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Copyright of Rehabilitation Sciences: Nursing, Physiotherapy, Occupational Therapy / Reabilitacijos Mokslai: Slauga, Kineziterapija, Ergoterapija is the property of Lithuanian Sports University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In state-of-art anomaly detection research, prevailing methodologies predominantly employ Generative Adversarial Networks and Autoencoders for image-based applications. Despite the efficacy demonstrated in the visual domain, there remains a notable dearth of studies showcasing the application of these architectures in anomaly detection within the sound domain. This paper introduces tailored adaptations of cutting-edge architectures for anomaly detection in audio and conducts a comprehensive comparative analysis to substantiate the viability of this novel approach. The evaluation is performed on the DCASE 2020 dataset, encompassing over 180 h of industrial machinery sound recordings. Our results indicate superior anomaly classification, with an average Area Under the Curve (AUC) of 88.16% and partial AUC of 78.05%, surpassing the performance of established baselines. This study not only extends the applicability of advanced architectures to the audio domain but also establishes their effectiveness in the challenging context of industrial sound anomaly detection. [ABSTRACT FROM AUTHOR]

The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions. [ABSTRACT FROM AUTHOR]

The article provides detailed insights into the arc welding of mixed joints on pressure equipment, emphasizing the quality requirements for welds in various technical regulations. It delves into the specific challenges posed by the diversity of materials and the need for optimal coordination of welding parameters, offering recommendations for achieving high-quality arc welding connections.

The aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed approach relies on a set of both low-order and high-order Yule-Walker equations and on a modified version of the overdetermined recursive instrumental variable method, leading to the estimation of both the AR coefficients and the additive noise variance. The main motivation behind our proposition is introducing model identification procedures suitable for implementation on edge-computing platforms and programmable logic controllers (PLCs), which are known to have limited capabilities and resources when dealing with complex mathematical computations (i.e., matrix inversion). Indeed, our development is focused on condition monitoring systems, with particular attention paid to their integration onboard industrial machinery. The performance of the recursive approach is tested using both numerical simulations and a laboratory case study. The obtained results are very promising. [ABSTRACT FROM AUTHOR]

The present study is directed to the problem of hardfacing and restoration of worn industrial equipment. Wear tests were carried out using especially built rig which reproduces working conditions of machinery applied in cement plants. The results of tribological tests on 20 Fe- and Ni-base hardfacings are presented. The effect of hardfacing hardness and chemical composition was evaluated. It was found in SEM examinations that matrix was removed from the zone adjacent to carbides which made them liable to cracking and digging out. The mechanism of matrix removal depended on its hardness and include microcutting and low cycle fatigue. Ni-based hardfacings outperformed Fe-based coatings. The abrasion resistance of the best Ni-base coating, the Stelcar 6 was 38.7 times higher than that of S235JR steel. Eutectics in Ni-base coatings disturb motion of abrasive grains and force them to rotate instead of sliding over hardfacing surface. Ni-based coatings can be considered in hardfacing or reclamation of numerous industrial components applied in cement plants. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/retrofitting. As reported in the 2022 McKinsey Global Survey (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products. [ABSTRACT FROM AUTHOR]

Sensor matrices are essential in various fields including robotics, aviation, health care, and industrial machinery. However, conventional sensor matrix systems often face challenges such as limited reconfigurability, complex wiring, and poor robustness. To address these issues, we introduce a one-wire reconfigurable sensor matrix that is capable of conforming to three-dimensional curved surfaces and resistant to cross-talk and fractures. Our frequency-located technology, inspired by the auditory tonotopy, reduces the number of output wires from row × column to a single wire by superimposing the signals of all sensor units with unique frequency identities. The sensor units are connected through a shared redundant network, giving great freedom for reconfiguration and facilitating quick repairs. The one-wire frequency-located technology is demonstrated in two applications-- a pressure sensor matrix and a pressure-temperature multimodal sensor matrix. In addition, we also show its potential in monitoring strain distribution in an airplane wing, emphasizing its advantages in simplified wiring and improved robustness. [ABSTRACT FROM AUTHOR]

Graphene-based materials have great potential for tribological applications. Graphene's unique properties such as low shear resistance, high stiffness, and thermal conductivity make it an attractive material for improving the properties of lubricants in a wide range of industrial applications, from vehicles to house refrigerators and industrial machinery such as gearboxes, large compressors, etc. The current review aims to give an engineering perspective, attributing more importance to commercially available graphene and fully formulated lubricants instead of laboratory-scaled produced graphene and base oils without additives. The use of lubricants with graphene-based additives has produced e.g., an increase in mechanical efficiency, consequently reducing energy consumption and CO2 emissions by up to 20% for domestic refrigerators and up to 6% for ICE vehicles. Potential effects, other than purely friction reduction, contributing to such benefits are also briefly covered and discussed. [ABSTRACT FROM AUTHOR]

Gear drives are widely used in various fields and applications due to their properties and capacity. Their versatility, durability, and ability to transmit high torques as well as precision and reliability make them extremely useful in many fields of technology. They are widely used in industrial and energy machinery, vehicle drive systems, aerospace, medical devices, and many other areas. Gears can be manufactured using many technologies. This work focuses mainly on machining with particular emphasis on high-performance new technologies. The process of mathematical modeling of the gear and the machined profile is strongly related to CNC machining technologies. A robust correlation of systems supporting the design and modeling of sliding gears needed for the manufacturing process is presented in the article. It is very important to properly assess gears with correct manufacturing in accordance with a specific standard. The article presents an analysis of available methods for controlling gears using coordinate measurement techniques. Gear machining methods were assessed in terms of the technologies used as well as their productivity and manufacturing tolerance. [ABSTRACT FROM AUTHOR]

In order to boost the cycles' thermal efficiency, their power generations and reduce industrial equipment costs the growing need to develop and improve the performance of power generation cycles has provided the basis for research. Currently, there are quite a few investigations aiming to consider Kalina cycles as a power generation system using low-grade heat sources. In this research, firstly KCS-11 (Kalina Cycle System11) as well as Ekalina cycle (enhanced KCS-11 with ejector) has been studied. The SEKalina cycle, which is a modification of Ekalina cycle, is introduced, examined, and simulated by EES software. In the structure of the EKalina, the throttle valve and absorber are removed and the ejector is used instead. The use of the ejector reduces pressure at the turbine's outlet and augments the difference between the enthalpy of two turbine ends, leading to enhancement of the thermal proficiency and net power output. Including an ejector and benefited from a split configuration, SEKalina cycle proposes a potential for efficiency improvement. Examining the results of the cycles, it is found that by employing the SEKalina cycle, compare to the two previously introduced cycles (EKalina and KCS-11), the thermal efficiency and net power output rise significantly. Moreover, as a result, the net power output in SEKalina cycle is 2% higher than that of EKalina cycle. [ABSTRACT FROM AUTHOR]

During the last three decades, financial sanctions have been imposed on Iran by the United States, the European Union, and the United Nations Security Council. These sanctions have had various effects on Iran's economic sectors. This paper aims at estimating the effect of financial sanctions on the import of capital and intermediate goods in Iran, which was carried out for two independent time periods. The first period (2010-2013) includes multilateral financial sanctions, and the second period (2016-2019) includes multilateral sanctions and the withdrawal of the United States from the JCPOA. We examined the impact using the difference-in-difference (DID) method. The results of the first period indicate that the decrease in the imports of capital and intermediate goods in Iran depends more on the countries that "provided the sanctions plan" than the countries that did not provide the sanctions plan, because the coefficient of dummy variable for implementation in the random effects model is statistically significant. The negative effect of 0.007 on imports shows that the effect is weak, because this group of countries behaved differently. Since the implementation of multilateral financial sanctions in 2012, some countries such as Australia greatly reduced the export of capital and intermediate goods to Iran, but other countries such as Italy increased the export of capital and intermediate goods to Iran. In the second period, the random effects model is statistically significant. In this model, the negetive effect of 0.22 on imports indicates a significant effect. Therefore, the reduction of Iran's imports in this period depends more on the countries that provided the sanctions plan than the countries that did not provide the plan. Since the withdrawal of the United States from the JCPOA in 2018, countries such as Korea, Germany, Russia, UK and Italy reduced the export of capital and intermediate goods to Iran. The comparison of two independent periods indicates that in the first period, the major share of Iran's imports belongs to the UAE, and in the second period, it belongs to China. Turkey has been Iran's trading partner in both periods and since the imposition of sanctions, it has had an 8% share in the export of capital and intermediate goods to Iran, and with the withdrawal of the United States from the JCPOA, this share has increased to 13%. [ABSTRACT FROM AUTHOR]

The problems of difficult dyeing, poor dyeing fastness, and dyeing pollution of aramid are always great obstacles in the practical application. In theory, constructing particular nanostructures instead of dyes on aramids to achieve bright structural colors will be an effective strategy. However, it is still challenging to construct particular nanostructures on aramid fabric surfaces because of their roughness. Here, beautiful and noniridescent structural colored aramids with excellent light fastness were achieved by spraying a thin layer of randomly distributed nanospheres on the surface of adhesive‐modified aramids. Moreover, different bright structural colors and colorful patterns were obtained by simply controlling the size or refractive index of the spheres. Importantly, the light fastness of the structural colored aramid can reach levels 6–7 or higher. The simple and eco‐friendly coloring technology can be compatible with industrial equipment to obtain stable multicolored aramid products, exhibiting great real potential applications. [ABSTRACT FROM AUTHOR]