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

Mikhail V. Bolgov

One of the important tasks of engineering hydrology and river runoff regulation, the solution of which is not fully regulated by current regulatory and methodological documents, is the determination of the design maximum discharges in the lower pools of hydraulic structures during flow regulation. These characteristics are calculated during the development of Water Resources Use Regulations and are valid for the duration of this document’s validity.

Ricardo Martinez, Juan D. Moreno-Ternero

We study the adjudication of water rights in international rivers. We characterize allocation rules that formalize focal principles to deal with water disputes in a basic model. Central to our analysis is a family of geometric rules that implement concatenated transfers downstream. They can be seen as formalizing Limited Territorial Sovereignty, as suggested in the Rio Declaration on Environment and Development. We apply our rules to the case of the Nile River, with a long history of disputes between downstream and upstream nations

Ovidio García-Oliva, Carsten Lemmen, Xiangyu Li et al.

Episodes of low dissolved oxygen concentration--hypoxia--threaten the functioning of and the services provided by aquatic ecosystems, particularly those of urban rivers. Here, we disentangle oxygen-related processes in the highly modified Elbe River flowing through the major German city of Hamburg, where low oxygen levels are frequently observed. We use a process-based biochemical model that describes particulate and dissolved organic matter, micro-algae, their pathogens, and the key reactions that produce or consume oxygen: photosynthesis, re-aeration, respiration, mineralization, and nitrification. The model analysis reveals pronounced spatial variability in the relative importance of these processes. Photosynthesis and respiration are more prominent upstream of the city, while mineralization, nitrification, and re-aeration prevail downstream. The city, characterized by rapid changes in bathymetry, marks a transitional area: pathogen-related micro-algal lysis may increase organic material, explaining the shift towards heterotrophic processes downstream. As the primary driver of seasonal changes, the model analysis reveals a differential temperature sensitivity of biochemical rates. These results may be extrapolated to other urban rivers, and also provide valuable information for estuarine water quality management.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Erum Haris, Anthony G. Cohn, John G. Stell

Navigating historical narratives poses a challenge in unveiling the spatial intricacies of past landscapes. The proposed work addresses this challenge within the context of the English Lake District, employing the Corpus of the Lake District Writing. The method utilizes a generative pre-trained transformer model to extract spatial relations from the textual descriptions in the corpus. The study applies this large language model to understand the spatial dimensions inherent in historical narratives comprehensively. The outcomes are presented as semantic triples, capturing the nuanced connections between entities and locations, and visualized as a network, offering a graphical representation of the spatial narrative. The study contributes to a deeper comprehension of the English Lake District's spatial tapestry and provides an approach to uncovering spatial relations within diverse historical contexts.

Jacob Serpico, B. A. Zambrano-Luna, Russell Milne et al.

Harmful cyanobacterial blooms (CBs) are increasingly prevalent worldwide, posing significant environmental and health concerns. We derive a stoichiometric model describing the population dynamics and toxicity of cyanobacteria in north-temperate freshwater ecosystems. Our model quantifies the hypoxic effects of CBs on fish mortality and evaluates the impact of microcystin-LR (MC-LR) on aquatic macro-invertebrates, phytoplankton, and fish species. Analyzing data from diverse north-temperate lakes with varying physical characteristics, we identify eutrophication as a pivotal catalyst in bloom proliferation. Under predicted warming scenarios coupled with increased eutrophication, peak MC-LR concentrations will surge dramatically, and blooms will occur earlier in the year. We uncover severe bioaccumulation of MC-LR in higher trophic species; the response to CBs among fish at intermediate trophic levels was heterogeneous across lakes. We compare our model against observations from several north-temperate lakes, demonstrating its robustness and applicability. Our insights are critical for informing targeted interventions to mitigate CBs.

Pakawan Pugsee, Kantika Soipetchakoon, Pongthep Thongsang

LIU Hechang, ZHAO Bohua, SUN Bo

Based on the measured flood data from 1952 to 2005 in Qianjiang Station,Liuzhou Station,and Wuxuan Station of Xijiang River,this paper selects the characteristic factors of flood forecasting by analyzing the correlation of flood flow at upstream and downstream stations.Meanwhile,the random forest algorithm is adopted to build a flood forecasting model for Wuxuan Station.The results are as follows.The certainty coefficients of the 12~48 h flood process in the studied station during the calibration period are more than 0.98 with the pass rates more than 98%.Additionally,the certainty coefficients of the 12~24 h flood process during the verification period are more than 0.72,with a pass rate of more than 82%.Thus,the proposed model has high forecast accuracy and little uncertainty and can provide a reference for flood forecasting methods.

SHAO Shipeng, WAGN Baoqiang, WEI Wenli

Mansour Jahantigh, Moien Jahantigh, Khodadad Dhemardhe et al.

Introduction Today, water security is one of the important limitations of development, especially in dry and desert areas. Because these areas not only have low rainfall, but also this low rainfall is not properly distributed. Despite the increase in irrigation efficiency in some agricultural methods, the limitation of freshwater resources in some areas makes it necessary to use salt water in agriculture. However, the use of these water sources has negative effects on the soil and the environment. So the salinity of soil and irrigation water reduces crop yield and puts soil resources at serious risk. It is possible to increase the crop yield and control soil erosion by using the appropriate irrigation method. The problem of salinity in plants is due to the accumulation of excessive amounts of sodium chloride, which is widely spread in coastal areas, soils of dry areas, and fertile lands. Studies have shown that the use of saline water, especially in conditions of equal fertilization between treatments, often reduces the absorption of important nutrients such as nitrogen due to the existence of a significant relationship between the absorption of water and nutrients. Research in the north of Golestan province showed that salinity causes a significant decrease in plant biomass. The effect of salinity stress on the accumulation of sodium, potassium, and chlorine in the plant was significant and the highest amount of ions was accumulated in the leaves. The plant's root system is selective in absorbing and transferring sodium to its aerial parts.   Materials and Methods To do this research, first, by selecting 36 experimental units, holes with a diameter of 50 cm and a depth of 60 cm were dug in the center of each one, and then the treatments were prepared. This research is in the form of treatments consisting of irrigation factor (clay and drip irrigation method), salinity level (salinity up to 1200, salinity up to 2200, and salinity up to 3200 µmhos cm-2), and plant (Rose and Hibiscus sabdariffa) in three repetitions and it was done factorial randomized complete blocks design. Three water sources each with a capacity of 200 liters were placed at a height of less than two meters from the ground. Rose plant was prepared in the form of potted seedlings and Hibiscus sabdariffa seeds were planted in the greenhouse and after about two weeks in March, it was transferred to the field. The growth height of the plants, as well as the crown, the diameter of the plant stem, and the number of their branches in the growing season were measured. Also, three soil samples were collected and their characteristics of salinity, acidity, and texture were measured. In order to analyze the data, the statistical method of analysis of variance (ANOVA) and the least significant difference (LSD) test were used to compare the average of the studied indicators using MSTAT-C software and SPSS software.   Results and Discussion The results of variance analysis of some growth traits of the studied species showed that seedling height and stem diameter were affected by different levels of salinity and the values ​​of this plant characteristic showed a statistically significant difference. The reason for the decrease in plant growth in a plant that is irrigated with more salinity is that the presence of salt in the soil exceeds the tolerance threshold of the plant, and as a result, the accumulation of excess salt in the root zone is a limiting factor for plant growth. According to the results of the effect of irrigation methods, as well as the interaction effect of salinity and irrigation method on the aforementioned indicators, there was no statistically significant difference. The interaction effect of plant and water salinity levels on the values ​​of these variables was significant. The comparison of the average data showed that the height of the studied species was significantly increased by using the clay irrigation method. The maximum diameter of the stem was also measured in the clay irrigation method, which was associated with an increase of 1.7\% compared to the drip irrigation method. Also, the results show that the highest values ​​of the studied variables are related to the rose flower plant, which is 1.7 and 3 times more than the sour tea plant, respectively. Clay irrigation causes water to be transferred to the root area of ​​the plant, which improves the performance and growth of the plant by providing the required moisture around the root. In other words, the way to distribution soil moisture in clay irrigation takes place in the form of percolation and uniformly around the root of the plant, which causes the moisture to be placed directly around the root area and thus affects the growth of the plant. In addition, the canopy data and the number of branches showed that there is no significant difference between them.   Conclusion This research tested the effect of different levels of water salinity and clay and drip irrigation on the establishment of plants in the Sistan plain, considering the existence of a water shortage crisis in the region, in order to use saline water on two plants, rose and Hibiscus sabdariffa. The results showed that clay irrigation performance was better than drip irrigation at all salinity levels. Because in the drip irrigation method, with the occurrence of drought stress, it reduces plant growth compared to the clay irrigation method. In addition, in the drip irrigation method, water is placed on the soil surface and deep penetration is limited, and as a result, the increase in humidity in the subsurface layers is less. In the clay irrigation method, due to deep penetration and uniform distribution of moisture in the soil profile, the amount of moisture stored in the soil increases.

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.

Ben Chen, Xuechao Zou, Kai Li et al.

Lake extraction from remote sensing imagery is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, with prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embedding through self- and cross-attention in the prompt decoder. Prompts are deactivated to ensure independence during inference, enabling automated lake extraction without introducing additional parameters and GFlops. Extensive experiments showcase performance improvements of our proposed approach compared to the previous state-of-the-art method. The source code is available at https://github.com/BastianChen/LEPrompter.

Ben Chen, Xuechao Zou, Yu Zhang et al.

Lake extraction from remote sensing images is challenging due to the complex lake shapes and inherent data noises. Existing methods suffer from blurred segmentation boundaries and poor foreground modeling. This paper proposes a hybrid CNN-Transformer architecture, called LEFormer, for accurate lake extraction. LEFormer contains three main modules: CNN encoder, Transformer encoder, and cross-encoder fusion. The CNN encoder effectively recovers local spatial information and improves fine-scale details. Simultaneously, the Transformer encoder captures long-range dependencies between sequences of any length, allowing them to obtain global features and context information. The cross-encoder fusion module integrates the local and global features to improve mask prediction. Experimental results show that LEFormer consistently achieves state-of-the-art performance and efficiency on the Surface Water and the Qinghai-Tibet Plateau Lake datasets. Specifically, LEFormer achieves 90.86% and 97.42% mIoU on two datasets with a parameter count of 3.61M, respectively, while being 20 minor than the previous best lake extraction method. The source code is available at https://github.com/BastianChen/LEFormer.

JIA Yuhao

The development of informatization and digitization of hydropower engineering is exposed to problems such as inconsistent data standards,uneven data quality,scattered data storage,weak correlation between data,and difficult data sharing.By referring to the concept of asset management,technologies including BIM,digital twins,and big data are used to construct an asset data model through the collection of engineering-related data assets and with the engineering asset codes as the link.In addition,an engineering digital asset platform is designed and developed to meet the asset management functions such as engineering data asset integration, management queries,download,and sharing,as well as the comprehensive application functions such as the overview of basic engineering information,multi-dimensional full life cycle asset management,and typical scenario application. In this way,the management and application of engineering digital assets are achieved in the whole life cycle of engineering including the planning and design, construction,and operation and maintenance of hydropower engineering.The construction management level and the production,operation,and maintenance capacity of hydropower engineering are improved,and the application and development of the concept of digital assets in hydropower engineering are promoted.

Meseret B. Addisie

The reliable estimation of groundwater recharge is fundamental to the appropriate use of groundwater resources. Shallow groundwater resource quantification for irrigation in highland regions remains challenging. Specifically, in the humid Ethiopian highlands, only limited research has been done on groundwater recharge estimation. Despite the various techniques used to determine recharge, the objective of this study was to better understand natural groundwater recharge using water table fluctuation (WTF) and empirical methods in the sub-humid Ethiopian highlands. The Ene-Chilala watershed was selected for this study. Precipitation, infiltration rate, and piezometric water levels were measured. Precipitation was measured over a 4-year period (2013–2016), whereas infiltration and the groundwater table were measured over a 1-year period (2014). Recharge rates using WTF were determined from the three slope positions and the median of all piezometers for the whole watershed. Infiltration rates on the upslope were greater compared to the mid- and downslopes. The rainfall intensity exceeded the infiltration rate in all slope positions, so the excess rainfall recharged the perched upslope aquifer and eventually drained as interflow to recharge the mid- and downslopes. The estimated groundwater recharge from WTF was less compared to the average of empirical estimations. Surprisingly, from the nine selected empirical equations, the modified Chaturvedi formula had a similar estimation to the WTF method. In conclusion, it is challenging to find long-term seasonal and spatial groundwater-level data. Long-term groundwater data should, therefore, be available in order to arrive at a reliable recharge estimate and for effective groundwater management practices. HIGHLIGHTS Various methods estimate groundwater recharge in tropical monsoon highlands.; Water table fluctuation (WTF) demonstrated recharge at different slope positions.; The modified Chaturvedi equation exactly estimates recharge as WTF using only rainfall.; WTF and empirical equations vary only by 1.8% of the estimated average recharge.;

Zeinab Abou Khalil, Stefano Zacchiroli

We introduce the General Index of Software Engineering Papers, a dataset of fulltext-indexed papers from the most prominent scientific venues in the field of Software Engineering. The dataset includes both complete bibliographic information and indexed ngrams (sequence of contiguous words after removal of stopwords and non-words, for a total of 577 276 382 unique n-grams in this release) with length 1 to 5 for 44 581 papers retrieved from 34 venues over the 1971-2020 period.The dataset serves use cases in the field of meta-research, allowing to introspect the output of software engineering research even when access to papers or scholarly search engines is not possible (e.g., due to contractual reasons). The dataset also contributes to making such analyses reproducible and independently verifiable, as opposed to what happens when they are conducted using 3rd-party and non-open scholarly indexing services.The dataset is available as a portable Postgres database dump and released as open data.

Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad et al.

Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.

Yevgeniy Kovchegov, Ilya Zaliapin, Efi Foufoula-Georgiou

The hierarchical organization and self-similarity in river basins have been topics of extensive research in hydrology and geomorphology starting with the pioneering work of Horton in 1945. Despite significant theoretical and applied advances however, the mathematical origin of and relation among Horton laws for different stream attributes remain unsettled. Here we capitalize on a recently developed theory of random self-similar trees to introduce a one-parametric family of self-similar critical Tokunaga trees that elucidates the origin of Horton laws, Hack's laws, basin fractal dimension, power-law distributions of link attributes, and power-law relations between distinct attributes. The proposed family includes the celebrated Shreve's random topology model and extends to trees that approximate the observed river networks with realistic exponents. The results offer tools to increase our understanding of landscape organization under different hydroclimatic forcings, and to extend scaling relationships useful for hydrologic prediction to resolutions higher that those observed.

D. Yilmazer, A. N. Berker, Y. Yilmaz

The fractal dimensions d_f of the shore lines of the Mediterranean, the Aegean, the Black Sea, the Bosphorus Straits (on both the Asian and European sides), the Van Lake, and the lake formed by the Ataturk Dam have been calculated. Important distinctions have been found and explained.