Abstract Background Because of the important role of bioactive compounds in functional foods and health care, more and more attention has been paid to the active components extracted by green extraction methods. However, how to ensure the extraction yield and biological activity during the extraction process is an urgent problem to be solved. Subcritical water extraction (SWE) is a promising engineering method, which provides an environmentally friendly technology for extracting various bioactive compounds from natural products. Especially, under high temperature and high pressure, subcritical water can change the polarity and dielectric constant of solvents, thus contributing to a better extraction process, improving the mass transfer efficiency of the extracts and maintaining its biological activities, which has a high application prospect. Scope and approach This review provided an update overview on the fundamental principles and bioactive compounds extraction with subcritical water. This will contribute to deepen the understanding of SWE and provide theoretical basis and reference value for further improving the application of subcritical water. Key founds and conclusions It is expected that this green and efficient extraction technology will be increasingly applied in the extraction of more bioactive ingredients in the near future. In addition, future research should consider combining SWE with other physical extraction techniques to ensure the retention of the biological ingredients better. These active compounds with SWE have great potential in health care medicine and functional foods. Meanwhile, future researches should focus on consumer acceptability, safety, legal aspects, and commercial availability of health products.
The article analyzes the applicability of hydrodynamic scaling laws in the design of fins for swimming humanoid robots intended to perform demanding and high‑risk underwater tasks in hydraulic engineering and sanitary technology. It is demonstrated that fin length plays a crucial role in the efficiency of rapid locomotion, which may be applied to tasks such as delivering equipment to divers in caissons or transporting materials in underwater environments. The presented mathematical relationships provide a tool supporting the selection of optimal fin parameters in engineering practice. The proposed modeling algorithms can be applied not only in the context of humanoid robots but also to human swimming, thereby extending the range of potential applications. Calculations are presented for the propulsion generated by the fins of a humanoid robot of the Morphobot type. The robot’s design enables transformation from a humanoid to a sirenoid configuration by replacing the lower body segment with a tail module integrated with a monofin. It is shown that the use of a single monofin attached to both legs eliminates functional redundancy, whereas integration of the monofin with a dedicated tail module increases reliability. The highest level of functional redundancy is achieved when two separate fins are mounted on each leg in elongated boots reaching mid‑calf, equipped with fastening straps and buckles. This solution, referred to as hybrid mounting, ensures functional redundancy by allowing task continuation even in the event of the loss of one fin. Keywords: fin hydrodynamics, scaling laws, humanoid robots, underwater construction, hydraulic engineering, sanitary technology, bioinspired design, robot locomotion, underwater safety, aquatic robotics
<p>Campi Flegrei, one of the most monitored and studied volcanic areas in the world, has recently attracted significant attention due to the reactivation of its peculiar activity, consisting of small earthquakes, geothermal phenomena and slow subsidence/rapid uplift cycles, known as bradyseism. While much of the research and of the attention focuses on potential eruptions or other volcanic-related activities, the potential hazard posed by gravitational instabilities has received little consideration. The interaction of the destabilized masses with water can trigger tsunamis, potentially affecting the whole coastline of the Gulf of Pozzuoli, which lies above the Campi Flegrei caldera. Moving from the limited available geomorphological studies of the area, a set of four landslide-tsunami scenarios (one subaerial and three submarine sources) are reconstructed. These are simulated through a sequence of numerical codes, accounting for all the phases of the tsunami process, providing insights into the distribution of tsunami energy and identifying the most affected coastal stretches. Additionally, the study explores the influence of dispersion effects in the tsunami propagation and the occurrence of resonance effects in some minor inlets of the Gulf, emphasizing the importance of accounting for complex and non-linear coastal processes when treating landslide-generated tsunamis.</p>
Cláudia Orsini Machado de Sousa, Rafael de Freitas Souza, Nuno Manoel Martins Dias Fouto
The increasing pressure on water resources has intensified the need to improve water supply efficiency, particularly by minimizing water loss. Non-revenue water (NRW) remains a critical issue in Brazil, with high loss levels impacting both financial sustainability and water security. This study employs a two-level hierarchical linear model (HLM2) to analyze NRW drivers in 310 Brazilian municipalities from 2017 to 2021. Ten factors that could influence water loss in the country were selected based on previous literature, and our results indicate that only four of them were statistically significant: population size, share of residential consumers, network length, and utility ownership. Municipalities with larger urban populations and longer distribution networks exhibited higher NRW levels, while a higher share of residential consumers correlated with lower NRW. Additionally, private utilities demonstrated lower water losses compared to public ones, likely due to stronger efficiency incentives. These findings highlight that the factors influencing NRW in Brazil differ from those commonly reported in studies based on developed countries. Therefore, policies designed to reduce NRW must consider regional specificities rather than relying solely on international benchmarks, which usually refer to developed countries. This study provides empirical evidence to support targeted interventions, helping water utilities and policymakers develop more effective NRW reduction strategies.
The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration status, or lack of flexible work options can significantly impact women's career progress, creating barriers for returning as lecturers, professors, or senior researchers. Although many companies promote gender diversity policies, such measures are less prominent and often under-recognized within academic institutions. Our goal is to explore the specific challenges women encounter when re-entering academic roles compared to industry roles; to understand the institutional perspective, including a comparative analysis of existing policies and opportunities in different countries for women to return to the field; and finally, to provide recommendations that support transparent hiring practices. The research project will be carried out in multiple universities and in multiple countries to capture the diverse challenges and policies that vary by location.
Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.
Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce PyTorchFire, an open-access, PyTorch-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model, we achieve millisecond-level computational efficiency, significantly outperforming traditional CPU-based wildfire simulators on real-world-scale fires at high resolution. Real-time parameter calibration is made possible through gradient descent on our model, aligning simulations closely with observed wildfire behavior both temporally and spatially, thereby enhancing the realism of the simulations. Our PyTorchFire simulator, combined with real-world environmental data, demonstrates superior generalizability compared to supervised learning surrogate models. Its ability to predict and calibrate wildfire behavior in real-time ensures accuracy, stability, and efficiency. PyTorchFire has the potential to revolutionize wildfire simulation, serving as a powerful tool for wildfire prediction and management.
The continuously growing effort towards developing real-world quantum technological applications has come to demand an increasing amount of flexibility from its respective platforms. This review presents a highly adaptable engineering technique for photonic quantum technologies based on the artificial structuring of the material nonlinearity. This technique, while, in a simple form, already featured across the full breadth of photonic quantum technologies, has undergone significant development over the last decade, now featuring advanced, aperiodic designs. This review gives an introduction to the three-wave-mixing processes lying at the core of this approach, and illustrates, on basis of the underlying quantum-mechanical description, how they can artificially be manipulated to engineer the corresponding photon characteristics. It then describes how this technique can be employed to realize a number of very different objectives which are expected to find application across the full range of photonic quantum technologies, and presents a summary of the research done towards these ends to date.
The current study investigates incompressible, MHD flow of Cross nanofluid containing of gyrotactic microorganisms and thermophoretic particle deposition over a sheet with activation energy and variable thermal conductivity. The variable characteristic of thermal conductivity is considered as a linear function of temperature. The present study’s insights can optimize the design of nanofluid-based systems, enhance drug delivery methods, improve environmental monitoring, refine materials engineering, advance microfluidics for diagnostics, boost renewable energy technologies, and upgrade electronics cooling solutions. Moreover, this study contribution to scientific understanding will catalyze further research across disciplines, fostering innovation and progress. Cross nanofluid containing iron oxide ( F e 3 O 4 ) nanoparticles, and based fluid ethylene glycol ( C 2 H 6 O 2 ) is used. In the current study, distributions of concentration, temperature, mass, microorganisms, and flow are examined in the presence of nanofluid while also accounting for thermophoretic particle deposition and a heat source. The proposed flow equations are transmuted into ODEs by employing the suitable similarity variables. RKF-45th approach is used to evaluate the reduced equations. Graphs are used to determine the effects of important factors on thermal, microorganism, concentration, and flow profiles. With a rise in the Marangoni ratio parameter, the velocity distribution is enhanced, whereas the temperature distribution exhibit inverse behavior.
Social development has led to the placement of high standards on ultra-long and ultra-deep mountain tunnels. Disasters may be encountered during the construction and maintenance of such mountain tunnels due to high geostress, high geotemperature, high hydraulic pressure, and special adverse strata, in addition to various other problems caused by engineering activities. To deal with uncertain geological conditions during mountain tunnel construction, comprehensive geological prediction, refined monitoring, and dynamic design and construction methods based on information technology should be adopted. For the operation and maintenance of ultra-long tunnels, the concepts of dynamic evacuation rescue, active protection, energy conservation, and environmental protection should be fully embodied in order to address significant problems related to ventilation, rescue situations, and energy consumption. Moreover, integrated construction and maintenance should be carried out to achieve digital sensing and intelligent maintenance. New ideas and technologies should be adopted to improve the quality and efficiency of the whole process of construction and operation, and to enable the construction of environmentally friendly tunnels, thus achieving the ultimate goals of safety, efficiency, greenness, and intelligence for ultra-long and ultra-deep rock tunnels. With the development of society, the economy, and transportation networks, the construction of ultra-long and ultra-deep tunnels through mountains has become increasingly inevitable. Ultra-long and ultra-deep tunnels are generally defined as tunnels that have a length exceeding 10 km and a depth exceeding 500 m [1]. Mountain tunnels mainly consist of road tunnels, railway tunnels, and hydraulic tunnels. Although an ultra-long and ultra-deep tunnel potentially features the advantages of safety, environmental friendliness, and speed, the cost and difficulty of project establishment, construction, and operation are considerable. Table 1 lists the ultra-long and ultra-deep mountain tunnels that have already been built or are under construction around the world. According to an incomplete survey, there are 56 ultra-long and ultra-deep mountain tunnels in China and 21 abroad. Among these tunnels, the 57.1 km Gotthard Basis Tunnel is the longest and deepest in the world, the 32.7 km New Guanjiao Tunnel is the world’s longest tunnel above 3000 m, the 18 km Highway Tunnel of Qinling Zhongnan Mountain is the world’s longest double-line highway tunnel, and the 16 km Qinling Tianhuashan Tunnel is Asia’s longest single-hole two-lane high-speed rail tunnel. With the increasing demand for and ongoing progress in tunnel construction technologies, the construction of ultra-long and ultra-deep mountain tunnels will usher in new development opportunities. Due to the high geostress, high geotemperature, and ultra-long construction and operation, these complex tunnel projects must handle unprecedented challenges in terms of design, construction, operation, and maintenance, which demand new ideas and engineering measures.
Pressure and residual chlorine concentration are among the key parameters in urban water distribution networks that require continuous monitoring and control. These networks must ensure that consumer water demands are met with adequate pressure while optimizing water quality parameters, such as residual chlorine concentration, to maximize service satisfaction. In this study, the Najaf Abad urban water distribution network was selected as a real large-scale case study. A simultaneous optimization model was developed to determine nodal average pressure, residual chlorine concentration, and network combined reliability. The multi-objective optimization problem was solved using the NSGA-II algorithm under two extreme water consumption scenarios-maximum and minimum water withdrawal during warm and cold seasons. A Pressure-Driven Analysis approach was employed to calculate network parameters. Additionally, three objective functions were optimized using the NSGA-II multi-objective optimization algorithm. The optimal solution was selected from the Pareto front using the TOPSIS method. The network under study includes four operational pressure-reducing valves; after determining their optimal set pressure values, the average network pressure was reduced by 2.9% during ward days and 13.5% during cold days. The average residual chlorine concentration did not undergo significant changes however, its further reduction was prevented through optimization, effectively achieving this objective as well. Lastly, the combined reliability increased by 1.7% and 1.3% for warm and cold days, respectively.
Technology, Water supply for domestic and industrial purposes
Urbanization is known to elevate storm runoff, but how it influences carbon cycle and ecosystem productivity through altering the evapotranspiration (ET) process is less clear. We examined the combined effects of urbanization including change in impervious surface area (ISA) and climate variability on the water and carbon balances of the Qinhuai River Basin (QRB) over 2001–2018. QRB represents a typical rice paddy-dominated region that experienced rapid urbanization in southern China. We improved a monthly scale water supply stress index ecosystem service model by integrating local eddy flux measurements and high-resolution remote sensing data. We found a significant downward trend in both ET (−4.6 mm yr ^−1 , p < 0.05) and gross primary productivity (GPP) (−10.4 gC m ^−2 yr ^−1 , p < 0.05) but a significant upward trend in water yield ( Q ) (+28.6 mm yr ^−1 , p < 0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 1.9-fold increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m ^−2 to 998 gC m ^−2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modeling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g. urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q . Our research offers insight about the interactions of carbon and water cycles through the critical ET processes under a changing climate and land surface characteristics at a watershed level. Our modeling tool and analysis provides land managers and policy makers information for designing effective ‘Urban Nature-based Solutions’ to mitigate the negative environmental effects of urbanization on carbon and water resources.
Marthinus Brits, Martin J. M. van Velzen, Feride Öykü Sefiloglu
et al.
Abstract The limited available data on human internal exposure poses a significant challenge in assessing the risks associated with micro and nanoplastics (MNPs) to human health. A contributing factor to this challenge is the scarcity of sensitive analytical methods to quantify the mass concentration of plastic polymers in human blood. In this study we present an improved and validated method for quantitatively analysing polyethylene (PE), polyethylene terephthalate (PET), poly(vinyl chloride) (PVC), poly(methyl methacrylate) (PMMA), polypropylene, and polystyrene in human whole blood samples. We introduce and apply stringent quality assurance and quality control procedures, including the validation of the method using quality control samples and continuous monitoring of batch analyses to ensure data reliability. Expanding upon prior pioneering work by Leslie et al. (2022), we optimised the pyrolysis-gas chromatography–mass spectrometry (Py-GC-MS) conditions to enhance method sensitivity and selectivity. Recovery experiments demonstrated a high level of accuracy and precision, with values ranging from 68 to 109% for quality control samples. Applying this method to whole blood samples (n = 68), we identified plastic polymers in 64 samples, with PE as the predominant polymer, followed by PVC, PET, and PMMA. In 17 blood samples, polymer concentrations were found to exceed the limit of quantitation, with a mean of 1070 ng/mL for the summed polymer concentrations, ranging between 170 and 2490 ng/mL. The mean of the sum of polymers across all blood samples (n = 68) was 268 ng/mL. These findings underscore the pressing need for further research aimed at comprehensive MNP quantification in human matrices, considering the potential health implications.
Environmental pollution, Polymers and polymer manufacture
Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco
et al.
Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.
In this practice paper, we propose a framework for integrating AI into disciplinary engineering courses and curricula. The use of AI within engineering is an emerging but growing area and the knowledge, skills, and abilities (KSAs) associated with it are novel and dynamic. This makes it challenging for faculty who are looking to incorporate AI within their courses to create a mental map of how to tackle this challenge. In this paper, we advance a role-based conception of competencies to assist disciplinary faculty with identifying and implementing AI competencies within engineering curricula. We draw on prior work related to AI literacy and competencies and on emerging research on the use of AI in engineering. To illustrate the use of the framework, we provide two exemplary cases. We discuss the challenges in implementing the framework and emphasize the need for an embedded approach where AI concerns are integrated across multiple courses throughout the degree program, especially for teaching responsible and ethical AI development and use.
Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber
et al.
Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.
Gus Henry Smith, Colin Knizek, Daniel Petrisko
et al.
State-of-the-art hardware compilers for FPGAs often fail to find efficient mappings of high-level designs to low-level primitives, especially complex programmable primitives like digital signal processors (DSPs). New approaches apply sketch-guided program synthesis to more optimally map designs. However, this approach has two primary drawbacks. First, sketch-guided program synthesis requires the user to provide sketches, which are challenging to write and require domain expertise. Second, the open-source SMT solvers which power sketch-guided program synthesis struggle with the sorts of operations common in hardware -- namely multiplication. In this paper, we address both of these challenges using an equality saturation (eqsat) framework. By combining eqsat and an existing state-of-the-art program-synthesis-based tool, we produce Churchroad, a technology mapper which handles larger and more complex designs than the program-synthesis-based tool alone, while eliminating the need for a user to provide sketches.
This study conducts a comprehensive literature review of articles on the triple bottom line (TBL) published from January 1997 to September 2018 to provide significant insights and support to guide further discussion. There were three booms in TBL publications, occurring in 2003, 2011, and 2015, and many articles attempt to address the issue of sustainability by employing the TBL. This literature analysis includes 720, 132, and 58 articles from the Web of Science (WOS), Inspec, and Scopus databases, respectively, and reveals the gaps in existing research. To discover the barriers and points of overlap, these articles are categorized into six aspects of the TBL: economic, environmental, social, operations, technology, and engineering. Examining the top 3 journals in terms of published articles on each aspect reveals the research trends and gaps. The findings provide solid evidence confirming the argument that the TBL as currently defined is insufficient to cover the entire concept of sustainability. The social and engineering aspects still require more discussion to support the linkage of the TBL and to reinforce its theoretical basis. Additionally, to discover the gaps in the data sources, theories applied, methods adopted, and types of contributions, this article summarizes 82 highly cited articles covering each aspect. This article offers theoretical insights by identifying the top contributing countries, institutions, authors, keyword networks, and authorship networks to encourage scholars to push the current discussion further forward, and it provides practical insights to bridge the gap between theory and practice for enhancing the efficiency and effectiveness of improvements.
Vineet Kumar, Sunil Kumar, J. H. P. Américo-Pinheiro
et al.
Waste Re-processing Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nagpur, India, Department of Forest Science, Soils, and Environment, School of Agronomic Sciences, São Paulo State University (UNESP), Ave. Universitária, Botucatu, Brazil, Graduate Program in Environmental Sciences, Brazil University, São Paulo, Brazil, University of Sri Jayewardenepura, Nugegoda, Sri Lanka, Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom