Hasil untuk "Water supply for domestic and industrial purposes"

Ruixuan Sun, Matthew Zent, Minzhu Zhao et al.

In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week real-user study with 120 U.S. news readers on the news recommendation experiment platform POPROX. With user interaction logs and survey responses, we found that algorithmic nudges can successfully increase exposure and consumption diversity, while the impact of LLM-based presentation nudges varied. User-level topic interest is a strong predictor of user clicks, while highlighting the relevance of news articles to prior read articles outperforms generic topic-based and no personalization. We also demonstrate that longitudinal exposure to calibrated news may shift readers' reading habits to value a balanced news digest from both domestic and world articles. Our results provide direction for future work on nudging for diverse consumption in news recommendation systems.

Hongyan Shao, Ka Yin Chau, Ahmad Zaman et al.

Abstract Climate change profoundly impacts hydropower productivity, a cornerstone of renewable energy, necessitating advanced predictive tools for sustainable water-energy management. This study presents novel machine learning (ML) frameworks to forecast climate-induced variations in hydropower output by synergistically integrating climate, hydrological, and operational data with reanalysis datasets. Distinct from existing approaches, our methodology introduces unique contributions, including synthetic climate scenario generation via Generative Adversarial Networks (GANs), neural network-driven feature ranking to prioritize key climate variables, and robust preprocessing techniques such as outlier detection, normalization, and time-series feature engineering. Using a dataset of 650 records with 12 features from a hydropower plant in the Middle East, split into 70% training, 15% validation, and 15% testing subsets, we evaluated the performance of ARIMA, GAN, Autoregressive Deep Neural Network (ARDNN), and Long Short-Term Memory (LSTM) models using RMSE and R² metrics. The LSTM model outperformed the others, achieving an RMSE of 2892.61, a MAPE of 1.3237, and an R² of 0.9985, owing to its superior ability to capture long-term temporal dependencies. These advancements surpass traditional models by offering enhanced predictive accuracy and adaptability, enabling optimized resource management and bolstering the resilience of hydropower systems against climate variability, thus contributing significantly to global sustainable energy strategies.

Betelhem W Demeke, Mesfin M Mekonnen, Kate A Brauman et al.

Global water footprint (WF) assessments guide critical water management decisions, yet their uncertainty remains poorly understood. While a few studies have assessed WF sensitivity to reference evapotranspiration and crop model inputs, uncertainty arising from harvested area (HA) variation has received little attention. This study quantified the uncertainty of WF for major crops (rice, maize, wheat, and soybeans) using five global HA datasets for the years 2000, 2010, and 2015. We applied statistical metrics, including bias, standard deviation, and coefficient of variation (CV), to quantify WF uncertainty and mapped spatial patterns to identify regional uncertainty hotspots. Results reveal substantial uncertainty in the total WF of the major crops, with average CVs of 28%, 32%, and 17% in 2000, 2010, and 2015, respectively. Green WF estimates exhibit even higher uncertainty than the total WF across all crops and years. In 2015, blue WF uncertainty exceeded total WF uncertainty for all crops except wheat, whereas in 2000 and 2010, this pattern was observed only for rice. Soybeans exhibited the highest variability in blue WF (24%, 28%, and 58% in 2000, 2010, and 2015) and green WF (53% in 2000, 69% in 2010), while wheat led in green WF variability in 2015 (35% CV). Hotspots of high WF variability of the major crops are found in China, India, Brazil, Southeast Asia, and parts of the US. Notably, WF uncertainty exceeds HA uncertainty, indicating amplification as uncertainty propagates from area inputs to WF estimates due to spatial variation in ET. These findings underscore the risk of misinformed water allocation and sustainability strategies, potentially exacerbating water and food insecurity. We highlight the need for systematic documentation of uncertainties and investment in crop area monitoring through enhanced field validation and farmer engagement.

Tsigereda Tumato, Hailu Gisha, Manamno Beza

ABSTRACT Although many people agree that groundwater is cleaner than surface water, it is affected by different factors that need assessment of its quality. However, little has been done so far in Damot Gale Woreda. Therefore, this study aimed to evaluate the groundwater quality in Damot Gale Woreda for drinking purposes. Fifteen samples were collected from 15 wells. The seasonal variation maps for each parameter were prepared using ANOVA, and the spatial distribution maps of quality parameters were prepared using the Kriging method in ArcGIS 10.3. The results of the physicochemical parameter analysis indicate variations in water temperature, pH levels, total dissolved solids, and electrical conductivity. The concentrations of both cations and anions were observed within a certain range. Additionally, the presence of iron and fluoride was detected, along with varying levels of turbidity. In the water quality index in both dry and wet seasons, 80% of the water fell in the excellent range, and 20% fell in the good range. Therefore, all the samples were suitable for drinking purposes. The results of the study concluded that evaluation was effective and can also be applied in decision-making for effective groundwater resources monitoring in the study area.

Md. Al-Amin, Muneeb Tahir, Amit Talukder et al.

Several noteworthy scenarios emerged in the global textile and fashion supply chains during and after the COVID-19 pandemic. The destabilizing influences of a global pandemic and a geographically localized conflict are being acutely noticed in the worldwide fashion and textile supply chains. This work examines the impact of the COVID-19 pandemic, the Russo-Ukraine conflict, Israel-Palestine conflict, and Indo-Pak conflict on supply chains within the textile and fashion industry. This research employed a content analysis method to identify relevant articles and news from sources such as Google Scholar, the Summon database of North Carolina State University, and the scholarly news portal NexisUni. The selected papers, news articles, and reports provide a comprehensive overview of the fashion, textile, and apparel supply chain disruptions caused by the pandemic and the war in Ukraine, accompanied by discussions from common supply chain perspectives. Disruptions due to COVID-19 include international brands and retailers canceling orders, closures of stores and factories in developing countries, layoffs, and furloughs of workers in both retail stores and supplier factories, the increased prominence of online and e-commerce businesses, the growing importance of automation and digitalization in the fashion supply chain, considerations of sustainability, and the need for a resilient supply chain system to facilitate post-pandemic recovery. In the case of the Russo-Ukraine war, Israel-Palestine war, and Indo-Pak war, the second-order effects of the conflict have had a more significant impact on the textile supply chain than the direct military operations themselves. In addition to these topics, the study delves into the potential strategies for restoring and strengthening the fashion supply chain

Zetian Shen, Hongjun Wang, Jiyuan Chen et al.

Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate and timely inference of supply chain resilience the capability to maintain core functions during disruptions is crucial for proactive risk mitigation and robust network design. However, existing approaches lack effective mechanisms to infer supply chain resilience without explicit system dynamics and struggle to represent the higher-order, multi-entity dependencies inherent in supply chain networks. These limitations motivate the definition of a novel problem and the development of targeted modeling solutions. To address these challenges, we formalize a novel problem: Supply Chain Resilience Inference (SCRI), defined as predicting supply chain resilience using hypergraph topology and observed inventory trajectories without explicit dynamic equations. To solve this problem, we propose the Supply Chain Resilience Inference Hypergraph Network (SC-RIHN), a novel hypergraph-based model leveraging set-based encoding and hypergraph message passing to capture multi-party firm-product interactions. Comprehensive experiments demonstrate that SC-RIHN significantly outperforms traditional MLP, representative graph neural network variants, and ResInf baselines across synthetic benchmarks, underscoring its potential for practical, early-warning risk assessment in complex supply chain systems.

Ritwik Murali, Akash Ravi

Software systems have grown as an indispensable commodity used across various industries, and almost all essential services depend on them for effective operation. The software is no longer an independent or stand-alone piece of code written by a developer but rather a collection of packages designed by multiple developers across the globe. Ensuring the reliability and resilience of these systems is crucial since emerging threats target software supply chains, as demonstrated by the widespread SolarWinds hack in late 2020. These supply chains extend beyond patches and updates, involving distribution networks throughout the software lifecycle. Industries like smart grids, manufacturing, healthcare, and finance rely on interconnected software systems and their dependencies for effective functioning. To secure software modules and add-ons, robust distribution architectures are essential. The proposed chapter enhances the existing delivery frameworks by including a permissioned ledger with Proof of Authority consensus and multi-party signatures. The proposed system aims to prevent attacks while permitting every stakeholder to verify the same. Critical systems can interface with the secure pipeline without disrupting existing functionalities, thus preventing the cascading effect of an attack at any point in the supply chain.

Martin Monperrus

Web3 applications, built on blockchain technology, manage billions of dollars in digital assets through decentralized applications (dApps) and smart contracts. These systems rely on complex, software supply chains that introduce significant security vulnerabilities. This paper examines the software supply chain security challenges unique to the Web3 ecosystem, where traditional Web2 software supply chain problems intersect with the immutable and high-stakes nature of blockchain technology. We analyze the threat landscape and propose mitigation strategies to strengthen the security posture of Web3 systems.

A. O. Olabamiji, C. A. Adejumobi, S. K. Alausa et al.

Abstract Water pollution poses a significant threat to human health and the overall well-being of ecosystems. Southwestern Nigeria, where the Ikere Gorge Dam is located, local water source is the major supply for daily life and agriculture purpose, there is need to ascertain the water quality. Twenty water samples were collected at each 100–200 m apart, the samples were filtered, acidified and digested, ICP-OES instrument was used to analyze the samples. The results showed the following concentration ranges: Cadmium: 0.001–0.017 mg/l, Chromium: 0.001–0.039 mg/l, Lead: 0.001–0.011 mg/l. Mercury: 0.001–0.111 mg/l, Arsenic: 0.001–0.005 mg/l, Nickel: 0.01–0.07 mg/l, Copper: 0.073–0.874 mg/l, Calcium: 29.038–293.516 mg/l, Iron: 4.840–49.119 mg/l and Potassium: 9.631–97.839 mg/l. Eighty percent of the samples were higher the global acceptable limit, three percent were within safe limit values, seven percent were below the recommended value. Exposure level of Cr, Cu, As were higher than the safe limit. Similarly, Cd, As and Hg have HQ > 1 indicating high risk non-carcinogenic health effects. In contrast, Cr, Pb, Cu, Ni, Zn, Fe, Mn have HQ < 1. ELCR values of Cd and As were higher than the acceptable limit, Cu, Hg, Ni, Zn, Fe, Mn, had no ELCR values. These findings recommended the need for improved water management in Nigeria, the need for long-term strategies to address population growth, climate change, and clean water for every nation, in line with United Nations Sustainable Development Goals 6 &7.

Davis Sibale, Gordana Kranjac-Berisavljevic, Shaibu Abdul-Ganiyu et al.

Abstract The study was conducted at Bontanga irrigation scheme in Northern Region of Ghana to know the extent of water losses in the scheme, identify deficiencies leading to water losses, propose solutions for reduction of water losses, and project the impact of water losses on water demand using Water Evaluation and Planning (WEAP) model. Assessment of water losses was based on conveyance, distribution, in-field, and total water losses. Out of the seasonal irrigation water supply of 8,391,118.37 m3, total water losses of 5,766,524.23 m3 (conveyance losses: 1,208,321.04 m3, distribution losses: 2,657,635.02 m3, and in-field losses: 1,900,568.17 m3) were recorded, representing 68.70% of the seasonal inflow into the irrigation system. Total water losses were beyond the acceptable limit of 40% for the surface irrigation system. Such significant water losses were attributed to lack of proper maintenance on canals, under-utilization of flow measuring structures, excessive lateral canal tailwater losses, and poor water management at field level. Without efforts to reduce water losses, WEAP model results revealed that unmet water demands are likely to reach 2,482,519 m3 by 2030. However, by reducing total water losses from 68.70 to 40%, an average seasonal water saving of 3,894,597.86 m3 is projected to be achieved during the simulation period from 2024 to 2030.The study has enlightened the significance of effective water loss management to meet competing water demands in the face of a changing climate. Future studies should investigate an in-depth synergy between crop water productivity and system’s water losses in the study area.

Alberto Rojas-Rueda, Jorge Ramón Carbajal-Hernández, Gonzalo Hatch-Kuri

En México, los consejos de cuenca son instrumentos de política hídrica sustentados en el marco legal que funcionan como espacios de coordinación gubernamental, consultivos, de concertación, asesoría y apoyo para la gestión integrada del agua dentro del territorio de una cuenca determinada. Actualmente existen 26 consejos de cuenca en el país. Su actuación está legitimada por un discurso político que los asume como mecanismos institucionalizados de participación ciudadana (MIPC) y espacios para la gobernanza del sector ambiental en el subsector hídrico. Este trabajo analizó el marco normativo en la materia, así como reglamentos internos de estos consejos para revelar su naturaleza jurídico-política, considerando los enfoques de la teoría democrática, la teoría de los derechos humanos y la teoría de la gobernanza. Para ello, se elaboró una base de datos a partir de la revisión de más de 150 archivos originales relacionados con la operación y el funcionamiento de los consejos de cuenca en México, así como 15 solicitudes de acceso a la información. Este conjunto de datos se procesó en un formato de tablas analíticas y descriptivas que, junto a las categorías de análisis, coadyuvó al análisis y procesamiento de la información. Los resultados indican que los consejos de cuenca representan espacios de concertación entre el Estado y el mercado, así como instrumentos de coordinación intergubernamental entre la federación, los estados y algunos municipios situados en las cuencas que representan dichos consejos. En ningún caso estos espacios pueden considerarse como MIPC de gobernanza democrática, dada la escasa inclusión y participación de otros actores civiles de manera individual o asociada. Pese a lo anterior, sus funciones de concertación y coordinación son importantes para la gobernanza del sector hídrico, sin que ello implique que ésta sea democrática.

Mohammad Javad Zeynali, Mohammad Nazeri Tahroudi, Omolbani Mohammadrezapour

In groundwater flow modeling, as in any modeling problem, a certain amount of error is inevitable. Recharge or discharge wells, acting as point sources or sinks, play a key role in modeling accuracy, and the way they are treated can either reduce or increase errors. In this study, two approaches were investigated: first, transferring the well to the nearest node in its neighborhood, and second, distributing the pumping rate of the well among the closest nodes. A hypothetical aquifer was examined under two conditions-unconfined and confined-and using both triangular and square meshes. The results indicated that simplifying the model by moving the pumping well to the nearest node is justified only for unconfined aquifers with triangular meshes. For other cases-including unconfined aquifers with square meshes and confined aquifers with either mesh-the second approach is recommended, as it significantly reduces errors in groundwater flow modeling. These findings can also be generalized to real aquifer studies. Quantitative results show that Approach 2 consistently reduces modeling errors: for unconfined aquifers, MAE values are below 0.03 for both mesh types, whereas confined aquifers exhibit larger reductions, particularly with triangular meshes, where MAE reaches 0.38 and maximum errors up to 1.17. These results highlight the robustness of Approach 2 across different mesh configurations and aquifer conditions, providing an effective and reliable numerical tool for groundwater modeling.

Aderonke Adetutu Okoya, James Uduohmoh Sule, Joshua Oluwole Olowoyo

Yash Vekaria, Rishab Nithyanand, Zubair Shafiq

Online advertising relies on a complex and opaque supply chain that involves multiple stakeholders, including advertisers, publishers, and ad-networks, each with distinct and sometimes conflicting incentives. Recent research has demonstrated the existence of ad-tech supply chain vulnerabilities such as dark pooling, where low-quality publishers bundle their ad inventory with higher-quality ones to mislead advertisers. We investigate the effectiveness of vulnerability notification campaigns aimed at mitigating dark pooling. Prior research on vulnerability notifications have primarily explored single-stakeholder contexts, leaving multi-stakeholder scenarios understudied. There is limited attention to complex multi-stakeholder supply chain ecosystems such as ad-tech supply chain, where resolving vulnerabilities often requires coordinated action across entities with misaligned incentives and interdependent roles. We address this gap by implementing the first online advertising supply chain vulnerability notification pipeline to systematically evaluate the responsiveness of various stakeholders in ad-tech supply chain, including publishers, ad-networks, and advertisers to vulnerability notifications by academics and activists. Our nine-month long automated multi-stakeholder notification study shows that notifications are an effective method for reducing dark pooling vulnerabilities in the online advertising ecosystem, especially when targeted towards ad-networks. Further, the sender reputation does not impact responses to notifications from activists and academics in a statistically different way. Overall, our research fosters industry-scale solution to combat ad inventory fraud and fosters future research on feasibility of multi-stakeholder vulnerability notifications in other supply chain ecosystems.

Hyung-il Ahn, Santiago Olivar, Hershel Mehta et al.

Supply chain networks in enterprises are typically composed of complex topological graphs involving various types of nodes and edges, accommodating numerous products with considerable demand and supply variability. However, as supply chain networks expand in size and complexity, traditional supply chain planning methods (e.g., those found in heuristic rule-based and operations research-based systems) tend to become locally optimal or lack computational scalability, resulting in substantial imbalances between supply and demand across nodes in the network. This paper introduces a novel Generative AI technique, which we call Generative Probabilistic Planning (GPP). GPP generates dynamic supply action plans that are globally optimized across all network nodes over the time horizon for changing objectives like maximizing profits or service levels, factoring in time-varying probabilistic demand, lead time, and production conditions. GPP leverages attention-based graph neural networks (GNN), offline deep reinforcement learning (Offline RL), and policy simulations to train generative policy models and create optimal plans through probabilistic simulations, effectively accounting for various uncertainties. Our experiments using historical data from a global consumer goods company with complex supply chain networks demonstrate that GPP accomplishes objective-adaptable, probabilistically resilient, and dynamic planning for supply chain networks, leading to significant improvements in performance and profitability for enterprises. Our work plays a pivotal role in shaping the trajectory of AI adoption within the supply chain domain.

Zhengdong Luo

Supply Chain Finance is very important for supply chain competition, which is an important tool to activate the capital flow in the supply chain. Supply Chain Finance-related research can support multiple applications and services, such as providing accounts receivable financing, enhancing risk management, and optimizing supply chain management. For more than a decade, the development of Blockchain has attracted widely attention in various fields, especially in finance. With the characteristics of data tamper-proof, forgery-proof, cryptography, consensus verification, and decentralization, Blockchain fits well with the realistic needs of Supply Chain Finance, which requires data integrity, authenticity, privacy, and information sharing. Therefore, it is time to summarize the applications of Blockchain technology in the field of Supply Chain Finance. What Blockchain technology brings to Supply Chain Finance is not only to alleviate the problems of information asymmetry, credit disassembly, and financing cost, but also to improve Supply Chain Finance operations through smart contracts to intelligent Supply Chain Finance and in combination with other technologies, such as artificial intelligence, cloud computing, and data mining, jointly. So there has been some work in Blockchain-based Supply Chain Finance research for different Supply Chain Finance oriented applications, but most of these work are at the management level to propose conceptual frameworks or simply use Blockchain without exploiting its deep applications. Moreover, there are few systematic reviews providing a comprehensive summary of current work in the area of Blockchain-based Supply Chain Finance. In this paper, we ...

Ahmed Akinsola, Abdullah Akinde

This article delves into the strategic approaches and preventive measures necessary to safeguard the software supply chain against evolving threats. It aims to foster an understanding of the challenges and vulnerabilities inherent in software supply chain resilience and to promote transparency and trust in the digital infrastructure that underpins contemporary society. By examining the concept of software supply chain resilience and assessing the current state of supply chain security, the article provides a foundation for discussing strategies and practices that can mitigate security risks and ensure security continuity throughout the development lifecycle. Through this comprehensive analysis, the article contributes to the ongoing effort to strengthen the security posture of software supply chains, thereby ensuring the reliable and secure operation of digital systems in a connected world

Xuanting Ji, Minghai Chen, Zhengheng Pu et al.

Precise value of scheduling decisions forms the cornerstone of water distribution network (WDN) scheduling optimization, which aims at conserving energy and enhancing network operational efficiency. This article proposes a computational methodology for evaluating the value chain of scheduling decisions in WDN. The scheduling process is modeled as a Markov decision process with immediate reward function, action and state space. Due to the periodicity of water supply and sequential nature of scheduling, the calculation quantifies cumulative value of scheduling decisions by incorporating state transition probability with expected value. The effectiveness and applicability of the proposed evaluation method are demonstrated using scheduling data from a real world WDN. The method provides rational values on scheduling period and strategies, offering practical feedback for scheduling decisions.

Hongzhi Liu, Takao Katsura, Katsunori Nagano

Inés Aberlí Nieves-Vilchez, Hiram Joshua Castillo-Infante, Josué Isac Carillo-Espinoza et al.

El objetivo del estudio fue estimar los escurrimientos promedio mensuales de una cuenca para el desarrollo de una pico central hidroeléctrica en un área rural-montañosa de la selva (Satipo) del Perú. Debido a la ausencia de datos hidrometeorológicos en cuencas montañosas y remotas en el Perú, una estimación hidrológica resulta indispensable para determinar el potencial hidroenergético en la cuenca de estudio. Primero se determinaron los parámetros geomorfológicos (de forma y de relieve) de la cuenca y, posteriormente, se realizó un análisis de precipitación tomando en cuenta los datos de 17 estaciones meteorológicas. Para verificar la homogeneidad de los registros pluviométricos, se realizó un análisis de consistencia mediante un análisis de dobles acumulaciones. Para estimar los caudales de la cuenca de estudio, a falta de información hidrométrica, se utilizó el método de transposición de caudal, en el cual se transportaron los caudales de la cuenca Ourohuari. Asimismo, se comprobó que las características geomorfológicas e hidrometeorológicas resultaran similares, para ello se realizó una prueba t-test para muestras independientes, con el fin de verificar la similitud en la precipitación anual entre ambas cuencas. Finalmente, se puede concluir, las características geomorfológicas y el caudal regular durante todo el año en la cuenca Cashingari favorece el desarrollo de una pico central hidroeléctrica.