Characteristics of Runoff Variation of Heishui River Basin in the Upper Reach of Minjiang River in Changing Environment by WANG Ning, WANG Wensheng, YU Siyi

“…This paper systematically analyzed the variation characteristics of runoff in the Heishui River Basin,the first tributary on the right bank upstream of Minjiang River,in a changing environment.The variation characteristics of runoff series were studied by using an improved heuristic segmentation algorithm,Mann-Kendall trend test,R/S analysis and wavelet analysis with the runoff data of Shaba hydrological station from 1960 to 2015.The results show the followings:① The abrupt change of annual runoff occurred in 1995.② The annual runoff showed a significant decrease trend with a decreasing rate of 0.32 m³/(s·a) in the past 56 years and may keep the decrease trend in the future.During 1960—1995,the annual runoff showed an insignificant decrease trend with a decreasing rate of 0.12 m³/(s·a),and the period of 1996—2015 witnessed an insignificant increase trend with an increasing rate of 0.80 m³/(s·a).③ Under the influence of environmental changes,the average annual runoff before the change point was larger than that after the point,whereas the inter-annual variation was smaller before the abrupt change.The intra-annual distribution form showed a double-peak curve before 1995 and a single-peak curve after 1995.The intra-annual variation range of runoff was significantly different before and after the change point,which could reduce flood control pressure in the basin but was not conducive to the utilization of water resources in non-flood seasons.④Annual runoff had multi-time-scale variation characteristics.During 1960—2015,the annual runoff had noticeable periods of 6 years,16 years and 28 years.Periods of 7 years and 16 years were found for 1960—1995 and 2 years and 5 years for 1996—2015.…”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…<italic>Methods:</italic> BucketAugment leverages principles from the Q-learning algorithm and employs validation loss to search for an optimal policy within a search space comprised of distributed stacks of 3D volumetric augmentations, termed &#x2018;buckets.…”
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Analyses of poverty indicators using PPI methodology by T. Merkulova, D. Kosiashvili

“…Developing the model was carried out using machine learning methods in several steps: 1) data processing and statistical analyses; 2) selection of significant indicators by the classification model; 3) clustering by k-mean algorithm; 4) hierarchical clustering; 5) comparing outcomes of modeling and interpretation of results. …”
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An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network by Zhijian Huang, Bowen Sui, Jiayi Wen, Guohe Jiang

“…Third, a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60% anchors. Last, the activation function is verified and optimized. …”
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Remdesivir-induced severe hypoglycemia in an elderly man without diabetes: a case report by Yasunori Nagano, Hisae Aoki, Juri David, Naoko Iwahashi Kondo, Makimi Aizawa, Toshiyuki Sumita, Yuki Hamada, Yuki Yamamoto, Kaoru Yamada

“…After discontinuation, his BG levels normalized. The Naranjo algorithm, a tool for evaluating the causality of adverse drug reactions, classified the event as “Probable” (6 points). …”
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Research Progress on High-Intermediate Frequency Extension Methods of SEA by Jintao Su, Ling Zheng, Zhaoxiang Deng, Yuhan Jiang

“…Neutron structure assembly and modeling, interval and mixed interval analysis, interval variable and mixed interval variable response are also described, so as to justify the development of a hybrid, large-scale interval algorithm. Finally, the engineering application of the above method is introduced, the limitations and shortcomings of SEA and intermediate frequency extension methods are reviewed, and unsolved problems are further discussed.…”
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Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud by Yurui Li, Danhui Dan, Ruiyang Pan

“…Additionally, the Random Sample Consensus (RANSAC) algorithm, the ordinary least squares, and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable. …”
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EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. …”
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…A multi-domain adaptation algorithm trains the MoE model for depression recognition. …”
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Real‐Time Detection, Location, and Measurement of Geoeffective Stellar Flares From Global Navigation Satellite System Data: New Technique and Case Studies by Manuel Hernández‐Pajares, David Moreno‐Borràs

“…Indeed, we will show that, unlike GSFLAI for solar flares, the new algorithm presented here (Blind GNSS search of Extraterrestrial EUV Sources [BGEES]) is able to detect EUV flares without the previous knowledge of the position of the source, which is also simultaneously estimated, providing an additional quality check of the detection. …”
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Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
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DIGITAL TRANSFORMATION AND THE USE OF ARTIFICIAL INTELLIGENCE IN SOCIAL INSURANCE AND INSURANCE by Elizaveta Pletneva, Daria Zagorska

“…The downsides may include data confidentiality issues, the exclusion of the human factor, the risk of algorithm bias, implementation complexity and high costs, and the threat of unemployment.…”
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A novel Complex q-rung orthopair fuzzy Yager aggregation operators and their applications in environmental engineering by Jabbar Ahmmad, Tahir Mahmood, Dragan Pamucar, Hafiz Muhammad Waqas

“…Furthermore, we have utilized this device algorithm in the selection of a temperature control system. …”
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Uncertainty analysis in river quality management considering failure probability: controllable and uncontrollable input pollutants by Mohsen Dehghani Darmian, Britta Schmalz

“…Using the Genetic Programming (GP) algorithm, a new accurate equation for assimilation capacity calculation is presented considering Pffor the first time. …”
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Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis by Weipeng Li, Yan Cao, Lijuan Li, Siyu Hou

“…The Gaussian mixture model (GMM) is an unsupervised clustering machine learning algorithm. This procedure involves the combination of multiple probability distributions to describe different sample spaces. …”
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Robotic constructor as a means of teaching C++ programming to high school students by Anton V. Eliseev

“…The control group was taught the topic “Linear Algorithm” in the traditional presentation of the textbook by K.Yu. …”
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Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion by Changle Song, Julien Monteil, Jean-Luc Ygnace, David Rey

“…We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. …”
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Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes by Chanwoo Park, Nan Jiang, Taesung Park

“…We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. …”
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Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall by M. R. Mustafa, R. B. Rezaur, H. Rahardjo, M. H. Isa, A. Arif

“…A multilayer perceptron neural network model was constructed using Levenberg-Marquardt training algorithm for prediction of soil pore-water pressure variations. …”
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Small world of the miRNA science drives its publication dynamics by A. B. Firsov, I. I. Titov

“…To tackle the problem of the institution name writing variability, we proposed the k-mer/n-gram boolean feature vector sorting algorithm, KOFER in short. This approach utilizes the fact that the contents of the affiliation are rather consistent for the same organization, and to account for writing errors and other organization name variations within the affiliation metadata field, it converts the organization mention within the affiliation to the K-Mer (n-gram) Boolean presence vector. …”
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