Hasil untuk "Environmental engineering"

Zhong-shuai Zhu, Shuang Zhong, Cheng Cheng et al.

Environmental catalysis has emerged as a scientific frontier in mitigating water pollution and advancing circular chemistry and reaction microenvironment significantly influences the catalytic performance and efficiency. This review delves into microenvironment engineering within liquid-phase environmental catalysis, categorizing microenvironments into four scales: atom/molecule-level modulation, nano/microscale-confined structures, interface and surface regulation, and external field effects. Each category is analyzed for its unique characteristics and merits, emphasizing its potential to significantly enhance catalytic efficiency and selectivity. Following this overview, we introduced recent advancements in advanced material and system design to promote liquid-phase environmental catalysis (e.g., water purification, transformation to value-added products, and green synthesis), leveraging state-of-the-art microenvironment engineering technologies. These discussions showcase microenvironment engineering was applied in different reactions to fine-tune catalytic regimes and improve the efficiency from both thermodynamics and kinetics perspectives. Lastly, we discussed the challenges and future directions in microenvironment engineering. This review underscores the potential of microenvironment engineering in intelligent materials and system design to drive the development of more effective and sustainable catalytic solutions to environmental decontamination.

Tab, Zhang, Bjorn De Sutter et al.

Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies in RE, balancing practical constraints with the need for meaningful, reproducible results.

Noemi Luna Carmeno, Tiago Domingos, Daniel W. O'Neill

Artificial Intelligence (AI) is changing the world, but its impacts on the environment and human well-being remain uncertain. We conducted a systematic literature review of 1,291 studies selected from 6,655 records, identifying the main impacts of AI and how they are assessed. The evidence reveals an uneven landscape: 72% of environmental studies focus narrowly on energy use and CO2 emissions, while only 11% consider systemic effects. Well-being research is largely conceptual and overlooks subjective dimensions. Strikingly, 83% of environmental studies portray AI's impacts as positive, while well-being analyses show a near-even split overall (44% positive; 46% negative). However, this split masks differences across well-being dimensions. While the impacts of AI on income and health are expected to be positive, its impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we suggest several areas for future research. Environmental assessments should incorporate water, material, and biodiversity impacts, and apply a full life-cycle perspective, while well-being research should prioritise empirical analyses. Evaluating AI's overall impact requires accounting for computing-related, application-level, and systemic impacts, while integrating both environmental and social dimensions. Bridging these gaps is essential to understand the full scope of AI's impacts and to steer its development towards environmental sustainability and human flourishing.

Esteban Parra, Sonia Haiduc, Preetha Chatterjee et al.

Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Himon Thakur, Armin Moin

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.

Yang Yue, Zheng Jiang, Yi Wang

The heterogeneity in the organization of software engineering (SE) research historically exists, i.e., funded research model and hands-on model, which makes software engineering become a thriving interdisciplinary field in the last 50 years. However, the funded research model is becoming dominant in SE research recently, indicating such heterogeneity has been seriously and systematically threatened. In this essay, we first explain why the heterogeneity is needed in the organization of SE research, then present the current trend of SE research nowadays, as well as the consequences and potential futures. The choice is at our hands, and we urge our community to seriously consider maintaining the heterogeneity in the organization of software engineering research.

Nitin Thombre, Pritesh Patil, Ankita Yadav et al.

Abstract The textile industry is one of the important and largest industry that consumes major chunk of the water in the world. This industry produces a large amount of wastewater during the processes such as sizing, de-sizing, scouring, bleaching, mercerizing, dyeing, printing, and finishing. The used water produced after such processes affects the environment heavily due to its composition such as mineral salts and oils present in suspended state, metals and metal complexes, dyes, various chemicals, some readily-biodegradable products and some constituents that are hard to biodegrade. The treatment of such hazardous effluent to reuse the water in certain water demanding processes is essential. Considering the worldwide application of the textiles, the appropriate management of water resources in the sector includes the treatment of effluent by efficient technology and the reuse of the water. This article displays an overview of waste management during textile industrial processes. It aims at giving oversight on waste minimization and reuse along with wastewater treatment methods. It also involves the cross-utilization of effluent between processes for achieving water efficiency. This review covers advanced waterless textile dyeing processes, zero liquid discharge techniques, advanced oxidation processes, biological treatment methods, which can be a sustainable and greener approach to reducing the waste generation.

Longfei Cui, Xinyu Niu, Haizhong Qian et al.

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. This approach enables the extraction of general shape features applicable to both line and polygon elements, generating high-dimensional embedded feature vectors. These vectors facilitate downstream tasks like shape classification, pattern recognition, and cartographic generalization. Our experimental results show that PSRT can extract vector shape features effectively without needing labeled samples and is adaptable to various types of vector features. Compared to the methods without pre-training, PSRT enhances training efficiency by over five times and improves accuracy by 5–10% in tasks such as line element matching and polygon shape classification. This innovative approach offers a more unified, efficient solution for processing vector shape data across different applications.

Tomi Suomi, Petri Ihantola, Tommi Mikkonen et al.

Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Zhenpeng Chen, Chong Wang, Weisong Sun et al.

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.

Krishna Ronanki, Simon Arvidsson, Johan Axell

The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.

Jian Liu, Fangyu Liu, Linbing Wang

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, which involves selecting aggregate gradation and binder content, is time-consuming and labor-intensive. To accelerate the traditional mix design procedure, this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning (ML) and a meta-heuristic algorithm. Specifically, ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity (Gmb) and air void (VV)) and both mixture component properties and mixture proportion, based on a dataset collected from literature with 660 mixture designs. Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization (MOGWO) algorithm, an automatic asphalt mix design was proposed to pursue three goals, including VV, cost, and CO2 emission. The results indicated that least squares support vector regression (LSSVR) and eXtreme gradient boosting (XGBoost) achieved the highest prediction accuracies (correlation coefficient: 0.92 for VV and 0.96 for Gmb). The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs. cost vs. CO2 emission. Compared to the traditional laboratory design, the optimal mixture with VV of 4% achieves a cost saving of 2.46% and a reduction of 4.03% in carbon emission. The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.

Roberto Castro-Muñoz

Today, chemistry and nanotechnology cover molecular separations in liquid and gas states by aiding in the design of new nano-sized materials. In this regard, the synthesis and application of two-dimensional (2D) nanomaterials are current fields of research in which structurally defined 2D materials are being used in membrane separation either in self-standing membranes or composites with polymer phases. For instance, pervaporation (PV), as a highly selective technology for liquid separation, benefits from using 2D materials to selectively transport water or other solvent molecules. Therefore, this review paper offers an interesting update in revising the ongoing progress of PV membranes using 2D materials in several applications, including solvent purification (the removal of water from organic systems), organics removal (the removal of organic molecules diluted in water systems), and desalination (selective water transport from seawater). In general, recent reports from the past 3 years have been discussed and analyzed. Attention has been devoted to the proposed strategies and fabrication of membranes for the inclusion of 2D materials into polymer phases. Finally, the future trends and current research gaps are declared for the scientists in the field.

Andrew Moses Obeti, Lawrence Muhwezi, John Muhumuza Kakitahi et al.

Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture plantations. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.

Dezhi Ran, Mengzhou Wu, Wei Yang et al.

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.

Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee et al.

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.

Maria-Victoria Vladucu, Hailun Wu, Jorge Medina et al.

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research.

Pablo López-Porfiri, Sebastián Ramos-Paredes, Patricio Núñez et al.

Abstract Membrane distillation (MD) is constantly acknowledged in the research literature as a promising technology for the future of desalination, with an increasing number of studies reported year after year. However, real MD applications still lag behind with only a few pilot-plant tests worldwide. The lack of technology transfer from academia to industry is caused by important gaps between its fundamental basis and the process design. Herein, we explore critical disconnections by conducting coupled mass and heat transfer modeling and MD simulations; we use well-known MD mass and heat transfer equations to model and simulate flux over a typical MD membrane for different geometries, areas, and operational conditions in direct contact configuration. From the analysis of the results, we propose research guidelines and process development strategies, and construct an MD module performance curve. From this graph, permeate flow rate, thermal energy consumption and outlet temperatures can be determined for given feed inlet conditions (temperature and concentration). Comprehensive tools such as this MD module curve and good communication between membrane developers and process engineers are required to accelerate the process of bringing the MD technology from a still-emerging status to a maturity level.

Einara Zahn, Elie Bou‐Zeid, Nelson Luís Dias

Abstract The Monin‐Obukhov Similarity Theory (MOST) links turbulent statistics to surface fluxes through universal functions. Here, we investigate its performance over a large lake, where none of its assumptions (flat homogeneous surface) are obviously violated. We probe the connection between the variance budget terms and departure from the nondimensional flux‐variance function for CO2, water vapor, and temperature. Our results indicate that both the variance storage and its vertical transport affect MOST, and these terms are most significant when small fluxes and near neutral conditions were prevalent. Such conditions are common over lakes and oceans, especially for CO2, underlining the limitation of using any MOST‐based methods to compute small fluxes. We further show that the relaxed eddy accumulation (REA) method is more robust and less sensitive to storage and transport, adequately reproducing the eddy‐covariance fluxes even for the smallest flux magnitudes. Therefore, we recommend REA over MOST methods for trace‐gas flux estimation.