Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching by Jennifer Leahy, Shabnam Jabari, Derek Lichti, Abbas Salehitangrizi

“…The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors.…”
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Research on water body information extraction and monitoring in high water table mining areas based on Google Earth Engine by Anya Zhong, Zhen Wang, Yulong Gen

“…The research findings offer a foundation for the formulation of land reclamation and ecological restoration plans in coal mining subsidence areas.…”
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Persistent pathogens as risk factors of community-acquired pneumonia and acute bronchitis in children by O. V. Zhukova, N. F. Brusnigina, S. V. Kononova, E. V. Speranskaya, E. I. Efimov

“…The concept of risk determination was based on the determination of the absolute risk in the exposed and the no exposed groups, attributable risk, relative risk, the population attributable risk, as well as determining the standard errors for each type of risk and confidence interval.Results. …”
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Centre-based landscape of muscular dystrophies in Gujarat: insight into genetic diagnosis and management by Gaurang Sindhav, Pooja Trivedi, Krishnakumari Patel, Dhruv Prajapati, Anil Shah, Sharad Gupta

“…Abstract DNA, RNA, or protein errors can lead to globally prevalent genetic disorders, though precise rates are difficult to determine due to underreporting. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. …”
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Cataract induction in an arthropod reveals how lens crystallins contribute to the formation of biological glass. by Amartya T Mitra, Shubham Rathore, Augusta Jester, Ruby Hyland-Brown, J Hassert, Joshua B Benoit, Annette Stowasser, Elke K Buschbeck

“…Our findings highlight the utility of T. marmoratus as a tractable model system to probe the aetiology of lens cataracts and refractive errors.…”
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Savi’s pipistrelle (Hypsugo savii) in Ukraine: analysis of records and evidence of expansion by Igor Polischuk, Igor Zagorodniuk

“…The only recent actual find, the description of which contains a number of dubious facts, is an indication of the detection of a hibernating Savi’s pipistrelle in the Sevastopol youth centre. …”
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Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption by Serene Goh, Rachel Sze Jen Goh, Bryan Chong, Qin Xiang Ng, Gerald Choon Huat Koh, Kee Yuan Ngiam, Mikael Hartman

“…However, its integration into clinical workflows faces several challenges, including unintended errors, the need for professional training, and ethical concerns. …”
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Artificial Intelligence in ECG interpretation - review article by Łukasz Fussek, Jagoda Niewiadomska, Borys Bondos, Aleksandra Stępień, Alicja Paluch, Jakub Skrzypek, Aleksandra Język Polski, Robert Kochan, Ewelina Wieczorek, Kacper Lee

“…The biggest limitations of artificial intelligence are the need to verify the diagnosis made by the algorithm in order to detect possible errors. In addition, the use of personal data to train an artificial intelligence algorithm to diagnose specific medical conditions can be controversial, which can interfere with data protection rules. …”
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An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms by Ahmad Almadhor, Stephen Ojo, Thomas I. Nathaniel, Shtwai Alsubai, Abdullah Alharthi, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…Conventional Schizophrenia diagnosis techniques are time- consuming and prone to error. The study proposes a novel automated technique for diagnosing Schizophrenia based on electroencephalogram (EEG) sensor data, aiming to enhance interpretability and prediction performance.MethodsThis research utilizes Deep Learning (DL) models, including the Deep Neural Network (DNN), Bi-Directional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM- GRU), and BiLSTM with Attention, for the detection of Schizophrenia based on EEG data. …”
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A Flow-Speed Model for Motorways in England: Analysis Under Various Weather Conditions by Ye Liu, Haibo Chen, Chuhan Yin, Vivi Michalaki, Phillip Proctor, Gavin Rowley, Xiaowei Wang, Hongyuan Wei

“…The proposed model was fitter on both four-lane and lane-by-lane conditions than the existing models, in terms of lower relative error and RMSE values and higher <i>R</i>² values (minimum <i>R</i>² = 0.79), which means the proposed model captured the speed–flow relationship better. …”
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Advancements in seasonal rainfall forecasting: A seasonal auto-regressive integrated moving average model with outlier adjustments for Ghana's Western Region by Francis Ayiah-Mensah, Senyefia Bosson-Amedenu, Emmanuel Mensah Baah, John Awuah Addor

“…Diagnostic plots validated the model's performance, and metrics such as the R-squared (99.02 %), mean absolute percentage error (MAPE, 5.97 %), and Theil's U statistic (0.05) demonstrated its accuracy and reliability. …”
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Weibull-Type Incubation Period and Time of Exposure Using <i>γ</i>-Divergence by Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi

“…Moreover, applications to real-world surveillance data on COVID-19 illustrate the practical advantages of the proposed method. These findings highlight the method’s robustness and efficiency in scenarios where data contamination from secondary or tertiary infections is common, showing its potential value for early outbreak detection and rapid epidemiological response.…”
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Sound Localization Training and Induced Brain Plasticity: An fMRI Investigation by Ranjita Kumari, Sukhan Lee, Pradeep Kumar Anand, Jitae Shin

“…These regions are associated with cognitive processes such as auditory processing, spatial working memory, planning, decision-making, error detection, and motor control. Conversely, a decrease in activation was observed in the left middle temporal gyrus, a region linked to language comprehension and semantic memory. …”
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GazeCapsNet: A Lightweight Gaze Estimation Framework by Shakhnoza Muksimova, Yakhyokhuja Valikhujaev, Sabina Umirzakova, Jushkin Baltayev, Young Im Cho

“…Our results show that GazeCapsNet achieves state-of-the-art (SOTA) performance on several benchmark datasets, including ETH-XGaze and Gaze360, achieving a mean angular error (MAE) reduction of up to 15% compared to existing models. …”
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Upgrading a Low-Cost Seismograph for Monitoring Local Seismicity by Ioannis Vlachos, Marios N. Anagnostou, Markos Avlonitis, Vasileios Karakostas

“…Specifically, the proposed system achieved a mean error of ±0.087 when benchmarked against the data collected by the high-cost commercial seismograph. …”
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Targeted Sequencing of Lung Function Loci in Chronic Obstructive Pulmonary Disease Cases and Controls. by María Soler Artigas, Louise V Wain, Nick Shrine, Tricia M McKeever, UK BiLEVE, Ian Sayers, Ian P Hall, Martin D Tobin

“…This pooled design maximised sample size and therefore power, but led to challenges during variant-calling since sequencing error rates and minor allele frequencies for rare variants can be very similar. …”
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Diagnostic model for COPD patients with nocardia infection: a study based on clinical features and risk factors by Kai Zhang, Kangli Yang, Hongmin Wang

“…The model demonstrated excellent discrimination (C-index: 0.895 infected, 0.829 noninfected) and calibration (mean absolute error: 0.127–0.170). ROC analysis revealed AUCs of 0.896 (95% CI: 0.90–0.97) and 0.830 (95% CI: 0.77–0.89) for infected and noninfected groups, respectively. …”
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Acoustic Fabry–Perot Resonance Detector for Passive Acoustic Thermometry and Sound Source Localization by Yan Yue, Zhifei Dong, Zhi-mei Qi

“…The findings open up a new avenue for the development of multifunctional acoustic detection devices and systems.…”
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Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation. by Nastaran Enshaei, Arash Mohammadi, Farnoosh Naderkhani, Nick Daneman, Rawan Abu Mughli, Reut Anconina, Ferco H Berger, Robert Andrew Kozak, Samira Mubareka, Ana Maria Villanueva Campos, Keshav Narang, Thayalasuthan Vivekanandan, Adrienne Kit Chan, Philip Lam, Nisha Andany, Anastasia Oikonomou

“…The severity-scoring module exhibited a high Pearson correlation of 93% and a low mean absolute error (MAE) of 2.35 compared to the radiologists' consensus. …”
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