Biomonitoring water quality is an essential method for maintaining the ecological health of resources. This study aims to analyze the temporal trends over a period (1984-2022) of four water quality-related indices (LST, TSS, NDCI, NDTI) in three Algerian dams (Beni slimene, Ledrat, Ghrib) using space remote sensing via the Google Earth Engine platform. The dataset is subjected to Mann-Kendall statistical tests and linear regression to detect any significant trends in the indices studied. The results show a clear opposition between the two-time scales. On a seasonal scale, there is no statistically significant trend in the three dams (p-values > 0.05; R² close to zero), indicating the absence of systematic seasonal cyclicity. On the other hand, the annual analysis reveals marked and highly significant trends: surface temperature (LST) and suspended solids (TSS) increase in all three dams (p < 10⁻⁵ for LST; p < 10⁻⁹ for TSS). The NDCI index (chlorophyll/organic matter) increased significantly in Beni Slimane (p = 3.9×10⁻³) and Ledrat (p = 1.24×10⁻⁵), but remained stable in Ghrib. The NDTI index (turbidity) decreases only at Ghrib (p = 5.52×10⁻⁵), with no detectable trend in the other two dams.
This paper deals with a system of fractional stochastic differential equations with delay. Under some regularity conditions we study the well posedness of the system and we establish the existence of stochastic optimal control.
This studyintroduces a refined approach to farming with sunlight, combining solar panels and crop growth while using connected sensors through long-range wireless signals alongside wiring data transfer. Farming under solar arrays saves space by producing food and electricity together on shared ground. Still, current setups struggle due touneven water delivery, unstable power handling, weak links among devices, and outdated plant behavior predictions when weather shifts occur. One wayaround these drawbacks begins with anewdesign:an IoT-based smartwater control setup paired with dynamic crop models. Instead of relying on extra wiring, power line communication moves data quickly using current electrical lines. For field sensors spread far apart, LoRaWAN handles connections efficiently - long reach, little power needed. Data flows nonstop - from earth dampness toweather shifts to plantstate - shapingsmarter watering plans. Health tracking for crops gets sharper. Energy use adjusts in response. Scaling up stays affordable. A key part of the system lies in its ability to fine-tune fertilizers according to both soil conditions and how crops develop over time. Because nutrients are applied more precisely, yields rise -alongside better use of water - while excess runoff and ecological harm drop off. Testing via MATLAB models, together with real-world examples, confirms gains across farming output, smarter watering practices, and improved energyoperations. When combined, programmable logic controls, long-range wireless communication, and smart nutrient handling open doors - not just for growing food sustainably - but also for sharing land wisely between farming and clean power generation.
Aim: This study aims to explore how economic, environmental, and social sustainability efforts influence the performance of a supply chain and to investigate how the role of government regulations and SDG 12 (Responsible Consumption and Production) may impact these effects. Method/Design: The study employed quantitative methods and utilised data collected from 365 professionals working in supply chains across various fields. The researchers used a questionnaire to assess factors such as sustainability dimensions, the overall regulatory framework, the incorporation of SDG 12, and performance in the supply chain. To assess the proposed hypotheses, structural equation modelling (SEM) was conducted using SmartPLS. Findings: The results indicate that environmental and economic sustainability practices have a positive influence on supply chain performance, whereas social sustainability does not show a significant impact. The rules and regulations in place had a direct effect and played a significant role in shaping how economic sustainability affected the company's performance. But none of the sustainability-performance relationships were significantly affected by SDG 12. They emphasise that strong institutions and economic cooperation are crucial for achieving better results in sustainable supply chains. Conclusion: This study brings a unique perspective to the field by examining sustainability in global supply chains through the lens of multiple theories, incorporating regulations and SDG 12 into the analysis. The research results help companies that aim to compete by following specific sustainability plans and understand how to interact with regulators.
Introduction: The process of cloud infrastructure management remains a high-friction and cognitively intensive endeavor since the Infrastructure-as-Code (IaC) platforms are platform-specific, syntactically dense, and coupled with provider-specific configurations. Since organizations are rapidly moving to cloud-native architecture to ensure they provide scalable and distributed applications, the operational cost of configuring, validating, and maintaining infrastructure has increased manifold. Manual provisioning processes demand extensive technical knowledge and they tend to have repetitive documentation reading, which makes configuration drift, deployment bugs, and ineffective operations more probable. These obstacles pose both a hindrance to enterprise-level efforts at implementing DevOps as well as to students and practitioners in need of a public, low-risk environment in which to learn and test out cloud technologies. Objectives: This paper seeks to create an AI-agent-based architecture, Cloudent, that provides a translation between natural-language user intent and executable cloud infrastructure by automating the process of IaC generation, enhancing deployment reliability, and cutting operational overhead by generating Intelligent Reasoning and Self-Correcting. Methods: Cloudent brings together an Agentic AI reasoning engine, based on LangChain and LangGraph, with the Pulumi Automation SDK within a Next.js application to write programs generating type-safe TypeScript IaC without any external CLI. The semantic retrieval layer translates the will of the user into cloud provisioning profiles and an iterative self-healing process translates the deployment logs and feedback on errors and adjusts configurations autonomously. The emulation based on LocalStack provides safe and cost-transparent environment of testing. Results: Analysis of the suggested framework indicates a high level in automating processes of infrastructure provisioning and still `maintaining contextual accuracy and operational consistency. The combined model recorded training accuracy of 96.50% and validation accuracy of 94.50% in interpreting and executing infrastructure tasks meaning that it was reliable in translating natural-language requirements into deployable resources. The self-healing system minimized the number of debugging cycles, increased the effectiveness of the deployment process, and allowed manual corrections to be performed iteratively, which speeded up the provisioning process and increased trust in automated DevOps operations. Conclusions: Cloudent illustrates how Agentic AI can change the DevOps processes through transforming the conversational requirements into production-deployable infrastructure. The structure provides a democratized and scalable cloud management solution through autonomous reasoning, correction through iteration and safe emulation, which increases the rate of deployment and improves confidence in operations.
