explainable ai

AI-driven PROTAC design overcomes oncogenic resilience by eliminating the CLIP1-LTK fusion protein.

The discovery of CAP-Gly domain-containing linker protein 1(CLIP1)-Leukocyte tyrosine kinase (LTK) as an oncogenic fusion reveals a unique dependency not only on LTK kinase activity but also on CLIP1-mediated multimerization, a noncatalytic function that drives oncogenic signaling. While this fusion is currently targeted with anaplastic lymphoma kinase inhibitors, their exclusive focus on kinase inhibition leaves the scaffolding function intact, necessitating a complete protein clearance strategy. Here, we report the AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1-LTK fusion protein. By integrating deep learning models for ternary complex prediction with structure-based molecular optimization, we designed DCL05, an orally bioavailable degrader of CLIP1-LTK fusion protein, achieving picomolar degradation potency (DC50 = 40 pM) and robust antitumor activity. DCL05 consistently outperformed existing kinase inhibitors across a broad spectrum of LTK resistance-associated mutations, both in vitro and in vivo. Collectively, our study explores resistance-associated contexts of LTK and establishes a structure-guided PROTAC development pipeline, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.

k-jinma/dogo-tutor: dogo-tutor v0.0.2 - Initial Release

This is the initial release of "dogo-tutor," an AI tutor designed for coding education. This release is created to archive the repository and obtain a DOI via Zenodo. Key Features Provides step-by-step hints (problem clarification, strategy, logic structuring, and partial code) rather than direct answers. Designed to offer deeper hints only when students explain "what they tried and what happened" in their own words. Automatically generates review notes and short quizzes (/review) based on the last 24 hours of dialogue history, focusing on concepts the student struggled with. Features a self-reflection tool (/log) that allows students to objectively track their dependency on deep hints. Employs a safe guidance design that strictly references URLs from actual lecture materials and official documentation specified by the instructor. Recent Changes Revamped the README for users and added an English summary. Adjusted the icon design for the VS Code extension. Updated various configuration files (e.g., package.json) for public release.

Neuro-Symbolic Reasoning via Temporal Logic Embedding

This paper introduces a novel approach to neuro-symbolic reasoning by embedding temporal logic rules directly into the weights of a neural network. The core challenge in combining neural networks with symbolic reasoning lies in the absence of a shared representation language. Our method addresses this by creating a mechanism for the neural network to perform logical inference over time, guided by the embedded temporal logic rules. We propose a technique for mapping temporal logic formulas to the weights of a neural network, enabling the network to reason about temporal relationships and constraints. The resulting system exhibits improved reasoning capabilities compared to purely neural or purely symbolic approaches. This work provides a foundation for building more robust and explainable AI systems capable of handling complex, time-dependent reasoning tasks. The key innovation resides in the learned, differentiable mapping between temporal logic and neural network parameters, offering a pathway to seamless integration of these two powerful paradigms. The presented approach is evaluated conceptually, focusing on the architecture and embedding process, without empirical experimentation.

Explainable AI-Driven Synthetic Data Generation

This paper explores the application of explainable artificial intelligence (XAI) techniques to the generation of synthetic data. The core claim is that XAI can significantly improve the quality and utility of synthetic data, ultimately facilitating the training of AI models while maintaining data privacy and ensuring desired data characteristics. The proposed mechanism involves leveraging XAI to analyze real-world data, identify key features, and generate synthetic data that accurately reflects these features. A key innovation lies in the ability to not only create synthetic data but to understand *why* that data was generated, leading to a higher degree of confidence in its validity and suitability for downstream AI model training. The generated synthetic data is evaluated for its effectiveness in mimicking the statistical properties of the original data, addressing concerns about data fidelity often associated with traditional synthetic data generation methods. This work contributes to the growing field of privacy-preserving data analytics and offers a pathway to more reliable and trustworthy AI model development.

naganjaneyulu75/NeuroCareIoT: NeuroCareIoT v1.0.0

NeuroCareIoT v1.0.0 Initial release of NeuroCareIoT — Reliable Multimodal Edge-Based Alzheimer's Home Monitoring Using NeuroFuseNet. Features SafeFallNet for IMU-based fall detection WanderSenseNet for indoor localization and wandering-risk assessment DailyRoutineNet for temporal routine-deviation detection VitalRhythmNet for physiological anomaly assessment NeuroFuseNet for reliability-aware multimodal fusion Probability calibration using Platt Scaling and Temperature Scaling Predictive uncertainty using Monte-Carlo dropout Uncertainty-gated alerting Context-aware alert policies Modality reliability scoring Personalized resident baselines Drift-aware adaptation Explainable AI support Controlled synchronized multimodal replay ONNX edge-model export support Edge benchmarking utilities Ablation and statistical evaluation support Public Datasets This implementation supports: UP-Fall UJIIndoorLoc CASAS Aruba PPG-DaLiA Edge Deployment The framework supports deployment-oriented experiments using: Raspberry Pi 4B NVIDIA Jetson Nano ONNX Runtime TensorRT-compatible inference Important Note NeuroCareIoT is a research and experimental framework. The public datasets are modality-specific and were not collected as a naturally synchronized Alzheimer's cohort. System-level multimodal evaluation therefore uses controlled synchronized replay. This software is not a certified medical device and is not intended for autonomous clinical or emergency decision-making.

Explainable Artificial Intelligence (AI) Modeling of Co-Exposure Synergies in Tobacco Farming Health Risk Assessment with Epidemiological Analysis

HOW DO INFORMATICS TEACHERS PERCEIVE THE CHANGING PURPOSES AND PRACTICES OF INFORMATICS EDUCATION IN THE ERA OF GENERATIVE AI?

AbstractGenAI is increasingly capable of performing activities central to Informatics learning, includinggenerating, explaining, modifying, and debugging code, raising questions about what studentsneed to learn and how teachers' roles may change. This study explored Informatics teachers'perceptions of the changing purposes and practices of Informatics education in the GenAI era.An exploratory qualitative design was employed with 12 Informatics teachers from a collegespecializing in Informatics education. Data were collected through semi-structured interviewsand analysed using reflexive thematic analysis. Three interconnected themes were identified.First, teachers perceived a shift in emphasis from producing correct computational outputstoward understanding, explaining, evaluating, and modifying both independently produced andAI-generated solutions, while continuing to regard foundational programming knowledge asessential. Second, teachers described their roles as increasingly involving the evaluation,contextualization, and mediation of AI-supported learning rather than primarily providinginformation and solutions. Third, participants negotiated a contextual boundary between AI asassistance and AI as substitution, particularly when evaluating whether successful taskperformance represented genuine student competence. The findings suggest that GenAI does notsimply introduce a new instructional tool but challenges established assumptions aboutcomputational competence, pedagogical expertise, and evidence of learning. Informaticseducation may therefore need to balance independent computational competence with criticaljudgement and purposeful human-AI collaboration.

Pathway to achieve sustainable value through task-AI fit: the critical roles of green knowledge management capabilities, competitive advantage and financial resilience

Purpose This study aims to investigate how artificial intelligence (AI)-based technologies are associated with sustainable value intentions in green-oriented organisations operating under circular economy principles in China. Drawing on an adapted view of task-technology fit and the resource-based view (RBV), this study examines how technology and task characteristics are associated with Task-AI Fit, and how this fit is associated with green knowledge management (KM) capabilities, competitive advantage and financial resilience, which serve as key enablers of sustainable value intentions. Design/methodology/approach A survey was conducted with 373 representatives from Chinese organisations committed to circular-economy goals. Using covariance-based SEM and statistical tools, including SPSS Statistics and SPSS AMOS, the study tested the hypothesised paths, the structural model and mediation and moderation effects. It also examined the association between perceived task-AI Fit and internal capabilities, and their subsequent relationship with sustainable value intentions, while assessing the moderating role of environmental uncertainty. Findings The findings show that perceived task-AI Fit is significantly associated with Green KM capabilities, competitive advantage and financial resilience. Specifically, the Green KM capabilities show the strongest association with intentions to realise sustainable value. Environmental uncertainty significantly moderates only the relationship between Green KM capabilities and sustainable value intentions. Research limitations/implications By proposing a Task-AI Fit-driven RBV, the study highlights that the perceived Task-AI Fit is associated with internal capabilities that can channel sustainable intentions in the face of environmental uncertainty. Practical implications Organisations must prioritise, adapt and practice AI characteristics and features to strengthen AI-task alignment and strategically develop sustainability-oriented knowledge routines and value realisation. Social implications Future work can examine cross-cultural aspects across regulatory and cultural contexts, adopt a longitudinal design and include objective sustainability performance indicators to identify which potential attributes fail to materialise and to track the development of AI-driven capabilities. Originality/value This research extends the understanding of AI-enabled sustainability transitions by integrating task-technology alignment with resource-based capabilities in the context of environmental uncertainty. It highlights the central role of green KM practices in explaining sustainable value intentions in circular economy-oriented firms.

Using Artificial Intelligence to Achieve Inclusive and Effective Education

Artificial intelligence, as one of the transformative technologies of the contemporary era, has considerable capacity to respond to learners’ individual differences, reduce barriers to access to education, and improve the quality of the teaching–learning process. The present study was conducted with the aim of explaining how artificial intelligence can be used to achieve inclusive and effective education. The research method is library-based and analytical; accordingly, the data were collected through the study, classification, and analysis of documents, scientific reports, and reputable peer-reviewed articles in the fields of artificial intelligence in education, inclusive education, and learning technologies. The findings indicate that AI-based tools, including adaptive learning systems, intelligent tutors, learning analytics, speech-to-text technologies, text-to-speech technologies, and assistive technologies, can align educational content, learning pace, feedback, and assessment methods with learners’ diverse needs, abilities, and circumstances. These capabilities can provide more equitable opportunities for participation in education, particularly for students with special educational needs, learners in under-resourced areas, and individuals with linguistic or cultural differences. However, the real effectiveness and inclusiveness of these technologies depend on observing principles such as the protection of personal data, algorithmic transparency, the mitigation of bias, equal access to digital infrastructure, and the preservation of the teacher’s central role (Miao & Holmes, 2023; Zawacki-Richter et al., 2019). It can therefore be concluded that artificial intelligence, when used responsibly and through a human-centered approach, can move beyond being merely a technological tool and become a means of strengthening educational equity, personalizing learning, and increasing the effectiveness of education.

Are Feature Selection and Feature Attribution the Same? A Comparative Survey -- Extended Version

Dynamic and Explainable Deep Learning Models

This paper presents a novel approach to developing dynamic and explainable deep learning models. The core challenge in deploying deep learning systems is often their "black box" nature, hindering trust and adoption. This work addresses this issue by integrating Explainable Artificial Intelligence (XAI) techniques with Reinforcement Learning (RL). The resulting model, termed a Dynamic Explainable Deep Learning (DEDL) model, not only produces predictions but also provides a traceable explanation of its decision-making process. Crucially, the model incorporates a feedback loop driven by user input, allowing it to adapt its parameters and improve both its predictive accuracy and the clarity of its explanations over time. The system aims to create a truly interactive and understandable AI, shifting from opaque prediction to transparent reasoning. This paper details the architecture, the learning process, and the explanation generation strategies employed within the DEDL framework. The focus is on the design principles and the core algorithms, demonstrating a pathway towards more trustworthy and adaptable deep learning systems. The system's performance is evaluated based on a combination of predictive accuracy metrics and the subjective quality of the generated explanations. The key innovation lies in the continuous interplay between explanation and learning, fostering a truly dynamic and explainable AI.

Intuitions and Directional Shifts in Minimal Computation Cosmology

This research genealogy traces the development of Minimal Computation Cosmology from its earliest prime-based intuitions to the separation of LAW and SOURCE, the Wonsik Reality-Renderer Architecture (WRRA), and a non-branching universe with a single accumulated history. Rather than presenting the model as a completed physical theory, the report documents how each intuition exposed a hidden assumption, missing information layer, forced input, or invalid identification. It follows the conceptual transition from prime operators, dimensional filtering, finite-window transport, and the common carrier to fifteen-channel accessibility, autonomous Hamiltonian evolution, dormant-state opening, Standard-Model phenotypes, quantum realization, and the distinction among Actual, Reality, and Record. The report also explains why known physical constants and Planck-scale values may be legitimate disclosed inputs; why LAW must remain distinct from SOURCE; why SOURCE includes not only values but dependencies, read order, addressing, and realization conditions; and why the universe need not branch into every possible outcome. In this framework, many futures may remain possible, but only one event enters the physical record at each realization. Each major intuition is linked to its related public research paper and clickable Zenodo Version DOI. The result is both a conceptual history of the model and a transparent map of its claims, failures, revisions, open boundaries, and research lineage. Keywords: Minimal Computation Cosmology, WRRA, Wonsik Reality-Renderer Architecture, LAW and SOURCE, quantum mechanics, quantum realization, Standard Model, general relativity, cosmology, finite computation, information physics, dimensional filter, prime operators, zeta function, common carrier, Hamiltonian constraint, dormant state, FLRW cosmology, rank opening, fifteen-channel carrier, Actual–Reality–Record, Born probability, single outcome, non-branching universe, open future, research genealogy, Zenodo, DOI, human–AI collaborative research.