide association study data pinpoints decidualized stromal cells and macrophages as most likely dysregulated in endometriosis. The HECA is a valuable resource for studying endometrial physiology and disorders, and for guiding microphysiological in vitro systems development.

Analysis of ELENA's Usage: Harmonization of TAM and TPB

This research analyzes perceptions of using ELENA from a student's perspective by harmonizing variables in the TAM and TPB theories. This research focused on students from the Department of Economic Education, Universitas Negeri Semarang (UNNES) class of 2021. Using the Slovin formula, a research sample of 205 students was obtained from 420 students. Research data was obtained using a questionnaire first tested for validity and reality. Descriptive analysis was used to determine the extent of students' perceptions of the research variables, and path analysis using the PLS method was used to test the research hypothesis. The research results show that perceived usefulness, attitude, subjective norms, and perceived behavioral control influence behavioral intention. Meanwhile, the variable that has no effect is perceived ease of use. According to the theoretical framework, attitude can mediate the influence of perceived usefulness and perceived ease of use on behavioral intention. Suggestions from this research to system developers are the need for a more straightforward manual guide and the need to simplify the system so that the system is user-friendly. Meanwhile, further research can use the same model as this research in other system applications.

Enhancing Software Testing Quality Assurance Using Agile Development Environment and Artificial Intelligence With Research Travelogue

This paper presents the analysis of software testing quality assurance using agile development environment and artificial intelligence with research travelogue. It explores the integration of Agile development methodologies with Artificial Intelligence (AI) to elevate the quality assurance (QA) processes in software testing. By leveraging Agile practices, such as iterative development and continuous feedback, alongside AI-driven tools for automation, predictive analytics, and anomaly detection, the approach aims to significantly enhance the accuracy and efficiency of QA processes. The research travelogue documents the journey through various Agile environments, detailing how AI technologies are applied to streamline testing workflows, identify potential issues earlier in the development cycle, and adapt to changing requirements. The findings reveal that this hybrid approach not only accelerates the testing process but also improves defect detection rates and overall software reliability, offering valuable insights for future advancements in software QA practices

A trust based framework for the envelopment of medical AI

The importance of a trust-based relationship between patients and medical professionals has been recognized as one of the most important predictors of treatment success and patients’ satisfaction. We have developed a novel legal, social and regulatory envelopment of medical AI that is explicitly based on the preservation of trust between patients and medical professionals. We require that the envelopment fosters reliance on the medical AI by both patients and medical professionals. Focusing on this triangle of desirable attitudes allows us to develop eight envelopment components that will support, strengthen and preserve these attitudes. We then demonstrate how each envelopment component can be enacted during different stages of the systems development life cycle and demonstrate that this requires the involvement of medical professionals and patients at the earliest stages of the life cycle. Therefore, this framework requires medical AI start-ups to cooperate with medical professionals and patients throughout.

SOCIAL IMPACT ASSESSMENT: A COMPARISON OF THE LEGAL FRAMEWORKS IN MALAYSIA AND NEW ZEALAND

Social Impact Assessment (SIA) has been implemented since the early 1990s, and it is a decision-making tool and methodology for determining the benefits or suffering that affect communities through the social outcomes data. This assessment is crucial in the planning phase as well as the development phase to monitor the impact of proposed development projects. Many countries, including Malaysia and New Zealand, have started implementing SIA within their legal framework to respond to the development cycle proactively. Thus, this paper seeks to compare and analyse the legal frameworks of SIA in Malaysia and New Zealand through qualitative methodologies: library research, content analysis, as well as comparative analysis. The findings show that both countries have legislation relating to SIA; however, the method of implementation is not the same, as SIA in New Zealand has emerged with the Environmental Impact Assessment (EIA) while SIA in Malaysia is implemented on a stand-alone basis.

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem

The necessary decarbonization efforts in energy sectors entail integrating flexible assets and increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving multi-stage TEP problems of different sizes based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options..