Hasil untuk "Automation"

D. Vrontis, M. Christofi, V. Pereira et al.

Abstract Although academic production in intelligent automation (e.g. artificial intelligence, robotics) has grown rapidly, we still lack a comprehensive understanding of the impacts of the utilization of these technologies in human resource management (HRM) at an organizational (firms) and individual (employees) level. This study therefore aims to systematize the academic inputs on intelligent automation so far and to clarify what are its main contributions to and challenges for HRM. In a systematic search of 13,136 potentially relevant studies published in the top HRM, international business (IB), general management (GM) and information management (IM) journals, we found 45 articles studying artificial intelligence, robotics and other advanced technologies within HRM settings. Results show that intelligent automation technologies constitute a new approach to managing employees and enhancing firm performance, thus offering several opportunities for HRM but also considerable challenges at a technological and ethical level. The impact of these technologies has been identified to concentrate on HRM strategies, namely, job replacement, human-robot/AI collaboration, decision-making and learning opportunities, and HRM activities, namely, recruiting, training and job performance. This study discusses these shifts in detail, along with the main contributions to theory and practice and directions for future research.

Jie Zheng, D. Baird, M. Borges et al.

Purpose of ReviewMendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel’s First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions.Recent FindingsIn this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR.SummaryIn conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.

S. Baduge, Sadeep Thilakarathna, J. S. Perera et al.

This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented

Shaoyong Yang, A. Bryant, P. Mawby et al.

P. Wriggers

C. Wickens

David Alejandro Pinzon, Gina Amado, Jader Rodriguez et al.

The cultivation of blueberry (<i>Vaccinium</i> spp.) has undergone an unprecedented global expansion, driven by its nutraceutical value and the diversification of production zones across the Americas, Europe, and Asia. Its consolidation as a strategic crop has prompted intensive scientific activity aimed at optimizing every stage of management from propagation and physiology to harvest, postharvest, and environmental sustainability. However, the available evidence remains fragmented, limiting the integration of results and the formulation of knowledge-based, comparative production strategies. The objective of this systematic review was to synthesize scientific and technological advances related to the integrated management of blueberry cultivation, incorporating physiological, agronomic, technological, and environmental dimensions. The PRISMA 2020 methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was applied to ensure transparency and reproducibility in the search, selection, and analysis of scientific literature indexed in the Scopus database. After screening, 367 articles met the inclusion criteria and were analyzed comparatively and thematically. The results reveal significant progress in propagation using hydrogel and micropropagation techniques, efficient fertigation practices, and the integration of climate control operations within greenhouses, leading to improved yield and fruit quality. Likewise, non-thermal technologies, edible coatings, and harvest automation enhance postharvest quality and reduce losses. In terms of sustainability, the incorporation of water reuse and waste biorefinery has emerged as key strategies to reduce the environmental footprint and promote circular systems. Among the main limitations are the lack of methodological standardization, the scarce economic evaluation of innovations, and the weak linkage between experimental and commercial scales. It is concluded that integrating physiology, technology, and sustainability within a unified management framework is essential to consolidate a resilient, low-carbon, and technologically advanced fruit-growing system.

YANG Jian, SHU Longyong, ZHANG Shulin et al.

Current research on gas concentration prediction in working faces of coal mines often suffers from limited feature dimensions and small dataset sizes, making it difficult to extract long-term fluctuation patterns from large-scale time-series data. To address this issue, this study proposes a Principal Component Analysis (PCA)-Transformer-based prediction algorithm for gas concentration in working faces. Firstly, raw gas concentration-related data was cleaned and normalized using min-max scaling. Then, PCA was applied to reduce the dimensionality of seven influencing factors (methane concentration at the upper corner, return airflow methane concentration, oxygen concentration, carbon monoxide concentration, temperature, net flow rate, and wind speed), effectively eliminating weakly correlated features. Finally, the processed training set was fed into a Transformer model, where the encoder and decoder extracted intrinsic patterns and features of gas concentration variations. Using monitoring data from working face 224 of a high-gas mine in Tongchuan as a sample, the PCA-Transformer model was compared with Long Short-Term Memory (LSTM), PCA-Long Short-Term Memory (PCA-LSTM), and Transformer models. The results show that: ① The PCA-Transformer model achieves a Mean Absolute Error (MAE) of 0.020 3, Mean Squared Error (MSE) of 0.047 2, and a runtime of 86 seconds, meeting the accuracy and timeliness requirements for gas concentration prediction in coal production. ② Compared to LSTM, PCA-LSTM, and Transformer models, the PCA-Transformer model better fits gas concentration trends, effectively identifies peak and trough sequences, and requires the least computational time, demonstrating its superior performance.

David N. Carruthers, Patrick C. Kinnunen, Yuerong Li et al.

Abstract Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Omar Yaakoubi, Yassir El Guenuni, Nisrine Srainy et al.

Since the 20th century, quality has become a major strategic concern for both researchers and organizations, serving as a lever for performance and a key factor of differentiation in an increasingly digital environment. However, despite the development of e-government services, the exploration and evaluation of their success, particularly concerning civil servants, remains limited. The implications suggest that for the success of such systems, governments must prioritize satisfaction and trust for the users in their technology strategies. This paper presents a comprehensive bibliometric analysis, based on the PRISMA model, focusing on the relationship between quality and electronic services (e-services). Using the Scopus database, 626 articles were initially identified, of which 204 were selected following strict inclusion and exclusion criteria. Covering the period from 2014 to 2024, the analysis highlights publication trends, journal distribution, author contributions, and keyword frequency. Special attention was given to 17 key publications, allowing for an in-depth exploration of the dynamics between perceived quality and the performance of e-services, using the VOSviewer tool. The findings reveal a strong interdependence between quality dimensions and the development of electronic services, particularly through technologies such as automation, intelligent interfaces, and interactive platforms. These elements play a crucial role in enhancing user experience, customer satisfaction, and operational efficiency.

Hou Shiyao, Zhang Yang, Yang Dongou, Zhao Yang, Chen Liqing

The development and application of ultra-high strength spring flat steel is of great significance in achieving automotive lightweight. Understanding the continuous cooling transformation behavior of this type of steel aids in developing its post-hot rolling cooling and quenching-tempering processes. On the basis of mechanical property testing， this study focuses on a newly designed ultra-high strength flat spring steel， 50SiMnCr， to investigate the effect of cooling rate on the microstructure and hardness of experimental steel by a combination of metallographic-hardness and thermal expansion method， thereby constructing continuous cooling transformation （CCT） curves. The results show that the strength and ductility of 50SiMnCr steel meet the requirements of the 2000 MPa level， with phase transition point Ac1=721 ℃， Ac3=758 ℃， Ms=220 ℃ and Mf=103 ℃. Ferrite and pearlite transformation occurs when the cooling rate is 0.05 ℃/s-0.5 ℃/s. Bainite transformation takes place when the cooling rate is increased to 1 ℃/s. Martensite transformation happens when the cooling rate is above 2 ℃/s. The hardness of the this steel increases with increasing the cooling rate.

Feng Zhang, Xinran Liu, Changxin Gao et al.

Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model’s generalizability across multiple real-world datasets.

Kuo-Chien Liao, Jian-Liang Liou, Muhamad Hidayat et al.

Pre-flight inspection and maintenance are essential prerequisites for aviation safety. This study focused on developing a real-time monitoring system designed to assess the condition of composite material structures on the exterior of aircraft. Implementing such a system can reduce operational costs, enhance flight safety, and increase aircraft availability. This study aims to detect defects in aircraft fuselages manufactured from composite materials by applying image visual recognition technology. This study integrated a drone and an infrared camera for real-time image transmission to ground stations. MATLAB image analysis software (MATLAB 2020b) was used to analyze infrared (IR) images and detect structural defects in the aircraft’s appearance. This methodology was based on the inspection of damaged engine cowlings. The developed approach compares composite material conditions with known defects before and after repair, considering mechanical performance, defect size, and strength. Simultaneously, tests were conducted on various composite material panels with unknown defects, yielding favorable results. This study underscores an integrated system offering rapid detection, real-time feedback, and analysis, effectively reducing time, and potential hazards associated with high-altitude operations. Furthermore, it addresses blind spots in aircraft inspections, contributing to effective flight safety maintenance.

George Volanis, Demitrios Galanakis, Nikolaos Bolanakis et al.

The stability of rock cliffs is a longstanding issue and is of practical significance. This case study demonstrates the application and use of advanced 3D modeling techniques, concentrating on the geological formations of the Xrobb l-Ġħaġin peninsula on the south-east coast of Malta, where the Xrobb l-Ġħaġin Neolithic site is located. In order to utilize a static and dynamic analysis of the investigated scenario, a 3D finite element model (FEM) of the geological formation in which the monument is set had to be created. To this end, 3D scanning, unmanned aerial vehicles (UAVs), and oblique photogrammetry were first used with state-of-the-art commercial packages for mesh reconstruction. As a result, a geometric and finite element model (FEM) was created, suitable for both static and dynamic analysis. In the second stage, a parametric investigation of the material properties of the structural system of the geological substrate was sought. The structural response of the system was evaluated for different loading scenarios assuming nonlinear finite element analysis. Collapse case scenarios were investigated for standard and weakened materials, predicting which components would collapse first and under which case of weakened materials the collapse occurs. Among other aspects, the main novelty of this paper lies in the integrated approach and multidisciplinary paradigm that supplement the available historical knowledge for this specific cultural heritage Neolithic site towards its conservation.

Oleksandr Diachenko, Maksym Delembovskyi, Kateryna Levchuk et al.

The production of concrete mixes, along with their use in the production of building materials and structures, is one of the key processes in the construction industry during the construction, restoration and repair of buildings and structures. Because of this, the need to create modern concrete mixing plants that will meet the requirements of minimum energy consumption and maximum productivity of concrete mixture production is an urgent task. Not only the main operations, which include the dosing of the components of the mixture and their mixing, but also the maintenance operations, namely operations that ensure the timely movement of the components of the concrete mixture from warehouses to the main technological equipment, affect the set rhythm of the concrete mixture production. Conveyors of various types and designs are used to move bulk materials, such as crushed stone and sand. For the rational selection of such equipment in accordance with the characteristics of the cargo to be transported, knowledge of the types of conveyors, their structures and parameters, understanding of operation issues and methods of parameter calculation are required. In addition, it is worth paying attention to the following parameters: maximum cargo transportation productivity, low energy consumption per unit of moved products, low metal content of the structure. The work reviewed the most common designs of conveyors used to move bulk materials in concrete mixing plants, analyzed the disadvantages and advantages of conveyors, as well as technical parameters. As a result, the predominant directions for the use of belt and plate conveyors at construction enterprises were determined. The advantages of belt conveyors, which contribute to their widespread distribution, are high productivity, simplicity of design, reliability, quiet operation, low specific power consumption. When choosing a conveyor, it is recommended to choose the equipment with the highest productivity and the lowest power of the drive motors, however, the performance should be clearly related to other technological equipment.

Camilo Garcia-Tenorio, Duvan Tellez-Castro, Eduardo Mojica-Nava et al.

This paper provides the theoretical foundation for the approximation of the regions of attraction in hyperbolic and polynomial systems based on the eigenfunctions deduced from the data-driven approximation of the Koopman operator. In addition, it shows that the same method is suitable for analyzing higher-dimensional systems in which the state space dimension is greater than three. The approximation of the Koopman operator is based on extended dynamic mode decomposition, and the method relies solely on this approximation to find and analyze the system’s fixed points. In other words, knowledge of the model differential equations or their linearization is not necessary for this analysis. The reliability of this approach is demonstrated through two examples of dynamical systems, e.g., a population model in which the theoretical boundary is known, and a higher-dimensional chemical reaction system constituting an original result.

C. L. Arthur, Lisa M. Killam, K. Buchholz et al.

Yongduan Song