Challenges in the establishment of a rare plant species monitoring program using community science volunteers by David J. Gibson, Christopher D. Benda, Ingrid Felsl, Gretel Kiefer

“…We propose a recruitment strategy to broaden the diversity of our volunteers by better engaging community members who are not typically involved with plant monitoring but are interested in hiking, walking in natural areas, gardening, and restoration activities, and others who would like the opportunity to collaborate with scientists and researchers in addressing an environmental issue. Practical implication: Community science plant monitoring programs face challenges in recruitment, retention, remoteness of field sites and data quality. …”
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Computational analysis of the functional impact of MHC-II-expressing triple-negative breast cancer by Yang Cui, Weihang Zhang, Xin Zeng, Yitao Yang, Sung-Joon Park, Kenta Nakai, Kenta Nakai

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Low-latency networking:architecture,key scenarios and research prospect by Xutong ZUO, Mowei WANG, Yong CUI

“…With the advent of delay-sensitive applications and ultra-low latency scenarios,research on low-latency networking is attracting attention from academia,industry,and standards organizations.Understanding the causes of latency and designing corresponding techniques to reduce latency enable the development of emerging applications.The sources of latency according to the layered architecture of the network was analyzed,and summarizes the techniques for reducing the latency.After that,three typical low-latency key scenarios and delay optimization techniques for data center network,5G and edge computing was analyzed.Finally,the opportunities and challenges that may be encountered in the development of low latency networks were presented from the perspectives of network architecture innovation,data-driven latency optimization algorithm and the design of new protocols.…”
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Assessing the efficacy and cost‐effectiveness of integrating autonomous recording units and point‐count surveys for population monitoring of northern bobwhite (Colinus virginianus)... by William B. Lewis, Clint Johnson, James A. Martin

“…We used empirical data and simulations of biologically realistic datasets to assess the relative benefits and costs associated with estimating abundance from ARU and point‐count data collected from northern bobwhite (Colinus virginianus), a species of cultural and commercial importance that has been declining across much of its range. …”
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Development of Concentration-Prediction Models in Multiple Manufacturing Environments Using NIR Spectroscopy by Yusuke Hayashi, Saho Okazaki, Kazuya Tanabe, Takuya Nagato, Hirokazu Sugiyama

“…Our results confirmed that concentration-prediction models could be extended to other humidity conditions by modifying measured NIR spectral data. The extension would be generally applicable to other compounds because it is based on the Beer–Lambert law. …”
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Defining critical SOC/clay thresholds for soil health in boreal croplands using satellite-based NDVI proxies for productivity and resilience by Jaakko Heikkinen, Riikka Keskinen, Kari Ylivainio

“…In this study, we developed a method to define a critical threshold value for SOC/clay ratio on the basis of crop productivity and resilience. The study integrated data from national soil monitoring (NSM) of Finnish cropland soils (n=505) with satellite-based normalized difference vegetation index (NDVI) obtained from the EcoDataCube (EDC) portal. …”
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AI-Integrated AR as an Intelligent Companion for Industrial Workers: A Systematic Review by Steven Yoo, Sakib Reza, Hamid Tarashiyoun, Akhil Ajikumar, Mohsen Moghaddam

“…This paper addresses this question by conducting a systematic review that delves into three specific research areas: the state-of-the-art of AR-based systems for industrial applications in terms of features and training/assistance capabilities, the existing gaps in transforming AR into an “intelligent companion” that adapts to both the work context and the user’s needs, and how these sources of multimodal data captured by AR headsets, wearables, and IoT sensors can be harnessed to interpret, predict, and guide task performance and learning through AR. …”
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Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. As Machine Learning algorithms become increasingly embedded in decision-making processes, particularly for traffic management and other high-level commitment applications, concerns regarding the transparency and trustworthiness of complex ‘black-box’ models have grown. …”
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A systematic review of lithium biology and pharmacology and toxicological evaluation of new organic lithium salts by I. Yu. Torshin, O. A. Gromova, V. V. Rastashansky, A. A. Garanin

“…Objective: to systematize scientific data on biomedical studies investigating trace element lithium over the past 70 years; evaluate toxic properties of lithium ascorbate (LiAsc) as an important promising candidate molecule.Material and methods. …”
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Delta-Adjust: Minimum Distance Interpolation by Ziad F. Doughan, Sari S. Itani

“…Delta-Adjust achieves this independently of the underlying data distribution, without any training steps, and without requiring explicit assumptions about global model structure.…”
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Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model by Sicong He, Yanbin Yuan, Zhen Li, Heng Dong, Xiaopang Zhang, Zili Zhang, Lan Luo

“…However, the fixed transit times of the satellites and severe data deficiencies restricted their applicability for revealing patterns of change in NO2 on sub-daily scales. …”
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Context-aware Location Privacy Protection Method by Haohua Qing, Roliana Ibrahim, Hui Wen Nies

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Convolutional neural network for dog breed recognition system by К. Хотінь, В. Шимкович, П. Кравець, А. Новацький, Л. Шимкович

“…The purpose of the study is to increase the efficiency of recognition of dog breeds using convolutional neural networks when implementing them in mobile applications. To achieve the goal, a neural network model based on the modified MobileNetV3-Large model is proposed, which is trained on the revised "70 Dog Breeds-Image Data Set" using data augmentation methods. …”
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Detailed Examples of Figure Preparation in the Two Most Common Graph Layouts by Izolda Gorgol, Hubert Salwa

“…They transform abstract concepts and data into visual formats that are easier to interpret, analyze, and apply in real-world engineering challenges. …”
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Structural biology meets typography: using protein structures to inspire creative expression and connect diverse audiences by Leonora Martínez-Núñez

“…Advances in structural bioinformatics and visualization have expanded access to molecular data, most notably through the Protein Data Bank (PDB). …”
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Manifold learning in statistical tasks by A.V. Bernstein

“…In many applications, real-world data occupy only a very small part of high-dimensional observation space, the intrinsic dimension of which is essentially lower than the dimension of this space. …”
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Class-aware contrastive optimization for imbalanced text classification by Grigorii Khvatskii, Nuno Moniz, Khoa D. Doan, Nitesh V. Chawla

“…Abstract The unique characteristics of text data make classification tasks a complex problem. …”
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Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis by Luis B. Elvas, Rafaela Santos, João C. Ferreira

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Improvement of the Regulatory Framework of Information Security for Terminal Access Devices of the State Information System by V. A. Sizov, D. M. Malinichev, V. V. Mochalov

“…They are designed to provide electronic services to citizens, such as paying taxes, obtaining certificates, filing of applications and other information. The processed personal data may belong to special, biometric, publicly available and other categories [3]. …”
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Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory by Guozhu Wang, Ruizhe Zhou, Fei Li, Xiang Li, Xinmin Zhang

“…This article proposes a data reconstruction method based on Center Nearest Neighbor (CNN) theory for fault diagnosis and abnormal variable identification. …”
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