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Keywords = nonproductive interfaces

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20 pages, 2480 KB  
Article
Development of Real-Time Water-Level Monitoring System for Agriculture
by Gaukhar Borankulova, Gabit Altybayev, Aigul Tungatarova, Bakhyt Yeraliyeva, Saltanat Dulatbayeva, Aslanbek Murzakhmetov and Samat Bekbolatov
Sensors 2025, 25(17), 5564; https://doi.org/10.3390/s25175564 - 6 Sep 2025
Viewed by 2027
Abstract
Water resource management is critical for sustainable agriculture, especially in regions like Kazakhstan that face significant water scarcity challenges. This paper presents the development of a real-time water-level monitoring system designed to optimize water use in agriculture. The system integrates IoT sensors and [...] Read more.
Water resource management is critical for sustainable agriculture, especially in regions like Kazakhstan that face significant water scarcity challenges. This paper presents the development of a real-time water-level monitoring system designed to optimize water use in agriculture. The system integrates IoT sensors and cloud technologies, and analyzes data on water levels, temperature, humidity, and other environmental parameters. The architecture comprises a data collection layer with solar-powered sensors, a network layer for data transmission, a storage and integration layer for data management, a data processing layer for analysis and forecasting, and a user interface for visualization and interaction. The system was tested at the Left Bypass Canal in Taraz, Kazakhstan, demonstrating its effectiveness in providing real-time data for informed decision-making. The results indicate that the system significantly improves water use efficiency, reduces non-productive losses, and supports sustainable agricultural practices. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Technology and Robotics Integration)
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27 pages, 7988 KB  
Article
Enhanced Computer Numeric Controller Milling Efficiency via Air-Cutting Minimization Using Logic-Based Benders Decomposition Method
by Hariyanto Gunawan, Didik Sugiono, Ren-Qi Tu, Wen-Ren Jong and AM Mufarrih
Electronics 2025, 14(13), 2613; https://doi.org/10.3390/electronics14132613 - 28 Jun 2025
Viewed by 478
Abstract
In computer numeric controller (CNC) milling machining, air-cutting, where the tool moves without engaging the material, will reduce the machining efficiency. This study proposes a novel methodology to detect and minimize non-productive (air-cutting) time in real-time using spindle load monitoring, vibration signal analysis, [...] Read more.
In computer numeric controller (CNC) milling machining, air-cutting, where the tool moves without engaging the material, will reduce the machining efficiency. This study proposes a novel methodology to detect and minimize non-productive (air-cutting) time in real-time using spindle load monitoring, vibration signal analysis, and NC code tracking. A logic-based benders decomposition (LBBD) approach was used to optimize toolpath segments by analyzing air-cutting occurrences and dynamically modifying the NC code. Two optimization strategies were proposed: increasing the feedrate during short air-cutting segments and decomposing longer segments using G00 and G01 codes with positioning error compensation. A human–machine interface (HMI) developed in C# enables real-time monitoring, detection, and minimization of air-cutting. Experimental results demonstrate up to 73% reduction of air-cutting time and up to 42% savings in total machining time, validated across multiple scenarios with varying cutting parameters. The proposed methodology offers a practical and effective solution to enhance CNC milling productivity. Full article
(This article belongs to the Special Issue Advances in Industry 4.0 Technologies)
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24 pages, 5637 KB  
Article
A 3D Printed Physical Human–Robot Interface Based on a Sizing System to Facilitate Customization: Wearable Robots for Paraplegia
by Chil-Yong Kwon, Jung-Sik Jang and Gyoo-Suk Kim
Appl. Sci. 2021, 11(11), 5143; https://doi.org/10.3390/app11115143 - 1 Jun 2021
Viewed by 3528
Abstract
Wearable robots that assist paraplegia patients should be manufactured according to the shape of the individual wearer. With the development of advanced three-dimensional (3D) printing technologies, such customizations are becoming available. However, conventional 3D printing customization requires extensive template remodeling, which is non-productive [...] Read more.
Wearable robots that assist paraplegia patients should be manufactured according to the shape of the individual wearer. With the development of advanced three-dimensional (3D) printing technologies, such customizations are becoming available. However, conventional 3D printing customization requires extensive template remodeling, which is non-productive and inefficient. This study proposes a 3D-printed (3DP) physical human–robot interface (pHRI) based on a sizing system that facilitates customization to body shapes. The proposed system is a pre-developed pHRI in various sizes and shapes using a human body shape database. Conformity of shapes and dimensions were evaluated visually via shape deviation analysis for 10 persons having paraplegia. With the proposed 3DP-pHRI, the trunk comprised 18 sections and the shank comprised nine. A biased trend of coverage rates in the shank 3DP-pHRI size system was identified. The trunk and shank subsystems were found to be adequate in terms of shapes and dimensions, with values within the mean deviation range of ±10 mm. A novel 3DP-pHRI size system facilitating customization tool for fabricating wearable robots for paraplegia patients according to body shape was developed, and its effectiveness was assured. The new system can be used for various wearable robot pHRIs, and the database is expected to supply a comprehensive pHRI template library. Full article
(This article belongs to the Section Robotics and Automation)
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20 pages, 3844 KB  
Review
RAS Nanoclusters: Dynamic Signaling Platforms Amenable to Therapeutic Intervention
by Que N. Van, Priyanka Prakash, Rebika Shrestha, Trent E. Balius, Thomas J. Turbyville and Andrew G. Stephen
Biomolecules 2021, 11(3), 377; https://doi.org/10.3390/biom11030377 - 3 Mar 2021
Cited by 23 | Viewed by 5582
Abstract
RAS proteins are mutated in approximately 20% of all cancers and are generally associated with poor clinical outcomes. RAS proteins are localized to the plasma membrane and function as molecular switches, turned on by partners that receive extracellular mitogenic signals. In the on-state, [...] Read more.
RAS proteins are mutated in approximately 20% of all cancers and are generally associated with poor clinical outcomes. RAS proteins are localized to the plasma membrane and function as molecular switches, turned on by partners that receive extracellular mitogenic signals. In the on-state, they activate intracellular signal transduction cascades. Membrane-bound RAS molecules segregate into multimers, known as nanoclusters. These nanoclusters, held together through weak protein–protein and protein–lipid associations, are highly dynamic and respond to cellular input signals and fluctuations in the local lipid environment. Disruption of RAS nanoclusters results in downregulation of RAS-mediated mitogenic signaling. In this review, we discuss the propensity of RAS proteins to display clustering behavior and the interfaces that are associated with these assemblies. Strategies to therapeutically disrupt nanocluster formation or the stabilization of signaling incompetent RAS complexes are discussed. Full article
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24 pages, 9458 KB  
Article
Advanced Process Simulation of Low Pressure Die Cast A356 Aluminum Automotive Wheels—Part II Modeling Methodology and Validation
by Jun Ou, Chunying Wei, Steve Cockcroft, Daan Maijer, Lin Zhu, Lateng A, Changhai Li and Zhihua Zhu
Metals 2020, 10(11), 1418; https://doi.org/10.3390/met10111418 - 25 Oct 2020
Cited by 25 | Viewed by 5793
Abstract
This manuscript presents an advanced modeling methodology developed to accurately simulate the temperature field evolution in the die and wheel in an industrial low-pressure die casting (LPDC) machine employed in the production of A356 automotive wheels. The model was developed in the commercial [...] Read more.
This manuscript presents an advanced modeling methodology developed to accurately simulate the temperature field evolution in the die and wheel in an industrial low-pressure die casting (LPDC) machine employed in the production of A356 automotive wheels. The model was developed in the commercial casting simulation platform ProCAST for a production die operating under production conditions. Key elements in the development of the model included the definition of the resistance to heat transfer across the die/casting interfaces and die/water-cooling channel interfaces. To examine the robustness of the modeling methodology, the model was applied to simulate production and non-production process conditions for a die cooled by a combination of water and air-cooling (Die-A), and to a second die for a different wheel geometry (Die-B) utilizing only water cooling for production conditions. In each case, the model predictions with respect to in-die and in-wheel temperature evolution were compared to industrially derived thermocouple (TC) data, and were found to be in good agreement. Once tuned to the process conditions for Die-A operating under production conditions, no further tuning of the die/casting interface resistance was applied. Additionally, the model results, in terms of the prediction of pockets of solid encapsulated liquid, were used to compare to x-ray images of wheels. This comparison indicated that the model was able to predict clusters of porosity associated with encapsulated liquid with an equivalent radius of ~27 mm. Full article
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