Search Results (31)

Computer-aided design (CAD) courses require students to develop both procedural skills and higher-order modeling competencies. This 10-year repeated cross-sectional study (N = 1952 students, 2013–2023) examined how replacing mandatory graded homework and the structured practice and feedback mechanisms it provided with optional short instructional videos affected performance. Using descriptive statistics, one-way ANOVA, interrupted time-series models, and student-level regression with clustered standard errors, we analyzed outcomes across pre-intervention (homework), post-intervention (video), and COVID remote-instruction periods. Results show that although homework and video instruction produced equivalent performance on basic modeling tasks (PE1), the homework period was associated with significantly higher performance on complex modeling tasks (PE2) and modestly higher final grades. The video-based period was associated with elevated failure rates, particularly for complex exams, whereas COVID period introduced temporary grade inflation that masked these effects. Findings indicate that while instructional videos effectively support basic procedural learning, structured homework with feedback remains essential for developing advanced modeling competencies. The results highlight the importance of aligning self-learning methods with task complexity and suggest that a combined approach may best support student success in CAD education. Full article

Descriptive geometry plays a fundamental role in developing spatial reasoning and geometric problem-solving skills in engineering education. This study investigates the comparative effectiveness of two instructional methodologies—Monge’s traditional projection system and the CADOP method, which integrates computer-aided design tools with orthographic projection principles. A quasi-experimental design was implemented with 90 undergraduate engineering students randomly assigned to two groups. Both groups followed the same instructional sequence and were evaluated using baseline surveys, rubric-based performance assessments, and post-training reflections. Quantitative analysis included mean comparisons, t-tests, and effect sizes, while inter-rater reliability confirmed scoring consistency. The results showed that CADOP students significantly outperformed those taught with Monge’s method across all criteria—conceptual under-standing, graphical accuracy, procedural consistency, and spatial reasoning—with very large effect sizes. Qualitative data indicated that CADOP enhanced clarity, efficiency, and confidence, while Monge promoted conceptual rigor but higher cognitive effort. The findings confirm that CADOP effectively reduces procedural complexity and cognitive load, supporting deeper spatial comprehension. Integrating CADOP with selected manual practices offers a balanced pedagogical approach for modernizing descriptive geometry instruction in engineering education. Full article

Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. We perform SHAP analysis on the constructed machine learning model to explain the effects of different components on the properties. Based on the trained machine learning models, we developed several ternary system prediction maps that can effectively predict the properties of glasses composed of different proportions of components. This study provides a method to design new oxyfluoride glasses only knowing the components of glasses, which is instructive for the development of new types of oxyfluoride glasses as well as for computer-aided reverse design. Full article

Despite substantial instructional attention to large-enrollment university courses in science, technology, engineering, and mathematics (STEM), they tend to have high rates of D, F, and Withdraw (DFW) at the introductory level that disproportionally disadvantage historically minoritized and underrepresented students, such as students identified as women, low-income, first-generation, or of color. While postsecondary institutions have recently explored big data and learning analytics to drive their institutional student success efforts, well-known shortfalls in student success in large STEM courses remain. This chapter documents an evidence-based approach at a large, R1 midwestern university that enriches robust data infrastructure with qualitative ethnographic methods. Applied to a gateway computer science course, these methods center students’ day-to-day learning realities, including disparate educational opportunities, in ways that interrogate barriers to and shortfalls in student success. The resulting case study describes our ethnographic approach, the shortfalls it uncovers, our future directions with this work, and how other faculty members and institutions can apply lessons learned to promote efficacy, attainment, and equity in gateway STEM courses. Implementations drawn from course vignettes point to revisions in design and preparation of group learning activities, strategic integration of lecture and lab sessions, course navigation aids, and pedagogical training for teaching assistants. Full article

For applications such as home surveillance systems and assisted living for elderly care, sensing capabilities are essential for tasks such as locating, determining the approximate position of a person, or identifying the status of a person (static or moving), since the effects caused by the presence of people can be captured in the power received by signals in an infrastructure deployed for these purposes. Human interference in Received Signal Strength Indicator (RSSI) measurements between different pairs of wireless nodes can vary depending on whether the target is moving or static. To test these ideas, an experiment was conducted using four nodes equipped with the ZigBee protocol in each corner of an empty 6.9 m × 8.1 m × 3.05 m room. These nodes were configured as routers, communicating with a coordinator outside the room that instructed the nodes to send back their pairwise RSSI measurements. The coordinator was connected to a computer in order to log the measurements, as well as the time at which the measurements were generated. The code was run for every iteration of the experiment, whether the target was static, moving, or when the number of targets was increased to five. The data were then statistically analyzed to extract patterns and other target relational parameters. There was a correlation between the change in the pairwise RSSI and the path described by the target when moving through the room. The data presented by the results can aid algorithms for device-free localization and crowd classification, with a low infrastructure cost for both, and shed light on the relevant characteristics correlated with the path and crowd size in indoor settings. Full article

Despite the effort invested to improve the teaching of programming, students often face problems with understanding its principles when using traditional learning approaches. This paper presents a novel teaching method for programming, combining the task-driven methodology and the case study approach. This method is called a task-driven case study. The case study aspect should provide a real-world context for the examples used to explain the required knowledge. The tasks guide students during the course to ensure that they will not fall into bad practices. We provide reasoning for using the combination of these two methodologies and define the essential properties of our method. Using a specific example of the Minesweeper case study from the Java technologies course, the readers are guided through the process of the case study selection, solution implementation, study guide writing, and course execution. The teachers’ and students’ experiences with this approach, including its advantages and potential drawbacks, are also summarized. Full article

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These “markerless” systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery. Full article

Artificial intelligence (AI) tools like ChatGPT demonstrate the potential to support personalized and adaptive learning experiences. This study explores how ChatGPT can facilitate self-regulated learning processes and learning computer programming. An evaluative case study design guided the investigation of ChatGPT’s capabilities to aid independent learning. Prompts mapped to self-regulated learning processes elicited ChatGPT’s support across learning tools: instructional materials, content tools, assessments, and planning. Overall, ChatGPT provided comprehensive, tailored guidance on programming concepts and practices. It consolidated multimodal information sources into integrated explanations with examples. ChatGPT also effectively assisted planning by generating detailed schedules. However, its interactivity and assessment functionality demonstrated shortcomings. ChatGPT’s effectiveness relies on learners’ metacognitive skills to seek help and assess its limitations. The implications include ChatGPT’s potential to provide Bloom’s two-sigma tutoring benefit at scale. Full article

IEEE 1588, also known as the Precision Time Protocol (PTP), is a standard protocol for clock synchronization in distributed systems. While it is not architecture-specific, implementing IEEE 1588 on Reduced Instruction Set Computer-V (RISC-V) low-power embedded devices demands considering the system requirements and available resources. This paper explores various approaches and techniques to achieve accurate time synchronization in such instruments. The analysis covers software and hardware implementations, discussing each method’s challenges, benefits, and trade-offs. By examining the state-of-the-art in this field, this paper provides valuable insights and guidance for researchers and engineers working on time-critical applications in RISC-V-based embedded systems, aiding in selecting the most-suitable stack for their designs. Full article

This article deals with the rationalization of manufacturing processes within the product life cycle with emphasis on the pre-production phase of production. A new methodology for evaluating the applicability of modern visualization tools in manufacturing processes is presented. This methodology includes a modified Z-score for categorizing manufacturing processes and has been validated by the successful implementation of 10 real projects. Ultimately, the methodology provides a practical decision-making aid for manufacturing companies in deploying such Computer Aided Instruction tools. For the pre-production phase of products and their development, the possibilities of using modern visualization tools to support CAD instructions and assembly instructions are being explored. These modern visualization tools are video tutorials, augmented reality tutorials and virtual reality tutorials. This paper explores the use of these tools for rationalization of pre-production processes. A methodology was designed to select the most appropriate tool for rationalizing process execution in preparation for production. The functionality of the methodology was verified by applying the methodology in industrial practice and subsequent implementation of the recommended solutions. The methodology was validated by testing key combinations that can arise based on the methodology directly in the operations of manufacturing companies. A total of 10 implementations in real production processes were tailored to this study and carried out over 2 years and the functionality of the methodology was confirmed (that consisted also of a new software development). It was found that there is a dependency between the visualization tools chosen to create the instructions in the context of organizational production preparation and the nature of the production processes. Full article

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased. Full article

In Industry 4.0., robots are regarded as one of the key components. In recent years, collaborative robots (cobots) have risen in relevance and have been included in the industry to perform tasks alongside humans. Robots have been used in many applications in manufacturing processes; for the scope of this paper, the emphasis on these applications is centered on welding and gluing. These applications need to be performed with specific speed, efficiency, and accuracy to attain optimal welding or bonding to the pieces. An operator cannot maintain such conditions consistently, with minimum variations, for an extended period; hence, robots are a more suitable option to perform those tasks. The robots used for these applications need to be instructed to follow a trajectory to either weld or apply the glue. This path must be programmed on the robot by an operator, and depending on the complexity of the trajectory, it can take up to extended periods of time to set all the required waypoints. There are specialized software environments that contribute to the automation of these tasks; however, the overall cost of the licenses is not affordable if the scale of the project only requires developing and programming trajectories a few times. This paper contains a proposal for an open-source Computer Aided Manufacturing (CAM) software to automatically generate the trajectories needed for the aforementioned welding and gluing applications. The procedure to develop the software starts by selecting the surface that will be welded or to which glue will be applied. The surface determines the model of the trajectory to be followed. Next, a processing system is fed with the individual points that make up the trajectory provided by their selection over the Computer Aided Drawing (CAD) model. This system then creates a program based on URScript^® that can be directly uploaded to and executed on the robot. A set of tests is presented to validate the applications and to demonstrate the versatility of the developed trajectory generation system. Full article

This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate computer and data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. The onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after undertaking initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, the uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skill levels, potentially starting novices with graphical IDEs before introducing Emacs. The key takeaways are as follows: literate programming aids comprehension but requires rigorous onboarding and reinforcement, and Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education. Full article

Robots equipped with legs have significant potential for real-world applications. Many industries, including those concerned with instruction, aid, security, and surveillance, have shown interest in legged robots. However, these robots are typically incredibly complicated and expensive to purchase. Iron Dog Mini is a low-cost, easily replicated, and modular quadruped robot built for training, security, and surveillance. To keep the price low and its upkeep simple, we designed our quadruped robot in a modular manner. We provide a comparative study of robotic manufacturing cost between our proposed robot and previously established robots. We were able to create a compact femur and tibia structure with sufficient load-bearing capacity. To improve stability and motion efficiency, we considered the novel Watt six-bar linkage mechanism. Using the SolidWorks modeling software, we analyzed the structural integrity of the robot’s components, considering their respective material properties. Furthermore, our research involved developing URDF data for our quadruped robot based on its CAD model. Its gait trajectory is planned using a 14-point Bezier curve. We demonstrate the operation of the simulation model and briefly discuss the robot’s kinematics. Computational methods are emphasized in this research, coupled with the simulation of kinematic and dynamic performances and analytical/numerical modeling. Full article

Heparan sulfate is a ubiquitous, variably sulfated interactive glycosaminoglycan that consists of repeating disaccharides of glucuronic acid and glucosamine that are subject to a number of modifications (acetylation, de-acetylation, epimerization, sulfation). Variable heparan sulfate chain lengths and sequences within the heparan sulfate chains provide structural diversity generating interactive oligosaccharide binding motifs with a diverse range of extracellular ligands and cellular receptors providing instructional cues over cellular behaviour and tissue homeostasis through the regulation of essential physiological processes in development, health, and disease. heparan sulfate and heparan sulfate-PGs are integral components of the specialized glycocalyx surrounding cells. Heparan sulfate is the most heterogeneous glycosaminoglycan, in terms of its sequence and biosynthetic modifications making it a difficult molecule to fully characterize, multiple ligands also make an elucidation of heparan sulfate functional properties complicated. Spatio-temporal presentation of heparan sulfate sulfate groups is an important functional determinant in tissue development and in cellular control of wound healing and extracellular remodelling in pathological tissues. The regulatory properties of heparan sulfate are mediated via interactions with chemokines, chemokine receptors, growth factors and morphogens in cell proliferation, differentiation, development, tissue remodelling, wound healing, immune regulation, inflammation, and tumour development. A greater understanding of these HS interactive processes will improve therapeutic procedures and prognoses. Advances in glycosaminoglycan synthesis and sequencing, computational analytical carbohydrate algorithms and advanced software for the evaluation of molecular docking of heparan sulfate with its molecular partners are now available. These advanced analytic techniques and artificial intelligence offer predictive capability in the elucidation of heparan sulfate conformational effects on heparan sulfate-ligand interactions significantly aiding heparan sulfate therapeutics development. Full article