Hasil untuk "Environmental engineering"

Klara Borowa, Andrzej Zalewski, Lech Madeyski

The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Kun Huang, Shuoshuo Hu, Yilin Zhou et al.

Introduction: Adult exposure to endocrine-disrupting chemicals (EDCs) may reduce muscle mass and strength; however, few studies considered EDC mixtures and their potential mechanisms. Objectives: We aimed to explore associations of EDC mixtures with adult muscle mass and strength, the modifying effects of diet and exercise, as well as the potential metabolic perturbations through plasma metabolome. Methods: We included 127 adults from a panel study that repeated measures across 3 seasons. We measured 110 EDCs spanning 12 groups in plasma and urine, along with the plasma metabolome. Bayesian kernel machine regression (BKMR), Bayesian weighted quantile sum regression, and quantile-based g-computation were employed to assess the mixture effects and relative contributions. Key EDCs were defined as those with weights exceeding the group average in at least two models. Stratified analyses were employed to investigate the modifying effects of diet and exercise. A meet-in-the-middle (MITM) approach was applied to characterize the underlying mechanisms. Results: BKMR results revealed significant negative associations between 7 EDC groups and both appendicular skeletal muscle mass (ASM) and hand-grip strength (HGS), namely per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, organophosphate pesticides, bisphenols, neonicotinoids, atrazine, and parabens. Three multi-exposure models identified 22 and 17 key EDCs linked to decreased ASM and HGS, respectively. Mixtures of these key EDCs were associated with decreases in both ASM and HGS, with significantly attenuated effects observed among participants with healthy diets or regular exercise. MITM approach identified overlapping pathways linking key EDC mixtures to ASM, including arachidonic acid, linoleic acid, and alpha-linolenic acid metabolism. Key EDC Mixtures were negatively associated with glycocyamine, which was positively associated with ASM. Conclusions: Adult exposure to EDC mixtures was linked to reduced ASM and HGS, whereas healthy diets and regular exercise mitigated such impairment. Downregulated glycocyamine and altered fatty acid metabolism were potential mechanisms underlying the decreased ASM.

Gengyang Li, Mason Peng, Qingguo Huang et al.

Per- and polyfluoroalkyl substances (PFAS) have drawn public concern recently due to their toxic properties and persistence in the environment, making it urgent to eliminate PFAS from contaminated water. Electrochemical oxidation (EO) has shown great promise for the destructive treatment of PFAS with direct electron transfer and hydroxyl radical (⋅OH)-mediated indirect reactions. One of the most popular electrodes is Magnéli phase titanium suboxides. However, the degradation mechanisms of PFAS are still unsure and are under investigation now. The main methodology is the first-principal density functional theory (DFT) computation, which is recently used to explore the degradation mechanisms and interpret by-product formation during PFAS mineralization. From the literature review, the main applications of DFT computation for studying PFAS degradation mechanisms by EO include bond dissociation energy, absorption energy, activation energy, and overpotential η for oxygen evolution reactions. The main degradation mechanisms and pathways of PFAS in the EO process include mass transfer, direct electron transfer, decarboxylation, peroxyl radical generation, hydroxylation, intramolecular rearrangement, and hydrolysis. In the recent 4 years, 11 papers performed DFT computation to explore the possible PFAS degradation mechanisms and pathways in the EO process. This paper’s objectives are to: 1) summarize the main degradation mechanisms of PFAS degradation in EO; 2) review the application of DFT computation for studying PFAS degradation mechanisms during EO; process; 3) review the possible degradation pathways of perfluorooctane sulfonoic acid (PFOS) and per-fluorooctanoic acid (PFOA) during EO process.

Tobias Heimann, Lara-Sophie Wähling, Tomke Honkomp et al.

Bioenergy with carbon capture and storage (BECCS) is a crucial element in most modelling studies on emission pathways of the Intergovernmental Panel on Climate Change to limit global warming. BECCS can substitute fossil fuels in energy production and reduce CO _2 emissions, while using biomass for energy production can have feedback effects on land use, agricultural and forest products markets, as well as biodiversity and water resources. To assess the former pros and cons of BECCS deployment, interdisciplinary model approaches require detailed estimates of technological information related to BECCS production technologies. Current estimates of the cost structure and capture potential of BECCS vary widely due to the absence of large-scale production. To obtain more precise estimates, a global online expert survey ( N = 32) was conducted including questions on the regional development potential and biomass use of BECCS, as well as the future operating costs, capture potential, and scalability in different application sectors. In general, the experts consider the implementation of BECCS in Europe and North America to be very promising and regard BECCS application in the liquid biofuel industry and thermal power generation as very likely. The results show significant differences depending on whether the experts work in the Global North or the Global South. Thus, the findings underline the importance of including experts from the Global South in discussions on carbon dioxide removal methods. Regarding technical estimates, the operating costs of BECCS in thermal power generation were estimated in the range of 100–200 USD/tCO _2 , while the CO _2 capture potential was estimated to be 50–200 MtCO _2 yr ^−1 by 2030, with cost-efficiency gains of 20% by 2050 due to technological progress. Whereas the individuals’ experts provided more precise estimates, the overall distribution of estimates reflected the wide range of estimates found in the literature. For the cost shares within BECCS, it was difficult to obtain consistent estimates. However, due to very few current alternative estimates, the results are an important step for modelling the production sector of BECCS in interdisciplinary models that analyse cross-dimensional trade-offs and long-term sustainability.

Hao He, Timothy P Canty, Russell R Dickerson et al.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM _2.5 observations exceeded 100 µ g m ^−3 , affecting major cities such as New York City and Philadelphia, while many areas lacked PM _2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM _2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM _2.5 observations, with linear regression results of R ^2 ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM _2.5 simulations by up to 40 µ g m ^−3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework’s ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

Courtney Grimes, Ramón A Alvarez

Mugeng Liu, Siqi Zhong, Weichen Bi et al.

Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Zhiwei Zhang, Dawei Pan, Yan Liang et al.

Field determination and ecological risk assessment of dissolved lead (Pb) were performed at two Yellow Sea sites in China using a continuous automated electrochemical system (CAEDS). This CAEDS instrument includes an automatic triple filter sampler and an electrochemical detection water quality analyzer, which might be operated automatically four times daily. The dissolved Pb concentrations varied from 0.29 to 1.57 μg/L in the South Yellow Sea over 16 days and from 0.32 to 2.28 μg/L in the North Yellow Sea over 13 days. During the typhoon and algal bloom periods, the Pb concentration was as high as ten times greater than usual. According to the calculation of contamination factors (C_f) and subsequent analysis, seawater quality was classified as Grade II. Through species sensitivity distribution (SSD) method experiments and ecological risk analysis, an average risk quotient (RQ) below 1 for both areas was obtained, indicating a low-to-moderate ecological risk. This system will be helpful for Pb monitoring and assessment in seawater and contribute to the biogeochemical cycling study of Pb.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

Haider K. AMMASH, Noora N. SHAFFAF

Based on the experimental test results of the authors, this investigation is concerned with the finite element analysis to examine and compare the load values and failure modes of the authors’ results. This research was conducted using the Abaqus software. The experimental work included the fabrication of twelve plate girders with honeycomb and flat web plate corrugation patterns, which were then tested under a single concentrated load at the midspan. According to the corrugation dimension or outer honeycomb web thickness, the honeycomb steel plate web girder is divided into three groups (60 mm, 80 mm and 100 mm). The specimens also involved plate girders with a flat web. The specimens were created with three lengths (600 mm, 1,200 mm and 1,800 mm). The Abaqus software was used in finite element models to simulate the concentrated load. The numerical results demonstrated that the 60 mm thick honeycomb web provides a greater load-bearing capacity and shear strength than other girders. The 20 mm honeycomb corrugation on the steel plate girder indicates the increased and improved shear resistance. The conclusion was that as the width of the corrugation increased, so did the steel web’s ultimate load and shear strength, resulting in a positive relationship between the critical shear buckling load of the web and the moment of inertia at the strong axis. When the dimension of the corrugation increases, the moment of inertia of the Y axis (Iy) decreases; thus, the plate girder will fail with a less critical buckling load (Pcr). Also, it can be concluded that as the steel plate thickness of the honeycomb web increases, the shear resistance increases as well. However, the spacing between the intermediate stiffener or the horizontal spacing of the web panel can enhance the shear resistance of honeycomb web girder if it was decreased due to increasing the action of tension field force that resists the diagonal tension developed at the web panel by the applied midspan concentrated force.

GAO Lan, MAO Huiqin, LU Xi*

Obtaining accurate emissions of methane (CH_4), one of the most important non-carbon-dioxide greenhouse gases, is the basis for formulating and validating emission reduction policies. In terms of shortcomings from the "bottom-up" approach, this study combined the vehicle-based monitoring and the AERMOD atmospheric dispersion modeling system to derive the emission rates and emission factors of main CH_4 sources in one demonstration coal mine in Jincheng city, Shanxi province. After systematically considering the topography, meteorological conditions, and infrastructure distribution of the coal mine, both the mobile and downwind stationary monitoring alternatives were adopted, using a platform equipped with a high-precision greenhouse gas analyzer. Results showed that the simulated CH_4 emission rate of a single ventilation shaft under non-production condition (763 kg/h) was about 15.9% lower than the data provided by the enterprise in production. If ignoring the fugitive emissions, the derived CH_4 emission factor of the coal mine was 15.09 m^3/t, which was 13.8% smaller than that in " bottom-up" inventory, indicating that the working conditions of the coal mine played a large role in CH_4 emissions. One ventilation shaft and two vent stacks in the gas gathering station were the main point sources, and six coal silos were the fugitive sources, the emission factors of which were 8.6 m^3/t( 43%), 6.49 m^3/t (33%) and 4.87 m^3/t (24%), respectively. The traditional "bottom-up" accounting without consideration of fugitive emissions, resulted in a nearly 24% under estimation of CH_4 emissions even under non-production conditions, which could be compensated by the methane quantification method based on vehicle-based monitoring.

Heechan Han, Boran Kim, Kyunghun Kim et al.

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations. HIGHLIGHTS Missing precipitation data is recovered using ANN and RF algorithms.; Air humidity and air pressure have a high correlation with precipitation occurrence.; Both models have high performance in detecting the precipitation occurrence.; ANN model has better performance than the RF model for recovering daily precipitation data in South Korea.;

Hai-Hsuan Cheng, Wan-Sheng Yu, Shu-Chuang Tseng et al.

Abstract According to the Taiwan Water Resources Agency, Ministry of Economic Affairs, the average water demand shortage is 530.6 million m3 yr−1 during the period of 2011 to 2019, and the situation will worsen in the near future due to global climate change. Therefore, reclaimed water has been an important new water source in Taiwan, particularly for industrial consumers such as high-tech industries in Science Parks. In order to meet the targeted reclaimed water supply of 1.32 million m3 d−1 (CMD) in 2031, Taiwan is focusing on two major reclaimed water sources, including reclaimed water from high water-consuming industries and municipal wastewater treatment plants. This report reviews current technologies used for reclaimed water including units for pretreatment, desalting, polishing, and reclamation. Case studies in Taiwan including reclaimed water from high water-consuming industries such as thin film transistor-liquid crystal display (TFT-LCD) and semiconductor industries, as well as from municipal wastewater treatment plants are presented. The TFT-LCD company Innolux and semiconductor company Advaned Semiconductor Engineering have implemented total recycled water system to recycle and reclaim wastewater from manufacturing processes, achieving a total recycled water of 290 million m3 yr−1 with about 97% recovery and 3.5 million m3 yr−1 with 80% recovery, respectively. The Fengshan reclaimed water treatment plant produces 40,436 CMD reclaimed water from municipal wastewater for the China Steel Cooperation’s steel-making processes, at an overall operation and maintenance cost of 11.5 NT dollars m−3. Meanwhile the Yongkang plant produces 15,500 CMD of reclaimed water for semiconductor and TFT-LCD manufacturing processes at an overall operation and maintenance costs of 25.8 NT dollars m−3, which is due to low urea and boron limits requested by the user. Finally, challenges and future prospects for promoting the use of reclaimed water to meet the targeted supply in 2031 will be discussed.

Jan Heine, Herbert Palm

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have" to a "must-have" capability for successful system developing organisations. The current state of the art, however, does not provide clear answers on how to map this need in terms of processes, methods, tools and competencies (PMTC) and how to successfully manage the transition within established industries. In this paper, we propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand. In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer and demonstrate the effectiveness of the newly proposed framework. By adopting the described agile SE Framework, companies can foster innovation and collaboration based on a learning, continuous improvement and self-reinforcing base.