Real-Time Threat Mitigation in Financial IT Infrastructures Using Quantum Computing by Jean Marie Vianney Sindayigaya

“…This research evaluates the application of quantum computing algorithms, specifically Continuous-Variable Quantum Neural Networks (CV-QNN), Crystals-Kyber cryptographic methods, and Quantum-enhanced Monte Carlo simulations, within financial IT infrastructures. Our findings indicate that quantum algorithms substantially enhance threat detection accuracy, reduce response latency, and ensure secure communication against quantum-powered threats. …”
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Analytical Validation of Wrist-Worn Accelerometer-Based Step-Count Methods during Structured and Free-Living Activities by Robert T. Marcotte, Shelby L. Bachman, Yaya Zhai, Ieuan Clay, Kate Lyden

“…Methods utilizing peak detection, threshold crossing, and frequency analysis have been used to detect steps from wrist-worn accelerometer data, but it remains unclear how different approaches perform across a range of walking speeds and free-living activities. …”
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Quantum Relay-Assisted Free-Space Optical Communication by Manav R. Bhatnagar, M. K. Arti, Kushagra Bhatnagar

“…This work proposes a quantum relaying method specifically designed for FSO links and examines the impact of two-hop relaying in terms of the probability of error. The Helstrom error bound is derived to assess the system's detection limits, while density operator calculations are performed to characterize the quantum relaying setup. …”
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SPECIFIC FEATURES OF ELECTROENCEPHALOGRAPHIC PAROXYSMAL ACTIVITY REGISTRATION IN OLD AGE GROUP PATIENTS by S. A. Gulyaev, I. V. Arkhipenko, S. E. Gulyaeva, A. A. Ovchinnikova, A. V. Ovchinnikov

“…According the data available in the literature, both overdiagnosis of epilepsy (20–25 % of all new diagnosed cases) and its underdiagnosis (as high as 10 %) are frequent diagnostic errors. Such errors are most commonly related to the misinterpretation of electrophysiological evidence in both young patients (in whom paroxysmal phenomena are a result of functional immaturity of brain structures) and elderly patients (in whom paroxysmal activity is a consequence of the development of degenerative processes in the neurons).Objective: to show examples of electroencephalographic (EEG) paroxysmal activity in old age group patients suffering from non-epileptic paroxysms.Now EEG investigation is a highly sensitive technique that requires a physician’s knowledge of neuroanatomy and neurophysiology to correct interpretation of the findings. …”
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”My AI is Lying to Me”: User-reported LLM hallucinations in AI mobile apps reviews by Rhodes Massenon, Ishaya Gambo, Javed Ali Khan, Christopher Agbonkhese, Ayed Alwadain

“…Abstract Large Language Models (LLMs) are increasingly integrated into AI-powered mobile applications, offering novel functionalities but also introducing the risk of “hallucinations” generating plausible yet incorrect or nonsensical information. These AI errors can significantly degrade user experience and erode trust. …”
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STARMAP: Spaceborne Target Acquisition Radar With Meta-RL Assisted Placement by Alireza Famili, Shihua Sun, Tolga Atalay, Angelos Stavrou

“…Through rigorous testing across various scenarios and dimensions, our findings demonstrate that STARMAP substantially improves localization accuracy by reducing geometry-induced errors compared to traditional placement strategies.…”
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Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms by Ruolan Liu, Shujie Yuan, Duanyang Liu, Lin Han, Fan Zu, Hong Wu, Hongbin Wang

“…In the second fog event, the addition of atmospheric pollutant concentration data from the boundary layer further improved results (ensemble model: for VIS < 200 m, Schemes 2 and 3 had MAEs of 20.1 m, corresponding to a relative error of less than 10.1%, and 11.4 m, corresponding to a relative error of less than 5.7%, respectively). …”
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Computer Vision-Based Safety Monitoring of Mobile Scaffolding Integrating Depth Sensors by Muhammad Sibtain Abbas, Rahat Hussain, Syed Farhan Alam Zaidi, Doyeop Lee, Chansik Park

“…A diverse dataset from multiple sources enhances the model’s applicability to real-world scenarios, while a status trigger module verifies worker behavior over a 3 s window, minimizing detection errors. The experimental results demonstrate high precision (0.95), recall (0.97), F1-score (0.96), and accuracy (0.95) for safe behaviors, with similarly strong metrics for unsafe behaviors. …”
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Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region by J. Zhao, J. Guo, X. Zheng

“…Further sensitivity experiments on vertical resolution and maximum detection height indicate that the RWP system configuration with the highest detection height achieves the best performance, while lower detection height degrades forecast quality. …”
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Advancing CH₄ and N₂O retrieval strategies for NDACC/IRWG FTIR observations with the support of airborne in situ measurements by I. Ortega, J. W. Hannigan, B. C. Baier, K. McKain, D. Smale

“…Uncertainty budgets combining random and systematic sources are provided. Random errors, mainly stemming from temperature profile uncertainties and measurement noise, dominate in the troposphere for both gases, with a retrieval random error of 0.5 %. …”
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Validation of MotionWatch8 Actigraphy Against Polysomnography in Menopausal Women Under Warm Conditions by Xinzhu Li, Mark Halaki, Chin Moi Chow

“…Significant proportional errors were observed, particularly for participants with prolonged sleep onset latency, high wake after sleep onset, and lower sleep efficiency. …”
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Applications of artificial intelligence in thoracic imaging: a review by Arjun Kalyanpur, Neetika Mathur

“…AI systems can help radiologists identify subtle findings that might otherwise be overlooked, thereby increasing efficiency and reducing diagnostic errors. …”
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Executive function deficits in adults with childhood‐onset type 1 diabetes mellitus: Evidence from Wisconsin card sorting test performance by Jing He, Wenjing Zou, Hongyu Du, Songzhuzi Liu, Xia Li, Zhiguang Zhou, Xiongzhao Zhu

“…Conclusions T1DM is associated with EF decrements, and there are differences in EFs between childhood‐onset and adult‐onset T1DM. These findings indicate that we should consider detecting and intervening in EF deficits in the T1DM population according to the age of onset.…”
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An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure by Zhenglin Li, Zhanjie Zhao, Wujun Gao, Li Jiao

“…Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. …”
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Enhancing Fault Location Accuracy in Transmission Lines Using Transient Frequency Spectrum Analysis: An Investigation into Key Factors and Improvement Strategies by Mustafa Akdağ, Mehmet Salih Mamiş, Düzgün Akmaz

“…The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. …”
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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis by Walid Abdelfattah, Munthar Kadhim Abosaoda, Krunal Vaghela, Gowrishankar J, Prabhat Kumar Sahu, Kamred Udham Singh, R. Sivaranjani, Rohit Chauhan, Siya Singla, Samim Sherzod

“…Among the evaluated models, the Decision Tree algorithm emerged as the most robust predictor, achieving an R² value of 0.771, a mean square error (MSE) of 16.182, and an average absolute relative error percentage (AARE%) of 11.848 % in testing. …”
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Machine learning the Ising transition: A comparison between discriminative and generative approaches by Difei Zhang, Frank Schäfer, Julian Arnold

“…Conversely, in a knowledge-driven setting where the system Hamiltonian is known, nonparametric generative approaches leveraging sufficient statistics are superior in terms of the accuracy reached at a given budget of computation time for all but the smallest sample sizes or highest error tolerances. Our findings provide a guide for the selection of machine-learning strategies for phase transition detection based on criteria such as the amount of available system knowledge, the number of data points, or computational constraints.…”
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Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model by Ahmad Zein Al Wafi, Febry Putra Rochim, Aisya Fathimah

“…Performance evaluation revealed that DenseNet201 outperformed the other models, achieving a precision of 0.99 and a recall of 0.61 for normal cases (F1-score of 0.75) and a precision of 0.81 with a recall of 0.99 for pneumonia cases (F1-score of 0.89). These findings demonstrate that DenseNet201 provides a reliable and effective solution for automated pneumonia detection, offering improved diagnostic efficiency and accuracy compared to traditional methods.…”
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Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo, Zhi Wang

“…Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.…”
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Improving phenological event identification in trees using manually measured dendrometer data: conventional approaches vs. the novel two-stage threshold approach by Przemysław A. Jankowski, Przemysław A. Jankowski, Rafael Calama, Jorge Aldea, Matías García, Guillermo Madrigal, Marta Pardos

“…However, year cessation was best estimated using a 95% relative growth threshold derived from the Richards function. These findings highlight the potential of the 2STA as a robust and objective method for phenological event detection using manually collected band dendrometer data, particularly for species with complex intra-annual growth patterns. …”
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