Effectiveness of Biological and Surgical Sampling for Detection of Mycobacterium Tuberculosis by R. N. Islamov, R. A. Sharipov, R. K. Tukfatullin, I. V. Sidorov, R. K. Yagafarova

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Generating Synthetic Malware Samples Using Generative AI by Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park

“…This issue is further compounded by a well-known limitation of machine learning models: their poor performance when training data is scarce. …”
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Ensuring representative sample volume predictions in microplastic monitoring by Richard K. Cross, Sarah L. Roberts, Monika D. Jürgens, Andrew C. Johnson, Craig W. Davis, Todd Gouin

“…Such sample volumes run the risk of wrongly concluding that microplastics are absent in samples and are not sufficient to be quantitative. …”
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Hierarchical multi‐modal video summarization with dynamic sampling by Lingjian Yu, Xing Zhao, Liang Xie, Haoran Liang, Ronghua Liang

“…This work proposes a dynamic sampling module that leverages frame‐level motion information to alleviate these issues. …”
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Advances in the detection methods for assessing the viability of cryopreserved samples by Hao Yan, Liu Zhicheng, Sun Heming, Zhai Wang, Sun Wenyu, Mu Long

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The FUTURE of the US Marine Seafloor and Subseafloor Sampling Capabilities by FUTURE 2024 PI‐team, Bruce Appelgate, Brandon Dugan, Nobuhisa Eguchi, Daniel Fornari, Tim Freudenthal, Patrick Fulton, Sean Kelley, Susan Q. Lang, Dana Manalang, Alan Mix, Rick Trask, Janine Andrys, Sarah Beethe, Hanna Bridgham, Haley Cabaniss, Samantha K. Cargill, Christian W. Conroy, Kassandra Costa, Alysia Cox, Andrew Cross, Deepa Dwyer, Justin Dodd, Jeffrey Donnelly, Valerie Finlayson, Mohammed Hashim, Daniel Heaton, Julie Huber, Brittany Hupp, Matthew G. Jackson, Claire Jasper, Hiroko Kitajima, Olga Libman‐Roshal, Christopher M. Lowery, Erica Maletic, Ashley N. Marranzino, Beatriz E. Mejía‐Mercado, Thomas Morrow, Lucien Nana Yobo, Celeste Pallone, Kurt Panter, Molly Patterson, Ally Peccia, Thomas A. Ronge, Ethan Roth, Alice Staro, Katherine Stelling, Jordan P. Todes, Man‐Yin Tsang, Scott T. Wieman, Kevin Konrad, Brendan Reilly, Matthew Schrenk, Maureen Walczak, Masako Tominaga

“…Cross‐cutting issues identified included weight and size limitations in the over‐boarding capabilities of the US‐ARF, a need to access material at depths greater than ∼20 m below the seafloor, sampling capabilities at the full range of ocean depths, technologies required for precise navigation‐guided sampling and drilling, resources to capitalize on the research potential of returned materials, and workforce development.…”
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How Many Samples are Enough When Data are Unbalanced? by Mehmet Mendeş

“…The aim of this study is to clarify some of the key issues regarding sample size and power 80 % when data are unbalanced. …”
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High rate of safety and fraud issues in commercially available cinnamon by M. Ghidotti, M. Behr, D. Pietretti, M. Jiménez, L. Garlant, S. Papoci, M. B. de la Calle Guntiñas

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Unification of documents in pre-revolutionary Russia: historical and legal issues by O. V. Marchenko

“…The study of the history of documentation practice in Russia allows us to conclude that the issues of document flow rationalization were of great importance since the XVII century. …”
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Development and validation of headspace gas chromatography with a flame ionization detector method for the determination of ethanol in the vitreous humor by Mihajlović Filip, Andrić Ivana, Slović Živana, Vujović Maja, Piskulić Kristina, Đorđević Snežana

“…Given the complexity of the issue, obtaining a realistic picture of lifelong alcoholemia requires supporting blood ethanol findings with analyses of alternative samples, primarily vitreous humor (VH).…”
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Considering the issue of separating antho­ cyanin pigments in table grape by А. Е. Модонкаєва, Е. А. Сластья, В. А. Бойко, Н. Н. Аппазова

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Chronic heart failure in out-patient practice: aetiology issues by S. M. Bogdanova, N. V. Makarova

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Issues and Challenges Facing the Greek Regional Press: Fight for Survival by Emmanouil Genikopoulos, Efstathios Dimitriadis

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Age-related issues of instruments screening for autism in young children by Thomaidis L, Choleva A, Kyprianou M

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Exercise Dependence and Perfectionism: a Novel Issue in Sport Environment? by P. Venuti, C. Furlanello, A.M. Peirò, T. Zandonai

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The Application of Molecularly Imprinted Polymers in Forensic Toxicology: Issues and Perspectives by Susan Mohamed, Simone Santelli, Arianna Giorgetti, Guido Pelletti, Filippo Pirani, Paolo Fais, Jennifer P. Pascali

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Microplastic Presence in Commercial Sea Salt: Sampling and Quantification Challenges by Cristina Di Fiore, Maria Pia Sammartino, Cristina Giannattasio, Pasquale Avino, Giovanni Visco

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Small sample data pricing research based on Reptile algorithm by Junxin Shen, Yi Yang, Fanghao Xiao

“…The methods proposed in this research are universally applicable for addressing the small sample problem in data pricing, providing a reference for solving similar issues. …”
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Psychometric properties of the self-rating organization scale with adult samples by Takeda T, Tsuji Y, Ando M

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Metric-based learning approach to botnet detection with small samples by Honggang LIN, Junjing ZHU, Lin CHEN

“…Botnets pose a great threat to the Internet, and early detection is crucial for maintaining cybersecurity.However, in the early stages of botnet discovery, obtaining a small number of labeled samples restricts the training of current detection models based on deep learning, leading to poor detection results.To address this issue, a botnet detection method called BT-RN, based on metric learning, was proposed for small sample backgrounds.The task-based meta-learning training strategy was used to optimize the model.The verification set was introduced into the task and the similarity between the verification sample and the training sample feature representation was measured to quickly accumulate experience, thereby reducing the model’s dependence on the labeled sample space.The feature-level attention mechanism was introduced.By calculating the attention coefficients of each dimension in the feature, the feature representation was re-integrated and the importance attention was assigned to optimize the feature representation, thereby reducing the feature sparseness of the deep neural network in small samples.The residual network design pattern was introduced, and the skip link was used to avoid the risk of model degradation and gradient disappearance caused by the deeper network after increasing the feature-level attention mechanism module.…”
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