Future Prospects of Labour Productivity in Algerian Agriculture: A 2030 Outlook DOI Creative Commons
Bouazza Elamine Zemrı,

Mohammed Fouad Gassem

Contemporary Agriculture, Год журнала: 2024, Номер 73(3-4), С. 238 - 249

Опубликована: Дек. 1, 2024

Summary The primary objective of this study was to forecast the labour productivity in Algeria's agricultural sector by year 2030 using seasonal autoregressive integrated moving average (SARIMA) model. Quarterly data spanning from first quarter 1991 fourth 2021 were analyzed, identifying SARIMA model (1, 1, 1) x 4) as most suitable for capturing variations and accurately fitting historical data. methodology utilized Python 3.11.5 processing modelling, thus enabling a comprehensive analysis trends patterns Algerian productivity. results obtained demonstrate robust steady growth attributable advancements farming techniques, technological innovations, evolving market conditions. These findings highlight critical role accurate forecasting effective policy-making resource allocation. By providing insights into future trends, research supports development strategies aimed at enhancing resilience sustainability sector, particularly face challenges posed climate change geopolitical tensions. conclusion underscores importance leveraging predictive models such informing policies ensuring long-term food security economic stability Algeria.

Язык: Английский

A comprehensive review on advancements in sensors for air pollution applications DOI

Thara Seesaard,

Kamonrat Kamjornkittikoon,

Chatchawal Wongchoosuk

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175696 - 175696

Опубликована: Авг. 26, 2024

Язык: Английский

Процитировано

28

Exploring deep learning models for roadside landslide prediction: Insights and implications from comparative analysis DOI

Tiep Nguyen Viet,

Dam Duc Nguyen,

Manh Nguyen Duc

и другие.

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер unknown, С. 103741 - 103741

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

5

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review DOI Open Access

Masoud Khanmohamadi,

Marco Guerrieri

Sustainability, Год журнала: 2024, Номер 16(19), С. 8336 - 8336

Опубликована: Сен. 25, 2024

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, radar that have been made available on CAVs are able to detect anomalies roads, including potholes, surface cracks, or roughness. also describes advanced data processing techniques of detected with sensors, machine learning algorithms, fusion, edge computing, which enhance accuracy reliability in assessment. Together, these support instant safety long-term maintenance cost reduction proactive strategies. Finally, this article provides a comprehensive review the state-of-the-art future directions monitoring systems traditional smart roads.

Язык: Английский

Процитировано

5

Unlocking robotic perception: comparison of deep learning methods for simultaneous localization and mapping and visual simultaneous localization and mapping in robot DOI Creative Commons
Minh Long Hoang

International Journal of Intelligent Robotics and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 24, 2025

Язык: Английский

Процитировано

0

On the use of LSTM-based estimation components for tokamak gas actuator control DOI Creative Commons
H. J. Baker, L. W. Brown,

A. Parrott

и другие.

Fusion Engineering and Design, Год журнала: 2025, Номер 215, С. 114932 - 114932

Опубликована: Апрель 6, 2025

Язык: Английский

Процитировано

0

Restoration of multi-channel signal loss using autoencoder with recursive input strategy DOI Creative Commons
Jae Jun Lee, Yonggyun Yu, Hogeon Seo

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 21, 2025

Abstract Multi-channel sensor data often suffer from missing or corrupted values due to failures, communication disruptions, environmental interference. These issues severely limit the accuracy of intelligent systems relying on integration. Existing restoration techniques fail capture complex correlations among channels, especially when losses occur randomly and continuously. To overcome these limitations, we propose an autoencoder-based recovery algorithm that recursively feeds reconstructed outputs back into model progressively refine estimates. A dynamic termination criterion monitors reconstruction improvements, automatically stopping iterations further refinements become negligible. This recursive input strategy significantly enhances computational efficiency compared conventional single-step methods. Experiments multivariate datasets show proposed method outperforms one-time autoencoder maintains robust performance across diverse scenarios. approach provides a scalable adaptable solution ensure integrity in networks, enabling improved reliability operational industrial technological applications.

Язык: Английский

Процитировано

0

Advancing quality prediction in polymer PBF-LB: a hybrid AI and physics-guided approach DOI Creative Commons
Matteo Calaon, Hao-Ping Yeh,

Shuo Shan

и другие.

CIRP Annals, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Solar Panel Fault Detection: Applying Convolutional Neural Network for Advanced Fault Detection in Solar-Hydrogen System at University DOI
Salaki Reynaldo Joshua, Sanguk Park, Ki-Hyeon Kwon

и другие.

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

2

Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting DOI Creative Commons

Maximilian Rudack,

Michael Rom, Lukas Bruckmeier

и другие.

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер unknown

Опубликована: Авг. 29, 2024

Abstract High-pressure die casting (HPDC) is a permanent mold-based production technology that facilitates the of near net shape components from nonferrous alloys. The pressure and temperature conditions within cavity impact cast product quality during after conclusion filling process. Die surface sensors can deliver information describing at die-casting interface. They are associated with high costs limited service lifetimes below achievable total cycle count inserts therefore ill-suited for industrial use cases. In this work, suitability long short-term memory (LSTM) recurrent neural networks (RNN) substituting physical virtually ramp-up or end sensor life investigated. Training LSTMs data 233 cycles different process parameters provides which then applied to 99 further cycles. prediction accuracy investigated time interval lengths in solidification cooling phase. For longer intervals, deteriorates, potentially due highly individual hardly ascertainable buildup distortion internal stresses. Overall, however, developed excellent temperatures good pressures.

Язык: Английский

Процитировано

1

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios DOI Creative Commons
Angel M. Gama Garcia, José M. Alcaraz Calero, Higinio Mora

и другие.

Sensors, Год журнала: 2024, Номер 24(4), С. 1236 - 1236

Опубликована: Фев. 15, 2024

In the era of Industry 4.0 and 5.0, a transformative wave softwarisation has surged. This shift towards software-centric frameworks been cornerstone highlighted need to comprehend software applications. research introduces novel agent-based architecture designed sense predict application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim enhance operational insights decision-making processes. The forecaster component uses random forest regressor known aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations an error underscore forecasting accuracy limitations. work establishes foundational understanding for behaviours, charting course future advancements components within evolving landscapes.

Язык: Английский

Процитировано

0