Real-world Projects Research Articles

Enhancing academic outcomes through industry collaboration: our experience with integrating real-world projects into engineering courses

This research investigates the integration of industry projects and mentorship into academic curricula, aiming to enhance student learning and professional readiness. By incorporating real-world projects and pairing students with industry mentors, this approach seeks to provide a unique blend of theoretical knowledge and practical application. Utilizing a mixed-methods research design, the study captures both quantitative and qualitative data to assess the educational outcomes of this innovative model. Quantitative metrics include final grades, attendance rates, participation rates, placement rates, project grades, and professional skills ratings, while qualitative feedback is gathered from students, mentors, and faculty. The study is set against the backdrop of two courses that have been redesigned to include elements of industry collaboration. The findings are expected to shed light on the effectiveness of this approach in preparing students for the demands of the modern workforce, offering insights into how industry mentorship and project-based learning can enhance academic curricula and better equip students for their future careers.

Development of a Photoshop course based on task-based Learning and Gagne's Teaching Model to Improve the Innovative Ability of First-year Students at Anhui Wenda University of Information Engineering

Background and Aim: Photoshop courses must incorporate both Gagne's teaching model and task-based learning to foster students' innovative thinking. This combination guarantees that students receive organized, efficient instruction while working on real-world projects, which enhances their creativity and problem-solving abilities. This research and development research aimed to 1) determine the effectiveness of the Photoshop course based on task-based learning and Gagne's teaching model to continuously improve the innovative ability by comparing the development of students' innovative ability after implementing with 4 times, and 2) compare students' satisfaction after implementing the Photoshop course based on task-based learning and Gagne's teaching model. Materials and Methods: The sample was a class of 37 first-year students, majoring in product design at Anhui Wenda University of Information Engineering. They were derived by cluster random sampling. The instruments used in this research were (1) a Photoshop course based on task-based learning and Gagne's teaching model, (2) lesson plans, (3) an innovative ability evaluation form with 0.89 reliability, and 4) a student satisfaction questionnaire with 0.98 reliability. The statistics used to analyze the data included the mean, standard deviation, one-way repeated measures ANOVA, and t-test for one sample. Results: Mauchly's test indicated a violation of sphericity, requiring Greenhouse-Geisser corrections. Significant improvements in students' innovative ability were observed across four-time points (F=251.830, p<0.001). Additionally, student satisfaction with the Photoshop course significantly exceeded the set criterion (t = 97.4, p < 0.05). Conclusion: The implementation results show that the difference in students' innovation ability each time different at the 0.05 level of significance, they were considered the students’ innovation ability was increasingly higher and continuously each time from the first time to the last time, students’ satisfaction was at a high level.

InnovatEd

The Melbourne InnovatEd program at the University of Melbourne supports innovation in educational technology, addressing the crucial need for innovative solutions in teaching and learning in higher education. By fostering a holistic approach, InnovatEd empowers educators and students to identify problems and devise impactful solutions. Established in 2019, the program offers a Bootcamp and an Incubation Funding Program, providing space, tools, support, and funding pathways. The Bootcamp engages participants in developing innovation skills, while the Incubation Program offers tailored support to bring ideas to fruition. Over five years, InnovatEd has supported 23 teams, enhancing teaching and learning through EdTech. The program has successfully cultivated connections within the University and industry, facilitating real-world project development and fostering an entrepreneurial mindset. Innovation programs in large organisations, such as InnovatEd, face challenges such as ensuring technology sustainability, providing post-program support, and addressing cultural barriers. InnovatEd aims to establish robust support structures and promote continuous innovation, essential for improving education in the age of AI.

FunFuzz : Greybox Fuzzing with Function Significance

Greybox fuzzing is dedicated to revealing software bugs by maximizing code coverage. Concentrating on code coverage, greybox fuzzing effectively exposes bugs in real-world programs by continuously executing the program under test (PUT) with the test inputs generated from initial seeds, making it a popular software testing technique. Although powerful, the effectiveness of greybox fuzzing can be restricted in some cases. Ignoring the significant degrees of executed functions, traditional greybox fuzzing usually fails to identify significant seeds that execute more significant functions, and thus may assign similar energy to significant and trivial seeds when conducting power scheduling. As a result, the effectiveness of greybox fuzzing can be degraded due to wasting too much energy on trivial seeds. In this paper, we introduce function significance (FS) to measure the significant degrees of functions. Our key insight is that the influential functions that connect to many other functions are significant to greybox fuzzing as they provide more probabilities to reach previously unexplored code regions. To quantify FS, we conduct influence analysis upon the call graphs extracted from the PUTs to obtain the centrality values of function nodes. With FS as the significance measurement, we further propose FunFuzz , an FS-aware greybox fuzzing technique, to optimize significant seeds and tackle the aforementioned restriction. To this end, FunFuzz dynamically tracks the functions executed by a seed during fuzzing, and computes the significance score for the seed by accumulating the FS values of the functions executed by it. Based on the computed FS values, FunFuzz then takes an estimation-based power scheduling to assign more (or less) energy to seeds that achieve over-estimated (or under-estimated) significance scores. Specifically, the seed energy is adjusted by multiplying with a scale factor computed regarding the ratio of the actual significance score achieved by executing the seed and the estimated significance score predicted by a linear model constructed on-the-fly. To evaluate FunFuzz , we prototype it on top of AFL++ and conduct experiments with 15 programs, of which 10 are from common real-world projects and five are from Magma, and compare it to seven popular fuzzers. The experimental results obtained through fuzzing exceeding 40,800 CPU hours show that: (1) In terms of covering code, FunFuzz outperforms AFL++ by achieving 0.1% \(\sim\) 18.4% more region coverage on 13 out of 15 targets. (2) In terms of finding bugs, FunFuzz unveils 114 unique crashes and 25 Magma bugs (which are derived from CVEs) in 20 trials of 24-hour fuzzing, which are the most compared to the competitor fuzzers and include 32 crashes and 1 Magma bug that the other fuzzers fail to discover. Besides the experiments focusing on code coverage and bug finding, we evaluate the key components of FunFuzz , namely the FS-centered estimation-based power scheduling and the lazy FS computation mechanism. The extensive evaluation not only suggests FunFuzz ’s superiority in code coverage and bug finding, but also demonstrates the effectiveness of the two components.

P200 Retrospective observational study on patients with metastatic renal cell carcinoma treated in first line with Pembrolizumab + Axitinib: A real world project of the campania oncology network

P200 Retrospective observational study on patients with metastatic renal cell carcinoma treated in first line with Pembrolizumab + Axitinib: A real world project of the campania oncology network

Summer School in Computational Physiology: A Collaborative Course in Modeling Excitable Tissues

ABSTRACT Computational modeling of physiology is a multiscale, multidisciplinary field requiring a diverse set of skills and backgrounds. The Simula Summer School in Computational Physiology aims to teach graduate students from around the world practical methods for multiscale modeling of excitable tissues (specifically, the heart and brain). This joint summer school is collaboratively based on the complementary expertise and shared educational goals of three institutions: Simula Research Laboratory, the University of Oslo, and the University of California San Diego. The summer school’s core goal is to promote successful research collaboration among the host institutions and to train excellent computational researchers. Keys to the success of this type of school include sustainable funding, close mentorship, and innovative, adaptive teaching tools. Using a combination of lectures, hands-on programming modules, and real-world projects in small groups with experienced scientific mentors, this unique program is an immersive way for young scientists to develop skills and network with future peers from around the world.

Spectral Subtraction Based Weighted Function for Pitch Extraction in Noisy Speech

Many speech-related works employ the pitch period as a crucial component. Speech signals are typically collected in challenging noisy settings for real-world projects. Therefore, it is now more important than ever for the algorithm to be noise resistant in order to estimate pitch accurately. However, when dealing with noisy speech files at a low signal-to-noise ratio (SNR) value, many state-of-the-art algorithms are unable to produce satisfactory results. In this work, a new noise-resistant pitch estimation algorithm based on spectral subtraction is presented, which uses a weighted function to lessen the impact of the vocal tract effect. Furthermore, to enhance the correlation between the pitch estimates and smoothen the pitch contours, we employ a weighted function that combines the spectral subtraction-based technique as the numerator and the circular average magnitude difference function (CAMDF) as the denominator. We have utilized two noisy speech databases using seven different kinds of recorded ambient noise, and we evaluated our system against three cutting-edge methods. The suggested method lowers the Gross Pitch Error (GPE) rate at practically all SNRs in white noise and performs best on the NTT and KEELE databases.

Scalability and Replicability Analysis in Smart Grid Demonstration Projects: Lessons Learned and Future Needs

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects.

Validation of the 2019 American Society of Colposcopy and Cervical Pathology Online Cervical Cancer Screening Program via 9 Large-Cohort Data of Chinese Women.

The aim of the study is to validate the applicability and performance of the 2019 US risk-based guideline for Chinese women. We analyzed 10,055 cases with data on human papillomavirus testing, cytology, and pathologically confirmed diagnosis (analysis-set). According to the 2019 US risk-based guideline, we recorded the risk value and triage recommendations for each case. Then, we assessed the concordance of the guideline triage recommendations with pathology diagnosis for the relevant case from the real-world projects. Among the analysis-set, 9,495 cases with an estimated risk value were identified as analysis cohort while the remaining 560 cases were cataloged as "special cases." Among the analysis cohort, 960 and 526 cases were pathologically confirmed as cervical intraepithelial neoplasia (CIN) 2+ and CIN3+, respectively. The US risk-based guideline recommended colposcopy or more aggressive interventions (Colp+) for 86.8% (833/960) of the CIN2+ and 95.8% (504/526) of the CIN3+ cases, with 87.1% sensitivity and 82.5% specificity for CIN3+ cases (AUC = 0.926, p < .0001). The US risk-based recommended no-Colp for 98.0% (6,142/6,269) of the pathologically confirmed CIN1 or benign cases. In addition, 97.3% (545/560) of the "special cases" cases were recommended as Colp+. The 2019 US risk-based guideline works well with satisfied clinical sensitivity for CIN2+ and CIN3+ and seems applicable for cervical cancer screening in China.

Non-termination Proving at Scale

Program termination is a classic non-safety property whose falsification cannot in general be witnessed by a finite trace. This makes testing for non-termination challenging, and also a natural target for symbolic proof. Several works in the literature apply non-termination proving to small, self-contained benchmarks, but it has not been developed for large, real-world projects; as such, despite its allure, non-termination proving has had limited practical impact. We develop a compositional theory for non-termination proving, paving the way for its scalable application to large codebases. Discovering non-termination is an under-approximate problem, and we present UNTer, a sound and complete under-approximate logic for proving non-termination. We then extend UNTer with separation logic and develop UNTer SL for heap-manipulating programs, yielding a compositional proof method amenable to automation via under-approximation and bi-abduction. We extend the Pulse analyser from Meta and develop Pulse ∞ , an automated, compositional prover for non-termination based on UNTer SL . We have run Pulse ∞ on large codebases and libraries, each comprising hundreds of thousands of lines of code, including OpenSSL, libxml2, libxpm and CryptoPP; it discovered several previously-unknown non-termination bugs and have reported them to developers of these libraries.

StarMalloc: Verifying a Modern, Hardened Memory Allocator

We present StarMalloc, a verified, efficient, security-oriented, and concurrent memory allocator. Using the Steel separation logic framework, we show how to specify and verify a multitude of low-level patterns and delicate security mechanisms, by relying on a combination of dependent types, SMT, and modular abstractions to enable efficient verification. We produce a verified artifact, in C, that implements the entire API surface of an allocator, and as such works as a drop-in replacement for real-world projects, notably the Firefox browser. As part of StarMalloc, we develop several generic datastructures and proof libraries directly reusable in future systems verification projects. We also extend the Steel toolchain to express several low-level idioms that were previously missing. Finally, we show that StarMalloc exhibits competitive performance by evaluating it against 10 state-of-the-art memory allocators, and against a variety of real-world projects, such as Redis, the Lean compiler, and the Z3 SMT solver.

Developing a Robust Multi-Skill, Multi-Mode Resource-Constrained Project Scheduling Model with Partial Preemption, Resource Leveling, and Time Windows

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim’s robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity.

FOBICS: Assessing project security level through a metrics framework that evaluates DevSecOps performance

Context:In today’s software development landscape, the DevSecOps approach has gained traction due to its focus on the software development process and bolstering security measures in projects, a task in light of the ever-evolving cybersecurity threats. Objective:This study aims to address the lack of metrics for quantitatively assessing its efficacy from both security and business logic perspectives. Methods:To tackle this issue, the research introduces the Framework of Business Index Concerning Security (FOBICS), a set of metrics designed to enable transparent evaluations of project security. FOBICS considers various perspectives relevant to DevSecOps practices. It includes factors such as project duration and financial outcomes, making it appealing for implementation in business settings. Results:The effectiveness of FOBICS is validated theoretically and empirically via its application in two real-world projects: the results from these implementations show a correlation between FOBICS metrics and the security strategies employed as the development methodologies adopted by diverse teams throughout the projects. Conclusion:Hence, FOBICS emerges as a tool for assessing and continuously monitoring project security, offering insights into areas of strength and areas that may require enhancement. FOBICS is shown to be effective in assessing the level of DevSecOps implementation. The ease of calculating FOBICS metrics makes them easily interpretable and continuously verifiable. Moreover, FOBICS summarizes most of the other quantitative and qualitative metrics in the literature.

Infrared non-uniformity correction method based on binocular detection system without blindsight

The output image of an infrared detector often suffers from non-uniformity due to the inhomogeneity of the device material and limitations in the manufacturing process, which can reduce the detector's capability to detect and identify targets. It is therefore necessary to adequately correct the non-uniformity to fully utilize the high temperature sensitivity of the detector. Calibration-based non-uniformity correction technology is one of the earliest and most effective methods of correcting non-uniformity. Typically, a relatively uniform shutter is used to mask the field of view for calibration correction. However, this process interrupts the observation due to the need for masking, severely limiting its use in real-world projects. In order to maintain the performance superiority of the calibration-based method and eliminate the defect of target loss caused by masking, this paper takes the occlusion calibration as the theoretical basis, which consists of dual detectors to form a binocular detection system, and uses one of the occluded calibration auxiliary detector to assist the other occlusion-free primary detector, and takes advantage of the fact that the radiation is the same when observing the same target at the same time to make the correct response based on the steepest gradient descent learning algorithm, which removes the non-uniformity while achieving detail fidelity, no ghosting and continuous output.

Implementation of a Practical Environmental Sustainable Design Project in Interior Architectural Education: A Case Study at the University of Jordan

Objectives: The purpose of this study is to discuss the implementing of a practical project as a new learning methodology to help students develop expertise in the functional, aesthetic, and creative aspects of environmental design. Methods: The research is qualitative in design and based on a case-study approach, in which sixty-three environmental practical projects were explored. The examined design projects were conducted by interior design students at the department of visual arts of the University of Jordan as part of an environmental design course during three semesters in 2022. Results: Nine themes emerged from the study, which were interpreted based on the selecting and processing of materials (either recycled or adapted from the local environment). These materials comprised recycled plastic, recycled paper, recycled metal, recycled wood, recycled glass, recycled threads, resin, ceramic, and plants. The study also found that working with and processing local or recycled materials in practical projects within the study curriculum helped students acquire skills and gain confidence in relation to environmental practice, and increased their awareness of the different usages of materials and their advantages from both a functional and aesthetic perspective. Conclusions: The significance of the study lies in its adoption of a practical approach as a new active learning methodology within art and design educational programmes to meet the current educational standards stipulated by the Ministry of Higher Education and Scientific Research in Jordan. Real-world projects foster students’ development of critical creativity to achieve the ultimate principles of environmental design and prepare them to deal with real challenges within the professional job market.

A Study on Offshore Anchor Selection with a Focus on Torpedo Anchor Stability and Performance

The global pursuit of renewable energy has significantly accelerated offshore wind energy development. Floating offshore wind turbines (FOWTs) are increasingly in the spotlight as they offer superior capabilities for harnessing abundant wind resources in deep-water areas, outperforming traditional fixed-bottom turbines. The deployment and station-keeping of these floating structures are critically dependent on robust mooring systems and the precise selection of anchoring solutions. This paper provides an overview of various anchors, including driven and suction caissons, torpedo, and drag anchors, and their applications in real-world projects. These commonly used anchors were discussed in relation to mooring types, soil conditions, and expected bearing capacities. Torpedo anchor installation failures have been reported in cases where excessive tilt angles occur during deployment. This motivates us to present an in-depth study on the importance of directional stability during installation of torpedo anchors. The study will utilize computational fluid dynamics Star-CCM+ 2402 to analyze and assess the impact of the center of gravity configuration on stabilization. Additionally, theoretical formulas will be used to estimate the holding capacity that the torpedo anchor can provide. These insights are designed to assist wind energy developers in making informed and effective anchoring decisions for floating wind turbine projects.

Innovations in recycled construction materials: paving the way towards sustainable road infrastructure

The expansive development of infrastructure has led to increased consumption of virgin aggregates in road construction, resulting in significant environmental impacts. To address this issue, there is a pressing need for sustainable alternatives that utilize recycled materials in pavement applications. This paper presents a comprehensive review of a decade-long research program focused on the development and evaluation of sustainable pavement materials, such as recycled and waste aggregates, industrial by-products, and natural fibers. The research encompassed a wide range of innovative materials and technologies, such as geopolymer-stabilized recycled aggregates, cement-stabilized waste materials, natural additive-modified cement stabilization, and recycled aggregate-geogrid reinforcement systems. The experimental framework employed a combination of mechanical testing, durability assessment, microstructural analysis, and environmental safety evaluation to assess the performance and sustainability of these materials. The key findings demonstrated the superior mechanical properties, improved durability, and environmental suitability of the recycled materials compared to conventional virgin aggregates. The successful implementation of these sustainable solutions in real-world projects highlights their potential to reduce the environmental footprint of road infrastructure development. Furthermore, the paper discussed the practical implications of the research outcomes for pavement design and construction, as well as future research directions to further advance the field of sustainable pavement engineering. The findings of this research report can be used as guidance for researchers, practitioners, and policymakers seeking to upcycle the widespread adoption of recycled materials in road application and contribute to the development of a more sustainable and resilient transportation infrastructure.

Building Student Sustainability Competencies through a Trash-Practice Nudge Project: Service Learning Case Study in Kuwait

This discussion describes an experimental behavioral economics class implemented in a service learning format. Students implemented two nudge interventions to influence public trash behavior, which is an issue throughout the Middle East/North Africa (MENA). The aim in one project was to encourage more use of trash bins in a multi-screen theater and in the other to sort plastic, glass, and paper when throwing trash in the public bins at a university. These two organizations paid the implementation costs, which were quite low, as common for most nudges. The class was co-taught by two university faculty and several personnel from governmental and UN offices responsible for building nudge capabilities in Kuwait. In each case, results in the student projects demonstrated that nudging resulted in a substantial improvement. Students gained strong competencies in applying sustainability principles to a specific problem and in carrying out a real-world project. They learned the importance of collaborating with stakeholders and got to see that their work was actually used in policy formation by the Kuwait government and the UNDP’s Kuwait office.

Modeling hydrogen markets: Energy system model development status and decarbonization scenario results

Hydrogen can be used as an energy carrier and chemical feedstock to reduce greenhouse gas emissions, especially in difficult-to-decarbonize markets such as medium- and heavy-duty vehicles, aviation and maritime, iron and steel, and the production of fuels and chemicals. Significant literature has been accumulated on engineering-based assessments of various hydrogen technologies, and real-world projects are validating technology performance at larger scales and for low-carbon supply chains. While energy system models continue to be updated to track this progress, many are currently limited in their representation of hydrogen, and as a group they tend to generate highly variable results under decarbonization constraints. The present work provides insights into the development status and decarbonization scenario results of 15 energy system models participating in study 37 of the Stanford Energy Modeling Forum (EMF37), focusing on the U.S. energy system. The models and scenario results vary widely in multiple respects: hydrogen technology representation, scope and type of hydrogen end-use markets, relative optimism of hydrogen technology input assumptions, and market uptake results reported for 2050 under various decarbonization assumptions. Most models report hydrogen market uptake increasing with decarbonization constraints, though some models report high carbon prices being required to achieve these increases and some find hydrogen does not compete well when assuming optimistic assumptions for all advanced decarbonization technologies. Across various scenarios, hydrogen market success tends to have an inverse relationship to success with direct air capture (DAC) and carbon capture and storage (CCS) technologies. While most model-scenario combinations predict modest hydrogen uptake by 2050 – <10 million metric tons (MMT) – aggregating the top 10 % of market uptake results across sectors suggests an upper range demand potential of 42–223 MMT. The high degree of variability across both modeling methods and market uptake results suggests that increased harmonization of both input assumptions and subsector competition scope would lead to more consistent results across energy system models. The wide variability in results indicates strongly divergent conclusions on the role of hydrogen in a decarbonized energy future.

DetectVul: A statement-level code vulnerability detection for Python

Detecting vulnerabilities in source code using graph neural networks (GNN) has gained significant attention in recent years. However, the detection performance of these approaches relies highly on the graph structure, and constructing meaningful graphs is expensive. Moreover, they often operate at a coarse level of granularity (such as function-level), which limits their applicability to other scripting languages like Python and their effectiveness in identifying vulnerabilities. To address these limitations, we propose DetectVul, a new approach that accurately detects vulnerable patterns in Python source code at the statement level. DetectVul applies self-attention to directly learn patterns and interactions between statements in a raw Python function; thus, it eliminates the complicated graph extraction process without sacrificing model performance. In addition, the information about each type of statement is also leveraged to enhance the model’s detection accuracy. In our experiments, we used two datasets, CVEFixes and Vudenc, with 211,317 Python statements in 21,571 functions from real-world projects on GitHub, covering seven vulnerability types. Our experiments show that DetectVul outperforms GNN-based models using control flow graphs, achieving the best F1 score of 74.47%, which is 25.45% and 18.05% higher than the best GCN and GAT models, respectively.