A Study of Engine Intake Noise Control Based on the Improved Filtered-x Least Mean Square Algorithm by Chaofeng Lan, Lei Zhang, Shou Lv, Pengwei Jiang

“…This algorithm not only has the advantages of fast convergence speed and small steady-state error but also adapts to the characteristics of a time-varying reference signal and easy selection of parameters. …”
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Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports by Kuo-Wei Wu, Wen-Fang Wu, Chung-Chih Liao, Wei-Ann Lin

“…We follow an identification method similar to fault tree analysis (FTA) with the help of the driving reliability and error analysis method (DREAM 3.0) and the Haddon matrix to find the potential key accident factors from all disengagement data. …”
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CO2 emissions response to GDP and crude oil price shocks: Evidence from India and China using SVAR Model by Matali Mahajan, Dr. Ash Narayan Sah

“…Global CO₂ emissions and warming remain critical challenges worldwide, with emission reduction central to attaining the UN Sustainable Development Goals. …”
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Hybrid Fuzzy Centroid with MDV-Hop BAT Localization Algorithms in Wireless Sensor Networks by Chakchai So-In, Weerat Katekaew

“…These combinations demonstrate the effectiveness in the simulation and location error reduction with time complexity trade-off.…”
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Improving Wireless Sensor Network Security Using Quantum Key Distribution by Laith H. Alhasnawy, Ameer K. AL-Mashanji

“…The efficiency of the work increased to 0.704 after using the Quantum Bit Error Rate equation, eventually increasing the network performance. …”
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Sensor placement optimization for critical-grid coverage problem of indoor positioning by Hui Wu, Zhe Liu, Jin Hu, Weifeng Yin

“…The results showed that the optimized schemes obtain a lower error (1.13, 1.21 m) and a higher reduction of sensor deployment cost than the uniform deployment scheme (1.44 m). …”
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Any-to-any voice conversion using representation separation auto-encoder by Zhihua JIAN, Zixu ZHANG

“…In view of the problem that it was difficult to separate speaker personality characteristics from semantic content information in any-to-any voice conversion under non-parallel corpus, which led to unsatisfied performance, a voice conversion method, called RSAE-VC (representation separation auto-encoder voice conversion) was proposed.The speaker’s personality characteristics in the speech were regarded as time invariant and the content information as time variant, and the instance normalization and activation guidance layer were used in the encoder to separate them from each other.Then the content information of the source speech and the personality characteristics of the target one was utilized to synthesize the converted speech by the decoder.The experimental results demonstrate that RSAE-VC has an average reduction of 3.11% and 2.41% in Mel cepstral distance and root mean square error of pitch frequency respectively, and has an increasement of 5.22% in MOS and 8.45% in ABX, compared with the AGAIN-VC (activation guidance and adaptive instance normalization voice conversion) method.In RSAE-VC, self-content loss is applied to make the converted speech reserve more content information, and self-speaker loss is used to separate the speaker personality characteristics from the speech better, which ensure the speaker personality characteristics be left in the content information as little as possible, and the conversion performance is improved.…”
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LX-mixers for QAOA: Optimal mixers restricted to subspaces and the stabilizer formalism by Franz G. Fuchs, Ruben Pariente Bassa

“…The method connects and utilizes the stabilizer formalism that is used in error correcting codes. This can be useful in the setting when the quantum approximate optimization algorithm (QAOA), a popular meta-heuristic for solving combinatorial optimization problems, is applied in the setting where the constraints of the problem lead to a feasible subspace that is large but easy to specify. …”
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Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation by Radhey Lal, Rajiv Kumar Singh, Dinesh Kumar Nishad, Saifullah Khalid

“…Validation on simulated datasets demonstrates a 50–70% reduction in mean absolute error and 2–3% improvements in gamma index metrics compared to conventional approaches. …”
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Scaling Laws for Emulation of Stellar Spectra by Tomasz Różański, Yuan-Sen Ting

“…Specifically, given a tenfold increase in training compute, achieving an optimal seven-fold reduction in mean squared error necessitates an approximately 2.5-fold increase in dataset size and a 3.8-fold increase in model size. …”
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Development of regional mixed-effects height–diameter models for natural black pine stands by Ramazan Ozçelik, Onur Alkan

“…Compared to the fixed-effects model, the mixed-effects model achieved a 32% reduction in the root mean square error (RMSE). The findings suggest that the proposed model is highly suitable for forest inventory studies to predict tree heights in black pine stands.…”
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Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

“…Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.…”
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AHerfReLU: A Novel Adaptive Activation Function Enhancing Deep Neural Network Performance by Abaid Ullah, Muhammad Imran, Muhammad Abdul Basit, Madeeha Tahir, Jihad Younis

“…We propose AHerfReLU, a novel activation function that combines the rectified linear unit (ReLU) function with the error function (erf), complemented by a regularization term 1/1+x2, ensuring smooth gradients even for negative inputs. …”
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Market Phases and Price Discovery in NFTs: A Deep Learning Approach to Digital Asset Valuation by Ho-Jun Kang, Sang-Gun Lee

“…Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates superior predictive accuracy compared to conventional approaches, achieving a 33.5% reduction in Mean Absolute Error versus LSTM models, a 29.7% improvement over Transformer architectures, and a 30.1% enhancement compared to LightGBM implementations. …”
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Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System by Ll Yi-Min, Yue Yang, Li Li

“…Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type-2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed-loop system and ensure all the signals bounded. …”
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Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning by Xinyu Huang, Franziska Schmelter, Christian Seitzer, Lars Martensen, Hans Otzen, Artur Piet, Oliver Witt, Torsten Schröder, Ulrich L. Günther, Lisa Marshall, Marcin Grzegorzek, Christian Sina

“…This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases.…”
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Facility-Based Delivery during the Ebola Virus Disease Epidemic in Rural Liberia: Analysis from a Cross-Sectional, Population-Based Household Survey. by John Ly, Vidiya Sathananthan, Thomas Griffiths, Zahir Kanjee, Avi Kenny, Nicholas Gordon, Gaurab Basu, Dale Battistoli, Lorenzo Dorr, Breeanna Lorenzen, Dana R Thomson, Ami Waters, Uriah G Moore, Ruth Roberts, Wilmot L Smith, Mark J Siedner, John D Kraemer

“…Limitations include the possibility of FBD secular trends coincident with the EVD period, recall errors, and social desirability bias.<h4>Conclusions</h4>We detected a 30% decreased odds of FBD after the start of EVD in a rural Liberian county with relatively few cases. …”
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Geometric line-of-sight guidance law with exponential switching sliding mode control for marine vehicles’ path following by Chengren Yuan, Chengren Yuan, Changgeng Shuai, Changgeng Shuai, Zhanshuo Zhang, Zhanshuo Zhang, Buyun Li, Buyun Li, Yuqiang Cheng, Yuqiang Cheng, Jianguo Ma, Jianguo Ma

“…It adapts to diverse compound paths by dynamically adjusting according to cross-track errors and local path curvature. Then, to enhance control performance, we present an improved exponential switching law for sliding mode control, enabling rapid convergence, disturbance rejection, and chatter reduction. …”
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Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data by Lei Xu, Xihao Zhang, Xi Zhang, Tingtao Wu, Hongchu Yu, Wenying Du, Zeqiang Chen, Nengcheng Chen

“…The results showed that compared with LSTM, the skills of the MAATL model were significantly improved, with an average correlation coefficient increase of 18.26% and a root mean square error (RMSE) reduction of 42.55%. Furthermore, 118 in-situ soil moisture stations are used for predictive validation and the proposed MAATL model demonstrates higher accuracy compared to the Global Forecast System (GFS) and the LSTM model, with an average correlation skill improvement of 16.02% and 15.08% for MAATL over GFS and LSTM, respectively. …”
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Compact H-Plane CLAF-SIW Horn Antenna With Phase Front Correction by Andres Biedma-Perez, Cleofas Segura-Gomez, Angel Palomares-Caballero, Pablo Padilla

“…By tuning the distribution of the unit cells of the metalens, it is possible to reduce phase errors at the aperture, a common issue in horn antennas. …”
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