Search Results (642)

The study examines how secondary school students interacted with an AI-powered educational chatbot, MyBotBuddy, while working on a programming task, and how observed dialogue structures were associated with differences in pre- to post-test performance. Fifty students first completed an unassisted pre-test, then attempted a chatbot-supported programming task, and finally completed an unassisted post-test. Based on score change, students were grouped into learning gain, no gain, and learning loss categories. Dialogue transcripts were analyzed using Epistemic Network Analysis to identify co-occurring discourse patterns, alongside descriptive sentiment analysis to characterize lexical tone. Students in the learning gain group showed more connected multi-turn patterns involving solution attempts, feedback uptake, knowledge-related contributions, and clarification following feedback. In contrast, the no gain and learning loss groups showed less iterative and less systematically connected interaction structures. Average sentiment polarity differed only slightly across groups and is interpreted cautiously because the dialogue was technical and programming focused. The findings are associational and exploratory rather than causal and suggest that learner engagement with a chatbot may be more informative than interaction frequency alone. We discuss implications for educational chatbot design, especially the potential value of multi-turn scaffolding and reflective prompting, while outlining the need for future validation, baseline-controlled analyses, and experimental work. Full article

Social bots significantly threaten online public opinion through manipulation and misinformation, posing detection challenges due to high anthropomorphism and concealment. GNN methods show superior performance but face deployment hurdles on real-world platforms because of their reliance on multi-hop neighbor information during inference. Conversely, pure text-based methods lack collective behavior modeling and robustness against advanced bots. This paper proposes LKD, a social bot detection framework for graph-less deployment. The framework utilizes large language models to summarize historical tweets, compressing long-text information to construct multi-source inputs including metadata, profiles, and tweets. By employing a GNN as the teacher and a pre-trained LM as the student, LKD transfers structural knowledge to a text-based model via dual-objective knowledge distillation across prediction distributions and feature spaces. Experiments on Cresci-2015 and TwiBot-20 datasets show that the graph-less LKD-LM mode outperforms state-of-the-art methods in accuracy and F1-score. It maintains stable performance in label-scarce and sparse-graph scenarios, providing an efficient, robust solution for social media platforms with restricted interfaces or real-time requirements. Full article

Conventional web analytics treats the human user as its fundamental unit of analysis, assuming stable preferences, identifiable intentions, and behavioral patterns that unfold over time. That assumption is under strain. Crawlers and traditional bots already account for a substantial fraction of online interactions, and autonomous AI agents are emerging as a further class of actors layered on top of this automated traffic. Unlike either, these agents do not possess persistent identities or psychologically grounded motivations. They are task-specific, dynamically instantiated processes whose behaviors are contingent and often orchestrated by external systems. Their presence weakens the interpretive value of core metrics, including sessions, engagement, conversion, and retention. A click may reflect an optimization routine, a proxy objective, or a recursive agent-to-agent exchange rather than meaningful human intent, and traditional inference frameworks cannot reliably distinguish among these possibilities. This is a position paper. It synthesizes literature across bot and agent detection, agent architecture, web measurement validity, governance of automated systems in adjacent sectors, and the epistemology of digital trace data, and it argues that web analytics should supplement, and in places replace, its human-centered model with an agent-aware model focused on interaction dynamics within hybrid ecosystems of human and non-human actors. The paper develops a working taxonomy of crawlers, traditional bots, AI agents, LLM-powered agents, and autonomous agents; identifies three properties of LLM agents (identity discontinuity by design, task-based instantiation, agent-to-agent loops) that distinguish the present challenge from prior bot-detection problems; examines opaque agent objectives, synthetic traffic loops, and the indistinguishability between human-originated and agent-mediated signals; and proposes five candidate measurement primitives (task chain, actor class, interaction provenance, objective alignment, signal authenticity) with explicit operational definitions. Governance machinery from energy systems and critical infrastructure offers a partial template, and we delimit which dimensions transfer and which do not. The contribution is conceptual and programmatic, presenting a vocabulary, set of candidate primitives, and research agenda for a field whose foundational unit of analysis is becoming unreliable. Full article

The Republic of Korea has implemented routine vaccination against foot-and-mouth disease virus (FMDV) in livestock using a bivalent vaccine comprising serotypes O and A following the massive FMD outbreak in 2010, while antigens for the remaining serotypes are maintained in overseas antigen banks. The recent geographic expansion of FMDV Southern African Territories 1 (SAT1) beyond Africa underscores the need for enhanced preparedness in previously unaffected regions. In this study, we evaluated the SAT1 BOT-R strain as a candidate vaccine seed for potential domestic vaccine production by optimizing antigen production conditions, assessing scalability, determining virus inactivation parameters, and examining immunogenicity in pigs. Optimal antigen yield was achieved at 20 h−24 h post infection with a multiplicity of infection of 0.005−0.01, with production remaining stable under mildly alkaline conditions. Antigen productivity was consistently maintained during scale-up from shake flasks to a bioreactor, yielding up to 9.5 μg/mL. Complete virus inactivation was achieved using binary ethylenimine at 2 mM for 24 h at 26 °C. Vaccines formulated from both flask- and bioreactor-derived antigens elicited comparable neutralizing antibody responses in pigs, reaching a median titer of 1:500 following booster immunization. Collectively, these findings demonstrate that the SAT1 BOT-R strain is a viable and scalable candidate for SAT1 antigen banking and future domestic vaccine production, providing a practical framework for strengthening national preparedness against potential incursions of FMDV SAT1. Full article

The Build–Operate–Transfer (BOT) model is one of the most widely used Public–Private Partnership (PPP) methods for financing large-scale infrastructure projects. In this model, the concession period, which is the most critical parameter of the contract between the government and the private-sector investor, is a decision variable that directly affects the interests of both parties and varies depending on many uncertainty factors. The vast majority of models in the existing literature have been tested on hypothetical projects, and it is observed that parameters such as country-specific legal regulations, traffic volume guarantees, and financing conditions affecting discounting over time are not sufficiently incorporated into existing models. This study develops an integrated stochastic financial model, building on the established NPV–Monte Carlo–bargaining framework in the literature, that determines the optimum concession period for highway projects to be tendered via the BOT model in Türkiye. In the proposed model, uncertain parameters (construction cost, inflation, loan interest rate, traffic volume, toll increase rate, operation and maintenance costs) are defined with probability distributions; the Net Present Value (NPV) based financial model is solved via Monte Carlo simulation; and the obtained concession range is narrowed using a Rubinstein-type alternating-offers bargaining-game framework. The model simultaneously integrates parameters that prior studies addressed only in isolation: the equity–debt structure, loan repayment conditions, the government’s traffic volume guarantee, expropriation costs, and legal limits specific to Türkiye. The proposed model was validated by applying it to the Ankara–Niğde Highway Project, which was tendered in 2017. The results indicate that the concession range calculated by the model (11 years, 9 months, 2 days–24 years, 4 months) is consistent with the actual bids in the tender process. Following the application of bargaining-game theory, the range was narrowed to between 13 years, 4 months, and 16 days and 13 years and 5 months; this interval represents the concession range that best balances the profitability of both parties. This study provides a multidimensional evaluation framework for decision-makers by presenting comprehensive profitability analyses under different scenarios (including/excluding guaranteed traffic volumes and the project being fully constructed by the state). Full article

Traditional Web Application Firewalls (WAFs) rely on static thresholds to detect automated threats. While effective against simple scripts, these deterministic rules struggle with “Ambiguous Traffic”—sophisticated bots that mimic human behavior and “Efficient Humans” (Power Users) who exhibit bot-like speed. In this paper, we introduce H2-MAS, a hierarchical security framework that combines a high-speed Random Forest classifier (Tier 1) with a Multi-Agent Cognitive Council (Tier 2) powered by Large Language Models (LLMs). Unlike standard “Black Box” LLM deployments, H2-MAS utilizes an adversarial “Prosecutor vs. Defender” protocol to resolve semantic paradoxes in real-time. We evaluated the system on a stratified dataset of 1000 high-uncertainty sessions ($0.2\le p\le 0.8$). Through simulated evaluation on a stratified semantic test set, the results demonstrate that the proposed architecture has the theoretical capacity to achieve up to 97.6% accuracy on specific edge cases where traditional heuristics fail. Notably, the Cognitive Council demonstrated the theoretical capacity to reduce the False Positive Rate to 0.00% within this constrained evaluated set, validating that the “Defender” agent can successfully protect legitimate power users from erroneous blocking. This architecture offers a cost-effective paradigm for Semantic Security, prioritizing user experience without compromising threat detection. Full article

To improve the adaptability and adhesion of wall-climbing robots on complex curved surfaces, a self-adaptive spherical magnetic wheel robot is proposed for inspecting three-dimensional variable-curvature structures. The robot employs a bilateral wheeled design with passive magnetic modules that automatically adjust to contact conditions, ensuring efficient adhesion without active control. A Halbach-array magnetic circuit further enhances adhesion without increasing size or weight. Simulations analyze the effect of swing angle on adhesion and determine the minimum adaptable curvature radius. Experiments show stable climbing on surfaces with radii of 100–350 mm, obstacle-crossing up to 7 mm, and a payload capacity of 16.63 kg. Compared with existing designs, the robot offers improved curvature adaptability and load capacity under similar size and weight constraints. Full article

Background/Objectives: Recently the European Association of Urology (EAU) guidelines presented the EAU Guidelines bot to assist urologists in the reading of the guidelines; however, there is a lack of up-to-date external validation. The aim of our study is to assess the accuracy, completeness, and clarity of the guidelines bot in urethral strictures. Methods: A total of 117 questions based on the EAU urethral strictures guidelines recommendations were developed. Each question was input to the EAU guidelines bot and the response was assessed by two expert urologists to assess the accuracy, completeness, and clarity. Moreover, 10 simple clinical cases were input. A 5-point Likert scale was used as a score and, in case of discrepancies, a third urologist was queried. Accuracy, completeness and clarity were assessed per chapter and per grade of recommendation. All questions and answers were recorded in an Excel file. Results: Overall 117 questions were developed. In terms of accuracy, 111/117 (95%) were defined as accurate (scores 4–5), 4/117 (3%) presented a fair accuracy (score 3), and 2/117 (2%) were deemed not accurate. In terms of completeness, 93/117 (80%) were defined as complete (scores 4–5), 22/117 (19%) presented a fair completeness (score 3), and 2/117 (2%) were deemed not complete. Finally, in terms of clarity, 104/117 (89%) were defined as clear (scores 4–5), 13/117 (11%) presented a fair clarity (score 3), and 0/109 (0%) were deemed not clear. When comparing strong and weak recommendations, no differences were recorded. Overall the answers to simple clinical cases were in line with the guidelines with good accuracy, completeness and clarity scores. Conclusions: The EAU guidelines bot represents an accurate tool for urethral stenosis guidelines. Some fine-tuning is needed to improve readability and clarity. Full article

Rising global food demand, increasing labor costs, and farm labor shortages have created significant challenges for specialty crop production, particularly in labor-intensive tasks such as fruit harvesting. Robotic harvesting offers a promising long-term solution, yet its adoption in orchard environments remains limited due to unstructured conditions, variable lighting, and difficulties in fruit recognition and manipulation. This study presents an improved robotic fruit harvesting system, Orchard roBot (OrBot), developed by the Robotics Vision Lab at Northwest Nazarene University, with the goal of advancing autonomous apple harvesting applications. The updated OrBot platform integrates a dual-camera vision system consisting of an eye-to-hand stereo camera with a wide field of view for fruit detection and an eye-in-hand RGB-D camera for precise manipulation. The control architecture was redesigned using Robot Operating System 2 (ROS2) and Python, enabling modular subsystem development and coordination. Fruit detection was performed using a YOLOv5 deep learning model, and visual servoing was employed to guide the robotic manipulator toward the target fruit. System performance was evaluated through laboratory experiments using artificial trees and field tests conducted in a commercial apple orchard in Idaho. OrBot achieved a 100% harvesting success rate in indoor tests and a 75–80% success rate in outdoor orchard conditions. Experimental results demonstrate that the dual-camera approach significantly enhances fruit search efficiency and harvesting efficiency. Identified limitations include sensitivity to lighting conditions, end effector performance with varying fruit sizes, and depth estimation errors. Overall, the results indicate a positive potential toward effective robotic fruit harvesting and highlight key areas for future improvement in vision, manipulation, and system robustness. Full article

In battery systems, external mechanical compression is commonly applied to pouch/prismatic cells to improve their electrical performance and mechanical integrity. However, cell clamping can hinder system heat rejection by introducing an additional thermal insulation layer. A novel battery clamping scheme was designed with reduced contact area to explore the system thermal behaviour under different cooling regimes. Experimental data obtained from battery characterisation and performance tests is analysed with a thermal-coupled equivalent circuit model to quantify changes in cell impedance and system thermal properties. By reducing the clamping area by 70%, the temperature rise of the cell was decreased by 0.5 °C in comparison to the reference condition of a cell with no clamping during a 1C discharge under natural convection. Under immersion cooling using BOT2100 dielectric liquid, the thermal benefit was amplified, resulting in temperature reductions of 0.9 °C at 1C and 4 °C at 3C. The principal conclusion of this work is that reshaping the clamping plate has the potential to reduce ohmic heating by lowering battery internal resistance, which outweighs the additional thermal resistance introduced by partial surface coverage. This novel experimental approach demonstrates the potential to improve battery thermal management through geometry-optimised cell clamping, particularly for high-power applications, and further directs the community towards cell clamping solution designed to optimise both thermal and mechanical cell performance. Full article

Accurate social bot detection relies on simulated data to alleviate the scarcity of labeled real-world datasets. Synthetic graph data serves as the core training resource for detection models within simulated data; nevertheless, edge deficiency in real social networks (induced by privacy constraints and data collection limitations) gives rise to “pseudo-isolated nodes” and distorts the quality of synthetic graph data. Furthermore, mainstream data-driven synthetic graph generation methods lack systematic and credible comparative analyses. To tackle these problems, this study optimizes two representative synthetic graph generation approaches (the Chung-Lu model and the Random Classifier-based Multi-Hop (RCMH) sampling + diffusion model) and puts forward an edge completion strategy grounded in sociological theories. Multiple groups of comparative experiments are conducted to assess the performance of the improved methods and the edge completion strategy. Experimental results demonstrate that the “interest + social association” edge completion strategy achieves an F1-score (F1) of 0.7051, and the improved sampling + diffusion model integrated with edge completion reaches an F1-score of 0.7071, which performs better than traditional and unmodified methods to a certain extent. This work preliminarily enhances the reliability of synthetic graph generation methods and provides relatively high-quality synthetic social graph data for social bot detection. It should be noted that the proposed methods are validated solely on Twitter-derived datasets, and their effectiveness remains to be verified in cross-platform adaptation and dynamic social network scenarios. Full article

Botulinum neurotoxin injections are used off-label to treat chronic pain, but their efficacy is limited and paralytic effects restrict clinical utility in these applications. Here, we investigated whether combining the light chain and translocation domains of botulinum neurotoxin A (BoNT/A) with the GM1-binding B subunit of cholera toxin would be beneficial in silencing pain-associated sensory neurons. Chimeric ChoBot was assembled via a coiled-coil linking technology and was shown to retain the enzymatic activity of BoNT/A in vitro and in vivo. In cultured dorsal root ganglion neurons, ChoBot cleaved SNAP25 in a calcitonin gene-related peptide (CGRP)-rich subpopulation of sensory neurons, resulting in marked inhibition of CGRP release. ChoBot had a lesser effect on the compound muscle action potentials of the rat gastrocnemius muscle than BoNT/A following subcutaneous injections. In rat models of pain, including chemotherapy-induced peripheral neuropathy, intraplantar administration of ChoBot significantly attenuated mechanical allodynia. Immunohistochemical analysis confirmed SNAP25 cleavage in NF200- and CGRP-expressing sensory fibres in the epidermis following a single injection. ChoBot also mediated SNAP25 cleavage in human neuroblastoma cells in culture. Together, these findings indicate that ChoBot enables a silencing of pain-associated sensory pathways, providing a new strategy for the development of new long-lasting analgesics for chronic pain. Full article

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

Background: Early childhood is a critical period for the development of motor competence, which is closely related to later physical activity, educational readiness, and broader developmental outcomes. However, the temporal dynamics of motor skill acquisition in preschool children, particularly the time required to reach initial and early refinement phases of learning, remain insufficiently described. The aim of this study was to examine whether different levels of previous participation experience in an organized kinesiology program are associated with differences in the speed and quality of novel motor skill acquisition in preschool children, and to explore the relationship between baseline motor proficiency and phase-based indicators of motor learning. Methods: A total of 161 preschool children aged 5–6 years participated in the study and were grouped according to their previous participation experience in an organized kinesiology program (0 h, ~120 h, ~350 h, and ~470 h). Following BOT-2 assessment, all participants completed a standardized 7-week motor learning program that included nine previously unfamiliar motor tasks. Using a phase-based video analysis protocol, three learning indicators were recorded: time to Phase 1 (F1; first successful execution), time to Phase 2 (F2; initial refinement of performance), and final performance quality (K). Group differences and associations were first examined descriptively and correlationally, after which additional multivariable regression models were performed to determine whether previous participation experience and baseline motor proficiency were independently associated with motor learning outcomes. Results: The findings showed consistent differences across groups, with children who had greater previous participation experience generally reaching F1 and F2 more rapidly and achieving higher final performance quality scores. Higher BOT-2 scores were also associated with shorter learning times and better final performance quality. In the multivariable models, both previous participation experience in an organized kinesiology program and BOT-2 total score were independently associated with Phase 1 attainment time and final performance quality, whereas only previous participation experience remained independently associated with Phase 2 attainment time. The applied phase-based observational protocol demonstrated good to excellent inter-rater reliability across the evaluated motor learning variables. Conclusions: These findings provide phase-based temporal indicators of motor learning progression in preschool children and suggest that previous participation experience in an organized kinesiology program and baseline motor competence are meaningfully associated with the speed and quality of acquiring new motor tasks. The findings also demonstrate the potential of phase-based approaches for quantifying motor learning dynamics in early childhood settings. Such indicators may offer useful reference information for instructional pacing and the planning of motor learning activities, while also serving as practically relevant predictors for adapting future kinesiology programs to children’s motor readiness. Future research should further examine these relationships using longitudinal and analytically expanded designs. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article