The Use of Organisational Learning Practices in Start-Ups Growth by Viktorija Baltrunaite, Jurgita Sekliuckiene

“…Start-ups are more likely to use the error-learning approach than cognitive or action learning practices. …”
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Hydration Tracking via Saliva Osmolarity in Recruit Firefighters Throughout a 12-Week Fire School by A. Maleah Holland-Winkler, Andrew R. Moore, Steven L. Parish, Tiffany J. Oberther

“…Methods: At the beginning (AM) and end (PM) of the workday for 13 weeks (a 12-week Fire School and an additional transition week), saliva osmolarity was measured, recorded, and relayed to each participant in the sample, which included 23 recruit firefighters. …”
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Data-Driven Carbon Emission Dynamics Under Ship In-Port Congestion by Weiyu Liu, Bowei Xu, Junjun Li

“…These insights inform targeted decarbonization strategies for port authorities, prioritizing operational efficiency and energy transition for high-emission ship categories.…”
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Fusion ConvLSTM-Net: Using Spatiotemporal Features to Increase Residential Load Forecast Horizon by Abhishu Oza, Dhaval K. Patel, Bryan J. Ranger

“…Power systems are undergoing a significant transition towards renewable energy technologies. To make the most of these energy sources, optimizing the generation, storage, and distribution of energy can be enhanced with accurate forecasts of future energy consumption. …”
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Prescribed Performance Bounded-H_∞ Control for Flexible-Joint Manipulators Without Initial Condition Restriction by Ye Zhang, Ruibo Sun, Jie Shang

“…An input control function (ICF) is employed to mitigate the impulse response of the control input, ensuring a smooth transition from zero. Furthermore, the improved tangent-type BLF enables the tracking error to rapidly converge to a small neighborhood of zero. …”
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Modeling of the"thin" structure probability discrepancy distribution in the indications of ADS-B and SSR. Uncompensated latency by I. V. Alipov, V. L. Kuznetsov

“…The upcoming transition to a new generation of the elements in the ATC system - AZN-B - has set a number of tasks related to confirming the integrity of the data received, and determining their accuracy characteristics. …”
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SELFADJUSTING AUTOMATIC CONTROL OF SOWING UNIT by A. Yu. Izmaylov, V. K. Khoroshenkov, E. S. Luzhnova, I. I. Afonina, V. A. Kolesnikova

“…It was noted that for regulation of a fertilizing dose it is necessary to complete the unit with the electric drive, and for error reduction use navigation GLONASS, GPS, Galileo receivers. …”
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Supply chain performance evaluation model for integrated circuit industry based on fuzzy analytic hierarchy process and fuzzy neural network by Chen Qian, Wang Xiangping

“…The results indicate that the error value output after running the validation sample is relatively small. …”
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Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration by Bahareh Ahkami, Morten B. Kristoffersen, Max Ortiz-Catalan

“…The evaluation of the platform and prediction algorithm relies on quantifying the transition error, signifying instances where the algorithm falls short in predicting shifts between different walking surfaces. …”
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Rational strategies for improving the efficiency of design and discovery of nanomedicines by Xiaoting Shan, Ying Cai, Binyu Zhu, Lingli Zhou, Xujie Sun, Xiaoxuan Xu, Qi Yin, Dangge Wang, Yaping Li

“…Abstract The rise of rational strategies in nanomedicine development, such as high-throughput methods and computer-aided techniques, has led to a shift in the design and discovery patterns of nanomedicines from a trial-and-error mode to a rational mode. This transition facilitates the enhancement of efficiency in the preclinical discovery pipeline of nanomaterials, particularly in improving the hit rate of nanomaterials and the optimization efficiency of promising candidates. …”
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Advanced neural network modeling with Levenberg–Marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems by A. Aziz, S.A.H. Shah, H.M.S. Bahaidarah, T. Zamir, T. Aziz

“…The model’s performance is evaluated using state transition (ST) index, error histogram (EH), mean squared error, and regression (R) analysis, demonstrating excellent agreement between predicted and reference solutions. …”
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An Ultra-Short-Term Wind Power Prediction Method Based on the Fusion of Multiple Technical Indicators and the XGBoost Algorithm by Xuehui Wang, Yongsheng Wang, Yongsheng Qi, Jiajing Gao, Fan Yang, Jiaxuan Lu

“…Wind power, as a clean and renewable energy source, plays an increasingly important role in the global transition to low-carbon energy systems. However, its inherent volatility and unpredictability pose challenges for accurate short-term prediction. …”
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The Relationship between Poverty and Environmental Damage in Indonesia: A VECM Analysis by Setyo Tri Wahyudi, Nurul Badriyah, Rihana Sofie Nabella, Kartika Sari, Amalia Rahmawati

“…These findings underscore the critical need for balanced strategies that simultaneously address poverty alleviation and promote ecological sustainability through inclusive policies and a transition to renewable energy. …”
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A deterministic and probabilistic framework based on corrected wind speed to improve Short-Term wind power forecasting accuracy by Nuttapat Jittratorn, Chao-Ming Huang, Hong-Tzer Yang

“…Each component contributes as follows: the wind speed correction reduces input error, improving input accuracy; the bidirectional long short-term memory enhances time-series learning to better capture temporal dependencies; the Markov chain integration improves deterministic forecasts by modeling transition probabilities; and the jackknife resampling quantifies prediction uncertainty to improve probabilistic forecast reliability. …”
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Data-Driven Dynamic Optimization for Hosting Capacity Forecasting in Low-Voltage Grids by Md Tariqul Islam, M. J. Hossain, Md Ahasan Habib

“…Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. …”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…The results from the model development show that the model demonstrates reasonable precision, with a mean square error of 0.0588, root mean square error of 0.24, mean absolute error of 0.1057 and a coefficient of determination of 0.95. …”
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The potential &amp; limitations of monoplotting in cross-view geo-localization conditions by Bradley J. Koskowich, Michael J. Starek, Scott A. King

“…Classical keypoint matching methods find the extreme pose transitions between cameras present in a CVGL configuration challenging to operate in, while deep neural networks demonstrate superb capacity in this area. …”
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Non-Myopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks by Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu, Quan Pan, Min Yang

“…This paper addresses beam scheduling for tracking multiple smart targets in phased array radar networks, aiming to mitigate the performance degradation in previous myopic scheduling methods and enhance the tracking performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem, where each bandit process is associated with a target and its TEC states evolve with different transition rules for different actions, i.e., either the target is tracked or not. …”
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A Survey of Holistic Approaches for Distributed Database Systems: From Conceptual Model to Deployment by Goncalo Carvalho, Jorge Bernardino, Bruno Cabral, Vasco Pereira

“…., Entity-Relationship (ER) diagrams, Unified Modeling Language (UML)) facilitate the transition to physical models, they still fall short when it comes to supporting distributed and multi-layered architectures. …”
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An improved Harris corner detection method for honeycomb structures by Pengfei Zheng, Wilson Byiringiro, Weiwei Xie, Zhengkai Jiang, Can Cui, Yongjun Wu

“…A Gaussian filter is then incorporated to smooth the noise and create a controllable multilevel grayscale transition, allowing the Harris corner detector to accurately identify corners. …”
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