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Study on Desktop Smart Production Line and Diagnosis Technology
Full text linkAbstract: Smart manufacturing is a development tendency in the manufacturing industry. Thus, this study aimed to construct a desktop smart production line using a virtual and a real system. The data measured by various sensors were collected and combined with an intelligent predictive diagnosis system to achieve online diagnosis, analysis, and prediction of the health status of the machine. We designed an interactive information collection service for the convenience of users. We allowed users to obtain specific information easily and quickly, improve the convenience of controllers and devices, and meet the need for long-term monitoring. Moreover, we focused on reducing production scenarios from cell manufacturing to factory product inspection using robotic arms, three-dimensional printers, and small and complex processing machines with intelligent predictive diagnostic systems. In this regard, the visual recognition function of the robotic arm can perform a product appearance inspection. Finally, in the machine network platform integrating all the controllers, when the machine fails, the information is sent to the user in real time through the communication service software, and the operator can take corresponding measures depending on the warning actions received, such as remote control of the machine, to ensure production efficiency and quality
Design and Analysis of a High-Precision Horizontal Machine Tools
Full text linkAbstract: The horizontal machine tool has an automatic exchange table, which can be combined with a flexible manufacturing system for automatic processing and production. Therefore, it requires higher performance stability than other machines. This study analyzes the static and dynamic characteristics of a horizontal machine tool structure. The finite element analysis (FEA) method is generally used to analyze the whole machine structure and improve the deformation and resonance of the horizontal machine tool. In this study, FEA was applied to the design process of the machine tool, including static deformation analysis, modal analysis, transient analysis, and harmonic analysis of the machine. The deformation of the whole machine due to acceleration of gravity and cutting force was analyzed. The modal shapes generated by the first and third modes directly affected the machining process of the machine tool. To further analyze the influence of vibration signal processing on processing quality, transient response analysis was carried out on the effect of axial cutting force during machining. Spectrum analysis of the machine was also carried out. This study is expected to help the structural design of a horizontal machine tool to improve the dynamic characteristics and stability of the horizontal machining system
Investigation and Fabrication of Brilliant Green Dye Material-Based Organic Heterojunction: Dielectric Response and Impedance Spectroscopy
Full text linkBrilliant green (BG) dye material-based heterojunction is fabricated by a spin coating route and characterized by impedance analyzer. Here, we study the impedance spectroscopy of such organic material based thin film onto silicon substrate. The capacitance and conductance versus voltage (C-V), (G-V) characteristics are plotted. So, the dielectric response of BG based heterojunction is studied via the plotting of the dielectric constant, modulus components, complex impedance and Nyquist diagram. Real and imaginary parts of electrical conductivity are also plotted at several frequencies. Real and imaginary parts of electric modulus M’-V and M”-V are investigated for all experimental frequencies. The Z’-V characteristics of our device-based BG organic heterojunction exhibit a peak of 1383 W. It is confirmed that an anomalous peak of Z’ is recorded within 0.5-1 V range. The drastic decay in Z”-V plot occurs for lower 100 and 200 kHz frequency range and Z” values become constant beyond 1V for all frequencies. Ac and Dc conductivity curves demonstrate a growth with frequency and angle phase approaches to 90º within reverse voltage. This result reveals a capacitive conduct of our device
Black Cool Pigments for Urban Heat Island (UHI) Control: from Cr-Hematite to Mn-Melilite
Full text linkBlack cool pigments are very interesting for its application in asphalt urban pavements and building floors for moderate the urban heat island effect (UHI) and improving air conditioning energy efficiency. Cool black pigments based on Cr doped hematite Fe2O3 (trigonal, R-3c), hexagonal perovskites YMnO3 (hexagonal, P63cm) and Sr4CuMn2O9 (trigonal, P321) and melilite Sr2(Mg0.5Mn0.5)Ge2O7 (tetragonal, P-421m) with high NIR reflectance synthesized by ceramic and coprecipitation method, are analyzed and compared from color yield in alkyd paint, ceramic glazes and porcelain stoneware, NIR reflectance, bandgap and photocatalytic activity on Orange II substrate. Sr4CuMn2O9 black powders show the nearest hue h to the reference carbon black and the highest NIR reflectance (51%). All pigments show high NIR reflectance in all tested applications. The Fe1.2Cr0.8O3 pigment shows good behavior in the free ZnO glaze and also in porcelain stoneware, YMnO3 and Sr4CuMn2O9 pigments are compatible with low temperature glazes, but Sr2(Mg0.5Mn0.5)Ge2O7 pigment loses the black color even in low temperature glazes. Sr4CuMn2O9 pigment shows moderate photoactivity on Orange II (t1/2=216 min) and the Fe1.2Cr0.8O3 pigment also shows some activity (t1/2=329 min)
A Passive Solar Air-House Conditioning System Integrated in Tunisian households
Full text linkIn Tunisia, the buildings’ space heating sector represents a major part of the total energy consumption budget. These issues have been increasingly prominent concerns since the energy crisis. Hence, interests have been growing to adopt renewable energies as viable sources of energy that offer a wide range of exceptional benefits with an important degree of promise, especially in the buildings sector. However, the management of renewable energy sources for space air heating/cooling is usually not economically feasible compared with the traditional carriers. In this chapter, we present a passive energy system, called air-conditioning cupboard which exploits renewable energies (hot water supplied from solar collector [40-50°C] and cold groundwater (19°C)) as thermal sources, is conceived and tested in our laboratory (Laboratory of Thermal Procedure, LPT Tunisia). To evaluate the air-conditioning cupboard efficiency indoor experiments were carried out under varied Tunisian environmental conditions for several days. Results show that the air-heating system has good thermal effectiveness (80 %). It permits to the maintenance of the temperature inside the experimented room at the range of [24-27°C] during the cold months and [20-23°C] during hot months. A theoretical model is employed for the sizing of the air-conditioning cupboard to obtain the required temperature values. This model allows also the determination of the air-cupboard conditioning thermal performances
Particle Filter-Based Robust Visual Servoing for UCF-MANUS-An Intelligent Assistive Robotic Manipulator
Full text linkA particle filter based tracking scheme is proposed to robustify visual servoing of objects in the UCF-MANUS camera-in-hand vision setup. Instead of simply fusing global and local information, a concatenation of the two sources of information is proposed here which enables the combination of the two independent measurements with a synergistic collaboration between them. A novel overlap metric to encode the degree and quality of overlap between two arbitrarily shaped Regions of Interest (ROIs) is defined to facilitate the prior and posterior pdfs in the particle filter setup.A sub-ROI is defined and utilized in the observation step to facilitate the global target detection. Based on extensive experimental results under a variety of scenarios obtained by using the UCF-MANUS assistive robotic testbed, it is seen that the proposed particle filter based fusion approach is superior to other non-fused global detection or local tracking approaches. The efficacy of the proposed approach has also been verified using standard data sets. Finally, robustification of a hybrid visual servoing technique is shown by implementing the proposed particle-filter based tracker during closed-loop operation in real-time
Computational Robotics: An Alternative Approach for Predicting Terrorist Networks
Full text linkIncreasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence
Magnetocaloric Effect in La0.7Sr0.3Mn0.95Ni0.05O3 Manganite Via Mean Field Theory
Full text linkThis study investigates the magnetocaloric effect of La0.7Sr0.3Mn0.95Ni0.05O3 manganite, with a primary focus on leveraging the mean-field theory as a powerful tool for analysis. By applying this theoretical framework, alongside the Law of Approach to Saturation (LAS), key parameters such as saturation magnetization (Mo), total angular momentum (J), gyromagnetic factor (g) , and exchange parameter (λ) were determined. The mean-field theory proved essential for simulating the isothermal magnetization M (H,T) and the magnetic entropy change -∆SM (T)curves, providing a comprehensive understanding of the material’s magnetocaloric behavior. Despite its simplifications, the mean-field approach serves as a crucial starting point for modeling complex magnetic systems and offers valuable insights into the material’s thermodynamic properties
A Multimodal and Dynamically Updatable Benchmark for Aviation Question Answering with Large Language Models
Full text linkWith the rapid advancement of artificial intelligence, large-scale language models (LLMs) have demonstrated strong capabilities in open-domain question answering, knowledge retrieval, and decision support. However, in safety-critical and knowledge-intensive industries such as aviation, existing evaluation benchmarks fall short in domain adaptation, comprehensiveness, and dynamic updating. As aviation increasingly integrates intelligent automation and robotic systems for maintenance, inspection, and manufacturing, reliable language-model evaluation becomes crucial for ensuring the safety and autonomy of such systems. This paper proposes a multimodal, multi-level benchmark dataset tailored to aviation QA tasks, alongside an automated updating mechanism and a multi-dimensional evaluation framework. The methodology integrates knowledge extraction from multimodal aviation documents, diverse QA pair generation, iterative complexity enhancement, and quality validation. Furthermore, dynamic updating is achieved via a hybrid strategy combining imitation and expansion, complemented by differentiated filtering and prompt optimization. To ensure rigorous assessment, a ten-dimension evaluation framework is introduced, covering accuracy, completeness, relevance, explainability, and safety, among others. By providing a reliable and dynamically evolvable benchmark, this work supports the integration of LLMs into robotic and automated decision-support systems in aviation, enabling more intelligent, autonomous, and safety-assured operations. Experimental results using aviation textbooks confirm the effectiveness of the proposed approach in generating high-quality, dynamically evolvable QA datasets. This work provides both methodological innovation and practical tools for the evaluation of LLMs in aviation, with potential extension to other knowledge-intensive domains
The Double-Edged Sword of Decentralization: Cryptocurrency Adoption and Risk
Full text linkThis study offers an in-depth examination of the transformative influence of cryptocurrencies on global economic and financial systems, emphasizing their interplay with financial inclusion, regulatory evolution, and decentralized economic frameworks. Employing a mixed methods design that combines quantitative regression modeling with qualitative analysis, the research uncovers new insights into cryptocurrency adoption, particularly within emerging economies and financially marginalized populations.
Unlike previous studies that focus primarily on technological or speculative dimensions, this paper critically investigates cryptocurrencies as both catalysts for financial democratization and potential sources of systemic risk. It develops a balanced framework for understanding how decentralized finance (DeFi) can coexist with regulatory oversight, proposing evidence-based policy recommendations that promote innovation while safeguarding market integrity and consumer protection.
Empirical findings demonstrate that cryptocurrencies facilitate broader access to financial services due to their decentralized structure and cost-efficient transactions. However, they also expose users to challenges such as extreme price volatility, cybersecurity risks, and inconsistent regulatory environments. Moreover, socio-economic analysis reveals that individuals with prior exposure to cryptocurrencies exhibit more favorable perceptions of their societal and economic impact.
The research concludes that sustainable cryptocurrency integration requires adaptive regulatory models, cross-border collaboration, and continuous monitoring of technological evolution. Future studies should expand on longitudinal and comparative analyses to evaluate how evolving governance and education strategies influence adoption and trust.
By situating cryptocurrencies within the broader discourse of digital transformation and economic sustainability, this paper contributes to shaping policy and industry practices that support an inclusive, resilient, and transparent financial ecosystem