bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application
Authors: Akhter Jamila , Naseer Noman , Nazeer Hammad , Khan Haroon , Mirtaheri Peyman
Published: May 2024

Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.
3D VSP Imaging Using DAS Recording of P- and S-Waves in Vertical and Lateral Well Sections in West Texas
Authors: Wang Yin-Kai , Stewart Robert R.
Published: May 2024

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P–P reflection and P–S volumes derived from the well’s vertical section. The Wolfcamp formation can be seen in two 3D volumes (P–P and P–S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north–south direction and 1500 ft (460 m) in the west–east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) –8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes.
3D Indoor Position Estimation Based on a UDU Factorization Extended Kalman Filter Structure Using Beacon Distance and Inertial Measurement Unit Data
Authors: Bodrumlu Tolga , Caliskan Fikret
Published: May 2024

The development of the GPS (Global Positioning System) and related advances have made it possible to conceive of an outdoor positioning system with great accuracy; however, for indoor positioning, more efficient, reliable, and cost-effective technology is required. There are a variety of techniques utilized for indoor positioning, such as those that are Wi-Fi, Bluetooth, infrared, ultrasound, magnetic, and visual-marker-based. This work aims to design an accurate position estimation algorithm by combining raw distance data from ultrasonic sensors (Marvelmind Beacon) and acceleration data from an inertial measurement unit (IMU), utilizing the extended Kalman filter (EKF) with UDU factorization (expressed as the product of a triangular, a diagonal, and the transpose of the triangular matrix) approach. Initially, a position estimate is calculated through the use of a recursive least squares (RLS) method with a trilateration algorithm, utilizing raw distance data. This solution is then combined with acceleration data collected from the Marvelmind sensor, resulting in a position solution akin to that of the GPS. The data were initially collected via the ROS (Robot Operating System) platform and then via the Pixhawk development card, with tests conducted using a combination of four fixed and one moving Marvelmind sensors, as well as three fixed and one moving sensors. The designed algorithm is found to produce accurate results for position estimation, and is subsequently implemented on an embedded development card (Pixhawk). The tests showed that the designed algorithm gives accurate results with centimeter precision. Furthermore, test results have shown that the UDU-EKF structure integrated into the embedded system is faster than the classical EKF.
Depth Control of an Underwater Sensor Platform: Comparison between Variable Buoyancy and Propeller Actuated Devices
Authors: Carneiro João Falcão , Pinto João Bravo , de Almeida Fernando Gomes , Cruz Nuno A.
Published: May 2024

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications.
Hadamard Error-Correcting Codes and Their Application in Digital Watermarking
Authors: Windisch Michael , Wassermann Jakob , Leba Monica , Stoicuta Olimpiu
Published: May 2024

In communication technologies such as digital watermarking, wireless sensor networks (WSNs), and visual light communication (VLC), error-correcting codes are crucial. The Enhanced Hadamard Error-Correcting Code (EHC), which is based on 2D Hadamard Basis Images, is a novel error correction technique that is presented in this study. This technique is used to evaluate the effectiveness of the video watermarking scheme. Even with highly sophisticated embedding techniques, watermarks usually fail to resist such comprehensive attacks because of the extraordinarily high compression rate of approximately 1:200 that is frequently employed in video dissemination. It can only be used in conjunction with a sufficient error-correcting coding method. This study compares the efficacy of the well-known Reed–Solomon Code with this novel technique, the Enhanced Hadamard Error-Correcting Code (EHC), in maintaining watermarks in embedded videos. The main idea behind this newly created multidimensional Enhanced Hadamard Error-Correcting Code is to use a 1D Hadamard decoding approach on the 2D base pictures after they have been transformed into a collection of one-dimensional rows. Following that, the image is rebuilt, allowing for a more effective 2D decoding procedure. Using this technique, it is possible to exceed the theoretical error-correcting capacity threshold of ⌊dmin−12⌋ bits, where dmin is the Hamming distance. It may be possible to achieve better results by converting the 2D EHC into a 3D format. The new Enhanced Hadamard Code is used in a video watermarking coding scheme to show its viability and efficacy. The original video is broken down using a multi-level interframe wavelet transform during the video watermarking embedding process. Low-pass filtering is applied to the video stream in order to extract a certain frequency range. The watermark is subsequently incorporated using this filtered section. Either the Reed–Solomon Correcting Code or the Enhanced Hadamard Code is used to protect the watermarks. The experimental results show that EHC far outperforms the RS Code and is very resilient against severe MPEG compression.
Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems
Authors: Osmani Khaled , Schulz Detlef
Published: May 2024

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.
Automatic Localization of Soybean Seedlings Based on Crop Signaling and Multi-View Imaging
Authors: Jiang Bo , Zhang He-Yi , Su Wen-Hao
Published: May 2024

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.
Electrical Sensor Calibration by Fuzzy Clustering with Mandatory Constraint
Authors: Yue Shihong , Fu Keyi , Liu Liping , Zhao Yuwei
Published: May 2024

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.
A Novel Lightweight Model for Underwater Image Enhancement
Authors: Liu Botao , Yang Yimin , Zhao Ming , Hu Min
Published: May 2024

Underwater images suffer from low contrast and color distortion. In order to improve the quality of underwater images and reduce storage and computational resources, this paper proposes a lightweight model Rep-UWnet to enhance underwater images. The model consists of a fully connected convolutional network and three densely connected RepConv blocks in series, with the input images connected to the output of each block with a Skip connection. First, the original underwater image is subjected to feature extraction by the SimSPPF module and is processed through feature summation with the original one to be produced as the input image. Then, the first convolutional layer with a kernel size of 3 × 3, generates 64 feature maps, and the multi-scale hybrid convolutional attention module enhances the useful features by reweighting the features of different channels. Second, three RepConv blocks are connected to reduce the number of parameters in extracting features and increase the test speed. Finally, a convolutional layer with 3 kernels generates enhanced underwater images. Our method reduces the number of parameters from 2.7 M to 0.45 M (around 83% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our Rep-UWnet effectively improves high-level vision tasks like edge detection and single image depth estimation. This method not only surpasses the contrast method in objective quality, but also significantly improves the contrast, colorimetry, and clarity of underwater images in subjective quality.
Full-Scale Modeling and FBGs Experimental Measurements for Thermal Analysis of Converter Transformer
Authors: Yang Fan , Gao Sance , Wang Gepeng , Hao Hanxue , Wang Pengbo
Published: May 2024

As the imbalance between power demand and load capacity in electrical systems becomes increasingly severe, investigating the temperature variations in transformers under different load stresses is crucial for ensuring their safe operation. The thermal analysis of converter transformers poses challenges due to the complexity of model construction. This paper develops a full-scale model of a converter transformer using a multi-core high-performance computer and explores its thermal state at 80%, 100%, and 120% loading ratios using the COUPLED iteration method. Additionally, to validate the simulation model, 24 FBGs are installed in the experimental transformer to record the temperature data. The results indicate a general upward trend in winding the temperature from bottom to top. However, an internal temperature rise followed by a decrease is observed within certain sections. Moreover, as the loading ratio increases, both the peak temperature and temperature differential of the transformer windings rise, reaching a peak temperature of 107.9 °C at a 120% loading ratio. The maximum discrepancy between the simulation and experimental results does not exceed 3.5%, providing effective guidance for the transformer design and operational maintenance.
Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection
Authors: Zhang Qianqian , Zhou Li , An Junshe
Published: May 2024

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.
Multi-Task Scenario Encrypted Traffic Classification and Parameter Analysis
Authors: Wang Guanyu , Gu Yijun
Published: May 2024

The widespread use of encrypted traffic poses challenges to network management and network security. Traditional machine learning-based methods for encrypted traffic classification no longer meet the demands of management and security. The application of deep learning technology in encrypted traffic classification significantly improves the accuracy of models. This study focuses primarily on encrypted traffic classification in the fields of network analysis and network security. To address the shortcomings of existing deep learning-based encrypted traffic classification methods in terms of computational memory consumption and interpretability, we introduce a Parameter-Efficient Fine-Tuning method for efficiently tuning the parameters of an encrypted traffic classification model. Experimentation is conducted on various classification scenarios, including Tor traffic service classification and malicious traffic classification, using multiple public datasets. Fair comparisons are made with state-of-the-art deep learning model architectures. The results indicate that the proposed method significantly reduces the scale of fine-tuning parameters and computational resource usage while achieving performance comparable to that of the existing best models. Furthermore, we interpret the learning mechanism of encrypted traffic representation in the pre-training model by analyzing the parameters and structure of the model. This comparison validates the hypothesis that the model exhibits hierarchical structure, clear organization, and distinct features.
Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics
Authors: Pan Zebang , Wen Guilin , Yin Hanfeng , Yin Shan , Tan Zhao
Published: May 2024

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model’s accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.
Laser-Based Mobile Visible Light Communication System
Authors: Hou Yuqi , Wang Zhichong , Li Zengxin , Hu Junhui , Ma Chicheng , Wang Xiaoqian , Xia Liang , Liu Guangyi , Shi Jianyang , Li Ziwei , Zhang Junwen , Chi Nan , Shen Chao
Published: May 2024

Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research.
Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information Management
Authors: Togo Taro , Togo Ren , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: May 2024

The advancements in deep learning have significantly enhanced the capability of image generation models to produce images aligned with human intentions. However, training and adapting these models to new data and tasks remain challenging because of their complexity and the risk of catastrophic forgetting. This study proposes a method for addressing these challenges involving the application of class-replacement techniques within a continual learning framework. This method utilizes selective amnesia (SA) to efficiently replace existing classes with new ones while retaining crucial information. This approach improves the model’s adaptability to evolving data environments while preventing the loss of past information. We conducted a detailed evaluation of class-replacement techniques, examining their impact on the “class incremental learning” performance of models and exploring their applicability in various scenarios. The experimental results demonstrated that our proposed method could enhance the learning efficiency and long-term performance of image generation models. This study broadens the application scope of image generation technology and supports the continual improvement and adaptability of corresponding models.
Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification
Authors: Omri Asma , Benothman Noureddine , Sayahi Sofiane , Tlili Fethi , Chaabane Ferdaous , Besbes Hichem
Published: May 2024

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.
An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System
Authors: Chi Tsun-Kuang , Chen Tsung-Yi , Lin Yu-Chen , Lin Ting-Lan , Zhang Jun-Ting , Lu Cheng-Lin , Chen Shih-Lun , Li Kuo-Chen , Abu Patricia Angela R.
Published: May 2024

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.
Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol
Authors: Igual Carles , Igual Jorge
Published: May 2024

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left–right, up–down and open–close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.
Remote Measurement of Tide and Surge Using a Deep Learning System with Surveillance Camera Images
Authors: Sabato Gaetano , Scardino Giovanni , Kushabaha Alok , Casagrande Giulia , Chirivì Marco , Fontolan Giorgio , Fracaros Saverio , Luparelli Antonio , Spadotto Sebastian , Scicchitano Giovanni
Published: May 2024

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.
An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques
Authors: Nunkhaw Maiyatat , Miyamoto Hitoshi
Published: May 2024

Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response to this issue of river-floating waste, our present research aimed to develop an automated waste measurement method tailored for real rivers. To achieve this, we considered three scenarios: clear visibility, partially submerged waste, and collective mass. We proposed the use of object detection and tracking techniques based on deep learning architectures, specifically the You Only Look Once (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT). The types of waste classified in this research included cans, cartons, plastic bottles, foams, glasses, papers, and plastics in laboratory flume experiments. Our results demonstrated that the refined YOLOv5, when applied to river-floating waste images, achieved high classification accuracy, with 88% or more for the mean average precision. The floating waste tracking using DeepSORT also attained F1 scores high enough for accurate waste counting. Furthermore, we evaluated the proposed method across the three different scenarios, each achieving an 80% accuracy rate, suggesting its potential applicability in real river environments. These results strongly support the effectiveness of our proposed method, leveraging the two deep learning architectures for detecting and tracking river-floating waste with high accuracy.
Modelling of a Lake Outburst as a Result of the Development of Piping
Authors: Pryakhina Galina , Rasputina Valeriia , Svirepov Stepan
Published: May 2024

The retreat of mountain glaciers inevitably leads to an increase in the number of outburst moraine lakes. One of the possible mechanisms of moraine dam outburst along with overflow over the crest is the formation of a filtration channel in the body of the moraine dam (piping). An algorithm for calculating the outburst flood hydrograph, describing the development of a filtration channel in the body of a moraine dam and the subsequent formation of water overflow when the soil above the channel collapses, is proposed in this paper. Verification of proposed methodology was carried out on the basis of experimental data and published data of real outbursts. Satisfactory results verifying this methodology made it possible to use the proposed methodology for the calculation of the hydrograph of the outburst of Lake Bashkara in the Elbrus region, which occurred on 1 September 2017. It is shown that the simulation results are quantitatively comparable with the estimates obtained from field data: the time of water discharge through the channel was 16 min, the period of the outburst wave passage was 40 min, and the maximum discharge was 636 m3/s. Thus, the possibility of applying the proposed methodology for calculating the destruction of natural moraine dams has been demonstrated.
Using Methods to Assess the Structure of Water and Water-Containing Systems to Improve the Properties of Living and Non-Living Systems
Authors: Sidorenko Galina , Brilly Mitja , Laptev Boris , Gorlenko Nikolay , Antoshkin Leonid , Vidmar Andrej , Kryžanowski Andrej
Published: May 2024

This paper explores how water and water-based systems change their structure under different conditions, such as pH, temperature, and electric fields. These changes affect the properties and performance of living and non-living systems that use water or water-based systems in various technologies. We can use pH, temperature, and electric fields to measure and control the structural changes in water and water-based systems and improve the outcomes of different technologies in biology and medicine. More research is needed to understand how various factors influence the structure of water and water-based systems and how this affects living and non-living systems.
Antimicrobial therapy: assessing physicians’ basic knowledge. KANT-I/II project
Authors:   FSBEI HE Mari State University, Yoshkar-Ola, Republic of Mari El, Bontsevich R. A. ,   FSAEI HE Belgorod National Research University,   Kazan State Medical Academy — Branch of the FSBEI CPE Russian Medical Academy of Continuous Professional Education, Zavitkevich G. I. ,   FSBEI HE Mari State University, Yoshkar-Ola, Republic of Mari El;, Eliseeva E. V. ,   FSBEI HE Pacific State Medical University,  Vladivostok, Feoktistova Yu. V. ,   FSBEI HE Pacific State Medical University, Vladivostok, Kompaniets O. G. ,   FSBEI HE Kuban State Medical University, Krasnodar, Ketova G. G. ,   6FSBEI HE South Ural State Medical University,Chelyabinsk, Maksimov M. L. ,   Kazan State Medical Academy — Branch of the FSBEI CPE Russian Medical Academy of Continuous Professional Education; FSAEI HE Pirogov Russian National Research Medical University of the Ministry of Health of Russia, Moscow\
Published: May 2024

This paper presents an assessment of health professionals' knowledge of antimicrobial stewardship within the first phases of the multicenter KANT project.
Identification of goods using the global trade item number for customs purposes
Authors:   Russian Customs Academy, Lyubertsy, Kondrashova E. S. , Krasilnikova E. V. ,   Russian Customs Academy, Lyubertsy, Popova A. A. ,   Russian Customs Academy, Lyubertsy
Published: May 2024

The article discusses one of the goods coding types, i.e. the Global Trade Item Number (GTIN), and its use for identification for customs purposes. A study was conducted to identify product samples using the GTIN. Based on the study, conclusions about the efficiency of the Global Trade Item Number for identification for customs purposes were drawn.
Impact of product quality on financial results on the case of AO GK "AZOT"
Authors:   Kemerovo State University, Kemerovo, Abdullaeva T. Z. , Mazhaeva D. A. ,   Kemerovo State University, Kemerovo
Published: May 2024

This article examines the features of the formation of financial results taking into account the quality of the produced goods and characterizes the factors that influence the level of quality of the goods produced by a business entity
La función consular en las relaciones entre españa y los países iberoamericanos
Authors: Martí Xavier
Published: May 2024

La función consular descansa sobre un complejo andamiaje normativo en el que sobresa-len los tratados internacionales, que resultan esenciales tanto para la fijación del derecho consular como para ordenar la colaboración entre Estados. España, por sus profundas relaciones con América Latina, ha acabado tejiendo con el paso del tiempo una tupida red de tratados y memorandos de contenido consular con los países de la región. Este análisis hace un repaso de la actividad convencional en el ámbito consular entre España y los paí-ses iberoamericanos, sistematiza los tratados y las principales áreas de colaboración exis-tentes en estos momentos y aborda, por último, los ámbitos en los que existe mayor mar-gen para estrechar los niveles de cooperación consular.
Menguatkan Narasi Kanibalisme (Anthropofagi) dalam Analisis Hyperrealitas Baudrillard pada Cagar Budaya Batu Persidangan di Kecamatan Simanindo
Authors: Puspitawati Puspitawati , Andriansyah Dedi , Rumapea Murni Eva Marlina , Nainggolan Erlin , Br Bangun Karina Ita Apulina
Published: May 2024

This research was conducted to describe the cannibalism storytelling of the trial stone legend and describe the strategy of the local community in building anthropophagic images of the trial stone legend in Simanindo District. This research was carried out using qualitative research methods with an ethnographic approach. Methods of data collection were carried out through literature studies, observation, and in-depth interviews. There are three narrative versions related to the legend of the trial stones, and the body parts consumed in the rituals carried out at Huta Siallagan. The three narrative versions in question are the community version, the local guide version, and the performance version. Reports related to cannibalism have previously appeared in various historical writings in Sumatra. The story of cannibalism in Huta Siallagan, Simanindo District, is still maintained and is still used as tourism support at this location. The hyperreality of cannibal stories that seem to really happen is strengthened by simulacra patterns (1) narrative through local guides, (2) legend performances trial stone, (3) animism geopark information board, and (4) generalization of the legend of trial stone legend. The stone legend of the trial and the story "Batak people eat people" are not only limited to influencing the economic aspects of society but also have various important values that can be used as a reference by today's society. The values in question are juridical values, political values , and social values.
Psychological Well-Being Ditinjau dari Dukungan Sosial Pada Remaja di Pinggir Rel Kelurahan Tanjung Gusta Medan
Authors: Hartini Sri , Tanadi Kelvin , Yakub Yakub , Utami Valerie , Wastuti Sri Ngayomi Yudha
Published: May 2024

Psychological well-being is a condition in which one feels psychologically well, by being able to seize self-acceptance, mastery of environment, appreciation and positive meaning of life. One’s psychological well-being degree, focusing on adolescents, can be affected due to several factors, one of which is social supports they received. This study aimed to discover the relationship between social support and psychological well-being of adolescents. Correlational quantitative was used in this study, with sampled by purposive technique, namely adolescents living at railroad tracks at Tanjung Gusta Medan with age range between 13 to 18 years. Data collection tool is used by including both psychological well-being and social support scale which were arranged in the form of Likert scale. Collected data were analyzed by Pearson Product Moment Correlation, using SPSS software version 26.0 for windows. The results of this study showed an effective contribution at 54,7 percent and correlation coefficient of 0.739. In another word, both variables are positively related. High social support will increase the one’s psychological well-being degree, whereas low social support will hinder their psychological well-being degree.
Analisis Usahatani Nanas (Ananas Comosus) Pada Lahan Gambut Di Kecamatan Panai Tengah (Studi Kasus : Desa Pasar Tiga)
Authors: Manurung Hildayani , Wahyuni Sri
Published: May 2024

Tujuan penelitian ini adalah untuk menganalisis dan mengetahui besar pendapatan usahatani nanas dan untuk mengetahui prospek usahatani nanas pada lahan gambut di Desa Pasar Tiga Kecamatan Panai Tengah.Adapun lokasi penelitian ini dilaksanakan di Desa Pasar Tiga Kecamatan Panai Tengah Kabupaten Labuhanbatu. Populasi dalam penelitian ini adalah seluruh petani nanas yang ada di Desa Pasar Tiga. Teknik pengambilan sampel dalam penelitian ini menggunakan accidental sampling dalam penelitian ini diambil sampel sebanyak 28 orang petani. Data yang digunakan dalam penelitian ini adalah data primer dan data sekunder. Teknik analisis data yang digunakan dengan menghitung biaya produksi, penerimaan, pendapatan,dan menggunakan nilai R/C Ratio untuk mengetahui prospek usahatani nanas.Hasil penelitian ini menunjukkan bahwa rata-rata total biaya produksi sebesar Rp. 6.179.714, rata-rata total penerimaan yang diperoleh adalah Rp. 11.318.571, dan rata-rata pendapatan bersih yang diperoleh adalah sebesar Rp. 5.138.857 permusim panen/tahun. Nilai rata-rata R/C Ratio menunjukkan hasil 1,8 dapat diartikan bahwa usahatani nanas memiliki prospek yang menguntungkan untuk dilakukan karena nilai R/C > 1 = Layak.
Implikasi Foreign Direct Investment Jepang melalui Toyota terhadap Perekonomian Indonesia Periode 2016-2022
Authors: Hardzi Aldella Alfia , Sugito Sugito
Published: May 2024

This article focuses on cooperation between Japan and Indonesia through the development of a Toyota subsidiary in Indonesia as an instrument for Japanese Foreign Direct Investment. Japan is a country that has quite an important role in the international world because of its development in the economic sector, where currently Japan can dominate the international market through advances in automotive technology. The rapid development of the automotive industry has encouraged Japan to invest in Indonesia. This article aims to find out what the results of Indonesia-Japan FDI cooperation are through the development of Toyota in Indonesia in 2016-2022. In order to approach this problem, references to the theories of liberalism, bilateral cooperation and Foreign Direct Investment are used. Data was collected through secondary data as a data source, as well as using desk review and analyzed qualitatively. This study concludes that Japanese FDI through the Toyota company has enabled Indonesia to improve its economy through a high increase in the value of exports to several countries, expanding employment opportunities, as well as providing a positive impact on improving environmentally friendly technology.
The Controversy of PBESI vs MPL Indonesia on Social Media: Crisis Communication Insights
Authors: Vigata Krisdian Majid Bella , Yunazar Flavia Maharani
Published: May 2024

This article aims to investigate the emerging issues in the regulation of online gaming in Indonesia. Specifically, it focuses on the regulatory violation claim case that has been filed by MPL Indonesia against the regulations issued by PBESI, which governs the Implementation of Esports Activities in Indonesia based on PBESI Regulation No: 034/PB-ESI/B/VI/2021. To approach this issue, we adopt the theoretical framework of crisis communication. The data we use in this research is collected through qualitative analysis methods, specifically Qualitative Content Analysis (QCA). Our data sources come from various scientific references and related literature. The results of our analysis show that the regulatory violation claim filed by MPL Indonesia cannot be upheld based on existing empirical evidence. Therefore, we argue that it is necessary to review the claim and resolve it based on crisis communication principles that emphasize the importance of clear messaging, consistency, and transparency. Thus, it is hoped that it can avoid protracted polemics and harm the Mobile Legends Community: Bangbang Community and the esports industry as a whole.
Penentuan Sistem Pusat Pelayanan Perkotaan Berdasarkan Data Point of Interest di Kota Pangkalpinang
Authors: Fitriansyah Hadi , Zulkia Dwi Rizka
Published: May 2024

An interconnected city service system exists in urban areas. A good urban space structure should be able to encourage sustainable development for the people and the countryside, as a process of changing the use of dynamic urban space. The high service requirements of the central area of Pangkalpinang City are not offset by the availability of adequate urban facilities. The purpose of this study is to describe the service center system in Pangkalpinang City. The method used in this research with kernel density analysis uses POI (Point of Interest) data related to social, cultural, economic, and/or public administration. Analysis results showed that the Padi Market Area is still a potential service center in Pangkalpinang City, with the main character of trade and services. This is in line with the 2011-2030 RTRW of Pangkalpinan City that makes the Pedi Market area the service center of Pangcalpinang Town. Several areas have the potential to develop functions and roles as a City Service Subcenter. The results of this analysis are expected to provide recommendations to policymakers in drawing up the structure of urban spaces in urban spatial documents.