Hasil untuk "Engineering economy"

Al Muttakin, Saikat Mondal, Chanchal K. Roy

Replication packages are crucial for enabling transparency, validation, and reuse in software engineering (SE) research. While artifact sharing is now a standard practice and even expected at premier SE venues such as ICSE, the practical usability of these replication packages remain underexplored. In particular, there is a marked lack of studies that comprehensively examine the executability and reproducibility of replication packages in SE research. In this paper, we aim to fill this gap by evaluating 100 replication packages published in ICSE proceedings over the past decade (2015 - 2024). We assess the (1) executability of the replication packages, (2) efforts and modifications required to execute them, (3) challenges that prevent executability, and (4) reproducibility of the original findings for those that are executable. We spent approximately 650 person-hours in total to execute the artifacts and reproduce the study findings. Our analysis shows that only 40 of the 100 evaluated artifacts were fully executable. Among these, 32.5% ran without any modification. However, even executable artifacts required varying levels of effort: 17.5% required low effort, while 82.5% required moderate to high effort to execute successfully. We identified five common types of modifications and 13 challenges that lead to execution failure, encompassing environmental, documentation, and structural issues. Among the executable artifacts, only 35% (14 out of 40) reproduced the original results. These findings highlight a notable gap between artifact availability, executability, and reproducibility. Our study proposes three actionable guidelines to improve the preparation, documentation, and review of research artifacts, thereby strengthening the rigor and sustainability of open science practices in SE research.

Huijuan Li, Shuni Xu, Yawen Liu et al.

Under China’s dual circulation strategy, traditional industries that are highly dependent on international supply chains face significant challenges requiring restructuring. This study develops a multi-scale analytical model that integrates the Global Trade Analysis Project (GTAP) with the enormous regional model (TERM), enhanced by sectoral decomposition techniques, to enable a detailed assessment of how free trade agreements (FTAs) influence spatial and industrial reconfigurations. Using China’s paper industry as a case study, we systematically examine how the Regional Comprehensive Economic Partnership (RCEP) reshapes domestic and global value chain dynamics. The analysis reveals three key structural changes: (1) upstream sectors exhibit material substitution, with increased imports of wood and non-wood pulp while domestic waste pulp production grows, signaling a strategic reallocation of resources; (2) midstream sectors, particularly in corrugated board and paperboard production, demonstrate geographic shifts, and coastal clusters are relocating capacity to RCEP partner countries, while inland areas see capacity growth driven by regional policy incentives; and (3) downstream industries benefit from upstream agglomeration effects, leading to a 5 % to 30 % increase in paper product output in Guangdong, Jiangsu, Zhejiang, and Anhui provinces. Quantitatively, the RCEP generates synergistic gains through international specialization and domestic clustering, contributing to a 0.33 % annualized compound growth rate in sectoral output. These findings empirically support the effectiveness of industrial transition strategies, suggesting three policy recommendations: (1) establishing strategic reserves for critical raw materials; (2) developing differentiated regional industrial roadmaps; and (3) leveraging agglomeration economies to optimize domestic production networks. Methodologically, this study advances computable general equilibrium (CGE) models in assessing the impacts of FTAs on industrial restructuring, offering actionable insights for globally dependent industries undergoing strategic transitions.

Nader Marzban, Marios Psarianos, Christiane Herrmann et al.

Integrated biorefineries play a transformative role in sustainable development by converting biomass and biogenic residues into high-value products while minimizing waste, emissions, and resource inefficiencies. This review explores innovations in biorefinery processes, emphasizing the synergy between thermochemical, biochemical, and biological technologies such as pyrolysis, fermentation, anaerobic digestion, hydrothermal carbonization, and algae and insect systems. Recent advancements, including hydrothermal humification and fulvification, enhance nutrient recovery, carbon sequestration, and near-zero waste production by generating artificial humic substances. Smart integrated biorefineries and the sustainable and circular bioeconomy systems are introduced as frameworks that promote synergy, interconnectivity, and resource optimization. These concepts emphasize that biomass valorization should be maximized before its final use. Biochar plays a multifaceted role beyond carbon sequestration. Rather than premature burial, it can be derived from fermented residues for lactic acid production or used to enhance fermentation and methane yields in anaerobic digestion. Additionally, nutrient-loaded biochar serves as a slow-release fertilizer, mitigating runoff, and GHG emissions. Meanwhile, heat from biochar production can generate electricity, and CO₂ emissions can support algae cultivation. Bio-oil, another byproduct, can be upgraded into platform chemicals, forming a closed-loop system that optimizes biomass utilization and minimizes environmental impact. Conventional biomass treatment methods, such as incineration, combustion, and composting, waste valuable resources and contribute to environmental degradation. Instead, a closed-loop, self-optimizing approach ensures full biomass utilization while addressing planetary boundaries. By integrating machine learning, digital twins, and decision-support systems, smart integrated biorefineries enhance resource efficiency, adapt to market demands, and accelerate the transition to a low-carbon, resource-efficient future.

Yiting Qiu, Adnan Khan, Danish

Nuclear energy is increasingly recognized as a critical component of circular economy frameworks due to its capacity to provide a stable, low-carbon energy source. Reducing dependency on fossil fuels promotes sustainable practices and aligns with circular economy goals such as resource efficiency, pollution reduction, and waste minimization. The existing literature has primarily focused on the contribution of nuclear energy to decarbonization, whereas the potential of nuclear energy in facilitating a circular economy has been largely neglected. In light of this context, this paper explores the impact of nuclear energy on the circular economy, thereby offering strong econometric evidence. The study used the advanced econometric tool Dynamic Auto-Regressive Distributive Lag (DYNARDL) method for empirical estimation to obtain long- and short-run estimates. The regression estimates, derived from a sample of China spanning 1990 to 2017, support the hypothesis that nuclear energy negatively impacts the circular economy in both the long- and short-run. Advanced econometric tests confirm the stability of the models, homoscedasticity, and the absence of serial correlation, ensuring the reliability of our findings. The study emphasizes the importance of policy strategies, including expanding nuclear energy adoption, advancing environmental technologies, and the effective use of nuclear energy by integrating comprehensive datasets and methodologies; this paper provides a foundation for scalable and equitable solutions as China moves toward a greener and more sustainable future.

F. Fiore, M. Elvis

Will it be possible in the future to realize large, complex space missions dedicated to basic science like HST, Chandra and JWST? Or will their cost be too great? Today's space scene is completely different from that of even five years ago, and certainly from that of the time when HST, Chandra and JWST were conceived and built. Space-related investments have grown exponentially in recent years, with a monetary investment exceeding half a trillion dollars per year since 2023. This boom is greatly aided by the rise of the so-called 'new space' economy driven by private commercial funding, which for the first time last year surpassed public investments in space. The establishment of a market logic to space activities results in more competition and a resulting dramatic cost and schedule reduction. Can space science take advantage of the benefits of the new space economy to reduce cost and development time and at the same time succeed in producing powerful missions in basic science? The prospects for Europe and the United States are considered here. We argue that this goal would be achievable if the scientific community could take advantage of the three pillars underlying the innovation of the new space economy: (1) technology innovation proceeding through both incremental innovation and disruptive innovation, (2) business innovation, through vertical integration, scale production, and service-oriented business model, and (3) cultural innovation, through openness to risk and iterative development.

Владислав Ярещенко, Віктор Косенко

Об’єктом дослідження в статті є технологія Network-on-Chip (NoC), яка стала популярним вибором для внутрішньокристалічної комунікаційної архітектури сучасних пристроїв System-on-Chip (SoC). Предмет дослідження – методи зниження розсіюваної потужності в NoC і SoC. Мета роботи: розроблення методу кодування з низьким енергоспоживанням, що дає змогу ефективно передавати або зберігати інформацію. У статті розв’язуються такі завдання: аналіз методів класифікації комбінаторних структур, побудова системи типових представників класів еквівалентності та аналіз їх характеристик. Методи дослідження основані на використанні теорії множин, теорії систем і комбінаторики. Досягнуті результати. Проаналізовано фактори, що впливають на розсіювану потужність, і принципи побудови енергоефективних кодів. Показано, що комутаційна активність сприяє значній долі загальної потужності та методи кодування з низьким енергоспоживанням є ефективними для зниження комутаційної активності під час зв’язку між пристроями або зв’язку на кристалі. Розроблено метод ієрархічної класифікації кодів єдиного розташування та алгоритми розв’язання поетапних задач. В основі методу лежить інваріантний підхід і побудова системи різних представників. Отримано оцінки їх кількості, визначено характеристики, сформовано каталоги типових представників. Висновки. У статті проаналізовано фактори, що впливають на розсіювану потужність, розглянуто принципи побудови енергоефективних кодів. Розроблено метод ієрархічної класифікації кодів єдиного розташування та алгоритми розв’язання поетапних задач, сформовано каталоги типових представників. Застосування розробленого методу дасть змогу розробникам аналізувати й обирати коди з кращими властивостями та, зрештою, отримувати кращі результати щодо мережних затримок, витрат на електроенергію та інших конструктивних обмежень для комп’ютерних систем.

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar et al.

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

Yevheniia Leha, Serhii Liashenko

У сучасному світі використання цифрових технологій у навчальному процесі стає все більш актуальним явищем. У зв’язку з цим оцінювання цифрової інфраструктури закладів вищої освіти є надзвичайно важливим. Для цього необхідно застосовувати відповідні методи й алгоритми, що дають змогу визначати якість цифрової інфраструктури та її ефективність у навчанні. З метою підвищення якості освіти й забезпечення максимальної доступності цифрової інфраструктури важливо проводити належне оцінювання. Предметом дослідження є методи та алгоритми оцінювання цифрової інфраструктури вишів. Мета роботи – аналіз методів оцінювання цифрової інфраструктури закладів вищої освіти та створення універсального підходу для вишів на базі проаналізованих прикладів. У статті вирішуються конкретні завдання: аналіз наукових джерел щодо методів і алгоритмів оцінювання рівня цифрової інфраструктури; розгляд основних методів, що використовуються для схожих завдань в Україні та світі; визначення наявних критеріїв і показників оцінювання; розроблення уніфікованих критеріїв, з огляду на специфіку кожного закладу вищої освіти; створення методу оцінювання за певними критеріями та вибір методів для виявлення значущих критеріїв; розроблення алгоритму для оцінювання критеріїв та отримання загальної оцінки; аналіз здобутих результатів. Для досягнення мети й вирішення поставлених завдань застосовані такі методи: науковий аналіз, аналіз ієрархій та аналіз експертних оцінок. Основним результатом дослідження є розроблення універсального методу й алгоритму оцінювання цифрової інфраструктури закладів вищої освіти, що дають змогу ефективно та більш об’єктивно визначати рівень розвитку цифрової інфраструктури у вишах і порівнювати різні навчальні заклади. Висновки: застосування методу допоможе порівнювати заклади вищої освіти з огляду на особливість кожного з них і дасть змогу більш об’єктивно оцінити цифрову інфраструктуру навчальних закладів.

Daniel Badawi, Eduardo Gildin

Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-informed neural networks (PINN) leverage the fact that neural networks are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations. The transient diffusivity equation is a fundamental equation in reservoir engineering and the general solution to this equation forms the basis for Pressure Transient Analysis (PTA). The diffusivity equation is derived by combining three physical principles, the continuity equation, Darcy's equation, and the equation of state for a slightly compressible liquid. Obtaining general solutions to this equation is imperative to understand flow regimes in porous media. Analytical solutions of the transient diffusivity equation are usually hard to obtain due to the stiff nature of the equation caused by the steep gradients of the pressure near the well. In this work we apply physics-informed neural networks to the one and two dimensional diffusivity equation and demonstrate that decomposing the space domain into very few subdomains can overcome the stiffness problem of the equation. Additionally, we demonstrate that the inverse capabilities of PINNs can estimate missing physics such as permeability and distance from sealing boundary similar to buildup tests without shutting in the well.

Fabio Calefato, Luigi Quaranta, Filippo Lanubile et al.

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.

Tao Chen, Miqing Li

Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.

A. Abdul Kareem, G. Yoganandham

India is known around the world for its diverse civilizations and mystical rituals. Scholars and philosophers of the time formed a century-old tradition in the depths of this culture. Despite a long history of being viewed as unscientific, scientists and doctors are now aware of the benefits of traditional Indian health care. Many investigations on traditional medicine and its apparently magical qualities in the treatment of terminal diseases are currently being done. Home remedies are used all around the world, but they are recognized as science in India only. Two traditional Indian medicinal traditions: Ayurveda and Siddha are progressively gaining traction in the global healthcare business. In this article, some of India’s most odd and effective medicinal practices, as well as the benefits of each therapy will be reviewed. Throughout history, traditional medicines were the only source of primary healthcare, and they made a substantial contribution. Knowledge of how to use medicinal plants to treat various ailments was highly valued by ancient cultures. Until the mid-nineteenth century, plants were the principal therapeutic agents used by humans, and they continue to play an important role in pharmaceutical formulations. Traditional medicine is used by around 80 percent of people in undeveloped countries for their primary health care needs because of its low prices, effectiveness, frequently restricted availability of modern medicine, and cultural and religious preferences. Plant research in the traditional system of medicine is becoming increasingly significant in the development of global healthcare and conservation efforts. Traditional medicine systems are being used to uncover biologically active chemicals that are useful to the pharmaceutical industry. To this end, as much information possible is presented about these areas in this article. There are a number of geographically specific traditional health behaviors and are well reviewed in this paper.

Jianbin Chen, Chengyu Yang, Zou Jianjun

The two-stage converter has become a popular off-board topology for high power charging due to its high efficiency and economy. However, a series of performance degradations caused by operating frequencies which deviate from the resonant frequency are its most significant disadvantage. This drawback has become a particularly significant challenge in industrial robot charging applications. This paper proposes a linear segmented voltage control strategy that allows the voltage to be manipulated in segments depending on the state of charge. It allows for a wider voltage range while achieving excellent control accuracy and compelling operational performance. Not only is the implementation of this control strategy discussed, but engineering issues such as which adjustments to the parameters of the LLC resonant tank are required are also analysed. In addition, it is discussed how the PFC module should be adjusted for the output voltage under this control strategy. Finally, a prototype with a maximum output power of 20 kW is produced to verify the various performance improvements of this strategy in high-power charging applications.

Shanghai Dong, Liying Wang, Weiyi Lou et al.

For the first time, piezocatalysis activity has been observed in bismuth-based MOFs (ultrasound vibration treatment) with two different morphologies, namely FCAU-17 (flakes) and CAU-17 (rods). CAU-17 and FCAU-17 were synthesized by solvothermal and ultrasonic methods, respectively, with the same organic ligand (1,3,5-benzenetricarboxylic acid) and metal salt (Bi(NO3)3·5H2O). Among these, the apparent rate constant k of CAU-17 in piezo-photocatalysis is 3.9 × 10−2 min−1, which is ∼3.9 and ∼ 1.5 times of those in photocatalysis and piezocatalysis, respectively. CAU-17 showed much high piezo-photocatalytic activity during degradation of RhB. Efficiently coupling between piezocatalysis and photocatalysis has been realized in rod-like CAU-17 (ultrasound vibration treatment). Our results provide a new strategy to improve catalytic performance of Bi MOFs through an efficient synergistic piezo-photocatalysis approach for environmental remediation.

Wen-Tao HU, Kai TIAN, Jia-hong LI et al.

Some beneficiation circulating water contains excess highly dispersed suspended particles, which are difficult to clarify only by simple concentration and sedimentation and cannot meet the requirements of reuse. To solve this problem, a clarification device was developed for removing the solid suspended matter from beneficiation circulating water, which consists of a hydraulic circulation area and a particle sedimentation area and integrating mixing, flocculation, and sedimentation. The flow field inside the gadget has a big influence on how well it works. The structural and operating parameters of the gadget were improved using the computational fluid dynamics approach to increase the device’s performance. A two-dimensional physical model of the deep clarification device for beneficiation circulating water was established. Numerical simulation research on its main structural parameters and operating parameters were conducted by using software Fluent and choosing the Mixture multiphase flow model and RNG k‒ε turbulence model. The effects of feed water nozzle length, throat to nozzle diameter ratio, sludge settling area opening size, and device diameter on the internal flow field were investigated. The average turbulent kinetic energy in the sludge settling zone can be reduced by reducing the length of the nozzle in the hydraulic circulation region, increasing the ratio of the throat to nozzle diameter and the opening size of the sludge settling area, and increasing the diameter of the device. Due to the fact that the turbulent kinetic energy is the kinetic energy of fluid produced by turbulent pulsation, the turbulent degree of the flow field in the sludge settling area is reduced, the effect of turbulent flow in the flow field on particle settling is weakened, and the removal effect of the device on suspended particles is improved. Simultaneously, it is found that at the same suspended solids concentration, reducing the inlet flow rate or increasing the suspended particle size helps to improve the removal rate of suspended solids. When the inlet flow rate is 0.1 m·s-1 and the coagulated suspended particles form particles with particle size more than 100 μm, the removal effect of slime particles in beneficiation circulating water is remarkable.

Ігор Алєксєєв, Оксана Курило , Павло Гориславець et al.

In the article from the standpoint of sustainable economic development, the introduction of circular economy principles into production activity is considered. A study of relevant publications was carried out, which reflected the latest achievements of science and practice of the leading countries of America, Europe, and Asia, and a typology of circular economy research was compiled. The author's feature of the typology is proposed - the search for directions for the formation and development of a scientific and technological cluster. As a means of implementing circular economy programs, projects, measures, the definition and formation of funding sources for scientific and technological clusters are proposed. The problem of financing research and development in Ukraine in comparison with the countries of Europe, North America and Asia was examined, which showed the impossibility of correcting the situation with the help of budget funding in the coming years. Based on the results of the analysis of the specific weight of research and development costs in relation to GDP, a low level of funding for this area of ​​activity in Ukraine was revealed. The need to involve business circles in the financing of research and development requires the use of such an organizational form of work as technoparks. Taking into account the European integration processes and the world experience of creating technology parks with the participation of universities, or territorially separated structures, as well as existing technology parks in Ukraine, it is proposed to form scientific and technological clusters. Scientific and technological clusters should include technoparks, universities, institutes of the National Academy of Sciences of Ukraine, design and technological structures (institutes, bureaus), and production enterprises. This will allow the most effective use of scientific and engineering potential and production capacity under the matrix system of managing all types of cluster resources in the current conditions. The article proposes the use of methods of analysis, analogy, comparison, induction, statistical, which allowed to thoroughly study the range of issues and draw the appropriate conclusions.

Zhilu Lai, Wei Liu, Xudong Jian et al.

The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.

Catalina Egea-Gilabert, Mario A. Pagnotta, Pasquale Tripodi

In the next decades, the agricultural systems will deal with major challenges linked to the expected population growth, climate changes and necessity of sustainable use of resources able to preserve the environment [...]

Xianglei Chen

The skilled technical workforce (STW) comprises workers in occupations that require significant science, technology, engineering, or mathematics (STEM) skills but not a bachelor’s degree for entry. The United States had over 17 million STW workers in 2017, and is expected to be short about 3.4 million workers who are qualified for the available STW positions by 2022. Despite the important contribution of the STW to the U.S. economy, the policy discourse on the STEM workforce has largely focused on workers with bachelor’s or graduate degrees, overlooking those without a 4-year degree. Consequently, knowledge about the STW is limited. This paper draws on a recently available national data source to provide a close look at STW workers through the lens of U.S. undergraduates who joined the STW after college. Multivariate results indicate that students who held STW jobs after college fared better than those who held nontechnical jobs on a range of employment outcomes, including salary, access to workforce benefits, alignment between college majors and intended careers, and job satisfaction. Multivariate analyses also confirmed that graduating from a less-than-4-year institution, earning a subbaccalaureate credential, and majoring in STEM, healthcare, and such technical fields as manufacturing, construction, repair, and transportation are common paths to STW careers. Despite the benefits of STW employment, however, relatively few students pursued STW jobs after college. Significantly fewer female than male students and fewer Black than White students pursued STW jobs, even after controlling for such factors as major field, type of last institution, STEM credits, and educational attainment. However, post-college STW participation did not differ between Hispanic and White students or vary by students’ family income or their parents’ education attainment.

Stephen G. MacDonell

Software project management makes extensive use of predictive modeling to estimate product size, defect proneness and development effort. Although uncertainty is acknowledged in these tasks, fuzzy inference systems, designed to cope well with uncertainty, have received only limited attention in the software engineering domain. In this study we empirically investigate the impact of two choices on the predictive accuracy of generated fuzzy inference systems when applied to a software engineering data set: sampling of observations for training and testing; and the size of the rule set generated using fuzzy c-means clustering. Over ten samples we found no consistent pattern of predictive performance given certain rule set size. We did find, however, that a rule set compiled from multiple samples generally resulted in more accurate predictions than single sample rule sets. More generally, the results provide further evidence of the sensitivity of empirical analysis outcomes to specific model-building decisions.