Search Results (64)

Liquid Crystal Display (LCD) waste from various sources, such as TVs, desktop monitors, and computer notebooks, is increasing and has become an environmental issue, requiring legalized disposal and practical recycling technologies to address it. LCD screens generally contain Indium–Tin–Oxide (ITO), which are critical and valuable metals worth recovering. The aim of this research is to optimize the thermal–physical pretreatment of LCD screens after dismantling to effectively recover indium via leaching and solvent extraction at an acceptable recovery rate. A comparative study was conducted on the recovery of indium from LCD waste from TVs and a mixed waste stream, where the latter source represents the total proportion of LCD waste accumulation. This comparison aimed to identify key factors for effective recovery from TV and mixed waste streams, containing lower and higher amounts of indium, respectively. The recovery process consisted of waste pretreatment, leaching, and solvent extraction. In the first pretreatment, ethanol immersion was found to be the most practical for polarizing film removal for both streams in comparison with manual peeling, water immersion, surface heating, and calcination. Comminution in the second pretreatment aimed to increase the surface area for the subsequent chemical reaction. For both streams, HCl leaching allowed effective indium recovery of >99.9% at the optimal conditions of 5 M HCl with 10% H₂O₂ addition and an S/L ratio of 500 g/L under ultrasonication for 1 h. Subsequent purification via solvent extraction was conducted to exclude iron as the key impurity. In solvent extraction, D2EHPA concentration, pH, and the organic-to-aqueous ratio (O:A) was investigated. Optimal solvent extraction was achieved using 0.25 M D2EHPA at pH 1 and an O/A ratio of 1:5 for 5 min, while ascorbic acid was added to eliminate undesirable iron. Although both waste streams achieved >99.9% leaching efficiency, they exhibited different extraction behaviors after scrubbing and stripping. The TV waste stream achieved 74.32% solvent extraction efficiency. The higher indium recovery of 84.34% via solvent extraction through the stripping of the mixed stream might be due to the higher initial indium concentration reacting with D2EHPA. This led to the overall recovery of the two waste streams as 70.22% and 80.24% for TV and mixed waste streams, respectively, possibly due to higher amounts of indium in the initial waste stream. The findings of this study serve as research and practical guidance for indium recovery and EOL–LCD recycling. Full article

This study introduces an open-source hybrid shape optimization workflow for acoustic wave problems that integrates acoustic wave propagation analysis with a two-stage optimization strategy. A coarse Parameter Sweep (PS) is first used for feasibility screening and global exploration, followed by derivative-free local refinement using the Nelder–Mead (NM) method. The framework is demonstrated on three benchmark problems: (i) an acoustic horn optimized for improved impedance matching and reduced reflections, (ii) a noise barrier reshaped to minimize acoustic pressure in the shadow zone, and (iii) a crescent-shaped scatterer designed to attenuate downstream pressure amplitude. Across all cases, the PS–NM strategy achieved lower objective values than baseline-initialized local optimization, at the expense of increased computational cost. All analyses were performed in the open-source FEniCS environment within Jupyter Notebooks. Comparisons with published results support the accuracy and consistency of the implementation. By combining accessibility with flexibility, the framework provides a reproducible methodology for acoustic shape optimization. Potential extensions include multi-objective formulations, frequency-adaptive designs, improved constraint-handling strategies, and integration with metamaterial concepts. Full article

Mechanistic interpretability seeks to reverse-engineer the computational circuits within large language models, but current methods rely on exhaustive or heuristic search over exponentially many feature interactions. This paper introduces Active Circuit Discovery (ACD), a framework that combines attribution-graph analysis with active inference to select interventions efficiently. ACD uses Anthropic’s circuit-tracer library as its attributiongraph backend, applying Edge Attribution Patching with transcoders to identify the active transcoder features for each prompt. A partially observable Markov decision process (POMDP) agent, implemented with pymdp, maintains a multi-factor generative model of feature importance, layer role, and causal influence. At each step, the agent selects both a target feature and an intervention type (ablation, activation patching, or feature steering) by minimising Expected Free Energy over the joint feature–action space, and it learns its observation model online through Dirichlet parameter updates. ACD is an interventionselection layer over existing attribution-graph tools; it is not a whole-circuit discovery method, and no claim of state-of-the-art circuit discovery is made. The framework is evaluated on Gemma-2-2B (26 layers) and Llama-3.2-1B (16 layers) across four settings: Indirect Object Identification (IOI), multi-step reasoning, feature steering, and a multidomain benchmark spanning geography, mathematics, science, logic, and history. With a budget of 20 interventions per prompt, an ablation-only agent scored by bounded oracle efficiency against the ablation oracle reaches 82.0% efficiency on Gemma IOI and 73.0% on Gemma multi-step. It exceeds random selection by 43.5% (relative) on Gemma IOI (paired permutation p = 0.031) and is competitive with greedy ranking, a heuristic UCB bandit, and a plain UCB baseline. A direct Edge-Attribution-Patching ranking is itself a strong baseline that the agent does not consistently surpass, and on Llama multi-step the agent reaches 9.3% efficiency (37.8% with finer layer-role bins). All comparisons report bootstrap 95% confidence intervals. The full multi-action agent is characterised separately by a Relative Cumulative KL, a steering-driven amplification factor reported apart from the bounded efficiency. Feature steering changes the top-1 prediction in a dose-dependent manner, but a matched random-feature control shows that circuit-selected features are only marginally, and not significantly, more steerable than random active features at large multipliers, indicating that part of the effect is generic activation scaling. Multi-domain analysis shows task-dependent circuit structure, with IOI circuits concentrated in late layers and reasoning and scientific knowledge recruiting early and middle layers. Code, notebooks (free T4), AMD64/aarch64 Docker images, and raw results are publicly available. Full article

The efficiency of data-driven research relies not only on high-quality data and sufficient computational resources but also depends sensitively on the personal knowledge management of the researcher. The multitude of digital artefacts created during a researcher’s daily workflow might comprise experimental results, simulation results, literate programming notebooks analysing experiments and simulations, statistical models, machine learning models, figures, tables, and conversations with generative Artificial Intelligence systems. In order to trace and track these interconnected research artefacts over several months of research or even extended research periods and different research projects, these artefacts need to be systematically named so that they can be referenced in note-keeping systems and research outputs. Therefore, the naming and referencing scheme for research artefacts needs to be flexible, consistent, efficient and support the linking of artefacts across different software frameworks and even classical laboratory notebooks. This article introduces a hierarchical naming scheme and the supporting open-source Python package contexere together with best practices for the personal knowledge management for postgraduate students and early career researchers, which provides a clear and linkable structure for data artefacts and thus supports effective personalised research workflows. Full article

Compact finite difference schemes approximate spatial derivatives through implicit relations between neighboring grid points. Despite using compact stencils and relatively simple algebraic structures, these schemes achieve high-order accuracy and spectral-like resolution, reducing dispersion errors while maintaining low numerical dissipation. These properties make them particularly attractive for problems requiring accurate spatial derivatives and computational efficiency, such as wave propagation, aeroacoustics, and turbulent flow simulations. This review presents the main ideas behind compact finite difference schemes, including their derivation from Taylor expansions and Padé approximations, their accuracy properties, and their resolution characteristics through modified wavenumber analysis. The manuscript is intended as a review and practical synthesis, rather than as the proposal of a new numerical scheme, and aims to connect the theoretical construction of compact schemes with their numerical behavior, practical implementation, and representative applications. To support reproducibility, we provide a fully documented open-source Python 3.11 notebook with a reference implementation of the schemes discussed in the paper. The examples include first- and second-order derivative calculations and representative one- and two-dimensional boundary-value problems, including Helmholtz-type equations. Finally, we survey applications across computational fluid dynamics, acoustics, geophysical flows, structural mechanics, biology, electromagnetism, and quantitative finance. Full article

Background: The aim of this study was to determine the optimal discipline position in the overall result of Olympic-distance triathlon. Methods: Data were extracted for free from the API (Application Programming Interface) service on the World Triathlon website and collected using a custom Python code. Statistical and machine learning analyses were employed within a Jupyter Notebook file. Linear and polynomial regressions were calculated between the overall race position and final positions in each discipline. Descriptive statistics and machine learning analyses were computed to identify the average position and most likely average position required in each discipline, respectively. A heatmap correlation analysis was conducted between the best overall triathletes and the best discipline triathletes. Differences between the two sub-databases were assessed using the student’s t-test. Results: Across all disciplines, the average position required in each segment remains consistently better than 13th place. The heat map shows a very small, negative correlation between the best time in each discipline and the overall best race time (p-values < 0.001). The student’s t-test establishes significant differences for all disciplines and overall race time (p-values < 0.001). Conclusions: Consistently high-level performance across all disciplines is essential for ensuring a podium finish or race victory in an Olympic triathlon. Achieving the best time in each discipline is not required to contend for victory, although running appears to be a strong predictor of overall race outcome. Full article

What is the fastest Artificial Intelligence Large Language Model (AI LLM) for generating quantum operations? To answer this, we present the first benchmarking study comparing popular and publicly available AI models tasked with creating quantum gate designs. The Wolfram Mathematica framework was used to interface with the six AI LLMs, including Google Gemini 2.0 Flash, Anthropic Claude 3 Haiku, WolframLLM Notebook Assistant For Mathematica V14.3.0.0, OpenAI ChatGPT Omni 4 Mini, Google Gemma 3 4b 1t, and DeepSeek Chat V3. Our novel study found the following: (1) Gemini 2.0 Flash is overall the fastest AI LLM of the models tested in producing average quantum gate designs at 2.66101 s, factoring in the “thinking” execution time and ServiceConnect network latencies. (2) On average, four out of the ten quantum operations that the six LLMs produced compiled in Python version 3.13.5 (40.8% success rate). (3) Quantum operations averaged approximately 21–45 Lines of Code (omitting nonsensical outliers). (4) DeepSeek Chat V3 produced the shortest code with an average of 21.6 lines. This comparison evaluates the time taken by each AI LLM platform to generate quantum operations (including ServiceConnect networking times). These findings highlight a promising horizon where publicly available Large Language Models can become fast collaborators with quantum computers, enabling rapid quantum gate synthesis and paving the way for greater interoperability between two remarkable and cutting-edge technologies. Full article

Background/Objectives: This study explored how attribute framing (lifestyle-focused vs. technology-focused product descriptions) and popularity cueing (presence or absence of a “best-seller” label) influenced purchase likelihood for a fictitious selection of hearing aids (HAs) among Canadian adults aged 40 years and above. The study further aimed to investigate whether the effects observed were unique to HAs or applicable to less-specialized consumer technology contexts. Method: A 2 × 2 × 2 mixed experimental design compared attribute framing and popularity cueing effects across HAs and notebook computers at three technology levels (entry-level, midrange, and premium). Participants (n = 122) provided ratings indicating their purchase likelihood for each product. Results: Attribute framing showed no significant influence on purchase decisions across technology levels. The presence of a popularity cue that the midrange HA was the best-seller negatively affected purchase likelihood for the entry-level HA, with higher purchase likelihood ratings observed when this cue was absent. Participants expressed stronger purchase likelihood for premium HAs compared to premium notebook computers. Notably, these two effects were not statistically significant following correction for multiple comparisons. Conclusions: Popularity cues for HAs may have inadvertent consequences for consumer perceptions of models with differing technology levels. Findings also suggest potentially greater willingness to invest in premium health-related technologies versus familiar consumer technology. Further research involving current HA users or candidates is needed to better understand these findings. Full article

This work introduces RAMA (Recursive Aesthetic Modular Approximation), a metaheuristic framework that models a restricted form of mathematical intuition inspired by the notebooks of Srinivasa Ramanujan. While Ramanujan often produced deep results without formal proofs, the heuristic processes guiding such discoveries remain poorly understood. RAMA treats large language models (LLMs) as proposal mechanisms within an iterative search that generates, evaluates, and refines candidate conjectures under an explicit energy functional balancing fit, description length, and aesthetic structure. A small set of Ramanujan-inspired heuristics—modular symmetries, integrality cues, aesthetic compression, and near-invariance detection—is formalized as micro-operators acting on symbolic states. We instantiate RAMA in two domains: (i) inverse engineering eta-quotients from partial q-series data and (ii) designing cyclotomic fingerprints with shadow gadgets for quantum circuits. In both settings, RAMA recovers compact structures from limited information and improves separation from classical baselines, illustrating how intuitive heuristic patterns can be rendered as explicit, reproducible computational procedures. Full article

This study presents the development of an AI-powered chatbot designed to facilitate accurate and efficient retrieval of information from the FDA drug labeling documents. Leveraging OpenAI’s GPT-3.5-turbo model within a controlled, document-grounded question–answering framework, Chatbot was created, which can provide users with answers that are strictly limited to the content of the uploaded drug label, thereby minimizing hallucinations and enhancing traceability. A user-friendly interface built with Streamlit allows users to upload FDA labeling PDFs and pose natural language queries. The chatbot extracts relevant sections using PyMuPDF and regex-based segmentation and generates responses constrained to those sections. To evaluate performance, semantic similarity scores were computed between generated answers and ground truth text using Sentence Transformers. Results across 10 breast cancer drug labels demonstrate high semantic alignment, with most scores ranging from 0.7 to 0.9, indicating reliable summarization and contextual fidelity. The chatbot achieved high semantic similarity scores (≥0.95 for concise sections) and ROUGE scores, confirming strong semantic and textual alignment. Comparative analysis with GPT-5-chat and NotebookLM demonstrated that our approach maintains accuracy and section-specific fidelity across models. The current work is limited to a small dataset, focused on breast cancer drugs. Future work will expand to diverse therapeutic areas and incorporate BERTScore and expert-based validation. Full article

Creative insight is a core phenomenon of human cognition, often characterized by the sudden emergence of novel and contextually appropriate ideas. Classical models based on symbolic search or associative networks struggle to capture the non-linear, context-sensitive, and interference-driven aspects of insight. In this work, we propose a computational model of insight generation grounded in continuous-time quantum walks over weighted semantic graphs, where nodes represent conceptual units and edges encode associative relationships. By exploiting the principles of quantum superposition and interference, the model enables the probabilistic amplification of semantically distant but contextually relevant concepts, providing a plausible account of non-local transitions in thought. The model is implemented using standard Python 3.10 libraries and is available both as an interactive fully reproducible Google Colab notebook and a public repository with code and derived datasets. Comparative experiments on ConceptNet-derived subgraphs, including the Candle Problem, 20 Remote Associates Test triads, and Alternative Uses, show that, relative to classical diffusion, quantum walks concentrate more probability on correct targets (higher AUC and peaks reached earlier) and, in open-ended settings, explore more broadly and deeply (higher entropy and coverage, larger expected radius, and faster access to distant regions). These findings are robust under normalized generators and a common time normalization, align with our formal conditions for transient interference-driven amplification, and support quantum-like dynamics as a principled process model for key features of insight. Full article

The durability and safety of modern concrete architecture and infrastructure are critically impacted by early-stage surface cracks. Timely and appropriate identification and management of these cracks are therefore essential to enhance structural longevity and stability. This study utilizes computer vision technology to construct a large-scale database, comprising 106,998 concrete surface crack images from various research sources. Through data augmentation, the database is extended to 140,000 images to fully leverage the advantages of deep learning models. For concrete surface crack detection, this study proposed a lightweight convolutional neural network (CNN) model, achieving 92.27% accuracy, 94.98% recall, and a 92.39% F1 score. Notably, the model runs smoothly on lightweight office notebooks without GPUs. Additionally, an image stitching algorithm that seamlessly stitches multiple images was proposed to generate high-quality panoramic views of bridges. The image stitching algorithm demonstrates robustness when applied to multiple images, successfully achieving stitching without visible seams or errors, providing efficient and reliable technical support for bridge panorama generation. The research outcomes demonstrate significant practical value in bridge inspection, providing robust technical support for safe and efficient bridge inspection. Moreover, our findings offer valuable references for future research and applications in related fields. Full article

We present a new method for trend analysis that integrates traditional foresight techniques with advanced data processing and artificial intelligence. It addresses the challenge of analyzing large volumes of information while preserving expert insight. The hybrid methodology combines computational analysis with expert validation across four phases: literature review, information systematization, trend identification, and analysis. Tools like Voyant Tools 2.6.18 and NotebookLMare used for semantic and statistical exploration. Among them, we highlight the use of the Defly Compass tool, a natural language processing tool based on semantic projections and developed by our team. The method produces mixed results, including both conceptual conclusions and quantifiable, reproducible outcomes adaptable to diverse contexts. Comparative case studies in agriculture, education, and public health identified key patterns within and across sectors. Cross-domain validation revealed universal trends such as digital infrastructure, data integration, and equity. Designed for accessibility, the method enables small, non-specialized teams to combine computational tools with expert knowledge for strategic decision making in complex environments. Full article

Social robots in Ambient Assisted Living (AAL) environments offer a promising alternative for enhancing senior care by providing companionship and functional support. These robots can serve as intuitive interfaces to complex smart home systems, allowing seniors and caregivers to easily control their environment and access various assistance services through natural interactions. By combining the emotional engagement capabilities of social robots with the comprehensive monitoring and support features of AAL, this integrated approach can potentially improve the quality of life and independence of elderly individuals while alleviating the burden on human caregivers. This paper explores the integration of social robotics with ambient assisted living (AAL) technologies to enhance elderly care. We propose a novel framework where a social robot is the central orchestrator of an AAL environment, coordinating various smart devices and systems to provide comprehensive support for seniors. Our approach leverages the social robot’s ability to engage in natural interactions while managing the complex network of environmental and wearable sensors and actuators. In this paper, we focus on the technical aspects of our framework. A computational P2P notebook is used to customize the environment and run reactive services. Machine learning models can be included for real-time recognition of gestures, poses, and moods to support non-verbal communication. We describe scenarios to illustrate the utility and functionality of the framework and how the robot is used to orchestrate the AAL environment to contribute to the well-being and independence of elderly individuals. We also address the technical challenges and future directions for this integrated approach to elderly care. Full article

This article presents an alternative variant of motion planning techniques for autonomous vehicles (AVs) centered on an inverse approach that concurrently optimizes both trajectory and speed. This method emphasizes searching for a trajectory and distributing its speed within a single road segment, regarded as a final element. The references for the road lanes are represented by splines that interpolate the path length, derivative, and curvature using Cartesian coordinates. This approach enables the determination of parameters at the final node of the road segment while varying the reference length. Instead of directly modeling the trajectory and velocity, the second derivatives of curvature and speed are modeled to ensure the continuity of all kinematic parameters, including jerk, at the nodes. A specialized inverse numerical integration procedure based on Gaussian quadrature has been adapted to reproduce the trajectory, speed, and other key parameters, which can be referenced during the motion tracking phase. The method emphasizes incorporating kinematic, dynamic, and physical restrictions into a set of nonlinear constraints that are part of the optimization procedure based on sequential quadratic optimization. The objective function allows for variation in multiple parameters, such as speed, longitudinal and lateral jerks, final time, final angular position, final lateral offset, and distances to obstacles. Additionally, several motion planning variants are calculated simultaneously based on the current vehicle position and the number of lanes available. Graphs depicting trajectories, speeds, accelerations, jerks, and other relevant parameters are presented based on the simulation results. Finally, this article evaluates the efficiency, speed, and quality of the predictions generated by the proposed method. The main quantitative assessment of the results may be associated with computing performance, which corresponds to time costs of 0.5–2.4 s for an average power notebook, depending on optimization settings, desired accuracy, and initial conditions. Full article