Software Design Research Articles

Modular soft pneumatic actuator mimics elephant trunk locomotion

Robots support and facilitate tasks in all life fields. Soft robots specifically have the advantages of inherent compliance, safe interaction and flexible deformability. Soft pneumatic network (Pneu-Net) is a soft pneumatic actuator (SPA) composed of network of chambers that is actuated by pneumatic power. Soft Pneu-Net fits the human interface applications perfectly. In this paper, a bio-inspired modular based design for Pneu-Net actuator is developed. The actuator mimics the elephant trunk curling to be employed for rehabilitation of human hand fingers. The actuator is an integrated four Pneu-Net modules actuator which is attached to hand’s finger. The main introduced advantages in the new developed actuator are: providing four degrees of freedom (DoF) essential for finger’s motion by single compound actuator and developing a methodology for a modular soft Pneu-Net actuator that is efficiently reproducible. The actuator’s design is developed using computer aided design (CAD) software SOLIDWORKS. The design is simulated using finite element modeling (FEM) software ABAQUS. Fabrication process uses 3D printed molds. Soft material is molded in the 3D printed molds, forming actuator’s modules. Actuator’s modules are integrated by adhesion using the soft material. A proposed non-standard hyper-elastic material biaxial tension test is introduced as a quick material properties identification method that can produce a test table used for material identification in the FEM. Enhanced version for the actuator uses reinforcement fibers. Results show advances for the reinforced actuator, as it limits the unwanted actuator’s strain and deformation. The reinforced actuator shows improved energy efficiency reaches to 46%.

Optimizing polyphenol extraction and UPLC-MS/MS analysis from red clover (Trifolium pratense L.) species using response surface methodology

Polyphenols were extracted from red clover (Trifolium pratense L.) plants following conventional extraction. The combined factors of ethanol concentration (60–80%), extraction time (15–45 min) and extraction temperature (40–80°C) were evaluated. Central composite design software was used to conduct Response Surface Methodology (RSM) modelling and optimize the polyphenol yield from the red clover. Total phenolic content, antioxidant activity and ultra-performance liquid chromatography mass-spectrometry (UPLC-MS/MS) analysis were used to quantify polyphenols. The RSM model identified ethanolic composition having greatest influence (p < 0.05) on the polyphenol yield with extraction yields increasing with percentage ethanol. The optimal extraction conditions were identified as 80% ethanol for 45 minutes at 40°C. When compared with an exhaustive polyphenol extraction method (70% ethanol for 21 h at room temperature), the optimal extraction resulted in a 25.6% increase in TPC yield. UPLC-MS/MS was performed to quantify the individual polyphenols present within the sample, in which biochanin A and formononetin were present in abundance. The outcomes of this modelling and optimization study demonstrate that polyphenols from red clover can be extracted efficiently with minimal environmental and energy cost and thus had the capability to serve as a source of valuable nutraceutical and pharmaceutical products.

Strengths and complementarity of systematic conservation planning and key biodiversity area approaches for spatial planning.

Developing biodiversity-inclusive spatial plans at a national level is the focus of Target 1 of the Kunming-Montreal Global Biodiversity Framework (KMGBF). There are 2 general approaches to identifying areas of value for biodiversity plans: criteria-based, such as the Key Biodiversity Areas (KBA) process, and systematic conservation planning (SCP) approaches, which apply complementarity to efficiently achieve specific quantitative targets. We examined the benefits of both approaches and considered how the KBA approach can best complement SCP. We reviewed 200 papers articles that applied SCP to real-world data with the Marxan conservation design software. Our review showed that targets for biodiversity elements are poorly selected in many SCP publications, with more than 75% of the studies applying uniform percentage target amounts to planning elements. Uniform targets favor more widespread species and ecosystems that are likely to be more common and less important for conservation. The strengths and complementarities of KBA and SCP approaches were reviewed and we identified the elements from both approaches that should be considered for spatial planning to achieve Target 1 in the KMGBF. In particular, the global approach of KBAs (i.e., identifying sites of global significance for species or ecosystems) better complements SCP, which often has a national or subnational focus. The KMGBF will fail if conservation of globally significant sites is not targeted and these sites are not incorporated in national spatial planning.

Extraction of Soybean Oil with Pressurized Ethanol: Prospects for a New Processing Approach with an Analysis of the Physical Properties of Crude Oil and Implementation Costs through Scale-Up in an Intermittent Process

New environmentally friendly methods for extracting vegetable oils are in development, with a focus on pressurized liquid extraction (PLE) in an intermittent process. Ethanol, a renewable and generally recognized as safe (GRAS) solvent, is gaining prominence in this process. It is crucial for these methods to maintain the physicochemical characteristics of the extracted oils and be economically viable on a large scale. Using SuperPro Design software v 8.5, a simulation of PLE scaled up to industrial levels was conducted. Measurements of oils extracted with pressurized hexane and ethanol showed minimal density variations, with slightly higher viscosity for ethanol-extracted oil. Accelerated oxidative degradation revealed a longer induction period for hexane-extracted oil, indicating that ethanol-extracted oil degrades more easily. The antioxidant activity of the oil extracted with hexane was lower than that with ethanol. In the differential scanning calorimetry analysis, the oils extracted with hexane and ethanol presented onset melting point temperatures of −43.2 and −36.1 °C, respectively. The economic assessment considered 16 scenarios, showing a return on investment ranging from 9.0 to 133.5% in the first year and payback times from 0.7 to 11.1 years. Scenario 8, involving two 5000 L extractors, ethanol recycling, and an annual production of 3,325,300 L of soybean oil at USD 1.25/L, demonstrated the best return on investment (133.5%) in less than one year. Overall, this study suggests that industrial-scale soybean oil extraction via PLE in an intermittent process can be more cost-effective than conventional methods, making implementation feasible.

The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications

The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed &amp;quot;Architectural Design Process-Cognitive Pilot Map&amp;quot; from an architectural perspective to aid AI programmers in developing architecture design software applications.

Analysis of Software Patterns to resolve Design Problems

A reproducible solution to a frequently occurring problem in software design was described as a software pattern, also known as a design pattern. It was a tested method for resolving kinds of software design issues that had been classified and identified by several years of experience and best practices. The key to making safe and accessible software was good software design. There were different types of design problems. Solving those problems was very important to produce good and efficient software. There were some tools, techniques, and patterns to solve these problems. Software design knowledge could be effectively captured and communicated using design patterns. Within a particular context of software design, a software design pattern was a standardized, repeatable solution to a frequently occurring problem. The patterns were effective for software design on several levels. In this paper, we identified the major design problems. Then we have analyzed those problems and present design patterns to develop an effective software design.

Implementation of Computerized Prescriber Order Entry Systems: A Review of Impacts, Barriers, and Facilitators.

This review evaluated the impact of a digitized computerized prescriber order entry (CPOE) system and described barriers and facilitators for introducing a digitized system. A systematic literature search was conducted in PubMed, Medline, and CINAHL using keywords. Articles in English during the last 10 years were included. Study selection was presented using a PRISMA flow diagram. Forty-eight studies were included. Data from the articles were presented to address each of the three objectives. CPOE systems improved the quality of care provided but also introduced new types of errors. Facilitating factors for implementation included leadership, stakeholder engagement, training, and user-centered design. Inadequate training, software design, changes in workload, and workflow disruptions were identified as barriers. Recommendations for implementation included dedicated training of users, user-centered design, a backup for system downtimes, and stakeholder engagement. Application of knowledge of the facilitators and barriers for the introduction of a CPOE system supports this change-management process within the specific context and augurs for more successful implementation.

Improving neural-network-based prediction models for misalignment in off-axis three-mirror anastigmat telescopes

The performance of a telescope system heavily relies on the precise alignment of the mirrors. The off-axis three-mirror anastigmat (TMA) telescope presents unique challenges due to its complex optical design. Each optical element within the off-axis TMA telescope is inherently introduced with theoretical eccentricity and tilt. Furthermore, the incorporation of freeform surfaces and other optical elements with intricate surface features typically leads to low initial alignment accuracy of the optical path. With this low initial alignment accuracy and the noise of the measurements, the prediction of the misalignment of the telescope is getting harder. A fully connected neural network architecture is proposed as a misalignment calculation method for an off-axis TMA telescope system with a freeform surface. Random training data is created by using optical design software. The sensitivity of the mirrors to the wavefront error is quantified and incorporated into the loss function of the neural network to improve prediction accuracy. Adding the noisy measurement samples to the training data creates a noise-immune neural network model. Simulation results show that our model can successfully predict misalignment of the mirrors with noisy measurement values.

Technical note: Measuring bicondylar length in computed tomography data.

With the increased use of 3D-generated images in biological research, there is a critical need to adapt classical anatomical measurements, traditionally conducted with calipers, to a virtual environment. We present detailed protocols for measuring bicondylar length, a critical dimension of the femur, using three different imaging software programs-3D Slicer™, Amira™, and Simpleware™. These protocols provide researchers and practitioners in radiology, orthopedics, biomechanics, and biological anthropology with accurate and reproducible measurement techniques. The objective is to standardize and support virtual osteology in biomechanical research, stature estimation, and related medical and anthropological studies. Adhering to standardized protocols, we adapted femoral bicondylar length measurements for computed tomography images from a New Mexican collection (n = 10). The method was designed for applicability and reproducibility across three software platforms. By comparing measurements from the same sample across different observers and different platforms, this study validates the accuracy and consistency of the adapted protocol, demonstrating its utility for research and clinical assessments. We present a step-by-step guide for each program, detailing bone alignment and measurement. We illustrate each step and provide video tutorials via links for an enhanced understanding of the process. Bicondylar length can be measured effectively in each software program following the provided instructions. However, ease of measurement varied among the programs, with some offering a more straightforward process. This variability underscores the importance of choosing appropriate software for the user's needs and proficiency. It also suggests areas for improvement and standardization in software design and instructional clarity.

Development of A Workshop on the Topic of the Bionically Inspired Lightweight Design Method for 3D Printed Components

Additive manufacturing methods, particularly 3D printing, are widely utilized in research and engineering for crafting lightweight yet durable materials capable of withstanding substantial forces. Leveraging insights from biomechanical structures offers a deeper understanding of reinforcement techniques and optimal design strategies to enhance strength. This paper focuses on the development of a Draisine design, historically significant in Karlsruhe, as part of the BWSplus project "Drais3D-Trinational" workshop. Through a comprehensive analysis of bionic influences on lightweight design and 3D printing parameters, this study aims to create an optimal design framework for the workshop. Utilizing Computer-Aided Design (CAD) software such as SolidWorks and Creo Parametric, along with Finite Element Analysis using ANSYS R2023 Workbench, deformation and stress analysis are conducted. Investigation into 3D printing parameters, including infill patterns, temperature, support systems, and orientation, seeks to identify optimal solutions considering factors like processing time, robustness, and filament wastage. The objective is to explore the biomechanical influence on construction methods, concept design, and parameter construction in preparation for the Drais3D-Trinational workshop in March 2023. The expected outcomes include the presentation of two main designs with varied parameters, alongside analyses such as Finite Element Analysis and optimization of 3D printing parameters, emphasizing the role of bionic structures in defining the optimal Draisine design.

A novel methodology for analysing dental implant positional changes from virtual planning to placement without CBCT.

Introduction This paper introduces a novel methodology for analysing dental implant positioning in vivo, advancing beyond traditional methods that rely on post-placement cone beam computed tomography (CBCT) scans.Method The core of the methodology is comparing stereolithography (STL) files, representing pre-planned and actual post-placement implant positions. These STL files, exported from guided surgery planning and computer-aided design software, focus on clinically significant key points, like the apical and coronal midpoints. Additionally, the method uses pose detection, differing fundamentally from CBCT scan approximations.Discussion Relying on pose detection instead of scanner resolution, this method aligns with international standards and overcomes CBCT and intra-oral scanner limitations. It allows for a more precise and accurate assessment of implant positions, independent of scanner technology constraints. Further refinements include potential detailed reporting of apical deviations, enhancing implant placement accuracy.Conclusion This research has significant implications for dental implantology, enhancing implant placement precision and overall procedure success. Introducing an automated process for report generation through a batch script improves efficiency and precision, streamlining the analysis process. This innovation sets the stage for future technological advancements, improving both the accuracy and reliability of implant placement assessments.

The impact of quantum computing on the development of algorithms and software

Introduction: There is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: An extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: This paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes.Conclusion: While there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions

INNOVATION IN LEARNING: DESIGNING KELADI APPLICATIONS USING C#

Technological developments such as the development of digital-based learning media have a major impact on the world of education. This study aims to design and develop the KelaDi (Digital Class) application using Visual Studio and the C# programming language as an interactive learning media for students. The KelaDi application has various features that support the teaching and learning process, such as e-books and e-book readers, learning videos, uploading assignments, and quizzes. The methodology used in this study is a qualitative methodology using the Waterfall model software development approach. The development process includes the stages of requirements analysis, system design, coding and testing program. Supporting software includes Visual Studio 2019 and SQL Server Management Studio 20 software. This program is built using NET 4.7.2 framework technology with the C# programming language. The author hopes that the birth of this program can help support digital-based learning

Accessible Adventures: Teaching Accessibility to High School Students Through Games

ABSTRACTAccessibility education has been rarely incorporated into the high school curricula. This is a missed opportunity to equip next‐generation software designers and decision‐makers with knowledge, awareness, and empathy regarding accessibility and disabilities. We taught accessibility to students (N = 93) in a midwestern high school through empathy‐driven games and interviewed three Computer Science high school teachers and one librarian who taught programming. Accessibility education is currently insufficient in high school, facing challenges such as teachers' knowledge and conflicted curriculum goals. The students exhibited increased knowledge and awareness of accessibility and empathy for people with disabilities after playing the games. With this education outreach, we aim to provide insights into teaching next‐generation software designers about accessibility by leveraging games.

Pipeline defect detection system based on phased array excitation technology

Abstract To address the challenges of non-destructive testing of professional industrial pipeline conditions, this paper designs a pipeline defect detection system based on phased array excitation technology. Online/offline detection equipment can be selected according to different application scenarios, achieving various functions such as phase delay calculation, ultrasonic data collection, and graphical display of calculation results. This article mainly elaborates on the principle of phased array excitation technology, the circuit design of detection equipment, and the overall design of upper computer software, proposing a practical solution for non-destructive testing of industrial pipelines.

Architecture designing of digital twin in a healthcare unit.

Objectives: This study proposes a novel architecture for designing digital twins in healthcare units. Methods: A systematic research methodology was employed to develop architecture design patterns. In particular, a systematic literature review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to answer specific research questions and provide guidelines for designing the architecture. Subsequently, a case study was designed and analyzed at a chemotherapy treatment center for outpatients. Results: System architecture knowledge was distilled from this real-world case study, supplemented by existing software and systems design patterns. A novel five-layer architecture for digital twins in healthcare units was proposed with a focus on the security and privacy of patients' information. Conclusion: The proposed digital twin architecture for healthcare units offers a comprehensive solution that provides modularity, scalability, security, and interoperability. The architecture provides a robust framework for effectively and efficiently managing healthcare environments.

Sctudies on adsorption of Brilliant Green from aqueous solution onto nutraceutical industrial pepper seed spent

The study proposed the removal of Brilliant Green, a cationic dye, by adsorption process from wastewater solution utilizing a low-cost adsorbent such as Nutraceutical Industrial Pepper Seed Spent (NIPSS). The study comprises an investigation of the parametric influence on the adsorption process. The parameters identified are pH, dye concentration, process temperature, quantity of the adsorbent, and particle size. The study of statistics found from experiments was carried out by incorporating Freundlich, Brouers-Sotolongo, Langmuir, Toth, Sips, Jovanovic, and Redlich-Peterson isotherm models. The adsorption kinetics were determined by implementing pseudo-first-order and second-order models, diffusion film models, and Dumwald-Wagner and Weber-Morris models. The experimental adsorption capacity qe was found to be about 130 mg/g. This value was closest to the maximum adsorption of 144.6mg/g predicted by the Brouers-Sotolongo isotherm which had a correlation coefficient (R2) of 0.998. The adsorption kinetics data was confirmed to be a pseudo-second-order model. The change in free energy, enthalpy change, and entropy change were vital thermodynamic factors in concluding that adsorption is almost spontaneous and endothermic process. Change in enthalpy (ΔH°) reduced value indicates the physical nature of the process. The adsorption of BG dye on the adsorbent surface was authenticated by FTIR spectroscopy and SEM imaging. A Central Composite Design (CCD) Quadratic model under Response Surface Methodology (RSM) was implemented for statistical optimization of adsorption capacity for the five parameters studied, namely, time, temperature, concentration of the dye, weight of the adsorbent, and pH. Software Design Expert 7.0 was used to evaluate 3D contour plots. The process of optimization yielded a value of 350 mg/g. Thus, incrementing the adsorption process by 84.2 %. The study provides insights on various dye and adsorbent interaction possibilities and derives that NIPSS is an efficient adsorbent to extract BG dye from wastewater solutions.

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

Software Architecture Design of ASV Rybitwa: Development of an Autonomous Surface Vehicle for Dynamic Navigation and Task Execution

Abstract This paper introduces the software design of an Autonomous Surface Vehicle (ASV) named ASV Rybitwa. It was designed as an easily transportable platform capable of autonomous navigation and performing tasks during the RoboBoat 2024 competition, with emphasis on its modularity, generality and extendibility. The paper presents a software architecture for a small autonomous surface vehicle using novel tools that provide easy system integration and expansion. To meet such requirements, a generic control box connecting an autopilot (PX4) and a onboard computer (NVIDIA Jetson Nano) was developed so as to host the Robot Operating System 2 (ROS2) and to interact with sensors and actuators. Computer vision from three Oak-D cameras, equipped with AI algorithm support (YOLOv8), was used for navigation and obstacle avoidance. The decision-making system was designed using behavioural trees and computer vision, allowing the vehicle’s capabilities to be adapted to the needs of the current tasks and mission. In addition, an Omni X propulsion system was proposed, providing full holonomy and enhancing dynamic positioning and navigation capabilities in complex navigational conditions. In addition this paper describes lessons learned from ASV Rybitwa’s real-world testing during the aforementioned competition.

Technology roadmap toward the completion of whole-brain architecture with BRA-driven development

The development of brain-morphic software holds significant promise for creating artificial general intelligence that exhibits high affinity and interpretability for humans and also offers substantial benefits for medical applications. To facilitate this, creating Brain Reference Architecture (BRA) data, serving as a design specification for brain-morphic software is imperative. BRA-driven development, which utilizes Brain Information Flow (BIF) diagrams based on mesoscale brain anatomy and Hypothetical Component Diagrams (HCD) for corresponding computational functionalities, has been proposed to address this need. This methodology formalizes identifying possible functional structures by leveraging existing, albeit insufficient, neuroscientific knowledge. However, applying this methodology across the entire brain, thereby creating a Whole Brain Reference Architecture (WBRA), represents a significant research and development challenge due to its scale and complexity. Technology roadmaps have been introduced as a strategic tool to guide discussion, management, and distribution of resources within such expansive research and development activities. These roadmaps proposed a manual, anatomically based approach to incrementally construct BIF and HCD, thereby systematically expanding brain organ coverage towards achieving a complete WBRA. Large Language Model (LLM) technologies have introduced a paradigm shift, substantially automating the BRA-driven development process. This is largely due to the BRA data being structured around the brain’s anatomy and described in natural language, which aligns well with the capabilities of LLMs for supporting and automating the construction and verification processes. In this paper, we propose a novel technology roadmap to largely automate the creation of WBRA, leveraging neuroscientific insights. This roadmap includes 12 activities for automating BIF construction, notably extracting anatomical structures from scholarly articles. Furthermore, it details 11 activities aimed at enhancing the integration of Hypothetical Component Diagrams (HCD) into the WBRA, focusing on automating checks for functional consistency. This roadmap aims to establish a cost-effective and efficient design process for WBRA, ensuring the availability of brain-morphic software design specifications that are continually validated against the latest neuroscientific knowledge.