Comparative analysis of antibodies and lymphocytes in pulmonary tuberculosis patients with negative and positive cultures by Fernando-Renato Cázares-Sosa, Alejandro Hernández-Solís, Atenea-Estela Andrés-Dionicio, Heliodora González-González, Miguel Ibáñez-Hernández, Raúl Cicero-Sabido, Herón Huerta, Raquel Tapia-Romero, Jorge-Luis de-la-Rosa-Arana

“…Further studies are needed to determine if this profile is predictive of the outcome of isolation. …”
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Body mass index as the main predictor for length of stay in COVID-19 patients with mild and moderate symptoms: a cross-sectional study in COVID-19 emergency hospital in Indonesia by Siti Rahayu Nadhiroh, Anisa Lailatul Fitria, Armedy Ronny Hasugian, Erwin Astha Triyono, Nono Tri Nugroho, Alfadhila Khairil Sinatrya, Hazreen B Abdul Majid

“…Only a few participants were hospitalized with comorbidities such as hypertension (11.6%) and diabetes mellitus (4.1%). The predictive model of LoS indicated that BMI was the main predictor of COVID-19 LoS, with higher BMI showed to prolong the LoS of mild to moderate symptoms patients. …”
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A novel indoor localization method using passive phase difference fingerprinting based on channel state information by Xiaochao Dang, Jiaju Ren, Zhanjun Hao, Yili Hei, Xuhao Tang, Yan Yan

“…During the online phase, the algorithm trains a back-propagation neural network using the fingerprint data and determines the modelled mapping relationship between the fingerprint data and the physical localization after carrying out the phase difference correction and the principal component analysis–based dimensionality reduction. …”
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Presenting a Fuzzy Multiobjective Mathematical Model of the Reverse Logistics Supply Chain Network in the Automotive Industry to Reduce Time and Energy by Saeed Aminpour, Alireza Irajpour, Mehdi Yazdani, Ali Mohtashami

“…Deterministic methods, genetic algorithm, particle swarm algorithm, and several scenarios with different aspects have been used to solve the model. …”
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…This model utilizes the pre-trained InceptionV3 for feature extraction on PID intrusion image dataset, followed by t-SNE for dimensionality reduction and subsequent clustering. When handling high-dimensional data, the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm faces efficiency issues due to its complexity and varying densities. …”
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Optimization of the Mixture Design of Low-CO2 High-Strength Concrete Containing Silica Fume by Seung-Jun Kwon, Xiao-Yong Wang

“…As abundant CO2 is released by high-strength concrete due to its high binder content, the reduction of CO2 emissions has become increasingly important. …”
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Shortcuts in multiple dimensions: the multiaxial racetrack filter by M.A. Meggiolaro, J.T.P. Castro, H. Wu

“…For uniaxial histories, the proposed algorithm exactly reproduces the classic racetrack filter. …”
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Permanent terrestrial laser scanning for near-continuous environmental observations: Systems, methods, challenges and applications by Roderik Lindenbergh, Katharina Anders, Mariana Campos, Daniel Czerwonka-Schröder, Bernhard Höfle, Mieke Kuschnerus, Eetu Puttonen, Rainer Prinz, Martin Rutzinger, Annelies Voordendag, Sander Vos

“…Many topographic scenes exhibit complex dynamic behavior that is difficult to map, quantify, predict and understand. A terrestrial laser scanner fixed on a permanent position can be used to monitor such scenes in an automated way with centimeter to decimeter quality at ranges of up to several kilometers. …”
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Visualization of Moisture Distribution in Stacked Tea Leaves on Process Flow Line Using Hyperspectral Imaging by Yuying Zhang, Binhui Liao, Mostafa Gouda, Xuelun Luo, Xinbei Song, Yihang Guo, Yingjie Qi, Hui Zeng, Chuangchuang Zhou, Yujie Wang, Jingfei Zhang, Xiaoli Li

“…In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. …”
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AI‐Powered Advancements in Food Analysis and Safety: Ensuring Quality, Protection, and Precision in Modern Food Systems: A Review by Ammar B. Altemimi, Farhang Hameed Awlqadr, Raqad R. Al‐Hatim, Syamand Ahmed Qadir, Mohammed N. Saeed, Aryan Mahmood Faraj, Tablo H. Salih, Hala S. Mahmood, Mohammad Ali Hesarinejad, Francesco Cacciola

“…The role of AI in contaminant detection and spoilage prediction is underlined. One of the applications is using near‐infrared spectroscopy on a machine learning algorithm sample to determine if it contains any adulterants in olive oil. …”
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An Indoor Unknown Radio Emitter Positioning Approach Using Improved RSSD Location Fingerprinting by Liyang Zhang, Kunlei Liu, Zhiyou Pan, Lei Pan, Rui Gao, Qian Zhang

“…The results show that the proposed algorithm can obtain a more superior performance compared with the conventional RSSD-based weighted k-nearest neighbor algorithm (RSSD-WKNN) and COS matching algorithm (RSSD-PCA-COS) in the case of different selected RP numbers, AP numbers, and grid distances.…”
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A Modified Nearest Level Control Scheme for Improved Submodule Current Sharing in a CHB Converter with Integrated EDLCs by Viktor Döhlen, Kent Bertilsson

“…A modification of the sort and select algorithm to determine which submodule is to be inserted and bypassed when using the Nearest Level Control algorithm is proposed to distribute the activation time and the experienced RMS current of the submodules. …”
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Artificial Intelligence and Ethical Dimensions of Automated Traffic Enforcement: Implications for Public Health, Healthcare Equity, and Social Justice by Patricia Haley

“…The analysis reveals how machine learning and predictive analytics in automated enforcement create disproportionate burdens on marginalized populations through three specific mechanisms: (1) biased algorithmic design that targets low-income neighborhoods more intensively, (2) punitive traffic fine structures that impose greater relative financial hardship on economically disadvantaged families, and (3) opaque implementation practices that limit community understanding and participation. …”
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Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer by Heping Qiu, Xiaolin Deng, Jing Zha, Lihua Wu, Haonan Liu, Yichen Lu, Xinji Zhang

“…Abstract Background Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. …”
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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…The deep-learning forecasting models produced more accurate predictions in the mid (500–3000 Hz) (MAE ∼0.4–1) and high (3000–24,000 Hz) (MAE ∼1.5–3) frequency ranges, where seasonal acoustic activity from fish and shrimp strongly influenced sound levels. …”
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Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Finally, we searched the drug targets of these hub genes, and established a nomogram model for predicting the risk of BPD.Results: There were 73 the differentially expressed and autophagy-related genes (DE-ARGs) by overlapping the DEGs in GSE8586 and ARGs. …”
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Formation of a heterogeneous group of UAVS with a reasonable number of false and real drones by Volodymyr Prymirenko, Andrii Demianiuk, Roman Shevtsov, Serhii Bazilo, Andrey Pilipenko, Mykola Vovchanskyi

“…The availability of the developed mathematical model, algorithm, and program code makes it possible to predict the possible results of the combat use of heterogeneous groups of UAVs based on the initial parameters and to substantiate recommendations for a possible composition of such groups.…”
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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. …”
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Construction of a minimizing sequence for the problem of cooling of the given segments of the rod with phase constraint by A.I. Egamov

“…The possibility of its reduction to a finite shortened system was demonstrated for the resulting countable system of differential equations. …”
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Machine learning in Alzheimer’s disease genetics by Matthew Bracher-Smith, Federico Melograna, Brittany Ulm, Céline Bellenguez, Benjamin Grenier-Boley, Diane Duroux, Alejo J. Nevado, Peter Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Sven van der Lee, Atahualpa Castillo, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermin Moreno, Jordi Pérez-Tur, María J. Bullido, Pau Pastor, Raquel Sánchez-Valle, Victoria Álvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Shima Mehrabian, Latchezar Traykov, Jakub Hort, Martin Vyhnalek, Nicolai Sandau, Jesper Qvist Thomassen, Yolande A. L. Pijnenburg, Henne Holstege, John van Swieten, Inez Ramakers, Frans Verhey, Philip Scheltens, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Jean-François Deleuze, Gael Nicolas, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Michael Wagner, Benedetta Nacmias, Marco Spallazzi, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, Carlo Masullo, Giacomina Rossi, Frank Jessen, Patrick Kehoe, Tsolaki Magda, Pascual Sánchez-Juan, Kristel Sleegers, Martin Ingelsson, Mikko Hiltunen, Rebecca Sims, Wiesje van der Flier, Ole A. Andreassen, Agustín Ruiz, Alfredo Ramirez, EADB, Ruth Frikke-Schmidt, Najaf Amin, Gennady Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Valentina Escott-Price

“…Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. …”
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