An early warning system based on machine learning detects huge forest loss in Ukraine during the war by Roberto Cazzolla Gatti, Rocìo Beatriz Cortès Lobos, Michele Torresani, Duccio Rocchini

“…We employed Random Forest, a supervised machine learning classification algorithm, in conjunction with high-quality satellite imagery, to quantify the forest loss in Ukraine during the war, between 2022 and 2023. …”
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Early-life management practices and their association with dairy herd longevity, productivity, and profitability by Gabriel M. Dallago, Daniel Warner, Elsa Vasseur

“…Cluster stability assessment was used to determine the best clustering algorithm and number of clusters. Associations between herd longevity, productivity, profitability, and early-life management practices were assessed using multivariate linear regression models. …”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…In this study, we present a workflow for producing peat occurrence maps for the whole of Finland. For this, we used random forest classification to map areas with peat thicknesses of ≥ 10 cm, ≥30 cm, ≥40 cm, and > 60 cm. …”
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The Use of Economic and Mathematical Modeling in Assessing the Competitiveness of Tourism Industry Enterprises by S. N. Medvedeva, V. P. Pavlyuk

“…The implementation of such a system is directly related to the analysis and assessment of the whole variety of conditions and factors for the functioning of the subjects of the tourism industry.…”
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Hybrid Model for 6G Network Traffic Prediction and Wireless Resource Optimization by Mohammed Anis Oukebdane, A. F. M. Shahen Shah, Md Baharul Islam, John Ekoru, Milka Madahana

“…Large-scale 6G traffic data with varied channel conditions and user scenarios was used to validate the model. An algorithm is presented to describe the training process of the proposed hybrid model. …”
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Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. Afterwards, ensemble learning and eight other conventional algorithms were used for model fitting and validation based on all features and selected features. …”
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A formal notation of legal rules applicable in the local space by Anna M. Barańska, Konrad Eckes

“…Following these assumptions, the general form of the behavioural test algorithm was formulated. The presented issues demonstrate complex relationships to space and its integrating rule in relation to legal rules. …”
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Rapid test for the qualitative simultaneous determination of cardiac fatty acid-binding protein and cardiac troponin I in the diagnosis of acute coronary syndrome by V. A. Kokorin, I. G. Gordeev, M. N. Arefyev, A. Ya. Goncharova, A. A. Yakovtsova

“…Further studies will clarify the place of this technique in the modern algorithm for the management of patients with ACS and evaluate the possibility of using the rapid test in predicting the course of the disease.…”
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Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning by Tianhao Li, Haoren Jing, Xinyu Gao, Te Zhang, Haitao Yao, Xipeng Zhang, Mingqing Zhang

“…Immune cell abundance quantification and statistical correlation analyses with IBD-associated transcripts were conducted via the CIBERSORTx deconvolution algorithm. To complement these findings, blood expression quantitative trait loci (eQTL) data from GTExv8.ALL.Whole_Blood were integrated with IBD genome-wide association statistics from the FinnGen consortium. …”
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Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…Methods A comparative differential methylation analysis was performed on whole blood samples from 94 anxiety disorder patients and 296 control samples using targeted bisulfite sequencing. …”
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Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques by Ch Gangadhar, P Pavithra Roy, R. Dinesh Kumar, Janjhyam Venkata Naga Ramesh, S. Ravikanth, N. Akhila

“…The suggested strategy outperformed modern fall identification techniques when tested using the labeled KFall database, achieving average precision of 95 percent, 96 percent, as well as 98 percent for Non-Falls, Pre-Falls, as well as detectable fall incidents, correspondingly. The whole assessment proved the algorithmic learning structure's efficacy. …”
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Space-time spectrum sharing based on game theory:dynamic access and penalty strategy by Yangyang NIU, Zhiqing WEI, Zhiyong FENG

“…To tackle the challenge of inefficient utilization of spatiotemporal spectrum resources in heterogeneous networks, a reliable spectrum sharing strategy based on game theory was proposed for scenarios where multiple secondary users dynamically access through sensing spatiotemporal spectrum opportunities.Firstly, secondary users sensed the activity state of primary users through channel sampling and subsequently seek suitable spatiotemporal spectrum opportunities.Each secondary user aimed to access more spectrum resources while preventing quality degradation between primary users.Consequently, a negotiation of individual utilities between secondary and primary users was needed, in order to reach a mixed Nash equilibrium.When the secondary user's transmission power was abnormal, its behavior would be regarded as a violation.Then, a backoff time compensation algorithm was presented to penalize the secondary users, according to the number of violations and active nodes in the network.Finally, simulation results validate the effectiveness of the spatiotemporal spectrum opportunities sharing strategy with the penalty mechanism.…”
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Prediction of magnetohydrodynamics fluid flow with viscous dissipation through artificial neural network by Sewli Chatterjee

“…The Levenberg–Marquardt algorithm to predict the Nusselt Number (NN) and Skin Friction Coefficients (SFCs). …”
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Cross-Layer Power-Control-Based Real-Time Routing Protocol for Wireless Sensor Networks by Lin Feng, Yang Lu, Zhenchun Wei, Zengxi Zhu, Xu Ding

“…Simulation results show that the energy consumption of the network is reduced while real-time end-to-end data transmission is guaranteed.…”
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Error-Tolerant and Energy-Efficient Coverage Control Based on Biological Attractor Selection Model in Wireless Sensor Networks by Takuya Iwai, Naoki Wakamiya, Masayuki Murata

“…A coverage problem is one of the important issues to prolong the lifetime of a wireless sensor network while guaranteeing that the target region is monitored by a sufficient number of active nodes. …”
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Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing by Bin LI, Sicong PENG, Zesong FEI

“…To address the dependence of traditional integrated sensing and communication network mode on ground infrastructure, the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption, signal blocking, and coverage blind spots in complex scenarios.Firstly, under the conditions of satisfying the user’s transmission power, radar estimation information rate and task offloading proportion limit, the system energy consumption was minimized by jointly optimizing UAV radar beamforming, computing resource allocation, task offloading, user transmission power, and UAV flight trajectory.Secondly, the non-convex optimization problem was reformulated as a Markov decision process, and the proximal policy optimization method based deep reinforcement learning was used to achieve the optimal solution.Simulation results show that the proposed algorithm has a faster training speed and can reduce the system energy consumption effectively while satisfying the sensing and computing delay requirements.…”
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AXIAL TENSILE STRENGTH ANALYSIS OF UNBONDED FLEXIBLE PIPE CONTAINING COMPOSITE TENSILE ARMORS by MA JinSheng, DU FaXi, LUO Dan

“…As offshore oil industry moves into ultra-deep water,replacing the steel tensile armors of unbonded flexible pipe with composite is becoming a scheme that can effectively reduce weight and provide better mechanical properties.The anisotropic parameters of composite are obtained by meso-mechanical method,then a model of axisymmetric structure response for composite flexible pipe with composite armored layer is established,which is based on both thick-wall cylinder theory and the differential equation of curved beam,and also the algorithm of cyclic iterative is combined.Failure models of different type of flexible pipes are obtained,and iso-strength method is used for composite armored pipe.A case study shows that composite armors can effectively enhance the tensile strength of unbonded flexible pipe while effectively reduce the overall weight.Results state that volume fraction,elastic modulus and strength of fiber are the important parameters which affect the axial stiffness and tensile strength of unbonded flexible pipe.…”
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Multi-objective optimization placement strategy for SDN security controller considering Byzantine attributes by Tao WANG, Hongchang CHEN

“…By giving the software defined network distributed control plane Byzantine attributes, its security can be effectively improved.In the process of realizing Byzantine attributes, the number and location of controllers, and the connection relationship between switches and controllers can directly affect the key network performance.Therefore, a controller multi-objective optimization placement strategy for SDN security controllers considering Byzantine attributes was proposed.Firstly, a Byzantine controller placement problem (MOSBCPP) model that comprehensively considered interaction delay, synchronization delay, load difference and the number of controllers was constructed.Then, a solution algorithm based on NASG-II was designed for this model, which included the initialization function, the mutation function, the fast non-dominated sorting function and the elite strategy selection function.Simulation results show that this strategy can effectively reduce interaction delay, synchronization delay, load difference and the number of controllers, while improving control plane security.…”
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An Empirical Configuration Study of a Common Document Clustering Pipeline by Anton Eklund, Mona Forsman, Frank Drewes

“…The results suggest that BERT embeddings combined with UMAP dimension reduction to no less than 15 dimensions provides a good basis for clustering, regardless of the specific clustering algorithm used. Moreover, while UMAP performed better than PCA in our experiments, tuning the UMAP settings showed little impact on the overall performance. …”
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Uplink assisted downlink channel estimation method of extra-large scale MIMO-OTFS system by Xumin PU, Kaiyuan DENG, Qianbin CHEN

“…A low complexity downlink channel estimation method for high-speed mobile scenarios was proposed for the extra-large scale multiple-input multiple-output (MIMO) orthogonal time-frequency space (OTFS) system.Different from the existing studies, the proposed method considers the significant spatial non-stationary characteristics of extra-large scale MIMO-OTFS system.Based on the visible path region, an enhanced sparse orthogonal matching pursuit (OMP) algorithm with low complexity was proposed, uplink-assisted downlink channel estimation was achieved using the mapping relationship between the uplink and downlink channels in the frequency-division duplex (FDD) mode.Simulation results show that the proposed uplink-assisted downlink channel estimation method can accurately represent the non-stationary of the system, and achieve significant improvement in the channel estimation performance while reducing the computational complexity , and still perform well in high-mobility IoT scenarios.…”
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