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

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

Feiyu Pan, Tianbin Zhang, Aoqian Zhang et al.

Modern data lakes have emerged as foundational platforms for large-scale machine learning, enabling flexible storage of heterogeneous data and structured analytics through table-oriented abstractions. Despite their growing importance, standardized benchmarks for evaluating machine learning performance in data lake environments remain scarce. To address this gap, we present LakeMLB (Data Lake Machine Learning Benchmark), designed for the most common multi-source, multi-table scenarios in data lakes. LakeMLB focuses on two representative multi-table scenarios, Union and Join, and provides three real-world datasets for each scenario, covering government open data, finance, Wikipedia, and online marketplaces. The benchmark supports three representative integration strategies: pre-training-based, data augmentation-based, and feature augmentation-based approaches. We conduct extensive experiments with state-of-the-art tabular learning methods, offering insights into their performance under complex data lake scenarios. We release both datasets and code to facilitate rigorous research on machine learning in data lake ecosystems; the benchmark is available at https://github.com/zhengwang100/LakeMLB.

Hancheng Ren, Gang Zhao, Shuo Wang et al.

River systems operate as inherently interconnected continuous networks, meaning river hydrodynamic simulation ought to be a systemic process. However, widespread hydrology data scarcity often restricts data-driven forecasting to isolated predictions. To achieve systemic simulation and reduce reliance on river observations, we present GraphRiverCast (GRC), a topology-informed AI foundation model designed to simulate multivariate river hydrodynamics in global river systems. GRC is capable of operating in a "ColdStart" mode, generating predictions without relying on historical river states for initialization. In 7-day global pseudo-hindcasts, GRC-ColdStart functions as a robust standalone simulator, achieving a Nash-Sutcliffe Efficiency (NSE) of approximately 0.82 without exhibiting the significant error accumulation typical of autoregressive paradigms. Ablation studies reveal that topological encoding serves as indispensable structural information in the absence of historical states, explicitly guiding hydraulic connectivity and network-scale mass redistribution to reconstruct flow dynamics. Furthermore, when adapted locally via a pre-training and fine-tuning strategy, GRC consistently outperforms physics-based and locally-trained AI baselines. Crucially, this superiority extends from gauged reaches to full river networks, underscoring the necessity of topology encoding and physics-based pre-training. Built on a physics-aligned neural operator architecture, GRC enables rapid and cross-scale adaptive simulation, establishing a collaborative paradigm bridging global hydrodynamic knowledge with local hydrological reality.

Jill, S., Baron et al.

Anatoly A. Krasnovsky

Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.

Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich et al.

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.

Rafael Brandão Ferreira de Moraes, Cláudia Gonçalves Vianna Bacchi, Fábio Veríssimo Gonçalves

ABSTRACT Inadequate urban planning has contributed to the sediment production in Urban Hydrographic Micro-basins (UHMs). The present study aims to develop and apply the Sediment Production Susceptibility Index (SPSI) in UHMs from Campo Grande – Mato Grosso do Sul (MS), Brazil, based on the Analysis Hierarchical Process (AHP) and Geographic Information System (GIS) aggregation. The indicators selected for the composition of the SPSI are Soil Class (49%), Average Slope (22%), Vegetation Cover (13%), and Unpaved Streets (16%). It is essentially to jointly analyze indicators from both spheres (natural and anthropogenic) to obtain greater reliability in studies related to sedimentation in urban areas. UHMs undergoing urbanization are more susceptible to sediment production than UHMs that are already densely occupied. SPSI can assist public managers in the urban and environmental planning and in the adoption of preventive measures against the silting of water bodies and obstruction of drainage systems.

Kleanthis Malialis, Nefeli Mavri, Stelios G. Vrachimis et al.

Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficiently manage resources. This work focuses on short-term forecasting of DMA consumption using deep learning and aims to address two key challenging issues. First, forecasting based solely on a DMA's historical data may lack broader context and provide limited insights. Second, DMAs may experience sensor malfunctions providing incorrect data, or some DMAs may not be monitored at all due to computational costs, complicating accurate forecasting. We propose a novel method that first identifies DMAs with correlated consumption patterns and then uses these patterns, along with the DMA's local data, as input to a deep learning model for forecasting. In a real-world study with data from five DMAs, we show that: i) the deep learning model outperforms a classical statistical model; ii) accurate forecasting can be carried out using only correlated DMAs' consumption patterns; and iii) even when a DMA's local data is available, including correlated DMAs' data improves accuracy.

Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais et al.

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e.~labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

Marvin Wyrich, Marvin Muñoz Barón, Justus Bogner

Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.

Chisato Nishimagi, Masami Yanagihara, Yiming Fang et al.

Recently, microplastic (MP) contamination of the aquatic environment has been reported. Marine MP pollution (especially terrestrial-sourced MPs derived from vehicle tires) is considered a global problem because marine organisms may ingest toxic substances. In this study, we analyzed the generation and occurrence of tire-derived MPs (TMPs) that originate from tire dust on roadways and also focused on driving behavior. The results suggested that the number of TMPs increased in proportion to the increase in traffic volume within the range of 10,000–30,000 vehicles/day. The influence of driving behavior was explored by comparing the number of TMPs at distances of 30, 50 and 70 m from the stop line and by assuming a difference in braking behavior. Traffic video was recorded in conjunction with sampling and was analyzed in parallel with the TMPs. The results demonstrated that brakes were applied for an acceleration rate of over −10 m/s2 at distances of 60 and 80 m from the stop line, which resulted in an approximate increase of 28% in the number of TMPs at approximately 70 m. With these results, it can be concluded that the number of TMPs increases due to the traffic volume and braking behavior. HIGHLIGHTS The number of MPs tended to increase with the traffic volume.; Tire dust increased after rainfall and reached a plateau after a certain period.; Braking affected the number of MPs, as suggested by vehicle behavior analysis.;

LI Jun, CUI Dehao, YOU Ri et al.

In view of the structural damage of an aqueduct pile-pillar supporting structure caused by ship collisions,relevant engineering data were collected,and on-site detection and monitoring analysis were carried out.In addition,the finite element method was used to establish a three-dimensional entity analytical model of the aqueduct pile-pillar structure.Under the joint action of the coupled foundation and pile foundation and superstructure,numerical simulation and calculation analysis were carried out on the pile-pillar collision process.The distribution and characteristics of the damage of the supporting structure caused by collisions were studied and verified.The finite element numerical simulation results show that the high-stress area of the aqueduct pile-pillar structure during the ship collision process can be divided into two categories according to the cause.One is the high-stress area caused by the direct contact load,which occurs in the collision contact surface and the adjacent area between the ship in a collision and the bearing platform,and the other is caused by the overall bending deformation of the pile-pillar structure,which occurs in the dangerous section of the pile-pillar structure and the bearing platform connection.In addition,the simulation analysis and on-site detection results are consistent.By assessing the influence of ship collisions and checking the potential safety hazards of the aqueduct,the research results lay a foundation for the maintenance,reinforcement,and design of the aqueduct.

ZENG Xiangyun, MO Jiehuan

This paper aims to analyze the spatial balance situation of water resources in the Nanpan River Basin from 2010 to 2019.Based on the social economic indicators,characteristics of water resources,and developmental utilization data of the Nanpan River Basin,the paper utilizes the load index of water resources,the matching factor of land and water resources,the benefit index of water resources,and their Gini coefficients and analyzes the utilization and the spatial balance situation of the water resources in the basin.Furthermore,a triangular system model involving the social economy,development and utilization of water resources,and characteristics of water resources is established to describe the stability and sustainability of the spatial balance of water resources in the basin.The results reveal that the load level of water resources in the Nanpan River Basin is relatively high,and the spatial distribution is uneven.The matching level of land and water resources and the spatial balance in the basin are low.The overall level of water utilization efficiency is still poor although there is an increasing trend year by year.The triangular system model analysis shows that the water resources system of the Nanpan River Basin is stable on the whole.However,in terms of the eight prefecture-level cities in the basin,the water resources system in half of the cities is unable to form a triangle system,while that in some regions is spatially unbalanced.

M. Di Martino, P. Linke, E. N. Pistikopoulos

The energy-water demands of metropolitan regions and agricultural ecosystems are ever-increasing. To tackle this challenge efficiently and sustainably, the interdependence of these interconnected resources has to be considered. In this work, we present a holistic decision-making framework which takes into account simultaneously a water and energy supply system with the capability of satisfying metropolitan and agricultural resource demands. The framework features: (i) a generic large-scale planning and scheduling optimization model to minimize the annualized cost of the design and operation of the energy-water supply system, (ii) a mixed-integer linear optimization formulation, which relies on the development of surrogate models based on feedforward artificial neural networks and first-order Taylor expansions, and (iii) constraints for land and water utilization enabling multi-objective optimization. The framework provides the operational profiles of all energy-water system elements over a given time horizon, which uncover potential synergies between the essential food, energy, and water resource supply systems.

Zhiyu Fan, Yannic Noller, Ashish Dandekar et al.

The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, particularly the teaching of introductory programming and advanced software engineering (SE) courses. First-year programming courses often face overwhelming enrollments, including interdisciplinary students who are not CS majors. The high teacher-to-student ratio makes it challenging to provide timely and high-quality feedback. Meanwhile, software engineering education comes with inherent difficulties like acquiring industry partners and the dilemma that such software projects are often under or over-specified and one-time efforts within one team or one course. To address these challenges, we designed a novel foundational SE course. This SE course envisions building a full-fledged Intelligent Tutoring System (ITS) of Programming Assignments to provide automated, real-time feedback for novice students in programming courses over multiple years. Each year, SE students contribute to specific short-running SE projects that improve the existing ITS implementation, while at the same time, we can deploy the ITS for usage by students for learning programming. This project setup builds awareness among SE students about their contribution to a "to-be-deployed" software project. In this multi-year teaching effort, we have incrementally built an ITS that is now deployed in various programming courses. This paper discusses the Intelligent Tutoring System architecture, our teaching concept in the SE course, our experience with the built ITS, and our view of future computing education.

Xiaofeng Wang, Henry Edison, Dron Khanna et al.

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.

Alexander A. Borisov, Nikita A. Goldobin

Th e internal dynamics of the water masses of river systems has a decisive influence on the reliability of the interpretation of the results of their monitoring, primarily in places of combined water consumption and sanitation. The category of such territories includes a site of variable backwater on the Kama River in the area of Berezniki city (Kama reservoir), since it is located in the zone of potential impact from the objects of the Berezniki agglomeration. Th is determines the need for systematic monitoring of its physico-chemical state.

Ian A. Cosden, Kenton McHenry, Daniel S. Katz

As software has become more essential to research across disciplines, and as the recognition of this fact has grown, the importance of professionalizing the development and maintenance of this software has also increased. The community of software professionals who work on this software have come together under the title Research Software Engineer (RSE) over the last decade. This has led to the formalization of RSE roles and organized RSE groups in universities, national labs, and industry. This, in turn, has created the need to understand how RSEs come into this profession and into these groups, how to further promote this career path to potential members, as well as the need to understand what training gaps need to be filled for RSEs coming from different entry points. We have categorized three main classifications of entry paths into the RSE profession and identified key elements, both advantages and disadvantages, that should be acknowledged and addressed by the broader research community in order to attract and retain a talented and diverse pool of future RSEs.

Md Jobair Hossain Faruk, Santhiya Subramanian, Hossain Shahriar et al.

Software Engineering is the process of a systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted in the software development life cycle that pour stakeholders towards the completion of final software product development. Within the boundary of advanced technology, various emerging and futuristic technology is evolving that really need the attention of the software engineering community whether the conventional software process techniques are capable to inherit the core fundamental into futuristic software development. In this paper, we study the impact of existing software engineering processes and models including Agile, and DevOps in Blockchain-Oriented Software Engineering. We also examine the essentiality of adopting state-of-art concepts and evolving the current software engineering process for blockchain-oriented systems. We discuss the insight of software project management practices in BOS development. The findings of this study indicate that utilizing state-of-art techniques in software processes for futuristic technology would be challenging and promising research is needed extensively towards addressing and improving state-of-the-art software engineering processes and methodology for novel technologies.

T. Valeev, Y. Rakhmanin, R. Suleymanov et al.

Introduction. The activities of enterprises engaged in the production, preparation, transportation and primary processing of oil are associated with the formation and accumulation of a large amount of waste (spent drilling fluids, drilling mud, oil sludge, spent catalysts, etc.), which leads to pollution of environmental objects and can contribute to the formation of adverse effects on public health. Materials and methods. The sanitary and hygienic state of atmospheric air, soil, the water of surface (rivers, lakes) and underground (wells, wells, springs) water sources, the water of centralized water supply systems in the territories of large oil fields the Republic of Bashkortostan is carried out. The origins of information were data from many years of in-house research, the regional information fund of the Sanitary Hygienic Monitoring, and departmental laboratories. Results. Studies have found that in areas of oil production, the content of chemicals in concentrations exceeding hygienic standards is detected: in the atmospheric air - dihydrosulfide, hydrocarbons, nitrogen dioxide, sulfur dioxide; in underground waters - chlorides, sulfates, nitrates, iron, strontium, increased mineralization and general hardness; in the soil - sulfates, chlorides, nitrates, petroleum products. Contamination of water from underground water sources and atmospheric air can contribute to the risk of adverse effects from individual organs and systems, as well as carcinogenic hazards. Based on the results of the study, ecological and hygienic recommendations were developed. Conclusion. As a result of the study, the level of public health risk associated with environmental factors in the oil-producing region was assessed, and a set of hygienic measures was justified.