Development of adsorbents for selenium and arsenic, which are particularly toxic, is urgently needed. arsenic was prepared for adsorption and removal. The experimental data of adsorption isotherms of As and Se oxyanions by NiZn adsorbents were well fitted to the Langmuir adsorption model, which is characteristic of monolayer adsorption, with a coefficient of determination of R2>0.94. The maximum adsorption (qmax) of Se(IV) by NiZn-Cl and NiZn-NO3 was similar to that of NiZn-AcO, and no difference in adsorption due to interlayer ions was indicated. On the other hand, NiZn-Cl had the highest qmax for Se(VI) and As(V). qmax for As(V) by NiZn-Cl was about twice that of NiZn-AcO, As(V) and Se(VI) adsorption performance was greatly improved.

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The challenge of coastal litter accumulation led to the creation of a detection system powered by deep learning, aimed at identifying microplastics. The system harnessed the yolov7 [1] deep learning architecture, known for its proficiency in real-time object detection, and integrated the SAHI (Slicing Aided Hyper Inference) [2] vision library to augment its capabilities. Within the scope of our study, we conducted four separate evaluations using two versions of yolov7—the base model and the advanced yolov7-e6e—alongside SAHI. The performance of each setup was measured against a set of metrics, including Intersection over Union (IoU), Precision, Recall, F-measure, and Detection Time, recorded in seconds. The dataset for the study was composed of images sourced from real-world beach clean-up sites, including Hokuto Mizukumi Park. The detection algorithm was subjected to 700 rounds of training, with an initial learning rate of 0.001. Our findings indicated that the system was adept at identifying relatively small microplastics.

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In recent times, the escalation of marine debris has emerged as a formidable challenge, particularly in its retrieval. Among this, coastal debris, which falls under the broader category of marine debris, can indeed be manually picked up, yet the diversity in its forms and sizes limits the efficiency of solely human retrieval efforts. In response to this issue, my research concentrated on the development of an autonomous mobile robot. This study zeroes in on the critical task of self-localization, a fundamental requirement for autonomous navigation. Within our simulation, we meticulously recreated a real-world coastal cleanup site and assessed the self-localization precision utilizing an Extended Kalman Filter (EKF) alongside various sensors.

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In recent years, three-dimensional computer graphics (3DCG) technology has been applied in various fields. One problem with this technique is the problem of contact between clothing and other objects (such as the body). In this paper, we verified simple animations concerning of hula costumes using three calculation methods, and found that we were able to create simple and stable animations using the FB Euler method. This paper reports that we were able to create a simple animation.

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An MCU(microcontroller unit)-based edge computing system for assessing the height of liquids in containers in this paper. This system uses a solenoid valve to strike the bottle and a microphone to capture sound waves. Signals are transformed from the time domain to the frequency domain by FFT (Fast Fourier Transform) and used to predict the water level by using AI (artificial intelligence) model. A dynamic label smoothing method enhances label correlation, and an ANN (Artificial Neural Network) model is employed on the MCU for classification. The system accurately predicts water levels from 200 to 250 milliliters at 1-milliliter intervals. Hyperparameter optimization balances accuracy with MCU memory and computational constraints. Experimental results show that the system achieves an accuracy of 81% under the limits of edge computing, verifying its effectiveness in liquid level measurement applications.

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With the development of AI, AI may substitute for physicians in radiological interpretations in the future. The purpose of this paper is to identify factors that could form the foundation for algorithms enabling AI to explore pathological findings. This study focuses on the "angles of gaze trajectory" and "average speeds of gaze movement," and conducts a comparative analysis between entry-level and experienced radiographers. Firstly, based on the results of angles of gaze movement, experienced radiographers exhibited frequent movements around the 180-degree mark compared to entry-level radiographer. Both entry-level and experienced radiographers commonly displayed frequent movements around 0 degrees and near 350 degrees. Secondly, according to the results of average speeds of gaze movement, entry-level radiographers were found to have faster movement speeds compared to experienced radiographers.

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Currently, the application of SLAM systems in the field of navigation is quite active. However, the detection accuracy of classical SLAM systems when applied in dynamic environments is not high. This is because some dynamic objects in dynamic environments may occlude the environmental features that should have been extracted by the SLAM system. In order to solve this problem, my research conducts analysis on the detection process in dynamic environments and designs a solution: to identify and remove dynamic objects in dynamic environments to improve detection accuracy. In this research, an object detection algorithm YOLOv5 is first used to detect, identify, and remove dynamic objects in the environment extracted by the SLAM system. Then, the remaining static environmental features are passed into visual SLAM for conventional SLAM environment calculation and localization work. Finally, the modified integrated algorithm is validated and analyzed on the TUM dataset for feasibility. The results indicate that this approach successfully removes dynamic objects and effectively improves the robustness of visual SLAM applications in dynamic environments.

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“Automatic narrative generation game” that we have been studying is a concept of game system for generating narratives through a game process. As this paper presents a game mechanism based on the concept of automatic narrative generation game, this research corresponds to an extension to a direction of the automatic narrative generation game. In particular, the proposed game uses ais the ongoing Russian invasion of Ukraine (beginning in February 2022) as the object. Moreover, our study of this kind of game refer to “tabletop role-playing game” or “table-talk role-playing game” (TRPG). TRPG is a type of analog game in which participants advance a shared storyline through dialog mainly. The proposed game system provides the knowledge of disinformation to the game user by simulating of the spread of disinformation. Through the game play, the game user can acquire knowledge for countering disinformation. We implemented two types of stories based on the timeline data of the Russian invasion of Ukraine. The first is a type of story in which Ukraine resists Russia and the second is a story of Ukraine’s collapse.

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It is difficult for mobile application developers to understand the structure of large and complex mobile applications. To support iOS application development, we proposed SwiftDiagram: a visualization of the static structure of Swift source code, and demonstrated its usefulness. To further support them, this paper has implemented RAGESS(Real-time Automatic Generation of SwiftDiagram System), which is a software visualization tool. RAGESS performs static analysis on Swift source code and automatically generates the corresponding SwiftDiagram whenever the target project build succeeds.

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The conceptual foundations of transdisciplinary researches with the use of methods and means of artificial intelligence, including ontological engineering, have been developed. The stages of formation of the theory of transdisciplinarity are defined, in which the processes of building the categorical level of concepts and integration of subject knowledge, the formation of clusters of convergence of subject disciplines, the scientific picture of the world and the corresponding global network of transdisciplinary knowledge take a special place. In this case, a special role belongs to informatics as a system-forming branch of knowledge. The development of the NBIC-cluster of convergence opens wide, so far completely not assessed, possibilities of a global knowledge-oriented Internet, but with it also the whole of modern civilization. Obviously, this development will follow first the path of creation of the applied distributed systems in specific subject areas (Internet of Things, Smart systems in telemedicine, environmental monitoring, information support of goods and services, energy systems, utilities, etc.). Grid-, Block-chain-technologies and Cloud-computing, as well as virtual organizations, structures and services will occupy the central place in them.

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At present, manual logistics handling is inefficient and limited, Manual handling Most of the command and dispatch with experience, it is difficult to carry tasks in real time, confirm the location of the handling, cargo inventory and other work tasks time-consuming and laborious, manual operation is prone to error, workers labor intensity, resulting in greatly reduced work efficiency. This paper presents a logistics handling robot based on AT89C52 single chip microcomputer. The integration of the power module, sensor, and drive motor module enables automatic obstacle avoidance and information collection. Integrating an ultrasonic obstacle avoidance module alongside an infrared tracking module significantly boosts obstacle evasion and pathfinding capabilities, adeptly adapting to various work environments. Handling robot arm structure reference Yaskawa MPL manipulator, the Yaskawa MPL manipulator is highly suitable for high-speed and high-precision palletizing, picking, packaging, and other industries.

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The rapid expansion of E-learning environments has highlighted the critical issue of cyberbullying within digital classrooms. This study introduces a novel approach for early detection of cyberbullying by analyzing student engagement and emotional states in real time. Our SER-YOLO model fuses an advanced You Only Look Once version 5 (YOLOv5) with a Student Emotion Recognition system, enriched by sophisticated methodological improvements. It features Soft NMS to refine the Non-Maximum Suppression (NMS) process, embeds the Channel Attention (CA) module to augment the network's backbone, and employs Enhanced Intersection over Union (EIOU) for bounding box regression. This method proactively detects changes in student engagement and emotional states, providing an effective mechanism for the early detection and management of cyberbullying in E-learning environments.

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In this paper, we propose ASLA, a segmentation and label generation system for document pictures. ASLA reduces the duration needed for separating document pictures into areas and label generation. By using the application example, we have verified that ASLA operates properly. We have evaluated the usefulness of ASLA in regard to time and accuracy. We have assessed the efficacy of the rule-based area correction method. As a result, we have verified that ASLA is useful.

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The expansion of Michinoeki along Japanese highway systems is widely considered to be a successful socio-economic experiment, which has enhanced interstate commerce as well as spurred tourism. Although descriptive studies on Michinoeki have been conducted, a review of the literature indicates that quantitative investigations are sparse. It is pivotal to identify the determinants that contribute to the success and efficiency of Michinoeki’s, which can provide insights to further augment their development, thus revitalizing the surrounding local economy. To address this gap in the literature, the present investigation drew data from the entire population of Michinoeki towards ascertaining their relative efficiency by employing the DEA model, which enables data-driven inferences as well as comparative analyses. Based on the findings of the study, managerial implications are identified and avenues for future research are forwarded.

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The proposed novel contactless speed sensor sounds intriguing and presents potential advantages over the conventional methods used in vessels, submarines, and aircraft. It is asserted that the utilization of a small-sized ball, dropped within a vacuum chamber, enables the direct measurement of the ground speed of these vehicles, as opposed to their speed relative to the water or air. This could be a significant improvement, as ground speed is often more relevant for navigation and operational purposes. A useful method for assessing the practicality and accuracy of the suggested sensor is to use simulation to verify its conceptual and mathematical model. However, it's important to note that simulations are different from real-world testing, and further experimental verification would be necessary to confirm the reliability of the sensor under various conditions and environments. One major advantage of this proposed sensor is its independence from the operational environment. Unlike current speed sensors that require contact with the air or water, the suggested sensor can be placed entirely inside the vehicle. By eliminating the necessity to mount the sensor externally, the risk of collision with external objects is minimized, resulting in an enhanced overall safety of the system.

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