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In this study, we developed a chatbot-based system for detecting early signs of depression and verified its effectiveness through experimental evaluations and user surveys. Em-phasizing that it does not rely on medical checklists, the system is designed to auto-matically extract three linguistic features associated with depression—frequent use of first-person pronouns, pessimistic expressions, and obsessive-compulsive writing styles—from natural user conversations. Multiple models were constructed for these features, and an ensemble layer integrates their outputs for a comprehensive judgment. The […]

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CAR-T cell therapy remains ineffective in most solid tumours because effector cells infiltrate poorly, undergo exhaustion, and face antigen escape within an immunosuppressive microenvironment. To address this, we developed a hybrid framework that combines a mechanistic spatiotemporal model with machine learning for limited patient-specific calibration. At its core, we employed a reaction-diffusion-chemotaxis model describing functional and exhausted CAR-T cells, antigen-positive and antigen-negative tumour subpopulations, a chemoattractant, an immunosuppressive factor, and hypoxia. Gradient boosting combined with nested cross-validation was […]

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Cognitive radio networks (CRNs) face significant challenges in dynamic spectrum access due to the complex interactions among multiple secondary users, sparse reward signals, and poor cross-domain generalization. Existing approaches, ranging from traditional optimization to single-agent deep reinforcement learning (DRL), struggle to balance spectral efficiency, collision avoidance, and adaptability in heterogeneous wireless environments. In this paper, we propose FedMA-DRL, a federated multi-agent deep reinforcement learning framework that integrates centralized training with decentralized execution (CTDE), graph neural network (GNN)-augmented Q-value […]

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Purpose: Hospital out-patient departments (OPDs) in India face severe queue inefficiencies with average waiting times of 90+ minutes and poor patient communication. Methodology: This study presents MediQueue — a full-stack intelligent queue management system built with React.js, Node.js, MySQL, Socket.io, and a self-learning ML engine. A dual-prediction architecture (Random Forest + equal-distribution fallback) predicts per-department wait times. A nightly recalibration scheduler updates slot capacities from verified treatment records. Findings: The system achieves a Mean Absolute Error (MAE) of […]

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The integration of large language models (LLMs) into peer review raises a concern beyond authorship and detection: the potential cascading automation of the entire editorial process. As reviews become partially or fully machine-generated, it becomes plausible that editorial decisions may also be delegated to algorithmic systems, leading to a fully automated evaluation pipeline. They risk reshaping the criteria by which scientific work is assessed. This paper argues that machine-driven assessment may systematically favor standardized, pattern-conforming research while penalizing […]

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For years, we’ve treated coding interviews as the gateway to software engineering. Solve a few algorithm problems under pressure, and you’re “in.” Struggle with them, and suddenly your ability as an engineer is questioned. But here’s the uncomfortable truth: LeetCode-style interviews have never been a reliable measure of engineering skill. At best, they’re a hit-or-miss signal. Sometimes you’re in the zone. Sometimes the pressure gets to you. Sometimes you’ve seen a similar problem before. Sometimes you blank out […]

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Efficient ground state search is fundamental to advancing combinatorial optimization problems and quantum chemistry. While the Variational Imaginary Time Evolution (VITE) method offers a useful alternative to Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA), its implementation on Noisy Intermediate-Scale Quantum (NISQ) devices is severely limited by the gate counts and depth of manually designed ansatz. Here, we present an automated framework for VITE circuit design using Double Deep-Q Networks (DDQN). Our approach treats circuit construction […]

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Software-Defined Network (SDN) renders flexible traffic engineering, but consumes a lot of energy. There is an overhead on the control-plane because of flow-rule updates are done always and the energy consumption by the forwarding hardware. Current energy-aware SDN methods mostly focus on static or greedy optimisations. This can cause too many Ternary Content-Addressable Memory (TCAM) updates and unstable rule churn when traffic changes over time. This article introduces a Dynamic Flow Rule Placement (DFRP) framework for real-time energy […]

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Stereo matching has witnessed rapid advances on curated benchmarks, yet deploying models in unconstrained real-world environments remains a fundamental challenge. This paper presents a sparse self-prompt guided network (SSPGNet) for stereo matching with strong generalization across diverse environments. Our core innovation lies in a sparse self-prompt guidance mechanism: 1) a sparse disparity map, used as a prompt, is self-estimated from visual foundation model features via cost aggregation; and 2) the sparse disparity is progressively refined into dense disparity […]

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