In recent years, advancements in artificial intelligence (AI) have led to the development of large language models like GPT-4, demonstrating potential applications in various fields, including education. This study investigates the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in achieving satisfactory performance on the Fundamentals of Engineering (FE) Environmental Exam. This study further shows a significant improvement in the model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating the utility of prompt modification as a viable approach to enhance AI performance in educational contexts. Furthermore, the findings reflect remarkable improvements in mathematical capabilities across successive iterations of ChatGPT models, showcasing their potential in solving complex engineering problems. Our paper also explores future research directions, emphasizing the importance of addressing AI challenges in education, enhancing accessibility and inclusion for diverse student populations, and developing AI-resistant exam questions to maintain examination integrity. By evaluating the performance of ChatGPT in the context of the FE Environmental Exam, this study contributes valuable insights into the potential applications and limitations of large language models in educational settings. As AI continues to evolve, these findings offer a foundation for further research into the responsible and effective integration of AI models across various disciplines, ultimately optimizing the learning experience and improving student outcomes.
Dario Cottafava, José R. Nicolás-Carlock, Marcel Llavero-Pasquina
This study investigates the interconnectivity of firms and Environmental Justice Organizations (EJOs) involved in socio-environmental conflicts worldwide, using data from the Environmental Justice Atlas (EJAtlas). By constructing a multilayer network that links firms, conflicts, and EJOs, the research applies social network analysis to evaluate the simultaneous involvement of these actors across multiple disputes. Both projected networks of firms and EJOs have been analysed by aggregating nodes by categories and countries to reveal structural differences. Findings reveal a stark contrast between the interconnectedness of firms and EJOs. Multinational corporations form a cohesive global network, enabling them to coordinate strategies and exert influence across regions. Conversely, EJOs are fragmented, often operating in isolated clusters with limited interconnection but forming a robust, decentralized and self-organized global network. Firms network present a strong dependence on pertaining conflict category while EJOs network does not depend on conflict category. This structural difference suggests a risk of systemic and structural coordination for firms towards exploitative expansion while EJOs dynamics seems to be led by a white blood cells defense-like mechanism. While fragmentation may represents a critical challenge for social movements, decentralization and self-organization show a more diffuse global networks supported by a limited number of central hub able to build stronger global alliances to effectively counter the power dynamics of transnational corporations. By providing robust evidence of these networks, this research contributes to discuss how structural differences in global coordination for companies and EJOs directly derives as emergent properties depending on the purpose of the network itself, sectorial expansion for firms while ecosystem preservation for EJOs.
Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.
Building-integrated photovoltaics (BIPVs) represent a pivotal technology for enhancing the utilization of renewable energy in buildings. However, challenges persist, including the lack of integrated design models, limited analytical dimensions, and insufficient consideration of climate change impacts. This study proposes a comprehensive performance assessment framework for BIPV that incorporates global climate change factors. An integrated simulation model is developed using EnergyPlus8.9.0, Optics6, and WINDOW7.7 to evaluate BIPV configurations such as photovoltaic facades, shading systems, and roofs. A multi-criteria evaluation system is established, encompassing global warming potential (GWP), power generation, energy flexibility, and economic cost. Future hourly weather data for the 2050s and 2080s are generated using CCWorldWeatherGen under representative climate scenarios. Monte Carlo simulations are conducted to assess performance across variable combinations, supplemented by sensitivity and uncertainty analyses to identify key influencing factors. Results indicate (1) critical design parameters—including building orientation, wall thermal absorptance, window-to-wall ratios, PV shading angle, glazing optical properties, equipment and lighting power density, and occupancy—significantly affect overall performance. Equipment and lighting densities most influence carbon emissions and flexibility, whereas envelope thermal properties dominate cost impacts. PV shading outperforms other forms in power generation. (2) Under intensified climate change, GWP and life cycle costs increase, while energy flexibility declines, imposing growing pressure on system performance. However, under certain mid-century climate conditions, BIPV power generation potential improves due to altered solar radiation. The study recommends integrating climate-adaptive design strategies with energy systems such as PEDF (photovoltaic, energy storage, direct current, and flexibility), refining policy mechanisms, and advancing BIPV deployment with climate-resilient approaches to support building decarbonization and enhance adaptive capacity.
<p>We used the CAM5 model to examine how different particle-bound polycyclic aromatic hydrocarbon (PAH) degradation approaches affect the spatial distribution of benzo(a)pyrene (BaP). Three approaches were evaluated: NOA (no effect of OA coatings state on BaP), shielded (viscous OA coatings shield BaP from oxidation under cool and dry conditions) and ROI-T (viscous OA coatings slow BaP oxidation in response to temperature and humidity). Results show that BaP concentrations vary seasonally, influenced by emissions, deposition, transport and degradation approach, all of which are influenced by meteorological conditions. All simulations predict higher population-weighted global average (PWGA) fresh BaP concentrations during December–January–February (DJF) compared to June–July–August (JJA), due to increased emissions from household activities and reduced removal processes during colder months. The shielded and ROI-T approaches, which account for OA coatings, result in 2–6 times higher BaP concentrations in DJF compared to NOA. The shielded simulation predicts the highest PWGA fresh BaP concentration (1.3 <span class="inline-formula">ng m<sup>−3</sup></span>), with 90 % of BaP protected from oxidation. In contrast, the ROI-T approach forecasts lower concentrations in middle to low latitudes, as it assumes less effective OA coatings under warmer, more humid conditions. Evaluations against observed BaP concentrations show the shielded approach performs best, with a normalized mean bias (NMB) within <span class="inline-formula">±</span> 20 %. The combined incremental lifetime cancer risk (ILCR) for both fresh and oxidized PAHs is similar across simulations, emphasizing the importance of considering both forms in health risk assessments. This study highlights the critical role of accurate degradation approaches in PAH modeling.</p>
Cadmium contamination found in paddy fields in the Maesot District of Tak Province, Thailand. This area was remediated using 50mg/L of ZVI. The study aimed to isolate and identify soil bacteria in the soil and rice roots and to investigate ZVI’s effect on the isolated bacterial cells. The results indicated no significant difference in soil bacteria content before and after remediation at the 95% confidence level. Twelve isolates of nitrogen-fixing bacteria were obtained. Those isolates could grow at high concentrations of 300 mg/L of ZVI. RH17 had a high tolerance for TSA with 300 mg/L of ZVI at only 10 CFU/ml. The effects of ZVI at 150 mg/L on RH17 cells, a small amount of ZVI was observed adhering to the cells’ surface and forming giant cells, while at 300 mg/L of ZVI, caused a reduction in growth by 81.0%. The nifH gene of RH17 was related to Rhizobium sp. strain 5-1-2. The results demonstrated the cadmium remediation process with 50mg/L of ZVI did not affect the cell count of soil bacteria in the paddy field. However, at 150 mg/L or higher, ZVI damaged the isolated Rhizobium sp. cell membrane. So, the remediation using ZVI must consider the appropriate concentration.
Paulo Roger Lopes Alves, German Andres Estrada-Bonilla, Antonio Marcos Miranda Silva
et al.
This study explored how different sugarcane vinasses influence the structure and composition of soil bacterial communities in two tropical Oxisols with contrasting textures. In a controlled microcosm experiment with sugarcane seedlings, two concentrations of three vinasse types were applied, and bacterial communities were monitored over 10, 30, and 60 days using T-RFLP and 16S rRNA gene sequencing. Across all treatments, vinasse application led to clear changes in bacterial community structure in both soils, regardless of the time point. Certain bacterial groups, such as <i>Sphingobacteriia</i>, <i>Alphaproteobacteria</i>, and <i>Gammaproteobacteria</i>, became more abundant—likely responding to increased carbon availability, higher pH, and greater soil moisture. At the same time, other groups declined, possibly due to excess nutrients like potassium and sulfur. Notably, these shifts occurred even when standard biochemical indicators suggested no major impact, highlighting the sensitivity of microbial community-level responses. These findings point to the importance of looking beyond traditional soil quality metrics when assessing the environmental effects of organic residue applications. Incorporating microbial indicators can offer a more nuanced understanding of how practices like vinasse reuse affect soil functioning in tropical agroecosystems.
Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.
Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.
Adriano Bressane, Ana Júlia da Silva Garcia, M. Castro
et al.
Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.
Phosphate solubilizing microorganisms (PSMs) are known as bacteria or fungi that make insoluble phosphorus in soil available to plants. To date, as beneficial microbes, studies on PSMs indicated they have potential applications in agriculture, environmental engineering, bioremediation, and biotechnology. Currently high cost and competition from local microbe are the most important factors hindering PSMs commercialization and application as for instance biofertilizer, soil conditioner or remediation agent, etc. There are several technical strategies can be engaged to approach the solutions of these issues, for instance mass production, advance soil preparation, genetic engineering, etc. On the other hand, further studies are needed to improve the efficiency and effectiveness of PSMs in solubilizing phosphates, promoting plant growth, soil remediation preferably. Hopefully, PSMs are going to be developed into ecofriendly tools for sustainable agriculture, environment protection and management in the future.
Lorenzo Pedroni, Daniel Zocchi Doherty, Chiara Dall’Asta
et al.
Mycotoxins are known environmental pollutants that may contaminate food and feed chains. Some mycotoxins are regulated in many countries to limit the trading of contaminated and harmful commodities. However, the so-called emerging mycotoxins are poorly understood and need to be investigated further. Fusaric acid is an emerging mycotoxin, noxious to plants and animals, but is known to be less toxic to plants when hydroxylated. The detoxification routes effective in animals have not been elucidated yet. In this context, this study integrated in silico and in vitro techniques to discover potential bioremediation routes to turn fusaric acid to its less toxic metabolites. The toxicodynamics of these forms in humans have also been addressed. An in silico screening process, followed by molecular docking and dynamics studies, identified CYP199A4 from the bacterium Rhodopseudomonas palustris HaA2 as a potential fusaric acid biotransforming enzyme. Its activity was confirmed in vitro. However, the effect of hydroxylation seemed to have a limited impact on the modelled toxicodynamics against human targets. This study represents a starting point to develop a hybrid in silico/in vitro pipeline to find bioremediation agents for other food, feed and environmental contaminants.
The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito
et al.
Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.
A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.
A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.
Lead dioxide (PbO2) materials have been widely employed in various fields such as batteries, electrochemical engineering, and more recently environmental engineering as anode materials, due to their unique physicochemical properties. Key performances of PbO2 electrodes, such as energy efficiency and space-time yield, are influenced by morphological as well as compositional factors. Micro-nano structure regulation and decoration of metal/non-metal on PbO2 is an outstanding technique to revamp its electrocatalytic activities and enhance environmental engineering efficiency. The aim of this review is to comprehensively summarize the recent research progress in the morphology control, the structure constructions, and the element doping of PbO2 materials, further with many environmental application cases evaluated. Concerning electrochemical environmental engineering, the lead dioxide employed in chemical oxygen demand detection, ozone generators, and wastewater treatment has been comprehensively reviewed. In addition, the future research perspectives, challenges and the opportunities on PbO2 materials for environmental applications are proposed.
Recently, single-atom catalysts (SACs) have attracted wide attention in the field of environmental engineering. Compared with their nanoparticle counterparts, SACs possess high atomic efficiency, unique catalytic activity, and selectivity. This review summarizes recent studies on the environmental remediation applications of SACs in (1) gaseous: volatile organic compounds (VOCs) treatment, NOx reduction, CO2 reduction, and CO oxidation; (2) aqueous: Fenton-like advanced oxidation processes (AOPs), hydrodehalogenation, and nitrate/nitrite reduction. We present the treatment activities and reaction mechanisms of various SACs and propose challenges and future opportunities. We believe that this review will provide constructive inspiration and direction for future SAC research in environmental engineering.