Search Results (194)

This study presents the first systematic empirical comparison of neural architectures for Universal Dependencies (UD) parsing in Old English, thus addressing central questions in computational historical linguistics and low-resource language processing. We evaluate three approaches—a baseline spaCy pipeline, a pipeline with a pretrained tok2vec component, and a MobileBERT transformer-based model—across datasets ranging from 1000 to 20,000 words. Our results demonstrate that the pretrained tok2vec model consistently outperforms alternatives, because it achieves 83.24% UAS and 74.23% LAS with the largest dataset, whereas the transformer-based approach substantially underperforms despite higher computational costs. Performance analysis reveals that basic tagging tasks reach 85–90% accuracy, while dependency parsing achieves approximately 75% accuracy. We identify critical scaling thresholds, with substantial improvements occurring between 1000 and 5000 words and diminishing returns beyond 10,000 words, which provides insights into scaling laws for historical languages. Technical analysis reveals that the poor performance of the transformer stems from parameter-to-data ratio mismatches (1250:1) and the unique orthographic and morphological characteristics of Old English. These findings defy assumptions about transformer superiority in low-resource scenarios and establish evidence-based guidelines for researchers working with historical languages. The broader significance of this study extends to enabling an automated analysis of three million words of extant Old English texts and providing a framework for optimal architecture selection in data-constrained environments. Our results suggest that medium-complexity architectures with monolingual pretraining offer superior cost–benefit trade-offs compared to complex transformer models for historical language processing. Full article

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

This study develops a high-accuracy indoor positioning system using ultra-wideband (UWB) technology and the time-of-arrival (TOA) method. The system is built using Arduino Nano microcontrollers and DW1000 UWB chips to measure distances between anchor nodes and a mobile tag. Three positioning algorithms are tested: the triangle centroid algorithm (TCA), inner triangle centroid algorithm (ITCA), and the proposed intersection midpoint algorithm (IMA). Experiments conducted in a 732 × 488 × 220 cm indoor environment show that TCA performs well near the center but suffers from reduced accuracy at the edges. In contrast, IMA maintains stable and accurate positioning across all test points, achieving an average error of 12.87 cm. The system offers low power consumption, fast computation, and high positioning accuracy, making it suitable for real-time indoor applications such as hospital patient tracking and shopping malls where GPS is unavailable or unreliable. Full article

Reliable and accurate docking remains a fundamental challenge for autonomous mobile robots (AMRs) operating in complex industrial environments with dynamic lighting, motion blur, and occlusion. This study proposes a novel multi-sensor fusion-based docking framework that significantly enhances robustness and precision by integrating YOLOv8-based AprilTag detection, depth-aided 3D localization, and LiDAR-based orientation correction. A key contribution of this work is the construction of a custom AprilTag dataset featuring real-world visual disturbances, enabling the YOLOv8 model to achieve high-accuracy detection and ID classification under challenging conditions. To ensure precise spatial localization, 2D visual tag coordinates are fused with depth data to compute 3D positions in the robot’s frame. A LiDAR group-symmetry mechanism estimates heading deviation, which is combined with visual feedback in a hybrid PID controller to correct angular errors. A finite-state machine governs the docking sequence, including detection, approach, yaw alignment, and final engagement. Simulation and experimental results demonstrate that the proposed system achieves higher docking success rates and improved pose accuracy under various challenging conditions compared to traditional vision- or LiDAR-only approaches. Full article

Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement. Full article

Understanding individual movement patterns in stream-resident salmonids is critical for conservation and river management, particularly in Mediterranean streams characterized by high environmental variability. We tagged 997 Mediterranean brown trout (Salmo trutta complex) and conducted an 11-month mark–recapture study using Passive Integrated Transponder (PIT) technology to assess movement behavior in the Flamisell River (Ebro Basin, northeastern Iberian Peninsula). Movements followed a leptokurtic distribution, with 81.8% of the individuals classified as sedentary (median movement = 24.9 m) and 18.2% as mobile (median movement = 376.2 m). Generalized linear models revealed distinct drivers of fish movement for each group. In sedentary trout, movement was mainly influenced by mesohabitat type, season, sex, and body size, with males and larger individuals moving farther. In mobile trout, mesohabitat type, density, and body size were key predictors. Movement patterns were repeatable over time, indicating consistent behavioral tendencies. These results support a bimodal movement strategy and highlight the importance of individual variation. Conservation planning should account for both sedentary and mobile groups to preserve functional and genetic connectivity and improve resilience of Mediterranean streams. Full article

Peat fires are a major hazard to human and animal health and can negatively impact livelihoods. Once peat fires start to burn, they are difficult to extinguish and can continue to burn for months, destroying biomass and contributing to carbon emissions globally. In areas with limited accessibility and periods of thick haze and fog, these fires are difficult to detect, localize, and tackle. To address this problem, thermal infrared cameras mounted on drones can provide a potential solution since they allow large areas to be surveyed relatively quickly and can detect thermal radiation from fires above and below the peat surface. This paper describes a deep learning pipeline that detects and segments peat fires in thermal images. Controlled peat fires were constructed under varying environmental conditions and thermal images were taken to form a dataset for our pipeline. A semi-automated approach was adopted to label images using Otsu’s adaptive thresholding technique, which significantly reduces the required effort often needed to tag objects in images. The proposed method uses a pre-trained ResNet-50 model as a backbone (encoder) for feature extraction and is augmented with a set of up-sampling layers and skip connections, like the UNet architecture. The experimental results show that the model can achieve an IOU score of 87.6% on an unseen test set of thermal images containing peat fires. In comparison, a MobileNetV2 model trained under the same experimental conditions achieved an IOU score of 57.9%. In addition, the model is robust to false positives, which is indicated by a precision equal to 94.2%. To demonstrate its practical utility, the model was also tested on real peat wildfires, and the results are promising, as indicated by a high IOU score of 90%. Finally, a geolocation algorithm is presented to identify the GNSS location of these fires once they are detected in an image to aid fire-fighting responses. The proposed scheme was built using a web-based platform that performs offline detection and allows peat fires to be geolocated. Full article

Biodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting and tracking mobile underwater targets that utilizes reflections from active acoustic emission of broadband signals received by a rigid hydrophone array. The method overcomes the problem of a high false alarm rate by applying a tracking approach to the sequence of received reflections. A 2D time–distance matrix is created for the reflections received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. The position and velocity are estimated using the debiased converted measurement Kalman filter. The results are analyzed for simulated scenarios and for experiments in the Adriatic Sea, where six Global Positioning System (GPS)-tagged gilt-head seabream fish were released and tracked by a dedicated autonomous float system. Compared to four recent benchmark methods, the results show favorable tracking continuity and accuracy that is robust to the choice of detection threshold. Full article

This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments. Full article

The structural analysis of glycans remains a major challenge due to their high isomeric complexity and conformational flexibility arising from diverse glycosidic linkages and dynamic three-dimensional structures. Ion mobility–mass spectrometry (IM–MS) has been attracting attention as a way to develop the structural analysis of glycans. In this study, the effects of ionization conditions—including different types of adduct ions, ionization temperatures, and solvent environments—on the ion mobility behavior of glycans were systematically investigated. IM–MS measurements of ethylamine-tagged glycans showed broad arrival time distributions of monoprotonated ions indicating the presence of multiple conformers of glycans. Increased ionization temperatures and the use of methanol as a solvent further broadened the distribution, suggesting the enhanced conformational dynamics of the glycan ions. In contrast, sodium adduct ions yielded narrower distributions, implying that the interactions between sodium ions and glycans constrained structural flexibility. These results demonstrate that ionization parameters have a significant impact on glycan conformational behavior and mobility in the gas phase. This study provides insights into the analytical conditions for IM–MS measurements of glycans and highlights the utility of this method as a powerful tool for elucidating glycan structure and dynamics. Full article

The MIPI (Mobile Industry Processor Interface) Alliance provides a security framework for in-vehicle network connections between sensors and processing electronic control units (ECUs). One approach within this framework is data integrity verification for sensors with limited hardware resources. In this paper, the security risks associated with image sensor data are described. Adversarial examples (AEs) targeting the MIPI interface can induce misclassification, making image data integrity verification essential. A CMOS image sensor with a message authentication code (CIS-MAC) is then proposed as a defense mechanism starting from the image sensor to protect image data from malicious manipulations, such as AE attacks. Evaluation results of the physically unclonable function (PUF) response and random number, which are utilized for generating the cryptographic key and MAC tag, are presented using a 2 Mpixel CMOS image sensor. The area of the CIS-MAC circuit is estimated based on a Verilog HDL design and synthesis using a 0.18 μm CMOS process. Various hash topologies are evaluated to select a hash function suitable for key generation and MAC tag generation within the CMOS image sensor. The estimated area of the CIS-MAC circuit is 67 kGE and $0.86{\mathrm{mm}}^2$, demonstrating feasibility for implementation in a CMOS image sensor typically fabricated using analog process technology. Full article

Thyroid hormone (TH) plays a vital role in ovarian follicle development, and glucose-regulated protein 78 (GRP78) is involved in these processes, which is regulated by TH. However, the mechanisms are still unclear. To evaluate the possible mechanism of TH on the regulation of GRP78 expression, Cleavage Under Targets and Tagmentation (CUT & Tag) sequencing, luciferase assays, and Electrophoretic Mobility Shift Assays (EMSA) were employed to delineate the binding sites of thyroid hormone receptor β (TRβ) on the GRP78 promoter and to confirm the interactions. Additionally, Co-Immunoprecipitation (Co-IP) and Immunofluorescence (IF) assays were used to investigate the interactions between TRβ and the coactivator peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) after triiodothyronine (T₃) treatment with different concentrations. Our findings identified a thyroid hormone response element (TRE) on the GRP78 promoter and demonstrated that TRβ can activate GRP78 expression by interacting with PGC-1α. In order to simulate the condition of hyperthyroidism, granulosa cells (GCs) extracted from rats were treated by T₃ with high concentrations, which decreased the expression of PGC-1α, resulting in decreased expressions of GRP78 and other ferroptosis-related markers such as glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11, xCT), thereby inducing ferroptosis in GCs. Taken together, the present study demonstrates that T₃ induces cellular ferroptosis by binding TRE of the GRP78 promoter in ovarian GCs via TRβ. As a switcher, PGC-1α is also involved in these processes. Full article

Radio Frequency Identification (RFID) assistive systems, which integrate RFID devices with IoT technologies, are vital for enhancing the independence, mobility, and safety of individuals with disabilities. These systems enable applications such as RFID navigation for blind users and RFID-enabled canes that provide real-time location data. Central to these systems are resource-constrained RFID devices that rely on RFID tags to collect and transmit data, but their limited computational capabilities make them vulnerable to cyberattacks, jeopardizing user safety and privacy. Implementing the Elliptic Curve Cryptography (ECC) algorithm is essential to mitigate these risks; however, its high computational complexity exceeds the capabilities of these devices. The fundamental operation of ECC is finite field multiplication, which is crucial for securing data. Optimizing this operation allows ECC computations to be executed without overloading the devices’ limited resources. Traditional multiplication designs are often unsuitable for such devices due to their excessive area and energy requirements. Therefore, this work tackles these challenges by proposing an efficient and compact field multiplier design optimized for the Montgomery multiplication algorithm, a widely used method in cryptographic applications. The proposed design significantly reduces both space and energy consumption while maintaining computational performance, making it well-suited for resource-constrained environments. ASIC synthesis results demonstrate substantial improvements in key metrics, including area, power consumption, Power-Delay Product (PDP), and Area-Delay Product (ADP), highlighting the multiplier’s efficiency and practicality. This innovation enables the implementation of ECC on RFID assistive devices, enhancing their security and reliability, thereby allowing individuals with disabilities to engage with assistive technologies more safely and confidently. Full article

In recent years, advancements in artificial intelligence (AI) have become an essential study for machine learning. The use of AI with the Robot Operating System (ROS) enables mobile robots to learn and move autonomously. Mobile robots can now be widely used in industrial and service sectors. Generally, robots have been operated on fixed paths requiring set points to function. This study utilizes Deep Q-Network (DQN) incorporating filtering to train and reward AprilTag images, paths, and obstacle avoidance. Training is conducted in a Gazebo simulation environment, and the collected data is verified on physical mobile robots. The DQN network excels in computing complex functions; AprilTag provides X, Y, Z, Pitch, Yaw, and Roll data. By employing DQN methods, recognition and path accuracy are simultaneously enhanced. The constructed DQN network can endow mobile robots with autonomous learning capabilities. Full article

The glycosylation process plays a crucial role in the structural integrity and biological activity of glycoproteins, where glycans are attached to a protein backbone. There are many kinds of glycans, the most common being N-glycans, which can be arranged into three classes, that is, complex, hybrid, and high mannoses, forming a structurally very diverse set of polar compounds that are difficult to detect and separate. Most commonly, N-glycans are labeled before separation by charged or fluorescence tags for better MS or fluorescence detection, respectively. This study examines the influence of ionic strength and organic modifier selection on the separation of fluorescently labeled dextran ladders in Hydrophilic Interaction Liquid Chromatography (HILIC). Using a Glycan BEH Amide column and varying the ammonium formate buffer concentration along with acetonitrile and methanol ratios, we investigated analyte retention, separation efficiency, and post-column conductivity changes. Our findings reveal that changes in the ionic strength of the mobile phase do not contribute to changes in selectivity, neither when acetonitrile nor methanol were used as organic modifiers to the mobile phase. However, the addition of methanol significantly changes the separation mechanism where two different prevailing separations mechanisms can be identified. It was assumed that the addition of methanol influences the folding pattern of dextrans around the permanent positive charge on the added tag, which influences the changes of separation selectivity. This work presents a systematic approach to altering mobile phase composition (buffer concentration, organic modifier type) to control retention and selectivity in complex glycan analysis. The discovery that methanol significantly alters separation behavior provides a potential new method for refining HILIC separations of polar compounds. Full article