DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…This paper proposes a novel hybrid technique, termed Decision Tree with Hill Climbing (DTHC), for efficient spectrum hole detection in CRNs. The objective of the DTHC method is to improve detection accuracy while minimizing false alarm rates. …”
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Elastostatic analysis of tapered FGM beams with spatially varying material properties by Justín Murín, Stephan Kugler, Juraj Paulech, Juraj Hrabovský, Vladimír Kutiš, Herbert Mang, Mehdi Aminbaghai

“…FGM beams of variable stiffness can be modeled efficiently, using only one finite element. …”
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Effects of two types of repetitive transcranial magnetic stimulation on brain network in Parkinson’s disease by Shuo Liu, Shuo Yang, Chen Wang, Jiarui Li, Lei Wang

“…The iTBS group showed post-treatment changes involving more brain regions in spatiotemporal variability. This study demonstrates that compared to 10Hz-rTMS, iTBS can reduce treatment time while providing superior improvement in functional brain connectivity for PD patients.…”
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Energy‐maximising moment‐based constrained optimal control of ocean wave energy farms by Nicolás Faedo, Giordano Scarciotti, Alessandro Astolfi, John V. Ringwood

“…The authors develop a framework under which the objective function (and system variables) can be mapped to a finite‐dimensional tractable quadratic program (QP), which can be efficiently solved using state‐of‐the‐art solvers. …”
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Improving Electrical Distance Accuracy for Cable Fault Location Based on the Prony Method by Xiang Ren, Zhiqiang Feng, Kai Zhou, Xueming Zhou, Jianjun Yuan, Yefei Xu, Li Zhang

“…By establishing a frequency-variable coupling model containing the transfer function of the cable with double joint structure, studying the mapping relationship between the cable attenuation constant and the electromagnetic wave velocity, and adopting the Prony estimation method combined with the singular value decomposition noise reduction algorithm to achieve the extraction of the attenuation constant and the decoupling of the segmented wave velocity, thus improving the electrical accuracy of fault location. …”
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Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment by Yalan Xu, Yu Qian, Kongming Guo

“…The numerical simulation results show the efficiency and effectiveness of the presented method for the identification of material parameter variability by using the measurable modal parameters of damaged FGM structures.…”
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity. by Ali Akbar Moosavi, Mohammad Amin Nematollahi, Mohammad Omidifard

“…Results revealed that all NN models particularly PSO-NNs were efficient in prediction of Kfs. However, further evaluations may be recommended for other soil conditions and input variables to quantify their potential uncertainties and wider potential and versatility before they are used in other geographical locations/soil conditions.…”
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High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics by Nauman Ahmed, Huigang Wang, Shanshan Tu, Norah A.M. Alsaif, Muhammad Asif Zahoor Raja, Muhammad Kashif, Ammar Armghan, Yasser S. Abdalla, Wasiq Ali, Farman Ali

“…The measuring parameters like robustness against noise and element quantity, estimation accuracy, computation complexity, various numbers of hydrophones, variability analysis, frequency distribution and cumulative distribution function of root mean square error (RMSE), and resolution ability are applied for analyzing the performance of the proposed model with additive white Gaussian noise (AWGN). …”
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Deep Reinforcement Learning With Dueling DQN for Partial Computation Offloading and Resource Allocation in Mobile Edge Computing by Ehzaz Mustafa, Junaid Shuja, Faisal Rehman, Abdallah Namoun, Mazhar Ali, Abdullah Alourani

“…The Dueling DQN alleviates the complexity of simultaneous offloading and resource allocation decisions, with each branch independently controlling a subset of the decision variables to scale efficiently with an increasing number of IoT devices, thereby avoiding the combinatorial explosion of potential actions. …”
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Dual RNA-seq reveals transcriptome changes during Fusarium virguliforme-Trichoderma afroharzianum interactions. by Mirian F Pimentel, Leonardo F Rocha, Arjun Subedi, Jason P Bond, Ahmad M Fakhoury

“…While extensive work has been done to understand Trichoderma antagonistic mechanisms, additional research is needed to fully understand how Trichoderma spp. recognize the pathogen-host and the intra-species variability i frequently observed upon interaction with a specific pathogen-host. …”
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Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration by Zhenjia Lin, Haoyong Chen, Jinbin Chen, Jianping Huang, Mengshi Li

“…Differing from existing interval optimization (IO) approaches that merely rely on the upper and lower boundaries of random variables, the distribution information retained in the historical data is introduced to the IO method in this paper. …”
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Drug Release Analysis and Optimization for Drug-Eluting Stents by Hongxia Li, Yihao Zhang, Bao Zhu, Jinying Wu, Xicheng Wang

“…The results demonstrate that the optimized design can efficiently improve the efficacy of drug deposition and penetration into the arterial walls.…”
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Genome Microscale Heterogeneity among Wild Potatoes Revealed by Diversity Arrays Technology Marker Sequences by Alessandra Traini, Massimo Iorizzo, Harpartap Mann, James M. Bradeen, Domenico Carputo, Luigi Frusciante, Maria Luisa Chiusano

“…However, scant information is available for these species in terms of genome organization, gene function, and regulatory networks. Consequently, genomic tools to assist breeding are meager, and efficient exploitation of these species has been limited so far. …”
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An Optimization Strategy for Generator Start-up Sequence After Blackouts Considering the Cyber System Fault by Mingming LI, Lei SUN, Yinghao MA

“…An optimal start-up sequence model of generators is proposed with the expectation of maximum power systems’ total generation as objective function, and taking into account the constraints including the start-up time constraint of generator units, the auxiliary variables constraint related to the cyber system fault, and the start-up power constraint of generator units. …”
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eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework by Abigail G. Keller, Ryan P. Kelly

“…Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. …”
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Automated Geographic Information System Multi-Criteria Decision Tool to Assess Urban Road Suitability for Active Mobility by Bertha Santos, Sandro Ferreira, Pollyanna Lucena

“…The treatment, representation, and study of the variables considered in the analysis are carried out with the aid of geoprocessing, using the spatial and network analysis tools available in the GIS. …”
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Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging by Momina Aisha, Moazma Ijaz, Nimra Tariq, Sehar Anjum, Sidra Siddiqui, Usman Hashmi

“…Techniques such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) play essential roles in identifying the physical and functional aspects of the lungs. Manual lung segmentation by radiologists, while adjustable, is time-consuming and subject to variability. …”
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Energy management of microgrid coalitions considering renewable energy prediction based on machine learning algorithms by Negar Dehghani Mahmoudabadi, Mehran Khalaj, Davood Jafari, Ali Taghizadeh Herat, Parisa Mousavi Ahranjani

“…Due to the inherent variability of renewable resources, an extreme learning machine (ELM) model is applied in order to forecast solar irradiation and wind power. …”
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Spatiotemporal changes of ecosystem health and the impact of its driving factors on the Loess Plateau in China by Xuepeng Zhang, Taixia Wu, Qiqi Du, Ninglei Ouyang, Wei Nie, Yang Liu, Peng Gou, Guangchao Li

“…Based on this idea, a new framework for assessing EH is proposed. The efficient machine learning model (light gradient boosting machine model) and the variable explanation model (SHapley Additive exPlanation model) are combined to quantify the functional relationships between various driving factors and EH, thereby exploring the impacts of climatic factors, socioeconomic development (SED), and ecological restoration projects (ERP) on EH. …”
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SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing by Gilberto Alvarado-Robles, Isac Andres Espinosa-Vizcaino, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes

“…Shadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. …”
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