Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor by Kuzmina Sofia, Lobanova Polina, Chepikova Svetlana Sergeevna

“…Then, using a Random Forest Machine Learning algorithm in the Classifier modification, we created models for each sea to predict the position of high-productivity zones (Chl-a > 1 mg m−3) using environmental parameters. …”
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The spatial extent and the dispersal strategy of species shape the occupancy frequency distribution of stream insect assemblages by I. Szivák, Z. Csabai, D. Schmera, A. Móra

“…For instance, the metapopulation dynamic model predicts bimodal OFD pattern indicating the dominance of dispersal processes in structuring the assemblages, while the niche‐based model predicts unimodal right‐skewed OFD pattern, and thus assemblages are driven mostly by niche processes. …”
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ML-Enabled Solar PV Electricity Generation Projection for a Large Academic Campus to Reduce Onsite CO₂ Emissions by Sahar Zargarzadeh, Aditya Ramnarayan, Felipe de Castro, Michael Ohadi

“…In the first phase, PVWatts gathered data to predict PV-generated energy. This was the foundation for Phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. …”
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Reconstructing the dynamics of HIV evolution within hosts from serial deep sequence data. by Art F Y Poon, Luke C Swenson, Evelien M Bunnik, Diana Edo-Matas, Hanneke Schuitemaker, Angélique B van 't Wout, P Richard Harrigan

“…HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. …”
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Patterns and variability of projected bioclimatic habitat for Pinus albicaulis in the Greater Yellowstone Area. by Tony Chang, Andrew J Hansen, Nathan Piekielek

“…Since intermodel variability from General Circulation Models (GCMs) lead to differing predictions regarding the magnitude and direction of modeled suitable habitat area, nine bias-corrected statistically down-scaled GCMs were utilized to understand the uncertainty associated with modeled projections. …”
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Identification of the feline humoral immune response to Bartonella henselae infection by protein microarray. by Adam Vigil, Rocio Ortega, Aarti Jain, Rie Nakajima-Sasaki, Xiaolin Tan, Bruno B Chomel, Rickie W Kasten, Jane E Koehler, Philip L Felgner

“…<h4>Methodology</h4>We report the development of a microarray comprised of proteins expressed from 96% (1433/1493) of the predicted ORFs encoded by the genome of the zoonotic pathogen Bartonella henselae. …”
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Social network heterogeneity promotes depolarization of multidimensional correlated opinions by Jaume Ojer, Michele Starnini, Romualdo Pastor-Satorras

“…For the simplest D=2 case and correlated initial opinions, we found that the depolarization threshold can vanish if the underlying connectivity is heterogeneous, as predicted by perturbation theory. Such an effect is due to the presence of hubs, which promote consensus in the population. …”
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Potential effects of climate change on the threatened Malagasy poison frogs: A multispecies approach by Albert Carné, David R. Vieites, Neftalí Sillero

“…The current distribution extent is a good predictor of both tolerance and marginality, and tolerance can predict the conservation status in the genus Mantella. …”
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An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study. by Abdulkerim Capar, Dursun Ali Ekinci, Mucahit Ertano, M Khalid Khan Niazi, Erva Bengu Balaban, Ibrahim Aloglu, Meryem Dogan, Ziyu Su, Fugen Vardar Aker, Metin Nafi Gurcan

“…The second proposed method, the Distance Based Cell Agreement Algorithm (DBCAA), eliminates the need for ground truth annotations in cell detection predictions. …”
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Detection of alpha-rod protein repeats using a neural network and application to huntingtin. by Gareth A Palidwor, Sergey Shcherbinin, Matthew R Huska, Tamas Rasko, Ulrich Stelzl, Anup Arumughan, Raphaele Foulle, Pablo Porras, Luis Sanchez-Pulido, Erich E Wanker, Miguel A Andrade-Navarro

“…Finally, we demonstrate the utility of these predictions in directing experimental work to demarcate three alpha-rods in huntingtin, a protein mutated in Huntington's disease. …”
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Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054 by Yingzi Wu, Yuewen Zhang, Xiaoyu Zhong, Huiling Xia, Mingyang Zhou, Wenjin He, Yi Zheng

“…Through Box–Behnken response surface methodology (RSM), the optimal medium composition was determined as sucrose 156.65 g/L, yeast extract paste 42 g/L, and $$\hbox {NH}_4\hbox {Cl}$$ NH 4 Cl 1.68 g/L, yielding an enzyme activity of 3249.00 ± 24.39 U/L (99.16% agreement with RSM predictions). Further optimization of cultivation conditions using a hybrid backpropagation neural network–genetic algorithm (BP–GA) model identified optimal parameters as pH 5.5, a liquid volume of 96.6 mL (in a 250 mL shaker), and inoculum size of 2.4 $$\times$$ × $$10^{4}$$ 10 4 spores/mL, achieving a final enzyme activity of 3422.14 ± 36.86 U/L (1.1% deviation from the predicted 3460 U/L), representing a 4.2-fold increase over initial conditions. …”
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Evaluation of future land use change impacts on soil erosion for holota watershed, Ethiopia by Abebe Chala Guder, Worku Firomsa Kabeta

“…The future LULC for 2050 was predicted using the CA–Markov chain model. Soil erosion for 2020 and 2050 LULC maps was estimated using the Revised Universal Soil Loss Equation (RUSLE). …”
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Fire spread simulations using Cell2Fire on synthetic and real landscapes by Minho Kim, Cristobal Pais, Marta C. Gonzalez

“…As wildfires have emerged into a global phenomenon with far-reaching impacts on the natural and built environments, FSM simulations provide crucial information to better understand and predict fire behavior in various landscapes. In this study, we tested Cell2Fire, a recently developed cellular automata-based FSM, against benchmarking models used in the U.S., Canada, and Chile. …”
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Cheby-KANs: Advanced Kolmogorov&#x2013;Arnold Networks for Applying Geometric Deep Learning in Quantum Chemistry Applications by Amgad Abdallah Mahmoud, Andreas Pester, Maha M. Muttardi, Frederic Andres, Shihori Tanabe, Nicolas Greneche, Hesham H. Ali

“…In this work, we present an enhanced version of the Kolmogorov&#x2013;Arnold network (KAN) algorithm, called Cheby-KAN, that offers a more efficient, more reliable, and faster alternative to conventional KANs. …”
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Research on an intelligent coagulant dosing system based on alum floc image recognition by FU Yuan, LEI Zhifeng, CUI Dongfeng, GUO Zhongquan, CAI Bohan, XIAO Yan, ZHOU Aijiao*

“…The system combines the YOLOv5 alum floc recognition algorithm and the Linear Regression dosing decision algorithm. …”
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Optimizing TCP Performance in Multi-AP Residential Broadband Connections via Minislot Access by Domenico Giustiniano, Eduard Goma, Alberto Lopez Toledo, George Athanasiou

“…We then introduce a simple analytical model that accurately predicts the TCP round-trip time (RTT) with a multi-AP TDMA policy and propose a resource allocation algorithm to reduce the observed TCP RTT with a very low computational cost. …”
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Experimental validation of the analytical modelling of a digitally created duffing acoustic nonlinear oscillator at low amplitudes by Morell Maxime, Collet Manuel, Gourdon Emmanuel, Ture Savadkoohi Alireza, De Bono Emanuele

“…Despite the several assumptions of the model, the analytical modelling is validated by the experiment, showing that the model is able to predict the two-degree-of-freedom system.…”
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Behaviour recognition of housed sheep based on spatio-temporal information by Lina Zhang, Bin Zhao, Chunming He, Fan Yang, Jue Zhang

“…Therefore, monitoring the behavior of sheep is helpful to predict the health status of sheep and thus safeguard the production performance of sheep. …”
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Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction by Yalong Li, Ye Jin, Yangqing Dan, Wenting Zha

“…Abstract Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. …”
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Oncological alertness in the practice of a primary care dentist by V. V. Shkarin, Y. A. Makedonova, O. Yu. Afanasyeva, A. V. Poroshin, L. A. Devyatchenko

“…In most cases, it is not possible to accurately predict the probability of malignancy, however, at a clinical appointment, the most important aspect is to identify both risk factors and initial signs of the development of the pathological process in the absence of complaints from the patient.  …”
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