Hasil untuk "Environmental engineering"

Xiao-qin Li, D. Elliott, Wei‐xian Zhang

Christopher E. Lawson, W. Harcombe, R. Hatzenpichler et al.

Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik et al.

Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.

Lili Chen, Winn Wing-Yiu Chow, Stella Peng et al.

PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion-referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution-based grading and how a GenAI-supported system can be designed to reliably identify student errors, provide high-quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI-assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion-referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high-quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.

Carolyn Seaman, Rashina Hoda, Robert Feldt

The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influential papers from the third decade of TSE's 50 years history. In this retrospective, the authors discuss the evolution of the use of qualitative methods in software engineering research, the impact it's had on research and practice, and reflections on what is coming and deserves attention.

Jose Marie Antonio Minoza, Rex Gregor Laylo, Christian F Villarin et al.

Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation. We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO$_2$ emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.

Zhimin Zhao

Foundation models (FMs), particularly large language models (LLMs), have shown significant promise in various software engineering (SE) tasks, including code generation, debugging, and requirement refinement. Despite these advances, existing evaluation frameworks are insufficient for assessing model performance in iterative, context-rich workflows characteristic of SE activities. To address this limitation, we introduce \emph{SWE-Arena}, an interactive platform designed to evaluate FMs in SE tasks. SWE-Arena provides a transparent, open-source leaderboard, supports multi-round conversational workflows, and enables end-to-end model comparisons. The platform introduces novel metrics, including \emph{model consistency score} that measures the consistency of model outputs through self-play matches, and \emph{conversation efficiency index} that evaluates model performance while accounting for the number of interaction rounds required to reach conclusions. Moreover, SWE-Arena incorporates a new feature called \emph{RepoChat}, which automatically injects repository-related context (e.g., issues, commits, pull requests) into the conversation, further aligning evaluations with real-world development processes. This paper outlines the design and capabilities of SWE-Arena, emphasizing its potential to advance the evaluation and practical application of FMs in software engineering.

Marc Bruni, Fabio Gabrielli, Mohammad Ghafari et al.

Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.

Shavindra Wickramathilaka, John Grundy, Kashumi Madampe et al.

The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisation needs. As current developer tools and practices encourage one-size-fits-all interfaces with limited potential to address the diversity of senior needs, there is a growing demand for approaches that support the systematic creation of adaptive, accessible app experiences. To this end, we present AdaptForge, a novel model-driven engineering (MDE) approach that enables advanced design-time adaptations of mobile application interfaces and behaviours tailored to the accessibility needs of senior users. AdaptForge uses two domain-specific languages (DSLs) to address age-related accessibility needs. The first model defines users' context-of-use parameters, while the second defines conditional accessibility scenarios and corresponding UI adaptation rules. These rules are interpreted by an MDE workflow to transform an app's original source code into personalised instances. We also report evaluations with professional software developers and senior end-users, demonstrating the feasibility and practical utility of AdaptForge.

Soo Ran Won, Yong Pyo Kim, Misheel Sainjargal et al.

In this study, 34 volatile organic compounds (VOCs) were analyzed using an online VOCs instrument at 30-min intervals from November 16 to November 23, 2023, in Ulaanbaatar (UB), the capital of Mongolia for the first time. The average concentration of the 34 VOCs was 13.0 ± 11.1 ppb, with the top 10 compounds, such as benzene, toluene, ethylbenzene, and xylenes (BTEX), constituting 80 % of the total. The concentrations of n-hexane, n-heptane, and undecane tended to increase significantly during high-concentration episode period (HEP). Compared to other studies, BTEX concentration levels in UB were higher than those in Seoul and Beijing, but lower than in Southeast Asian cities. Positive matrix factorization (PMF) identified four VOCs sources: vehicle exhaust (33.8 %), industrial/coal combustion (25.3 %), secondary formation precursors (21.3 %), and solvent usage (19.6 %). Vehicle exhaust and industrial/coal combustion sources increased during rush hours and were strongly correlated with nitrogen oxides. During HEP, stagnant air mass led to increased contributions from vehicle exhaust and industrial/coal combustion sources, indicating a significant local impact. Solvent usage appeared to be influenced by building materials and exterior painting which increased with high relative humidity. Secondary formation precursors increased in concentration during daytime and were highly correlated with ozone. Among the measured compounds, benzene was assessed for lifetime health risk, showing that adults with the prolonged exposure exhibited higher risk than infants and children. However, during HEP, children were also at increased risk, despite their shorter exposure duration. Based on the concentration levels of VOCs and the associated health risks, the results highlight that the need for a policy on ambient VOCs management in UB, with a particular focus on local source management.

Ying Dou, Junling Guo, Junke Shao et al.

Abstract Over the past decade, the most fundamental challenges faced by the development of lithium–sulfur batteries (LSBs) and their effective solutions have been extensively studied. To further transfer LSBs from the research phase into the industrial phase, strategies to improve the performance of LSBs under practical conditions are comprehensively investigated. These strategies can simultaneously optimize the sulfur cathode and Li‐metal anode to account for their interactions under practical conditions, without involving complex preparation or costly processes. Therefore, “two‐in‐one” strategies, which meet the above requirements because they can simultaneously improve the performance of both electrodes, are widely investigated. However, their development faces several challenges, such as confused design ideas for bi‐functional sites and simplex evaluation methods (i. e. evaluating strategies based on their bi‐functionality only). To date, as few reviews have focused on these challenges, the modification direction of these strategies is indistinct, hindering further developments in the field. In this review, the advances achieved in “two‐in‐one” strategies and categorizing them based on their design ideas are summarized. These strategies are then comprehensively evaluated in terms of bi‐functionality, large‐scale preparation, impact on energy density, and economy. Finally, the challenges still faced by these strategies and some research prospects are discussed.

Shekhar Sharan Goyal, Raviraj Dave, Rohini Kumar et al.

Abstract Intensive agricultural practices have powered green revolutions, helping nations attain self-sufficiency. However, these fertilizer-intensive methods and exploitative trade systems have created unsustainable agricultural systems. To probe the environmental consequences on production hubs, we map the fate of Nitrogen and Phosphorus in India’s interstate staple crop trade over the recent decade. The nation’s food bowls, while meeting national food demand, are becoming pollution-rich, sustaining around 50% of the total surplus from trade transfer, accounting for 710 gigagrams of nitrogen per year and 200 gigagrams of phosphorus per year. In combination with water balance analysis, surplus nutrient conversion to a graywater footprint further highlights an aggravated situation in major producer regions facing long-term water deficits. Given India’s role in global food security, identifying the nation’s environmental vulnerability can help in designing appropriate policy interventions for sustainable development.

Pranav Gupta, Raunak Sharma, Rashmi Kumari et al.

Environment Sound Classification has been a well-studied research problem in the field of signal processing and up till now more focus has been laid on fully supervised approaches. Over the last few years, focus has moved towards semi-supervised methods which concentrate on the utilization of unlabeled data, and self-supervised methods which learn the intermediate representation through pretext task or contrastive learning. However, both approaches require a vast amount of unlabelled data to improve performance. In this work, we propose a novel framework called Environmental Sound Classification with Hierarchical Ontology-guided semi-supervised Learning (ECHO) that utilizes label ontology-based hierarchy to learn semantic representation by defining a novel pretext task. In the pretext task, the model tries to predict coarse labels defined by the Large Language Model (LLM) based on ground truth label ontology. The trained model is further fine-tuned in a supervised way to predict the actual task. Our proposed novel semi-supervised framework achieves an accuracy improvement in the range of 1\% to 8\% over baseline systems across three datasets namely UrbanSound8K, ESC-10, and ESC-50.

Eduard C. Groen, Kazi Rezoanur Rahman, Nikita Narsinghani et al.

The farming domain has seen a tremendous shift towards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation.

Fengbao Liu, Jinsheng Sun, Xiao Luo

Drilling fluid systems for deep and ultra-deep wells are hampered by both high-temperature downhole environments and lengthy cycle periods. Suppose that the gel particle-plugging agent, the primary treatment agent in the system, fails to offer durable and stable plugging performance. In such a scenario, the borehole wall is susceptible to instability and landslide after prolonged immersion, leading to downhole accidents. In this study, novel core-shell gel particles (modified ZIF) with ZIF particles employed as the core material and organosilicon-modified polyethylene polyamine (PEPA) as the polymer shell were fabricated using PEPA, in-house synthesized (3-aminopropyl) triethoxysilane (APTS), and the ZIF-8 metal-organic framework (MOF) as the raw materials to enhance the long-term plugging performance of gel plugging agents. The modified ZIF particles are nanoscale polygonal crystals and differ from conventional core-shell gel particles in that they feature high molecular sieve catalytic activity due to the presence of numerous interior micropores and mesopores. As a result, modified ZIF exhibits the performance characteristics of both rigid and flexible plugging agents and has an excellent catalytic cross-linking effect on the sulfonated phenolic resin (SMP-3) and sulfonated lignite resin (SPNH) in drilling fluids. Consequently, a cross-linking reaction occurs when SMP-3 and SPNH flow through the spacings in the plugging layer formed by the modified ZIF particles. This increases the viscosity of the liquid phase and simultaneously generates an insoluble gel, forming a particle-gel composite plugging structure with the modified ZIF and significantly enhancing the long-term plugging performance of the drilling fluid.

Karen Anderson, Brandi M. Shabaga, Serge Wich et al.

Summary This journal (Drone Systems and Applications; DSA) conducted a targeted “horizon scan” during 2022 within our team of editors and associate editors. We asked—Which research areas currently under-represented in Drone Systems and Applications would you like to see more heavily represented in the future? The process highlighted five areas of interest and potential growth: Drones in the geosciences Aquatic drones Ground drones Drones within calibration/validation experiments Drones and computer vision Over the past two years (2020–22), the journal has published over 50 papers with a strong leaning towards aerial drones for ecology and also with an engineering focus. DSA is keen to receive new submissions addressing the five highlighted areas, which lie firmly within the aims and scope of the journal. Further to the horizon scan, we propose two special collections for the coming year—one addressing drone applications (drones in geoscience applications) and a second addressing drone systems (aquatic drone systems). We would like to hear from scientists and practitioners in these fields as both contributors and (or) collection editors.

Mirella Kanerva, Nguyen Minh Tue, Tatsuya Kunisue et al.

The Atlantic salmon (Salmo salar) population in the Baltic Sea consists of wild and hatchery-reared fish that have been released into the sea to support salmon stocks. During feeding migration, salmon migrate to different parts of the Baltic Sea and are exposed to various biotic and abiotic stressors, such as organohalogen compounds (OHCs). The effects of salmon origin (wild or hatchery-reared), feeding area (Baltic Main Basin, Bothnian Sea, and Gulf of Finland), and OHC concentration on the differences in hepatic proteome of salmon were investigated. Multi-level analysis of the OHC concentration, transcriptome, proteome, and oxidative stress biomarkers measured from the same salmon individuals were performed to find the key variables (origin, feeding area, OHC concentrations, and oxidative stress) that best account for the differences in the transcriptome and proteome between the salmon groups. When comparing wild and hatchery-reared salmon, differences were found in xenobiotic and amino acid metabolism-related pathways. When comparing salmon from different feeding areas, the amino acid and carbohydrate metabolic pathways were notably different. Several proteins found in these pathways are correlated with the concentrations of polychlorinated biphenyls (PCBs). The multi-level analysis also revealed amino acid metabolic pathways in connection with PCBs and oxidative stress variables related to glutathione metabolism. Other pathways found in the multi-level analysis included genetic information processes related to ribosomes, signaling and cellular processes related to the cytoskeleton, and the immune system, which were connected mainly to the concentrations of Polychlorinated biphenyls and Dichlorodiphenyltrichloroethane and their metabolites. These results suggest that the hepatic proteome of salmon in the Baltic Sea, together with the transcriptome, is more affected by the OHC concentrations and oxidative stress of the feeding area than the origin of the salmon.

Ye Yuan, Yongtong Zhu, Jiaqi Wang et al.

IntroductionSpiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-dependent plasticity (STDP), structural plasticity, and homeostatic plasticity, making network connection patterns and weights to change continuously during the lifecycle. However, it is unclear how these plasticities interact to shape neural networks and affect neural signal processing.MethodHere, we propose a reward-modulated self-organization recurrent network with structural plasticity (RSRN-SP) to investigate this issue. Specifically, RSRN-SP uses spikes to encode information, and incorporate multiple plasticities including reward-modulated spike timing-dependent plasticity (R-STDP), homeostatic plasticity, and structural plasticity. On the one hand, combined with homeostatic plasticity, R-STDP is presented to guide the updating of synaptic weights. On the other hand, structural plasticity is utilized to simulate the growth and pruning of synaptic connections.Results and discussionExtensive experiments for sequential learning tasks are conducted to demonstrate the representational ability of the RSRN-SP, including counting task, motion prediction, and motion generation. Furthermore, the simulations also indicate that the characteristics arose from the RSRN-SP are consistent with biological observations.

Oleksii M. Shushura, Liudmyla A. Asieieva, Oleksiy L. Nedashkivskiy et al.

The widespread use of computer technology, its rapid development and use in almost all areas of human activity requires constant updating of information security issues. The activities of many enterprises in the field of IT, construction, and other areas are of a project nature and therefore further research on information security management of projects is relevant. Appearance of changes and the current state of the project results at certain points of time describe the documents that accompany it. In this paper, the information structure of the project is considered as a set of specific documents. During the life cycle of each project document, which includes the creation, transfer, preservation and transformation, there are generally threats to its confidentiality, integrity, accessibility and authenticity. This paper develops a method for assessing the risks of violation of the availability of project documents in solving information security problems. A formal description of many project documents in the form of a generalized hierarchical structure is presented, the connection of documents with the operations performed on them and information systems used during these operations is formalized. Given the incompleteness and dimension of the data, the based on fuzzy logic model was developed to assess the risk of document accessibility. Approaches to the assessment of the damage from the violation of the availability of the project document and the method of calculating the overall assessment of the risk of violation of the documents availability are proposed. The results presented in this paper can be used in decision-making processes regarding information security of projects in organizations that have project activities. The approaches proposed in this paper can serve as a basis for the creation of specialized information technologies to automate the calculation of project risk assessments.

Grace Chang, Galen Egan, Joseph D. McNeil et al.

Abstract Novel analysis of in situ acoustic and optical data collected in a shallow, wave‐ and current‐driven environment enabled determination of (1) particle characteristics that were most affected by near‐bed physical forcing over seasonal scales and (2) characteristic shear stress, τchar, at which the rate of change to particle characteristics was most pronounced. Near‐bed forcing and particle responses varied by season. Results indicated that moderate τchar values of 0.125 Pa drove changes in particle composition during summer. In winter, particle concentration effects were most affected at τchar of 0.05 Pa, suggesting dominance of fluff layer resuspension. Changes to particle size were most relevant during a biologically productive springtime period, with initiation of particle disaggregation occurring most commonly at τchar of 0.25 Pa. These results suggest that it may be more important to parameterize τchar, as opposed to critical shear stress for erosion, for sediment transport models.