Hasil untuk "Environmental technology. Sanitary engineering"

Mateus Cavalcante Sá, Marco Aurélio Holanda de Castro

Building Information Modeling (BIM) é uma tecnologia promissora na indústria da construção civil. Percebe-se sua consolidação, e as aplicações dessa tecnologia tornam-se indispensáveis para o dia a dia de empresas e projetistas. No segmento de projetos de infraestrutura, especificamente no setor de saneamento, a metodologia BIM vem sendo implementada e disseminada. Uma implementação apropriada facilita os processos de projeto e construção, podendo resultar em obras de melhor qualidade e prazos reduzidos. Nesse sentido, este trabalho apresenta uma proposta de automatização da geração de redes de abastecimento de água em BIM, utilizando uma interface entre o sistema UFC e o Civil 3D. Essa interface foi desenvolvida por meio de rotinas em Dynamo e linguagem Python. Entre as funcionalidades, a interface permite realizar conversões de arquivos do formato .inp para .xlsx. Para validar sua funcionalidade, realizaram-se testes de geração automática em BIM de redes de abastecimento de diversos formatos, incluindo tubos e conexões dimensionados com o sistema UFC, o que permitiu uma avaliação mais ampla. Os resultados demonstraram a aplicabilidade da ferramenta, que constitui uma alternativa viável para integração em fluxos de trabalho.

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Daniel Straub, Wolfgang Betz, Mara Ruf et al.

Information value, a measure for decision sensitivity, can provide essential information in engineering and environmental assessments. It quantifies the potential for improved decision-making when reducing uncertainty in specific inputs. By contrast to other sensitivity measures, it admits not only a relative ranking of input factors but also an absolute interpretation through statements like ''Eliminating the uncertainty in factor $A$ has an expected value of $5000$ Euro''. In this paper, we present a comprehensive overview of the information value by presenting the theory and methods in view of their application to engineering and environmental assessments. We show how one should differentiate between aleatory and epistemic uncertainty in the analysis. Furthermore, we introduce the evaluation of the information value in applications where the decision is described by a continuous parameter. The paper concludes with two real-life applications of the information value to highlight its power in supporting decision-making in engineering and environmental applications.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Gabriela Haag Coelho, Joice Cristini Kuritza Denck Gonçalves, Raynner Menezes Lopes et al.

Em sistemas de abastecimento de água (SAA), os grandes volumes de perda de água não apenas acarretam prejuízos financeiros, mas, também, representam desperdício de recursos naturais, sendo estes alguns dos principais desafios enfrentados pelo setor no Brasil. Neste estudo, realizou-se ranqueamento para avaliar os sistemas de abastecimento de água de 397 municípios em relação às perdas de água, por meio de indicadores de desempenho disponíveis no Sistema Nacional de Informações em Saneamento Básico (Sinisa). A análise foi conduzida em dois contextos: uma avaliação geral, abrangendo todos os municípios incluídos no estudo, e uma avaliação estratificada, considerando a divisão dos municípios em diferentes estratos populacionais. Os resultados indicaram os municípios com maiores oportunidades de melhoria em implementação de medidas de redução de perdas e os que podem ser utilizados como referência para coleta de informações sobre boas práticas de redução e controle de perdas de água.

Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach et al.

Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

P. Voit, M. Heistermann

<p>Counterfactuals are scenarios that describe alternative ways of how an event, in this case an extreme rainfall event, could have unfolded. In this study, we present the results of a counterfactual search for flash flood events in Germany. We used a radar-based precipitation dataset from Germany's national meteorological service (Deutscher Wetterdienst) to identify the 10 most extreme precipitation events in Germany from 2001 to 2022 and then assumed that any of these top 10 events could have happened anywhere in Germany. In other words, the events were shifted around all over Germany. For all resulting positions of the precipitation fields, we simulated the corresponding peak discharge for any affected catchment smaller than 750 km<span class="inline-formula">²</span>. From all the realizations of this simulation experiment, the maximum peak discharge was identified for each catchment.</p> <p>In a case study, we first focused on the devastating flood event in July 2021 in western Germany. We found that a moderate shifting of the event in space could change the event peak flow at the Altenahr gauge by a factor of 2. Compared to the peak flow of 1004 m<span class="inline-formula">³</span> s<span class="inline-formula">⁻¹</span> caused by the event in its original position, the worst-case counterfactual of that event led to a peak flow of 1311 m<span class="inline-formula">³</span> s<span class="inline-formula">⁻¹</span>. Shifting another event that had occurred just 1 month earlier in eastern Germany over the Ahr River valley even effectuated a simulated peak flow of 1651 m<span class="inline-formula">³</span> s<span class="inline-formula">⁻¹</span>.</p> <p>For all analysed subbasins in Germany, we found that, on average, the highest counterfactual peak exceeded the maximum original peak (between 2001 and 2022) by a factor of 5.3. For 98 % of the basins, the factor was higher than 2.</p> <p>We discuss various limitations of our analysis, which are important to be aware of, namely, the quantification and selection of candidate rainfall events, the hydrological model, and the design of the counterfactual search experiment. Still, we think that these results might help to expand the view of what could happen in the case that certain extreme events occurred elsewhere and thereby reduce the element of surprise in disaster risk management.</p>

Fetcia Jackulin, P. Senthil Kumar, Gayathri Rangasamy

Among the azo dye, Tartrazine is widely used for most of applications like pharmaceuticals, cosmetics, food, etc. As the demand for dye application is increased, the disposal of dye is also increasing. However it is very difficult to cleave due to its stability. Different methods are available, but the Advanced Oxidation Process (AOP) is an emerging technique used for treating various contaminants. In this study, sulfate radical (SO4−.) based AOP was performed to degrade tartrazine dye using iron oxide (Fe3O4) nanoparticles (NP). This NP was synthesized using the co-precipitation method, analyzed by X-Ray Diffraction (XRD), revealed the crystalline structure of the material and the average size of the particle was 16.17 nm also High Resolution- Scanning Electron Microscope (HR-SEM) showed spherical and cube shape of the particles with agglomeration. Response surface methodology (RSM) was carried out to determine the optimum condition based on central composite design. The optimum conditions were found to be pH-5.34, time- 113.58 min, NP- 0.89 g, SPS- 15.40 mM, and predicted degradation efficiency - 97.22% which was correlated to the experimental value- 96.66% with minimal error. Application of SO4−. radical implied an efficient degradation due to the involvement of both SO4−. and hydroxyl (OH-.) radical. Excess formation of SO4−. radicals, Fe2+ was majorily responsible for suppressive degradation. The intermediate compound was identified from Gas Chromatography-Mass Spectrometry (GC-MS), proved the absence of parent dye and occurrence of degradation due to Fe3O4/PS system.

Abdelfattah Amari, Ahmad Ismael Saber, Haitham Osman et al.

Abstract Water pollution is a pressing global concern, with per- and polyfluoroalkyl substances (PFAS) being considered as “forever contaminants.” Among them, perfluorooctanesulfonic acid (PFOS) has received significant attention for its adverse effects on human health and aquatic ecosystems. This study aimed to design an innovative adsorbent for effective PFOS removal with exceptional water stability, improving its cost-performance trade-off. The current work simultaneously improved the stability of water of Cu-based metal–organic framework (CMOF) and increased its PFOS removal capacity by modifying it with amine-functionalized SiO2 nanoparticles (AF-CMOF). AF-CMOF presented a lower specific surface area of 999 m2 g−1 compared to CMOF with a surface area of 1098 m2 g−1. AF-CMOF showed remarkable PFOS uptake performance of 670 mg/g compared to the performance of the Cu-based MOF which exhibited a PFOS uptake capacity of only 22 mg/g. The most suitable pH for PFOS removal using both adsorbents was determined to be 3. In addition, AF-CMOF demonstrated excellent water stability, retaining its structural integrity even after seven days of water contact, while CMOF structure collapsed rapidly after four days of water exposure. Moreover, the study identified the significant pH influence on the PFOS uptake process, with electrostatic interactions between protonated amine functionalities and PFOS molecules identified as the dominant mechanism. The study’s findings present the potential of synthesized adsorbent as a superior candidate for PFOS uptake and contribute to the development of effective water treatment technologies.

Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen et al.

The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.

Romulus Costache, Hazem Ghassan Abdo, Arun Pratap Mishra et al.

AbstractIn this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) and Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) were generated, which had as input data the values of 10 flood predictors and a number of 158 points where historical floods occurred. In the first step, the Certainty Factor values were calculated, which were then used in the Fuzzy-Analytical Hierarchy Process and Multiclass Alternating Decision Tree models. It should be mentioned that the Multiclass Alternating Decision Tree model was optimized with the help of the Iterative Classifier Optimizer. In the case of both ensemble models the slope angle was the most important flood conditioning factor. Moreover, according to Certainty Factor modelling the 8 classes/categories achieved the maximum value of 1. Next, the susceptibility to floods on the surface of the study area was derived. On average, about 20% of the study area has areas with high and medium susceptibility to flash floods. After evaluating the quality of the models through Receiver Operating Characteristics (ROC) Curve, the following results emerged: Success Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.985) and Flood Potential Index (FPI) Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.967); Prediction Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.952) and Flood Potential Index Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.913). At the same time, the accuracies of the models were: Training dataset − 0.943 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.931 (Fuzzy-Analytical Hierarchy Process – Certainty Factor); Validating dataset − 0.935 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.926 (Fuzzy-Analytical Hierarchy Process – Certainty Factor). As main conclusion, it can be mentioned that the 2 ensemble models outperform the previous machine learning models applied on the same study area before.

Jones Yeboah, Feifei Pang, Hari Priya Ponnakanti

This paper represents preliminary work in identifying the foundation for the discipline of Software Engineering and discovering the links between the domains of Software Engineering and Information Technology (IT). Our research utilized IEEE Transactions on Software Engineering (IEEE-TSE), ACM Transactions on Software Engineering and Methodology (ACM-TOSEM), Automated Software Engineering (ASE), the International Conference on Software Engineering(ICSE), and other related journal publication in the software engineering domain to address our research questions. We explored existing frameworks and described the need for software engineering as an academic discipline. We went further to clarify the distinction difference between Software Engineering and Computer Science. Through this efforts we contribute to an understanding of how evidence from IT research can be used to improve Software Engineering as a discipline.

Michael A. Heroux

Software plays a central role in scientific discovery. Improving how we develop and use software for research can have both broad and deep impacts on a spectrum of challenges and opportunities society faces today. The emergence of Research Software Engineer (RSE) as a role correlates with the growing complexity of scientific challenges and diversity of software team skills. In this paper, we describe research software science (RSS), an idea related to RSE, and particularly suited to research software teams. RSS promotes the use of scientific methodologies to explore and establish broadly applicable knowledge. Using RSS, we can pursue sustainable, repeatable, and reproducible software improvements that positively impact research software toward improved scientific discovery.

Parvin Gharbani, Ali Mehrizad, Seyyed Amir Mosavi

Abstract In this study, the photocatalytic degradation of Methylene Blue was investigated using CdSe nanoparticles. CdSe nanoparticles were synthesized via a simple method and were characterized by FTIR, XRD, FESEM, BET, DRS and EDS techniques. The photocatalytic performance of the CdSe nanoparticles was optimized using Response Surface Methodology (RSM) under visible light. The independent variables involved initial pH, MB concentration, photocatalyst dosage, and irradiation time were evaluated and the optimum photodegradation efficiency of MB dye removal was achieved ˜ 92.80% at pH = 8, 20 mgL−1 of MB concentration, 0.02 g 50 mL−1 of CdSe dosage, and 20 min of irradiation time. Also, the photodegradation of MB by CdSe is obeyed pseudo-first-order kinetic model (k = 0.038 min−1). The thermodynamic results revealed that the photocatalytic degradation of MB is spontaneous and endothermic. Also, the evaluation of various scavengers confirmed that the MB photodegradation was mainly done by photogenerated holes and hydroxyl radicals.

Rodrigo Henrique Barbosa Monteiro, Maurício Ronny de Almeida Souza, Sandro Ronaldo Bezerra Oliveira et al.

Gamification has been used to motivate and engage participants in software engineering education and practice activities. There is a significant demand for empirical studies for the understanding of the impacts and efficacy of gamification. However, the lack of standard procedures and models for the evaluation of gamification is a challenge for the design, comparison, and report of results related to the assessment of gamification approaches and its effects. The goal of this study is to identify models and strategies for the evaluation of gamification reported in the literature. To achieve this goal, we conducted a systematic mapping study to investigate strategies for the evaluation of gamification in the context of software engineering. We selected 100 primary studies on gamification in software engineering (from 2011 to 2020). We categorized the studies regarding the presence of evaluation procedures or models for the evaluation of gamification, the purpose of the evaluation, the criteria used, the type of data, instruments, and procedures for data analysis. Our results show that 64 studies report procedures for the evaluation of gamification. However, only three studies actually propose evaluation models for gamification. We observed that the evaluation of gamification focuses on two aspects: the evaluation of the gamification strategy itself, related to the user experience and perceptions; and the evaluation of the outcomes and effects of gamification on its users and context. The most recurring criteria for the evaluation are 'engagement', 'motivation', 'satisfaction', and 'performance'. Finally, the evaluation of gamification requires a mix of subjective and objective inputs, and qualitative and quantitative data analysis approaches. Depending of the focus of the evaluation (the strategy or the outcomes), there is a predominance of a type of data and analysis.

Alex Serban, Koen van der Blom, Holger Hoos et al.

Following the recent surge in adoption of machine learning (ML), the negative impact that improper use of ML can have on users and society is now also widely recognised. To address this issue, policy makers and other stakeholders, such as the European Commission or NIST, have proposed high-level guidelines aiming to promote trustworthy ML (i.e., lawful, ethical and robust). However, these guidelines do not specify actions to be taken by those involved in building ML systems. In this paper, we argue that guidelines related to the development of trustworthy ML can be translated to operational practices, and should become part of the ML development life cycle. Towards this goal, we ran a multi-vocal literature review, and mined operational practices from white and grey literature. Moreover, we launched a global survey to measure practice adoption and the effects of these practices. In total, we identified 14 new practices, and used them to complement an existing catalogue of ML engineering practices. Initial analysis of the survey results reveals that so far, practice adoption for trustworthy ML is relatively low. In particular, practices related to assuring security of ML components have very low adoption. Other practices enjoy slightly larger adoption, such as providing explanations to users. Our extended practice catalogue can be used by ML development teams to bridge the gap between high-level guidelines and actual development of trustworthy ML systems; it is open for review and contribution

Austen Rainer

BACKGROUND: Software engineering is a human activity. People naturally make sense of their activities and experience through storytelling. But storytelling does not appear to have been properly studied by software engineering research. AIM: We explore the question: what contribution can storytelling make to human--centric software engineering research? METHOD: We define concepts, identify types of story and their purposes, outcomes and effects, briefly review prior literature, identify several contributions and propose next steps. RESULTS: Storytelling can, amongst other contributions, contribute to data collection, data analyses, ways of knowing, research outputs, interventions in practice, and advocacy, and can integrate with evidence and arguments. Like all methods, storytelling brings risks. These risks can be managed. CONCLUSION: Storytelling provides a potential counter--balance to abstraction, and an approach to retain and honour human meaning in software engineering.

Yikan Meng, Yang Li, Na Zheng et al.

Metal contamination of skin care products that occurs during their production poses potential health risks, which are of increasing concern, to consumers. Here, we collected 570 responses to an online survey to analyze the usage pattern of skin care products across China. Then a total of 30 commonly used skin care products with various prices and applications were purchased. The concentrations of metals (Al, Zn, Cu, Ni, Cr, Pb, Hg, and Cd) and metalloid As, were determined. Next, we improved the frequency calculation method and used the weighted exposure frequency to calculate the dermal absorption dose (DAD). The amounts of Zn, Cr, and Al that were assimilated by the human body via uptake were approximately 2 orders of magnitude greater than those of Pb, Hg, Cd, Ni and metalloid As. In addition, younger consumers were at higher risk of metals exposure than older consumers because of their higher frequency of use of skin care products. Al and Zn posed higher risk to consumers because of its high DAD. There was no significant chronic non-carcinogenic health risk (hazard index < 1) posed by skincare product use.