Hasil untuk "Irrigation engineering. Reclamation of wasteland. Drainage"

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

Ali Mehrabinejad, Nasrollah Kalantari, Farshad Alijani et al.

The drinking water of Izeh city in northeastern Khuzestan province is supplied from limestone wells in the Nal-e-asbi Karst aquifer south of the plain, which is in direct hydrogeological communication with the alluvial aquifer. Due to nitrate pollution infiltration from the alluvial aquifer to the Karst aquifer, determining the nitrate status in the aquifer and the effect of the natural denitrification process on reducing nitrate concentration is very important. In this research, temporal changes in nitrate, ammonium, and nitrite, and spatial changes in nitrate in the urban aquifer of Izeh were investigated to determine the denitrification mechanism. Over a one-year period (December 2023 - November 2024), samples were taken from 9 wells over 6 periods. Then, nitrate isoconcentration maps were prepared, and along with temporal changes of nitrogenous species, the nitrate chemograph was interpreted. Finally, spatial zoning of denitrification and the relationship between denitrification and other hydrogeochemical parameters was analyzed using composite diagrams. The results showed that with the leaching of pollutants from the surface and chemical fertilizers applied to agricultural lands, which are mainly applied in January and February in the Izeh region, the nitrate concentration in the urban aquifer of Izeh had a sharp increasing trend from December 2023 to April 2024, but decreased throughout the aquifer from April to June 2024. Groundwater in the north of Izeh city and adjacent to Miangaran wetland, due to severe contamination with Izeh city sewage, is under reducing and severe denitrification conditions and has the lowest nitrate concentration among the water wells. Based on the denitrification zoning map, the highest and lowest constant rate of denitrification are related to the northeast and southwest of Izeh city, respectively.

XUAN Ziyu, ZHANG Xiaotao, DANG Hongkai et al.

【Objective】Reducing groundwater use for irrigation has been a key mandate in the Hebei Plain to promote sustainable agriculture. Improving irrigation water use efficiency is therefore essential to achieving this goal. This study aims to develop a method to optimize irrigation scheduling for winter wheat in this region.【Method】A field experiment was conducted from October 2022 to June 2023 at a winter wheat field in Hengshui City, Hebei Province. Four irrigation schedules were compared: irrigating once (W1), twice (W2), three times (W3), and four times (W4) during the growing season. Experimental data were used to calibrate and validate the AquaCrop model. The calibrated parameters were then applied to optimize irrigation scheduling.【Result】The calibrated AquaCrop model accurately simulated canopy development, above-ground biomass, and soil moisture dynamics. The coefficient of determination (R2), root mean square error (RMSE), normalized RMSE (NRMSE), Nash-Sutcliffe efficiency (EF), and index of agreement (d) between the simulated and measured values all fell within acceptable ranges, with R2＞0.85, RMSE＜4.5, NRMSE＜12, EF＞0.8, and d＞0.9. The average error of the simulated grain yield was higher in 2021 than in 2022. Water use efficiency under the W3 treatment was 1.88 kg/m3 in 2021 and 2.35 kg/m3 in 2022.【Conclusion】Among the four irrigation schedules, W3 (irrigating three times) was the most effective in improving both grain yield and water use efficiency for winter wheat in the Hebei Plain. The calibrated AquaCrop model accurately simulated winter wheat growth and can be used to optimize irrigation scheduling and improve irrigation water use efficiency in this region and other areas with similar environments.

MA Guangyuan, HU Aiping, CHEN Hucheng et al.

【Objective】 Improving water and nutrient use efficiency is essential for promoting sustainable agriculture. This study investigates the combined effects of water and phosphorus applications on growth, photosynthetic characteristics, and yield of alfalfa. 【Method】The experiment was conducted in the Yinda Irrigation District of Gansu Province using the variety Jieyi alfalfa as the model plant. The experiment consisted of three water treatments with soil moisture content kept at 45%-60% (low, W1), 60%-75% (medium, W2), and 75%-90% (high, W3) of the field capacity. For each soil moisture treatment, there were three phosphorus treatments by applying 0 (low, P0), 50 kg/hm2 (medium, P1) and 100 kg/hm2 (high, P2) of phosphorus fertilizer. In each treatment, we measured plant traits, photosynthesis, and hay yield. 【Result】① Under low and medium soil moisture conditions, plant height, stem diameter, leaf-to-stem ratio, photosynthetic performance, and irrigation water use efficiency all increased with phosphorus application, peaking when phosphorous application was 100 kg/hm2. ② Under high soil moisture conditions, these parameters increased initially and then declined with increasing phosphorus application, reaching their maximum when phosphorus application was 50 kg/hm2. ③ When phosphorus application was the same, the impact of soil moisture on hay yield followed the order: high＞medium＞low soil moisture. Among all treatments, the highest yield was achieved under W3P1, which, compared with the low water and low phosphorus treatment, increased the hay yield by 11.78%, 9.51%, and 13.63%, respectively. 【Conclusion】Among all treatments, the optimal water and phosphorus application for maximizing alfalfa hay yield was W3P1, where soil moisture was maintained at 75%-90% of the field capacity throughout the growing period by applying 50 kg/hm2 of phosphorus.

Oishee Bintey Hoque, Nibir Chandra Mandal, Abhijin Adiga et al.

Accurate mapping of irrigation methods is crucial for sustainable agricultural practices and food systems. However, existing models that rely solely on spectral features from satellite imagery are ineffective due to the complexity of agricultural landscapes and limited training data, making this a challenging problem. We present Knowledge-Informed Irrigation Mapping (KIIM), a novel Swin-Transformer based approach that uses (i) a specialized projection matrix to encode crop to irrigation probability, (ii) a spatial attention map to identify agricultural lands from non-agricultural lands, (iii) bi-directional cross-attention to focus complementary information from different modalities, and (iv) a weighted ensemble for combining predictions from images and crop information. Our experimentation on five states in the US shows up to 22.9\% (IoU) improvement over baseline with a 71.4% (IoU) improvement for hard-to-classify drip irrigation. In addition, we propose a two-phase transfer learning approach to enhance cross-state irrigation mapping, achieving a 51% IoU boost in a state with limited labeled data. The ability to achieve baseline performance with only 40% of the training data highlights its efficiency, reducing the dependency on extensive manual labeling efforts and making large-scale, automated irrigation mapping more feasible and cost-effective.

Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio et al.

The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.

Martin Obaidi, Marc Herrmann, Elisa Schmid et al.

Sentiment analysis is an essential technique for investigating the emotional climate within developer teams, contributing to both team productivity and project success. Existing sentiment analysis tools in software engineering primarily rely on English or non-German gold-standard datasets. To address this gap, our work introduces a German dataset of 5,949 unique developer statements, extracted from the German developer forum Android-Hilfe.de. Each statement was annotated with one of six basic emotions, based on the emotion model by Shaver et al., by four German-speaking computer science students. Evaluation of the annotation process showed high interrater agreement and reliability. These results indicate that the dataset is sufficiently valid and robust to support sentiment analysis in the German-speaking software engineering community. Evaluation with existing German sentiment analysis tools confirms the lack of domain-specific solutions for software engineering. We also discuss approaches to optimize annotation and present further use cases for the dataset.

Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski et al.

Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.

Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan

The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.

Nadia Alshahwan, Mark Harman, Inna Harper et al.

In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.

Marvin Wyrich, Justus Bogner

LinkedIn is the largest professional network in the world. As such, it can serve to build bridges between practitioners, whose daily work is software engineering (SE), and researchers, who work to advance the field of software engineering. We know that such a metaphorical bridge exists: SE research findings are sometimes shared on LinkedIn and commented on by software practitioners. Yet, we do not know what state the bridge is in. Therefore, we quantitatively and qualitatively investigate how SE practitioners and researchers approach each other via public LinkedIn discussions and what both sides can contribute to effective science communication. We found that a considerable proportion of LinkedIn posts on SE research are written by people who are not the paper authors (39%). Further, 71% of all comments in our dataset are from people in the industry, but only every second post receives at least one comment at all. Based on our findings, we formulate concrete advice for researchers and practitioners to make sharing new research findings on LinkedIn more fruitful.

Sonja Hyrynsalmi, Ella Peltonen, Fanny Vainionpää et al.

In the extant literature, there has been discussion on the drivers and motivations of minorities to enter the software industry. For example, universities have invested in more diverse imagery for years to attract a more diverse pool of students. However, in our research, we consider whether we understand why students choose their current major and how they did in the beginning decided to apply to study software engineering. We were also interested in learning if there could be some signs that would help us in marketing to get more women into tech. We approached the topic via an online survey (N = 78) sent to the university students of software engineering in Finland. Our results show that, on average, women apply later to software engineering studies than men, with statistically significant differences between genders. Additionally, we found that marketing actions have different impacts based on gender: personal guidance in live events or platforms is most influential for women, whereas teachers and social media have a more significant impact on men. The results also indicate two main paths into the field: the traditional linear educational pathway and the adult career change pathway, each significantly varying by gender

Jieke Shi, Zhou Yang, David Lo

Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This paper aims to redirect the focus of the research community towards the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance of LLM4SE and highlights the need for efficient and green LLM4SE solutions. Subsequently, the paper presents a vision for a future where efficient and green LLM4SE revolutionizes the LLM-based software engineering tool landscape, benefiting various stakeholders, including industry, individual practitioners, and society. The paper then delineates a roadmap for future research, outlining specific research paths and potential solutions for the research community to pursue. While not intended to be a definitive guide, the paper aims to inspire further progress, with the ultimate goal of establishing efficient and green LLM4SE as a central element in the future of software engineering.

PAN Deng, ZHANG Yanfang, HU Haodong et al.

【Objective】 Predicting change in groundwater table in response to natural and anthropogenic influence is a prerequisite in groundwater resource management. This paper compares several methods commonly used in studying groundwater dynamics based on the shallow groundwater exploration in plain areas in central China. 【Method】 The study was based on shallow groundwater resources in Puyang City in Henan province. We analyzed the groundwater dynamics in the region using three methods: water balance method, dewatering-volume method, and time series method. 【Result】 The time series method is applicable only to areas where groundwater development and utilization do not show considerable change with time. In contrast, the water balance method and dewatering volume method are more mechanistic, providing more accurate results as they consider potential changes in groundwater usage and management. As the dewatering volume method divides the studied region into subunits based on administrative regions rather than hydrogeological conditions, it could lead to significant differences when compared to measurements from monitoring boreholes. The water balance method considers factors such as current groundwater development, hydrogeological conditions and groundwater management measures, and it is hence more accurate for analyzing groundwater dynamics. 【Conclusion】 Results calculated by the improved groundwater balance method proposed in this paper are consistent with ground-truth data. It is more reliable for evaluating the response of shallow groundwater table to change in management and usages in plain areas.

Mostafa Yosefian, Ali Shahnazari, Mirkhalegh Ziatabar Ahmadi et al.

Due to drought crisis in recent years, the use of alternative cropping methods that saves water without any decrease in yield is increasing. Regulated deficit irrigation is a method of irrigation in which, by supplying part of the maximum crop requirement at specified times, water management is addressed and therefore the root zone is in a dry area most of the time. Using this method will partially stop the growth of the plant and usually decrease yield (Jovanovic et al., 2010). Root Partial Drying (PRD) is a modified form of the deficit, which includes watering one section of the root zone in each irrigation and dry up another side, so the moisture of this section before next irrigation largely come down (Ahmadi et al., 2010). In the root drying method, the roots of the plant simultaneously with soil drying with the production of Abscisic Acid, prevent leaf expansion and reduce stomatal conductance. Simultaneously with this process, the root in the wet part with adequate water uptake puts the plant in proper moisture (Yousri Ibrahim Atta, 2008). Abscisic acid is a plant hormone whose production is increased by root in dry soils and transported by water flow into the stem. (liu et al., 2008). On the other hand, The quality of production is as important in all crops and especially in rice production in Iran. (Salehifar  et al., 2009) In order to evaluate some of the physical and qualitative properties of rice grain and the amount of nitrogen uptake in rice (Hashemi variety) under different irrigation levels and comparing it with continuous waterlogging, an experiment was conducted during two years of cultivation of 2015 and 2016.

راضیه خسروی طا ئمه, جمال محمدولی سامانی, سیده لیلا رضوی طوسی

سدها علاوه بر منافع اجتماعی بسیاری که دارند امکان دارد با گذشت زمان نیز خطرات اجتماعی، اقتصادی، زیست‌محیطی و فنی‌مدیریتی را داشته باشند. سد پدیده عظیمی درطبیعت محسوب می‌گردد و اثرات آن بر روی محیط‌زیست به‌صورت گسترده است. یکی از مسائلی که می‌باید در زمان بهره‌برداری سدها موردبررسی قرار می‌گیرد، آثار زیست‌محیطی منفی در منطقه احداث سد است. از اثرات زیست‌محیطی منفی برای سدها به‌عنوان مثال می‌توان به رسوب‌گذاری در مخزن و دریاچه سد، تشدید فرسایش در اراضی پایین‌دست، شورشدن اراضی کشاورزی به‌علت بالاآمدن سطح آب‌های زیرزمینی اشاره کرد. اثرات ریسک‌های نام برده‌شده برای سدسازی، ممکن است جبران‌ناپذیر باشد. این پژوهش، با هدف ارزیابی اثرات ریسک‌های چهار معیار اجتماعی، اقتصادی، فنی ‌مدیریتی و زیست‌محیطی متأثر از احداث سد‌ها بر حوضه‌آبخیز موردبررسی قرار گرفته است. تصمیمات مربوط به مدیریت این سدها نیازمند شناخت خطرات و ریسک‌های موجود در سد و اولویت‌بندی آن‌هاست. استفاده از روش‌های تصمیم‌گیری چندشاخصه‌ای، ابزار مناسبی برای ارزیابی ریسک‌های مختلف در این سدها می‌باشد. در این مقاله از روش ترکیبی دیماتل- فرایند تحلیل شبکه‌ای، جهت شناسایی روابط بین معیارها و زیرمعیارهای ریسک و اولویت‌بندی آن‌ها استفاده ‌شده است. بنابراین، چهار معیار و 42 زیرمعیار برای ارزیابی ریسک‌های سه سد طالقان، ماملو و لار به‌روش فرایند تحلیل شبکه‌ای استفاده شده و نتایج نشان می‌دهد که سد طالقان در اولویت اول ریسک قرار دارد و زیرمعیار اشتغال، مربوط به معیار اقتصادی، با وزن 0720/0 دارای بیش‌ترین ‌ریسک و زیرمعیار تملک اراضی، مربوط به معیار فنی ‌مدیریتی با وزن 0029/0 دارای کم‌ترین ریسک نسبت به‌کل زیرمعیارها می‌باشد. هم‌چنین با انجام تحلیل حساسیت بیش‌ترین حساسیت نسبت به افزایش و کاهش 50 درصدی وزن معیار اقتصادی و کم‌ترین حساسیت نسبت به افزایش و کاهش 30 درصدی وزن معیار فنی‌مدیریتی می‌باشد.

منا مسعودی آشتیانی, احمد شرافتی, حمید حمید کاردان مقدم

مفهوم ریسک به عنوان یک روش‌ و تکنیک در بیان مدیریت ظهور و ارائه‌دهنده خطرات تلقی می‌گردد. رویکردهای مختلفی برای ارائه این مفهوم وجود دارد که یکی از این تکنیک‌ها استفاده از روش استخوان ماهی است. از طرفی نیز تعیین ریسک بدون شناخت ابعاد مفهومی بی‌معنی است لذا هدف از این مطالعه استفاده ترکیبی از روش‌های تصمیم‌گیری طوفان فکری، SWOT و استخوان ماهی جهت تجزیه و تحلیل علل اصلی حوادث و ریسک‌های مترتب به قنوات، پرداختن به جزئیات، دسته‌بندی آن‌ها و تبدیل تهدیدها به فرصت‌هاست. پس از برگزاری جلسات طوفان فکری برای شناسایی آسیب‌های وارد بر قنوات ماتریس SWOT مبتنی بر نقاط ضعف، قوت، فرصت و تهدید تکمیل شده و با استفاده از روش استخوان ماهی ترسیم علل ریشه‌ای تحلیل گردید. نتایج روش SWOT نشان داد که راهبردهای پیشنهادی می‌بایست در وضعیت قوت از منظر عوامل داخلی با امتیاز 57/2 و تهدید از منظر عوامل خارجی با امتیاز 49/2 بصورت راهبردهای محافظه‌کارانه تعریف شود. از طرفی نیز نتایج ریسک‌های بهره‌برداری از قنات به دو دسته خطرات بالقوه قنات‌داری و خطرات بیرونی تاثیرگذار تقسیم‌بندی شده و نتایج حاکی از بالابودن اهمیت ریسک‌های تکنولوژیک با میانگین امتیاز 85/7 بوده که براین اساس راهبردهای محافظه‌کارانه مبتنی بر شرایط ریسک تعریف گردید. نتایج و راهبردهای ارائه شده دراین مطالعه می‌تواند رویکردهای مدیریتی مناسبی را در اختیار متولیان جهت بهره‌برداری بهینه از این منبع آب در اختیار قرار دهد.

Marcel Grote, Justus Bogner

Today, many systems use artificial intelligence (AI) to solve complex problems. While this often increases system effectiveness, developing a production-ready AI-based system is a difficult task. Thus, solid AI engineering practices are required to ensure the quality of the resulting system and to improve the development process. While several practices have already been proposed for the development of AI-based systems, detailed practical experiences of applying these practices are rare. In this paper, we aim to address this gap by collecting such experiences during a case study, namely the development of an autonomous stock trading system that uses machine learning functionality to invest in stocks. We selected 10 AI engineering practices from the literature and systematically applied them during development, with the goal to collect evidence about their applicability and effectiveness. Using structured field notes, we documented our experiences. Furthermore, we also used field notes to document challenges that occurred during the development, and the solutions we applied to overcome them. Afterwards, we analyzed the collected field notes, and evaluated how each practice improved the development. Lastly, we compared our evidence with existing literature. Most applied practices improved our system, albeit to varying extent, and we were able to overcome all major challenges. The qualitative results provide detailed accounts about 10 AI engineering practices, as well as challenges and solutions associated with such a project. Our experiences therefore enrich the emerging body of evidence in this field, which may be especially helpful for practitioner teams new to AI engineering.

Eduardo A. Oliveira, Leon Sterling

This paper describes how motivational models can be used to cross check agile requirements artifacts to improve consistency and completeness of software requirements. Motivational models provide a high level understanding of the purposes of a software system. They complement personas and user stories which focus more on user needs rather than on system features. We present an exploratory case study sought to understand how software engineering students could use motivational models to create better requirements artifacts so they are understandable to non-technical users, easily understood by developers, and are consistent with each other. Nine consistency principles were created as an outcome of our study and are now successfully adopted by software engineering students at the University of Melbourne to ensure consistency between motivational models, personas, and user stories in requirements engineering.

Walid Maalej, Yen Dieu Pham, Larissa Chazette

Requirements Engineering (RE) is the discipline for identifying, analyzing, as well as ensuring the implementation and delivery of user, technical, and societal requirements. Recently reported issues concerning the acceptance of Artificial Intelligence (AI) solutions after deployment, e.g. in the medical, automotive, or scientific domains, stress the importance of RE for designing and delivering Responsible AI systems. In this paper, we argue that RE should not only be carefully conducted but also tailored for Responsible AI. We outline related challenges for research and practice.