Hasil untuk "River, lake, and water-supply engineering (General)"

R. L. Shreve

Junwei Yu, Mufeng Yang, Yepeng Ding et al.

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

Yi Lyu, Pei-Chieh Lo, Natan Lidukhover

Data lakes have emerged as a flexible and scalable solution for storing and analyzing large volumes of heterogeneous data, including structured, semi-structured, and unstructured formats. Despite their growing adoption in both industry and academia, there is a lack of standardized and comprehensive benchmarks for evaluating the performance of data lake systems. Existing benchmarks primarily target traditional data warehouses and focus on structured SQL workloads, making them insufficient for capturing the diverse workloads and access patterns typical of data lakes. In this work, we propose a new benchmarking framework for data lakes that aims to provide an objective and comparative evaluation of different data lake implementations. Our benchmark covers multiple data types and workload models, including data retrieval, aggregation, querying, and similarity search, which is a common yet underexplored operation in existing benchmarks. We measure key performance metrics such as query execution time, metadata generation time, and metadata size across different scale factors. The benchmark is designed to be extensible and reproducible, enabling users to generate datasets and evaluate data lake systems under realistic and diverse scenarios. We conduct our experiments on CloudLab and demonstrate how the proposed benchmark can be used to compare both commercial and open-source data lake platforms.

Mark Looi

The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study the usage patterns of 147 professional developers, examining perceived correlates of AI tools use, the resulting productivity and quality outcomes, and developer readiness for emerging AI-enhanced development. We describe a virtuous adoption cycle where frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest. The study finds no perceptual support for the Quality Paradox and shows that PP is positively correlated with Perceived Code Quality (PQ) improvement. Developers thus report both productivity and quality gains. High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption, though security concerns remain a moderate and statistically significant barrier to adoption. Moreover, AI testing tools' adoption lags that of coding tools, opening a Testing Gap. We identify three developer archetypes (Enthusiasts, Pragmatists, Cautious) that align with an innovation diffusion process wherein the virtuous adoption cycle serves as the individual engine of progression. Our findings reveal that organizational adoption of AI tools follows such a process: Enthusiasts push ahead with tools, creating organizational success that converts Pragmatists. The Cautious are held in organizational stasis: without early adopter examples, they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy. Policy itself does not predict individuals' intent to increase usage but functions as a marker of maturity, formalizing the successful diffusion of adoption by Enthusiasts while acting as a gateway that the Cautious group has yet to reach.

HUANG Zexi, SUN Wei, CHEN Xinlin et al.

This study took the“22·6”flood event at the Gaodao Station along the Lianjiang River in the middle and upper reaches of the Beijiang River in Guangdong Province as an example to explore the flow prediction method combining physical models with deep learning, aiming to improve the accuracy of hydrological predictions under extreme weather conditions. The study adopted a combination of the hydrologic engineering center-hydrologic modeling system (HEC-HMS) distributed hydrological model and the long short-term memory (LSTM) network to construct three types of coupled models, namely the HEC-LSTM model based on error correction, the HECo1-LSTM model based on single-station flow, and the HECo2-LSTM model based on multi-sub-basin output. Through prediction experiments with forecast periods of three hours, six hours, and 12 hours, the performance of each coupled model and the single hydrological model in runoff forecasting and extreme flood events was compared. The results show that the HEC-HMS model has limitations when the flow fluctuates greatly; the error correction-based HEC-LSTM model significantly improves the prediction accuracy in the short and medium term, with the root mean square error (RMSE) reduced by approximately 46% in the training set and 25% in the validation set. The HECo1-LSTM and HECo2-LSTM models perform outstandingly in long-term forecast periods, with the HECo2-LSTM model reducing the RMSE by 58% in the training set and 33% in the validation set and maintaining a high prediction accuracy (Nash-Sutcliffe model efficiency coefficient of 0.91) even in the 12-hour forecast period. This study provides a new coupling method for hydrological simulation and prediction in river basins, which is expected to significantly improve the accuracy and reliability of hydrological forecasts under extreme weather conditions.

GUO Chu, WANG Danna, ZHANG Tao et al.

Based on Euler multiphase model, RNG <italic>k</italic>-<italic>ε </italic>turbulence model, and SIMPLEC algorithm, a numerical simulation of solid-liquid two-phase flow in an axial flow pump was carried out to study the pressure fluctuation characteristics of the axial flow pump when transporting a sediment-laden flow. The pressure fluctuation characteristics of the axial flow pump unit at different positions under the condition of clean water were emphasized, and the pressure pulsation characteristics with clean water medium and solid-liquid two-phase flow under the design condition were compared. The results show that the pressure pulsation at the impeller inlet and outlet of the axial flow pump unit presents obvious periodicity under the condition of clear water medium, and the amplitude increases gradually from the hub to the rim; the pulsation amplitude at the impeller outlet is less than that at the impeller inlet. The pulsation amplitude in the middle of the guide vane is smaller than that at the inlet and outlet of the impeller, and the pressure pulsation amplitude decreases first and then increases along the radial direction; the pressure pulsation amplitude at the outlet of the guide vane distributes irregularly along the radial direction. There is no obvious difference between the pressure pulsation amplitudes at the inlet of the impeller under the condition of clean water and solid-liquid two-phase flow, but the pressure pulsation waveform phase under the condition of two-phase flow lags behind about 0.04 s compared with that under the condition of clean water. The distribution law of pressure pulsation in the middle and at the outlet of the guide vane is quite different, and the amplitude of pressure pulsation is relatively large under the condition of solid-liquid two-phase flow. The vortex intensity distribution in the middle section of the guide vane is more complex than that in the clear water condition when the flow is laden with sediment, and the intensity is larger. The research results can provide some reference for the optimal design of axial flow pump units.

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

E. H. Colombo, A. B. García-Andrade, Ismail et al.

Rivers exhibit fractal-like properties that are associated with scaling laws linking geometry and size. The optimal channel network (OCN) model, which is a mathematically tractable representation of river networks often used in theoretical studies, is based on the fractal properties of rivers and consequently reproduces geometric scaling laws. However, purely geometric relationships may not fully capture the interaction between river structure and species' movement strategies that is most relevant to many large-scale ecological processes. In contrast, connectivity, which is a concept that blends habitat geometry and individual movement, has been shown both theoretically and empirically to influence relevant large-scale ecological outcomes across a broad array of ecosystems. Here, we analyze networks from more than 1000 major rivers around the world, including the Amazon, Mississippi, and Nile, to investigate how river network connectivity metrics scale with system size. Specifically, we found clear power-law scaling of both the harmonic centrality and betweenness centrality network connectivity metrics. To assess the extent to which OCNs can capture these empirical connectivity patterns, we generated synthetic river networks by fitting an OCN model to each real river. We found excellent agreement between empirical and OCN-based scaling laws, supporting the notion that OCNs can accurately represent rivers in network-based models and analyses. Finally, we examined the robustness of the connectivity scaling laws to species movement strategies ranging from ideal shortest-path navigation to suboptimal random-path navigation. Surprisingly, we found that random navigation breaks the power-law scaling relationship for harmonic centrality, but not for betweenness centrality.

Nadia Ouyahia, Abdelhamid Saou, Mustapha Maza et al.

Understanding the critical relationship between the Soummam River and its alluvial aquifer is crucial for the protection of this vital water resource. The approach is based on monitoring the spatial and temporal evolution of physico-chemical parameters and identifying their origin through correlation with the geology and piezometry of the alluvial aquifer; this will be achieved using differential gauging and hydrogeochemical tracing. This will provide valuable information for the management and protection of this precious water resource. The case study focuses on the alluvial aquifer of the lower Soummam Valley in Bejaia, Algeria, where a unique natural barrier upstream creates a close hydraulic relationship between the river and the aquifer. This allows for water exchange, which we investigated through two sampling campaigns (high and low water) at 32 water points (boreholes, wells and stations). By tracking the movement of special chemical tracers in both the Soummam River and the underlying alluvial aquifer, this study confirms a direct hydraulic connection between them. This means that water can flow from the river into the aquifer, highlighting the potential risk of water pollution. This has helped us to identify areas where pollution from the river could seep into the groundwater, threatening the drinking water supply. HIGHLIGHTS Determination of the existing exchanges between the river and the alluvial aquifer.; Monitoring of the spatial and temporal evolution of physico-chemical parameters.; Identification of the origin of the physico-chemical parameters.; Selection of differential gauging methods and hydrogeochemical tracing.; Identification of areas vulnerable to water pollution in the alluvial aquifer of the lower Soummam Valley.;

JIN Gaoyang, ZHU Sanhua, CAO Yihao

To study the changes in the riverbed of the Northwest River Delta and their relationship with saltwater intrusion,this paper builds a one-dimensional unsteady flow chlorine concentration mathematical model based on the collection and organization of previous research results,hydrological,meteorological,and river terrain data.The hydrodynamic and chlorine concentration verification results meet the relevant regulatory requirements and can be applied to calculate and analyze the impact of riverbed changes on saltwater intrusion.The research results are as follows.① From 1999 to 2016, the riverbed of the Northwest River Delta network was in an uneven downward cutting state,and the volume of the river continued to increase,resulting in a decrease in the resistance of the estuary to rising and falling tides.The upward tracing of high saltwater masses along the estuary was simpler,and the upward tracing of salt tides became increasingly severe.② The rising tide at each Koumen station increases,and the probability of removing freshwater from the saltwater boundary decreases.Under different inflow conditions in Sixianjiao,the average upward movement of the saltwater boundary at Modaomen Waterway is 3 735 m,and the average downward movement of the freshwater from Pinggang Pumping Station is 5.33%.The average upward movement of the saltwater boundary at Shawan Waterway is 2 369 m,and the average downward movement of the freshwater from Shawan No.1 Water Plant is 5.14%.These seriously affect the ecosystem and normal water intake demand in the estuarine area.The research results can provide basic support for the research and engineering design of water supply safety measures in the Northwest River Delta and estuarine areas.

Ricardo Villalba-Briones, Paola Calle, Marynes Montiel et al.

Hydrogeochemical and microbiological parameters of groundwater samples in the Paipayales agricultural community in western Ecuador were studied to evaluate groundwater origin, contamination, and suitability for domestic use and irrigation. The water wells studied are typically shared by multiple families which account for 37% of the total community population. A total of 31 parameters of water samples from the wells used by the community were analysed by four laboratories at the ESPOL University. The parameters analysed included microbiological and chemical compounds, along with physical characteristics typically influencing water quality. As regards the World Health Organization (WHO), U.S. Environmental Protection Agency (EPA), and Ecuadorian standards, all samples failed to meet the required concentrations for at least one compound. The chemical analysis showed eight elements (cadmium, aluminium, ammonia, iron, manganese, chloride, and bromide) exceeded the maximum limits for drinking water in at least one well. Seventy percent of sampled wells failed to meet the maximum permissible limits for at least one chemical parameter. Water in all wells showed the presence of microbiological contaminants. The high natural groundwater salinity limits the ability to use this groundwater for irrigation purposes. Water in open and closed wells shows different hydrochemical and microbiological patterns. The presence of domestic animals and the lack of protection for wells may influence the quality of water. It is highly recommended that the authorities increase water supply and storage capacity to improve the availability of drinkable water in rural communities in the area.

Wenpeng Wang, Ming Tang, Yanlei Li et al.

Although regional and seasonal water scarcity occurs frequently in China, and the contradiction among domestic, production and ecological water is prominent in some watersheds, the Chinese government still attaches great importance to the determination and implementation of ecological flow of rivers or lakes. Practitioners have been seeking methods to determine the ecological flow of rivers or lakes and how to ensure its implementation. Taking the Dingnan River watershed as a case, drawing on the experience of ‘Hedging rule’, the ‘Determination-Assessment-Reduction’ for the ecological flow nexus approach (the D-A-R approach) is introduced, which includes the determination of the annual ecological flow process through the river section, the assessment of water scarcity degree of the watershed and various water reduction strategies, respectively, and respond to the three scenarios of ‘general type, saving type and constrained type’ during the gap period. The results show that it is possible to use the D-A-R approach to proactively and dynamically adjust the ecological flow according to the probability estimate of that amount of water inflow per month, which the adjusted ecological flow threshold can better adapt to water scarcity at different levels and alleviate the contradiction among domestic, production and ecological water in the watershed during the dry period. HIGHLIGHTS Drawing on the experience of the ‘Hedging rule’, the ‘synchronous reduction strategy’ about the demand for domestic, production and ecological water is proposed.; The D-A-R approach is an ecological flow determination approach combined with assessment and reduction.; The new approach is proposed to strengthen water allocation to water users with potential future water scarcity to degrade the probability of suffering more serious water scarcity events in the later stage.;

Federico Vilaseca, Santiago Narbondo, Christian Chreties et al.

RESUMENUn sólido estudio hidrológico diario es una tarea desafiante en regiones caracterizadas por una alta variabilidad hidro-climática, como Uruguay. Por esta razón, los modelos hidrológicos de base física de diferentes escalas temporales y espaciales (concentrados, semi-distribuidos y distribuidos) han pasado por un largo período de desarrollo y aplicación local. En los últimos años, los modelos basados en datos se están usando con éxito para resolver problemas hidrológicos. Hasta ahora, estos diferentes tipos de modelos se han estudiado individualmente para evaluar su capacidad para simular el proceso diario de precipitación-escorrentía. Este trabajo proporciona una profunda comparación entre un modelo agregado (GR4J), un modelo semi-distribuido (SWAT) y otro basado en datos (Random Forest (RF)) para simular el proceso diario de precipitación-escorrentía de dos cuencas hidrográficas ubicadas en Uruguay (una con reservorio y la otra sin). El rendimiento de cada modelo se analizó comparando numéricamente y gráficamente el caudal observado versus el simulado en términos de correspondencia temporal y cuantiles. En general, RF presenta un mejor rendimiento en comparación con los otros modelos físicamente basados. Sin embargo, carece de la capacidad de generalización que caracterizó a los otros dos enfoques. GR4J y SWAT logran un desempeño similar en nuestros casos de estudio.

Wanrui Wang, Yaning Chen, Yapeng Chen et al.

Intense anthropogenic activities in arid areas have great impacts on groundwater process by causing river dried-up and phreatic decline. Groundwater recharge and discharge have become hot spot in the dried-up river oases of arid regions, but are not well known, challenging water and ecological security. This study applied a stable isotope and end-member mixing analysis method to quantify shallow groundwater sources and interpret groundwater processes using data from 186 water samples in the Wei-Ku Oasis of central Asia. Results showed that shallow groundwater (well depth < 20 m) was mainly supplied by surface water and lateral groundwater flow from upstream, accounting for 88 and 12%, respectively, implying surface water was the dominant source. Stable isotopes and TDS showed obviously spatiotemporal dynamic. Shallow groundwater TDS increased from northwest to southeast, while the spatial variation trend of groundwater δ18O was not obvious. Surface water and groundwater in non-flood season had higher values of stable isotopes and TDS than those in flood season. Anthropogenic activities greatly affect groundwater dynamics, where land-cover change and groundwater overexploitation are the main driving factors. The findings would be useful for further understanding groundwater sources and cycling, and help restore groundwater level and desert ecosystem in the arid region. HIGHLIGHTS The sources of shallow groundwater in the dried-up river oasis of central Asia were quantified.; Surface water was the dominant source of shallow groundwater.; Anthropogenic activities greatly affect groundwater dynamic and cycle.;

Johan Bergelin, Per Erik Strandberg

There is an increasing interest in research on the combination of AI techniques and methods with MDE. However, there is a gap between AI and MDE practices, as well as between researchers and practitioners. This paper tackles this gap by reporting on industrial requirements in this field. In the AIDOaRt research project, practitioners and researchers collaborate on AI-augmented automation supporting modeling, coding, testing, monitoring, and continuous development in cyber-physical systems. The project specifically lies at the intersection of industry and academia collaboration with several industrial use cases. Through a process of elicitation and refinement, 78 high-level requirements were defined, and generalized into 30 generic requirements by the AIDOaRt partners. The main contribution of this paper is the set of generic requirements from the project for enhancing the development of cyber-physical systems with artificial intelligence, DevOps, and model-driven engineering, identifying the hot spots of industry needs in the interactions of MDE and AI. Future work will refine, implement and evaluate solutions toward these requirements in industry contexts.

Etienne Scholly, Pegdwendé Sawadogo, Pengfei Liu et al.

The rise of big data has revolutionized data exploitation practices and led to the emergence of new concepts. Among them, data lakes have emerged as large heterogeneous data repositories that can be analyzed by various methods. An efficient data lake requires a metadata system that addresses the many problems arising when dealing with big data. In consequence, the study of data lake metadata models is currently an active research topic and many proposals have been made in this regard. However, existing metadata models are either tailored for a specific use case or insufficiently generic to manage different types of data lakes, including our previous model MEDAL. In this paper, we generalize MEDAL's concepts in a new metadata model called goldMEDAL. Moreover, we compare goldMEDAL with the most recent state-of-the-art metadata models aiming at genericity and show that we can reproduce these metadata models with goldMEDAL's concepts. As a proof of concept, we also illustrate that goldMEDAL allows the design of various data lakes by presenting three different use cases.

Jennifer Hehn, Daniel Mendez

Effective Requirements Engineering is a crucial activity in softwareintensive development projects. The human-centric working mode of Design Thinking is considered a powerful way to complement such activities when designing innovative systems. Research has already made great strides to illustrate the benefits of using Design Thinking for Requirements Engineering. However, it has remained mostly unclear how to actually realize a combination of both. In this chapter, we contribute an artifact-based model that integrates Design Thinking and Requirements Engineering for innovative software-intensive systems. Drawing from our research and project experiences, we suggest three strategies for tailoring and integrating Design Thinking and Requirements Engineering with complementary synergies.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda et al.

Requirements Engineering (RE) requires the collaboration of various roles in SE, such as requirements engineers, stakeholders and other developers, and it is thus a highly human dependent process in software engineering (SE). Identifying how human aspects such as personality, motivation, emotions, communication, gender, culture and geographic distribution might impact RE would assist us in better supporting successful RE. The main objective of this paper is to systematically review primary studies that have investigated the effects of various human aspects on RE. A systematic literature review (SLR) was conducted and identified 474 initial primary research studies. These were eventually filtered down to 74 relevant, high-quality primary studies. Among the studies, the effects of communication have been considered in many RE studies. Other human aspects such as personality, motivation and gender have mainly been investigated to date related to SE studies including RE as one phase. Findings show that studying more than one human aspect together is beneficial, as this reveals relationships between various human aspects and how they together impact the RE process. However, the majority of these studied combinations of human aspects are unique. From 56.8% of studies that identified the effects of human aspects on RE, 40.5% identified the positive impact, 30.9% negative, 26.2% identified both impacts whereas 2.3% mentioned that there was no impact. This implies that a variety of human aspects positively or negatively affects the RE process and a well-defined theoretical analysis on the effects of different human aspects on RE remains to be defined and practically evaluated. Findings of this SLR help researchers who are investigating the impact of various human aspects on RE by identifying well-studied research areas, and highlight new areas that should be focused on in future research.

Pegdwendé Sawadogo, Jérôme Darmont

Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are mainly characterized by volume, velocity, variety and veracity issues. Big data-related issues strongly challenge traditional data management and analysis systems. The concept of data lake was introduced to address them. A data lake is a large, raw data repository that stores and manages all company data bearing any format. However, the data lake concept remains ambiguous or fuzzy for many researchers and practitioners, who often confuse it with the Hadoop technology. Thus, we provide in this paper a comprehensive state of the art of the different approaches to data lake design. We particularly focus on data lake architectures and metadata management, which are key issues in successful data lakes. We also discuss the pros and cons of data lakes and their design alternatives.