Predicting accrual success for better clinical trial resource allocation by Sisi Ma, Yinzhao Wang, John Wagner, Steve Johnson, Serguei Pakhomov, Constantin Aliferis

“…We built predictive models for accrual failure using state-of-the-art supervised machine learning protocols and methods. Models resulted in good predictive performance that was stable over a 10-year time period, with predictive performance of cross-validation AUC = 0.744 (+/-0.018) and prospective validation AUC = 0.737 (+/-0.038). …”
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Improvement of reading platforms assisted by the spring framework: A recommendation technique integrating the KGMRA algorithm and BERT model by Yawen Su

“…Concurrently, the BERT model uses deep learning to generate robust semantic representations of article content, enhancing the system's capacity to understand and predict user interests with greater precision. …”
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Dystrophy of the Sense of Incomprehension and its Consequences by O. A. Donskikh

“…This situation has superimposed some previous tendencies, particularly the simplification of tasks and diminishing of challenging conditions of education from the very beginning, which leads to diminishing the joy of learning and efforts to overcome difficulties. The trend towards early specialization plays the same role, because in reality it usually only leads to the exclusion of some subjects. …”
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Driving Innovation to Support Pupils with SEND Through Co-Production in Education and Research: Participatory Action Research with 22q11.2 Deletion Syndrome Families in England by Michelle Jayman, Sophie Edmonds, Maria Gudbrandsen

“…However, evidence suggests pupils’ learning and wellbeing needs are often missed or unmet and legislation designed to increase families’ decision-making in education provision has not been translated into practice. …”
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Securing Urban Landscape: Cybersecurity Mechanisms for Resilient Smart Cities by Qiang Lyu, Sujuan Liu, Zhouyuan Shang

“…The implications of this approach are profound for securing urban infrastructures in smart cities. By combining deep learning with evolutionary algorithms, the CNN-GA framework offers a dynamic and adaptive solution to address the complex and evolving nature of cyber threats. …”
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Grade students attending classes in primary schools and at gymnasiums at national mathematics competitions by Vulović Nenad R., Milenković Aleksandar Z., Milikić Milan P.

“…For decades, mathematics education researchers have been focusing on students with special abilities in mathematics and mathematics competitions, and examining the impact of various organizational forms of work, such as grouping students and enriching teaching and learning programs, on students' success in competitions. …”
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Training Technologies as Means for Forming the Professional Identity of Police Officers during Professional Genesis by O. Y. Shlomin

“…Since the training of modern police officers in higher educational institutions with specific learning conditions should focus on professional self-development of personality, formation and development of professional identity and other necessary personal and professional qualities, the aim of the study was to identify the effectiveness of training procedures on the dynamics of professional identity. …”
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Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review by Nafizul Alam, Sk Hasan, Gazi Abdullah Mashud, Subodh Bhujel

“…Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. …”
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Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework by Supu Xiu, Ying Zhang, Hualong Chen, Yuanqiao Wen, Changshi Xiao

“…Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. …”
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Resource-Exploration-Oriented Lunar Rocks Monocular Detection and 3D Pose Estimation by Jiayu Suo, Hongfeng Long, Yuebo Ma, Yuhao Zhang, Zhen Liang, Chuan Yan, Rujin Zhao

“…The experimental results show that the SYHT method performs exceptionally well on simulated lunar rock data, achieving a mean per-joint position error (MPJPE) of 37.93 mm and a percentage of correct keypoints (PCK) of 99.94%, significantly outperforming existing methods. Finally, transfer learning experiments on real-world datasets validate its strong generalization capability, highlighting its effectiveness for lunar rock pose estimation in both simulated and real lunar environments.…”
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A 20 m spatial resolution peatland extent map of Alaska by Mark J. Lara, Roger Michaelides, Duncan Anderson, Wenqu Chen, Emma C. Hall, Caroline Ludden, Aiden I. G. Schore, Umakant Mishra, Sarah N. Scott

“…Ground-data were used to train machine learning classifiers to detect peatlands using a fusion of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM), that were spatially constrained by a peatland suitability model. …”
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Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images by Jian Wang, Xian-Hua Han, Yingying Xu, Lanfen Lin, Hongjie Hu, Chongwu Jin, Yen-Wei Chen

“…This study investigates an improved codebook model for the fined-grained medical image representation with the following three advantages: (1) instead of SIFT, we exploit the local patch (structure) as the local descriptor, which can retain all detailed information and is more suitable for the fine-grained medical image applications; (2) in order to more accurately approximate any local descriptor in coding procedure, the sparse coding method, instead of K-means algorithm, is employed for codebook learning and coded vector calculation; (3) we evaluate retrieval performance of focal liver lesions (FLL) using multiphase computed tomography (CT) scans, in which the proposed codebook model is separately learned for each phase. …”
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Decoding Sentiment in Online Communication: A Social Media Analysis of Fashion Companies by Karolina Mania, Monika Jedynak, Aneta Kuźniarska, Karolina Woszczyna

“…Analysis was performed at sentence, document, and aspect levels, employing both lexicon-based methods and machine learning. Findings: The results showed a preponderance of strong positive sentiment among the messages seen on the social media channels of the companies analyzed. …”
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Games and gamers: the influence of participating players on the process and outcome of regional spatial energy games by Hartmut Dumke, Pia Nabielek

“…Results and conclusions Our empirical study shows the impact of players with different stakeholder attributes on two game results: the first game result is a spatial energy scenario (output) and the second result is the collective and place-based learning experience during the game (outcome). The paper closes with three concluding recommendations: It is important to pay attention to player’s attributes as well as to constellations of players since they influence game experience (outcome) and achieved scenario (output). …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. …”
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Misères et splendeurs d’un mendicant dans 'Le Seigneur vous le rendra' de Mahi Binebine by Mohamed Semlali

“…If the monstrous is here inseparable from the tale, it also inscribes the hero in a process of transformation made possible by intellectual (literature) and sentimental (love) learning. Thus the protagonist goes from being a simple fairground beast who flaunts his disproportionate physique for a few coins to becoming a storyteller, a fabulator, a creator of worlds. …”
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Indigenous Environmental Activism and Media Depiction by Raphaela G O Pavlakos

“…The purpose of this article is to elucidate media bias as a way of informing our individual teaching and learning practice, as well as shaping how we engage with and talk about Indigenous issues. …”
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Making Things Right: Nurses' Experiences with Workplace Bullying—A Grounded Theory by Donna A. Gaffney, Rosanna F. DeMarco, Anne Hofmeyer, Judith A. Vessey, Wendy C. Budin

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How Speech–Language Pathologists Adapt This Is Me Digital Transition Portfolios to Support Individuals with Intellectual/Developmental Disabilities and Communication Challenges Acr... by Eve Müller, Jamie R. Kleiner, Danielle Evans, Ann Kern, Dawn Reikowsky, Katherine Smidl

“…., strengths, hobbies, and preferences) and information about communication, learning styles, and behavior/emotion regulation. While school and inpatient units demonstrated similar patterns of domain use, TiME created in an inpatient context contained more information about behavior plans/supports and were almost twice as long on average. …”
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Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties by Tianyi Wu, Sina Kheiri, Riley J. Hickman, Huachen Tao, Tony C. Wu, Zhi-Bo Yang, Xin Ge, Wei Zhang, Milad Abolhasani, Kun Liu, Alan Aspuru-Guzik, Eugenia Kumacheva

“…Here, we introduce the Autonomous Fluidic Identification and Optimization Nanochemistry (AFION) self-driving lab that integrates a microfluidic reactor, in-flow spectroscopic nanoparticle characterization, and machine learning for the exploration and optimization of the multidimensional chemical space for the photochemical synthesis of plasmonic nanoparticles. …”
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