Systems Development Research Articles

Paleoclimate data provide constraints on climate models' large-scale response to past CO2 changes

The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.

Using boundary objects and methodological island (BOMI) modeling in large-scale agile systems development

AbstractLarge-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. Here, there is a conflict between coordination and group autonomy, and it is challenging to determine what necessary coordination information must be shared by what teams or groups, and what can be left to local team management. We introduce a way to manage this complexity using a modeling framework based on two core concepts: methodological islands (i.e., groups using different development methods than the surrounding organization) and boundary objects (i.e., artifacts that create a common understanding across team borders). However, we found that companies often lack a systematic way of assessing coordination issues and the use of boundary objects between methodological islands. As part of an iterative design science study, we have addressed this gap by producing a modeling framework (BOMI: Boundary Objects and Methodological Islands) to better capture and analyze coordination and knowledge management in practice. This framework includes a metamodel, as well as a list of bad smells over this metamodel that can be leveraged to detect inter-team coordination issues. The framework also includes a methodology to suggest concrete modeling steps and broader guidelines to help apply the approach successfully in practice. We have developed Eclipse-based tool support for the BOMI method, allowing for both graphical and textual model creation, and including an implementation of views over BOMI instance models in order to manage model complexity. We have evaluated these artifacts iteratively together with five large-scale companies developing complex systems. In this work, we describe the BOMI framework and its iterative evaluation in several real cases, reporting on lessons learned and identifying future work. We have produced a matured and stable modeling framework which facilitates understanding and reflection over complex organizational configurations, communication, governance, and coordination of knowledge artifacts in large-scale agile system development.

Impact of Artificial Intelligence on Mechanical Engineering: A Comprehensive Overview

Artificial Intelligence (AI) is coming to mainstream and as emerged as a transformative force in various fields not just limited including mechanical engineering. This paper provides an overview of the profound impact of AI on the practice and evolution of mechanical engineering. The usage of AI technologies in the field of mechanical engineering has potential to revolutionize traditional design, manufacturing, and maintenance processes. With AI-powered design tools engineers now can generate optimized designs faster with greater efficiency, leading to enhanced product performance and reduced development cycles. Further, Predictive/forecasting method of AI in maintenance systems facilitate early detection of equipment failures, thereby minimizing downtime and maintenance costs.

Virtual Prototyping of the RM Active Grid®: a DEM-MBD Study

AbstractNumerical simulation is an established tool to improve knowledge of complex systems and to accelerate the development of new prototypes. In this contribution, the patented Active Grid® made by the company RM RUBBLE MASTER HMH GmbH is examined.In an initial step, representative system parameters (spring properties) are found via a multibody dynamic simulation; at this point, without particles acting on the system components. After that, the virtually defined Active Grid® system is loaded with particles in order to include the interaction effects of various bulk materials acting on the system’s mechanical components. For this purpose, a DEM-MBD co-simulation, extending the discrete element method (DEM) towards multibody dynamics (MBD) simulation, is performed, accounting for the bulk materials via DEM and the interacting system components via MBD. This bi-directional co-simulation enables the consideration of interactions between the bulk materials (the particles) and the driven spring-damper system (the moving system components).With this virtual prototype of the Active Grid®, performance benefits and characteristics in terms of screening efficiency for different material types and varying drive speeds are analysed. The overall aim is to achieve a scalable DEM-MBD model that allows virtual prototyping and, especially, optimisation for future system developments.

Identification of digital device hardware vulnerabilities based on scanning systems and semi-natural modeling

Objectives. The development of computer technology and information systems requires the consideration of issues of their security, various methods for detecting hardware vulnerabilities of digital device components, as well as protection against unauthorized access. An important aspect of this problem is to study existing methods for the possibility and ability to identify hardware errors or search for errors on the corresponding models. The aim of this work is to develop approaches, tools and technology for detecting vulnerabilities in hardware at an early design stage, and to create a methodology for their detection and risk assessment, leading to recommendations for ensuring security at all stages of the computer systems development process.Methods. Methods of semi-natural modeling, comparison and identification of hardware vulnerabilities, and stress testing to identify vulnerabilities were used.Results. Methods are proposed for detecting and protecting against hardware vulnerabilities: a critical aspect in ensuring the security of computer systems. In order to detect vulnerabilities in hardware, methods of port scanning, analysis of communication protocols and device diagnostics are used. The possible locations of hardware vulnerabilities and their variations are identified. The attributes of hardware vulnerabilities and risks are also described. In order to detect vulnerabilities in hardware at an early design stage, a special semi-natural simulation stand was developed. A scanning algorithm using the Remote Bitbang protocol is proposed to enable data to be transferred between OpenOCD and a device connected to the debug port. Based on scanning control, a verification method was developed to compare a behavioral model with a standard. Recommendations for ensuring security at all stages of the computer systems development process are provided.Conclusions. This paper proposes new technical solutions for detecting vulnerabilities in hardware, based on methods such as FPGA system scanning, semi-natural modeling, virtual model verification, communication protocol analysis and device diagnostics. The use of the algorithms and methods thus developed will allow developers to take the necessary measures to eliminate hardware vulnerabilities and prevent possible harmful effects at all stages of the design process of computer devices and information systems.

Functional characterization of BbEaf6 in Beauveria bassiana: Implications for fungal virulence and stress response

ABSTRACT The Eaf6 protein, a conserved component of the NuA4 and NuA3 complexes in yeast and MOZ/MORF complexes in humans, plays crucial roles in transcriptional activation, gene regulation, and cell cycle control. Despite its significance in other organisms, the functional role of Eaf6 in entomopathogenic fungi (EPF) remained unexplored. Here, we investigate the function of BbEaf6, the Eaf6 homolog in the entomopathogenic fungus Beauveria bassiana. We demonstrate that BbEaf6 is predominantly localized in nuclei, similar to its counterpart in other fungi. Deletion of BbEaf6 resulted in delayed conidiation, reduced conidial yield, and altered conidial properties. Transcriptomic analysis revealed dysregulation of the genes involved in asexual development and cell cycle progression in the ΔBbEaf6 mutant. Furthermore, the ΔBbEaf6 mutant exhibited decreased tolerance to various stresses, including ionic stress, cell wall perturbation, and DNA damage stress. Notably, the ΔBbEaf6 mutant displayed attenuated virulence in insect bioassays, accompanied by dysregulation of genes associated with cuticle penetration and haemocoel infection. Overall, our study elucidates the multifaceted role of BbEaf6 in stress response, development, and virulence in B. bassiana, providing valuable insights into the molecular mechanisms governing fungal pathogenesis and potential targets for pest management strategies.

4E analysis and multi-objective optimization of a novel multi-generating cycle based on waste heat recovery from solid oxide fuel cell fed by biomass

The present study optimizes a novel developed cycle including solid oxide fuel cell (SOFC) fed by synthesis gas produced from biomass as well as gas turbine (GT), supercritical carbon dioxide cycle (SCO2), transcritical carbon dioxide cycle (TCO2), Organic Rankine Cycle (ORC), thermoelectric generator (TEG), and reverse osmosis (RO)- based desalination. Energy, exergy, exergoeconomic and exergoenvironmental analyses on the developed cycle were investigated. Multi-objective optimization was carried out using of Genetic algorithm using generated power and exergy destruction as objective functions. Sankey diagram data indicate that afterburner holds the highest portion of the total exergy destruction 46.5% (692.24 kW), followed by SOFC which is 20.48% (304.51 kW). Moreover, optimization results showed that the total net power, first and second laws of thermodynamic efficiencies increased by 2.6%, 0.96% and 0.83%, respectively, while exergy destruction decreased by 1%. Furthermore, such a power increase (18.53 kW) using the freshwater produced by RO leads to daily production of 17040 liters of drinking water. According to the exergoeconomic analysis, the minimum flow value pertains to GT at a value of 0.0119 $/GJ, while the TCO2 turbine has the highest value which is 0.2867 $/GJ. The system product cost rate and exergy destruction cost rate reached 27.0353 $/h, and 10.7012 $/h, respectively. In the case of the exergoenvironmental one, the maximum environmental impact is related to the SCO2 turbine 0.0212 Pts/GJ, while SOFC has the lowest (0.0002 Pts/GJ). The system product environmental impact and exergy destruction were achieved at optimum values of 2.7503 $/h, and 4.1576 ×10-7 $/h, respectively.

A profound study of damage behavior for Al 2024-T3 alloy worksheet produced by constrained groove pressing in the superior practical condition

Constrained groove pressing (CGP) process is one of the most efficient and novel methods of severe plastic deformation to manufacture ultra-fine sheet metal. The present research work is related to the study of CGP process of 2024-T3 Aluminum alloy sheet. The empirical and numerical simulation of CGPed- specimens have been examined at both room and elevated temperatures. In order to simulate the constrained groove pressing process in Abaqus software, the Johnson–Cook material model has been employed. The geometrical variables of the CGP process include the teeth number, teeth angle, and sheet thickness, and the criterion for the superiority of the numerical test is the maximum reduction of two equivalent strain and equivalent force factors, simultaneously. To determine the weight’s coefficients of the target factors and select the superior test, the Shannon’s Entropy and Simple Additive Weighting (SAW) methods have been used, respectively. After validating the numerical findings, the variation of damage parameter, stress triaxiality and the equivalent plastic strain distribution in the superior practical condition have been evaluated in detail. Based on the Entropy-SAW hybrid technique, the weight’s coefficients of equivalent strain and equivalent force target factors were computed to, in turn, 0.38 and 0.62. Also, the results revealed that the CGP process could not be performed at room temperature based on high damage evolution. But, at elevated temperature, the damage parameter (D) doesn't exceed 0.26. Also, the maximum stress triaxiality and equivalent plastic strain (PEEQ) at this process temperature reached to 0.5 and 0.1, respectively.

Green building progress assessment: analysis of registered and certified buildings for LEED rating system

Green buildings are of great importance to overcome climate change effects and the depletion of natural resources. This paper aims to provide a review of the progress in green building constructions that have used the LEED (Leadership in Energy and Environmental Design) rating system for the last two decades. To do this the researchers have analyzed the data of around 61000 LEED-registered buildings (from the U.S. Green Building Council (USGBC) website) Using Google data analysis module studying different factors affecting green building growth and trying to spot any possible correlation between LEED and each factor. The review will provide the researchers and the construction industry with the necessary information about the analysis of green building development. The results showed the trends in the green building progress and provided insight analysis for the reasons behind this progress which can help rating system developers, green building designers, and governmental decision-makers, to promote green building worldwide.

Pengujian Alpha dan Beta Testing Pada Aplikasi TIJE

This research discusses the software testing process on the TIJE application using the alpha (blackbox) and beta test methods. Alpha testing is carried out to identify and fix internal errors by the development team, while beta testing involves end users to ensure the application meets user needs and expectations. Test results show that various critical bugs were successfully identified and fixed, improving the quality and stability of the application before being released to the public. The conclusion of this research shows the importance of alpha and beta testing in the software development cycle.

A Review on FPGA-based Architectures for Noise Removal of Digital Images using High-Level Design

Image corruption during the acquisition and transmission operations may be caused by a variety of disturbances. Scientists are still having trouble getting rid of the noise in the original picture. picture denoising is a technique used to improve a picture's visual quality and extract small details from a damaged image. Improving an image to make it crisper than it was originally is the aim of image restoration. The field of image processing is vast. This paper presents the results of some significant work in the subject of image denoising. It looks into image denoising using some of the most recent methods, such as SEPD (Simple Edge Preserved De-noising) and RSEPD (Reduced Simple Edge Preserved De- noising).Image corruption during the acquisition and transmission operations may be caused by a variety of disturbances. Scientists are still having trouble getting rid of the noise in the original picture. picture denoising is a technique used to improve a picture's visual quality and extract small details from a damaged image. Improving an image to make it crisper than it was originally is the aim of image restoration. The field of image processing is vast. This paper presents the results of some significant work in the subject of image denoising. It looks into image denoising using some of the most recent methods, such as SEPD (Simple Edge Preserved De-noising) and RSEPD (Reduced Simple Edge Preserved De- noising). Keywords— Power consumption analysis, Digital logic circuits, Ultra-low power, Subthreshold operation, Static CMOS gates

Predicting hospital length of stay using machine learning on a large open health dataset

BackgroundGovernments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.MethodsWe investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.ResultsThe study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.ConclusionOur study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

Convolutional neural network model compression method

In the field of deep learning, Convolutional Neural Networks (CNN) has become a focal point due to its multi-layered structure and wide application. The success of deep learning is due to the model has more layers and more parameters, which gives it a stronger nonlinear fitting ability. Traditionally, CNNs are primarily run on Central Processing Units (CPUs) and Graphics Processing Units (GPUs). However, CPUs have lower computational power, and GPUs consume a lot of energy. In contrast, Field-Programmable Gate Arrays (FPGAs) offer high parallelism, low power consumption, flexible programming, and rapid development cycles. These combined advantages make FPGAs more suitable for the forward inference processes of deep learning compared to other platforms. However, CNNs has the characteristics of parameter redundancy, the storage cost of deploying it on FPGA is too high. In order to apply CNNs to FPGA, we need to optimize CNNs for compression. Because after compression, This paper analyzes several specific cases of model compression for convolutional neural networks, and summarizes and compares the efficient methods of model compression. The results show that the model compression methods are mainly divided into the structure change of convolutional neural network and the quantization of parameters. The two compression methods can be cascaded at the same time to achieve a better optimization effect.

Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space

Abstract The article explores the usability of the geodiscussion administrative panel, an online tool aiding Public Participation Geographic Information Systems. This tool was specifically designed and developed for distribution in a Software as a service model, offering geodiscussion users the flexibility to leverage the tool without the need for substantial investments in expensive infrastructure or complex deployments. The research primarily centers on the geodiscussion administrative panel, a key component empowering organizers of public consultations in spatial planning to efficiently create and manage consultation processes. In contrast to previous studies that focused on the usability for consultation participants, this research uniquely targets individuals responsible for organizing and overseeing the consultation processes. A diverse array of usability research methods were employed, ranging from the creation of personas and conducting direct interviews based on test scenarios to non-invasive methods for recording user activities. The study involved 682 participants who had prior experience with public consultations across various public administration units and representatives from consulting firms. Given that the usability studies were an integral part of the geodiscussion administrative panel’s development process, the research team has the opportunity to influence the further implementation of the applied functionalities. This implies that insights gained from the research can actively shape and enhance the tool’s features as it progresses in its development cycle.

Financial Development, Monetary Policy, and the Monetary Transmission Mechanism—An Asymmetric ARDL Analysis

This paper’s objective is to examine the asymmetric cointegration and asymmetric effects of financial development and monetary policy on monetary transmission mechanisms in the Nigerian context using annual data spanning the period from 1986 to 2023. This study pushes the frontiers of knowledge by providing information on the nonlinear impacts of monetary policy and financial sector innovations on monetary transmission mechanisms in Nigeria to help policymakers tailor their strategies to local conditions, enhancing the effectiveness of monetary interventions in the economy. To achieve this, this paper adopted nonlinear ARDL models to understand how changes in the direction of monetary policy and developments in the financial system induce changes in the transmission of monetary policy. The findings document the existence of asymmetries in both the short and long run, revealing that the impacts of financial development and monetary policy on the different monetary policy channels are not uniform. These asymmetries indicate that the responses of various economic variables to monetary policy actions differ depending on the level of financial development. These findings underscore the complexity of the monetary transmission mechanism and the necessity for a nuanced understanding of how financial development and monetary policy interact in different contexts. Consequently, this finding is symptomatic of some characteristics of those financial markets on their way toward advanced developments. As the financial system matures, monetary policy may have a greater impact on the cost of short-term funding for banks without having any discernible effect on the rates at which businesses and households access funding. Therefore, this paper recommends focusing on the policies that will foster the financial system across the banking sector, capital market, bond market, and overall financial sector to improve the efficiency of the monetary transmission process.

Implementation of Internal Organizational Communication in the Motor Vehicle Registration and Identification (Regident) System in the South Sulawesi Police Traffic Directorate

The purpose of this study is to analyze the implementation of organizational communication from the aspect of internal communication in the Motor Vehicle Registration and Identification (Regident) System at the South Sulawesi Regional Police Traffic Directorate. The research uses a quantitative and qualitative approach with an Explanatory Design research design. The results show that internal organizational communication plays a very important role in the context of implementing information technology for the Motor Vehicle Registration and Identification (Regident) System services at the South Sulawesi Regional Police Traffic Directorate. Here are some key points to consider: first, Coordination and Collaboration where internal organizational communication facilitates effective collaboration between various units or departments within the Traffic Directorate. Second is Information Dissemination where effective communication ensures that all staff understand the latest changes or developments in the Regident system, including the implementation of new technology or improvements made. Third is Motivation and Engagement where good communication can increase staff motivation and their engagement in achieving organizational goals, and fourth is Conflict Resolution where open and clear communication helps in addressing conflicts or disagreements among staff or departments that may arise in the management of Regident.

Enabling radical and potentially disruptive innovations through interdisciplinarity: challenges and practices in industrial companies

The motivation behind this paper and the underlying research is to shed light on how companies deal with interdisciplinary innovation activities, especially looking at the development of radical and potentially disruptive innovations. This includes the analysis of methods, processes, and organizational structures applied to support interdisciplinary innovation activities as well as challenges faced. Whereas this thematic field has been widely investigated for academic institutions and, in companies on a team level, there is only a little information on industrial companies’ current state. This understanding is especially important as shortening development cycles, and technological convergence combined with intersectoral innovations might lead to increases in radical and potentially disruptive innovations and thereby create the need for improved methods and structures throughout the innovation process. To gain insights into challenges and current practices of interdisciplinary collaboration in innovation activities, semi-structured exploratory interviews were carried out with representatives from seven industrial companies. Among others, the results of the study revealed that whereas many methodologies are said to support interdisciplinary innovation activities, a wide majority of these methodologies are applied for other reasons which make supporting interdisciplinarity seem like a kind of byproduct.

Development of motion control function library based on PLCopen specification

The motion control library is a standard set of functional blocks integrated in a programmable integration environment, which is of great significance for the rapid development of reconfigurable Programmable Automation Controllers (PAC). However, due to the encapsulation and concealment of function blocks and the incompatibility between software and hardware, the development efficiency of function blocks is low, which is not conducive to the development of autonomous, controllable PAC. Aiming at the above problems, the methods of function block opening and key technology are studied, and a kind of motion control function library architecture and rapid development method based on the PLCopen specification are proposed. A series of motion control function libraries are developed on domestic PLC software. An experimental platform based on domestic hardware and software was designed to verify the typical motion control function library. Experimental results show that this method can effectively shorten the development cycle and that the developed motion control module can meet the actual control needs.

Reinterpreting the Nursing Record for an Electronic Context: Development Principles.

The full potential for electronic health record systems in facilitating a positive transformation in care, with improvements in quality and safety, has yet to be realised. There remains a need to reconceptualise the structure, content and use of the nursing component of electronic health record systems. The aim of this study was to engage and involve a diverse group of stakeholders, including nurses and electronic health record system developers, in exploring together both issues and possible new approaches to documentation that better fit with practice, and that facilitate the optimal use of recorded data. Three focus groups were held in the UK and USA, using a semi-structured interview guide, and a common reflexive approach to analysis. The findings were synthesised into themes that were further developed into a set of development principles that might be used to inform a novel electronic health record system specification to support nursing practice.

Sustainability in Federal Universities: An analysis based on management reports

This study examines the integration of sustainability in Brazilian federal universities based on the analysis of 2022 management reports. In the current global scenario, the integration of environmental and social sustainability into the policies of higher education institutions is crucial. Brazil's federal universities play a vital role in nurturing leaders and producing knowledge that can positively influence society and the environment. This study questions how these institutions are incorporating sustainability into their management strategies, contributing to a more sustainable future. The study aims to analyze how Brazilian federal universities approach sustainability in their activities and decisions, using the 2022 management reports as a basis. The research is descriptive in nature and involved the selection of 12 recently created federal universities, whose 2022 management reports were analyzed. Content analysis was performed in two stages: coding and interpretation. The actions described in the reports were categorized into themes related to sustainability, including education, planning, control, technology, infrastructure, collective applicability, and administrative actions. Of the 12 reports analyzed, 8 universities demonstrated a commitment to sustainability, dedicating specific chapters to this theme. The economic dimension was the one that presented the most actions, with 78 initiatives identified, reflecting a conscious strategy to ensure financial and operational sustainability. The analysis reveals that Brazilian federal universities are increasingly integrating sustainability into their operations and management. Economic initiatives stand out as fundamental for the viability of institutions, promoting a virtuous cycle of sustainable development.