First-Arrival Picking for Microseismic Monitoring Based on Deep Learning by Xiaolong Guo

“…In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. …”
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Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature by Zhiyong Li, Pengfei Li, Xiaoping Yu, Mervat Hashem

“…This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). …”
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A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. …”
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Data-Augmented Deep Learning Models for Assessing Thermal Performance in Sustainable Building Materials by Ana Carolina Rosa, Carles Mateu, Assed Haddad, Dieter Boer

“…Energy efficiency in buildings drives the development of sustainable materials, with Phase Change Materials standing out for their contribution to the construction sector. …”
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Mathematical modeling of heat and mass transfer processes in a reciprocating engine of a vehicle with a hybrid powertrain by Fedіr Abramchuk, Vitaliy Krivda, Olha Sakno, Valentyna Olishevska, Kostyantyn Kornilenko, Olena Sakno

“…The third scenario considers a decrease in ambient temperature. This study modeled the dependence of engine wall temperatures over time for these three operating conditions, enabling control of thermal modes and prediction of ICE performance to enhance the efficiency of the vehicle's hybrid power unit. …”
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Base mathematical models of process of transmission of mechanical energy during drilling of exploraty and geotechnological wells by V. V. Kulikov, M. S. Frolova

“…The base proportional mathematical model of transmission of mechanical energy is characterized by the followings properties: the values of the coefficient of efficiency of the parts of transmission of energy ratios stay unchanging; general coefficient of efficiency of all areas remains unchanging; at the different areas of transmission of energy the spent power changes in different number of times; the areas of transmission of energy mutually influence on each other; the maximum degree of influence is possessed by the last area, the least one is in the first area.…”
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Relative efficiency of government expenditure on secondary education by Beata Gavurova, Kristina Kocisova, Lubomir Belas, Vladimir Krajcik

“…Governments of OECD countries are under pressure to improve public sector performance and at the same time to reduce the government expenditure. In the process of improvement of public sector performance it is necessary ensure the efficiency in the provision of education, at the same time, the countries are required to provide their educational services by minimizing the amount of public money directed to them. …”
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Evaluating QoS in Dynamic Virtual Machine Migration: A Multi-Class Queuing Model for Edge-Cloud Systems by Anna Kushchazli, Kseniia Leonteva, Irina Kochetkova, Abdukodir Khakimov

“…The numerical results show that the blocking probability for the first VM for Policy 1 is 2.1% times lower than for Policy 2 and the same metric for the second VM is 4.7%. …”
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Time deterministic routing algorithm and protocol based on time-varying graph over the space-ground integrated network by Hongyan LI, Tao ZHANG, Jingqian ZHANG, Keyi SHI, Pengcheng ZENG

“…In terms of the difficulties to construct the end-to-end path and improve the utilization of network resources,caused by the time-varying multi-dimensional resources and diverse services over the space-ground integrated networks,the time-varying graph-based time deterministic routing algorithm and protocol for the space-ground integrated network was proposed.Firstly,a time-varying continuous graph model was constructed to describe the spatial and temporal attributes of multi-dimensional resources,such as the topology,link connectivity opportunities,node caching and so on.Then,a service-oriented time deterministic routing algorithm was proposed.According to the calculation rules of link cumulative traffic,and the constraints of node flow conservation and cache,the time-varying path with the shortest transmission delay was constructed.After that,a delay-guaranteed time deterministic routing protocol was designed by combining the proposed routing algorithm with both the segment routing technology and the time sensitive network technology,which supported the dynamic topology discovery,the efficient calculation of deterministic routing and the timing forwarding of data packets on the time-varying network.The simulation results show that,compared with the routing algorithm based on snapshot graph and contact graph,the proposed routing algorithm has higher link resource utilization rate and the packet successful delivery rate by jointly using link and node storage resources by association,and ensures the end-to-end transmission delay of services.…”
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Studying and Incorporating Efficiency into Gastrointestinal Endoscopy Centers by Lukejohn W. Day, David Belson

“…Improving efficiency has been the focus of management in many industries; however, it has not been until recently that incorporating efficiency models into healthcare has occurred. …”
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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

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A Quantified Analysis of Lean First Then Automate for a Synergetic Effect by Sharmin Akter Urmee, Mosharraf Hossain

“…Using ARENA simulation software, four distinct simulation models were created using the data: the current assembly line model, the model with a lean tool (Inline Quality Check), the model with only automation, and the model with lean applied first and automation applied later. …”
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Communicative Specificity of the First National Printed Newspaper “Peter the Great’s Vedomosti” by I. Yu. Kuksa

“…The structural complexity and different functional roles of the newspaper texts creators, as well as the way the author introduces new information, including the emergence of a news note model is emphasized. It reveals the communicative position of the author of the newspaper text in terms of the space-time approach and the means of its representation. …”
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Cluster-based efficient information dissemination in dynamic networks by Zhiwei Yang, Weigang Wu, Yong Li, Yishun Chen

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Remaining Useful Life Prediction Method for Bearings Based on Pruned Exact Linear Time State Segmentation and Time–Frequency Diagram by Xu Wei, Jingjing Fan, Huahua Wang, Lulu Cai

“…As an efficient time-series prediction model, the Informer excels at handling long time series by leveraging a sparse self-attention mechanism to effectively capture the long-term dependencies in the signals. …”
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Computationally Efficient Neural Architecture Search for Image Denoising by Esau Alain Hervert Hernandez, Yan Cao, Nasser Kehtarnavaz

“…In this paper, this challenge is addressed in two ways. First, deep reinforcement learning is leveraged with weight-sharing for optimal architecture search, creating a shared model that enables efficient weight reuse across architectural configurations. …”
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Efficient detection and counting method for maize seedling plots by Feiyun Wang, Hanlu Jiang, Jincan Wu, Fupeng Li, Bo Zhao, Wenhua Mao, Chengxu Lv, Liming Zhou, Qingzhong Xu

“…To efficiently detect and count maize seedlings in complex field conditions, this study first developed a sample dataset under diverse backgrounds and lighting scenarios and introduced a data augmentation technique called “M_AUG.” …”
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Efficient heuristic algorithm for the mobile sink routing problem by YUAN yuan1, PENG Yu-xing1, LI Shan-shan2, TANG Wen-sheng3

“…In large-scale monitoring region,randomly deployed wireless sensor networks may not be fully connected with high probability.Using mobile sink for data collection is one of the feasible solutions.Mobile sink shortest routing prob-lem can be regarded as a special case of TSP with neighborhoods（TSPN） problem,since the neighborhoods are the radio ranges of the sensor nodes,which can be modeled as possibly overlapped disks with diverse sizes.This kind of TSPN problem has no polynomial algorithms so far.To handle it,a novel approximation algorithm was proposed,which first forms a "racetrack" by utilizing the non-intersecting loop property of TSP routes,and then through the inner lane heuris-tic,the bend heuristic and the shortcut searching,the algorithm can find an approximation solution within O（n2） computa-tion time.The formal proofs and the large-scale simulations all verify that our algorithm can achieve a good approxima-tion ratio and can be more efficient than the related algorithms.…”
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Joint modeling of predictors of adverse drug reactions and viral load changes over time among adult patients on ART at Nigist Eleni Mohammed Memorial comprehensive specialized hosp... by Mesfin Menza Jaldo, Awoke Abraham, Bereket Aberham Lajore, Hamdi Fekredin Zakaria, Tagese Yakob

“…Current evidence is limited by methodological constraints, as most prior studies have analyzed these factors separately rather than employing integrated approaches that account for the endogenous nature of time-varying biomarkers like viral load. Joint modelling techniques offer a robust solution by simultaneously analyzing longitudinal and time-to-event data, providing more efficient and precise estimates of this critical association. …”
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Multiscale Convolutional Fusion Network for Efficient Monaural Speech Separation by Rui Yang, Shanliang Pan

“…To enable deployment in resource-limited scenarios, a speech separation system must balance separation performance, computational efficiency, and inference speed. In time-domain approaches, recurrent convolutional neural network (RCNN)-based models employ multiscale convolutional architectures to model the long sequences of encoded mixture signals. …”
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