Techno International New Town

Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-augmented pipelines achieve progress in fluency but remain vulnerable to visual-linguistic biases, structural misalignment, and lack of explicit multi-hop clinical reasoning. We present NeuroSymb-MRG, a unified framework that integrates NeuroSymbolic abductive reasoning with active uncertainty minimization to produce structured, interpretable reports. The system maps image features to probabilistic clinical concepts, composes differentiable logic-based reasoning chains, decodes those chains into templated clauses, and refines the textual output via retrieval and constrained language-model editing. An active sampling loop driven by rule-level uncertainty and diversity guides targeted training optimization and promptbook refinement. Experiments on standard benchmarks demonstrate consistent improvements in structural coherence and interpretability and standard language metrics compared to representative baselines.

The rapid advancement of intelligent technologies has driven a transformative shift in healthcare, giving rise to Healthcare 5.0, a new paradigm centered on patient-focused, digitally empowered medical services. This study presents a comprehensive review of cutting-edge Deep Learning (DL) techniques that are redefining Healthcare 5.0 through applications in disease prediction and early diagnosis, medical image analysis and radiology, and multimodal deep learning (MMDL). We analyze a broad spectrum of DL architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Gated Recurrent Units (GRUs), Generative Adversarial Network (GANs), transformers, autoencoders, transfer learning, and MMDL, highlighting the unique features that make them particularly suited for healthcare tasks. Using bibliometric analysis of 1,342 Scopus-indexed publications (2018-2025) via VOSviewer, we uncover key research trends, thematic clusters, and emerging focus areas. Furthermore, we assess DL models across diverse real-world healthcare datasets and discuss critical challenges, including data privacy, model interpretability, and system integration. Following the PRISMA methodology, this review also synthesizes recent research on advanced DL architectures for disease diagnosis and Healthcare 5.0, alongside widely used medical imaging and multimodal datasets. The manuscript has been substantially strengthened by incorporating a comparative analysis, quantitative aggregation of reported results, an explicit novelty and contribution statement, and a systematic comparison of DL survey studies across Healthcare 4.0 and Healthcare 5.0, thereby providing a more critical, data-driven, and clearly differentiated perspective beyond prior reviews. Finally, we address significant issues, including ethical considerations, privacy concerns, and model limitations, while outlining promising directions for future research. This work serves as a valuable resource for researchers and practitioners, offering both a broad overview and deep insights into the latest developments in DL for Healthcare 5.0 systems.

Rapid industrialization had led to increase in water pollution due to the discharge of toxic waste directly into the water bodies without proper treatment leading to increase in the concentration of heavy metal ions (HMIs) in the consumable water. The current study illustrates an opto-electronically tunable CuO/ZnO heterojunction based device for the detection of HMIs in water at a high selective rate. The device showed a response of 160% toward As(V) ions in the presence of UV light and 47% toward Ni(II) ions in the presence of red light for 100 ppm of the ions in the cutoff region and a response of 30% for UV light toward Hg(II) and 22% for red light toward As(III) ions for 100 ppm in the linear region. The device also demonstrated a response time of 5 and 3 s for As(V) and Hg(II) under UV light, respectively, and 10 and 19 s for Ni(II) and As(III) in the presence of red light, respectively. The device demonstrated excellent repeatability with a maximum error of approximately ±1.9%. The device also showed high response toward UV exposure compared to red light. The device’s high sensitivity toward different ions under various optical conditions demonstrated the optical tunability of the sensor toward different HMIs, thereby making it a suitable candidate over its existing hardware intensive counterparts. An adsorption based mechanism illuminated the device’s behavior toward different HMIs. Hence, this type of sensors can be used as an alternative route for the development of cost-efficient and industry-friendly HMI sensors.

The growing threat of heavy metal ion (HMI) contamination in drinking water calls for highly sensitive, rapid, and field-deployable detection technologies. Here we report a p-type reduced graphene oxide (rGO)/graded ZnO field-effect transistor (FET) that enables tunable, low-level detection of copper (Cu(II)) and iron (Fe(II)) ions in aqueous media. The vertically aligned ZnO nanorods, synthesized via a seed-layer-assisted chemical bath deposition method, serve as both the gate dielectric and receptor layer, while rGO functions as the conducting channel. The heterostructured FET demonstrates strong gate-voltage-dependent selectivity, showing a maximum response of similar to 4406% for Cu(II) ions at V-GS = -4V and similar to 4699% for Fe(II) at V-GS = -5V. Low limits of detection of similar to 1.0 ppb for both Cu(II) and Fe(II) ions are achieved experimentally, with rapid response and recovery times of 4s and 3.5s for Cu(II) ion and 10s and 3s for Fe(II) ion, respectively. The sensing mechanism is attributed to adsorption-driven reduction of target ions on the ZnO surface, which modulates the interfacial dipole and channel conductivity of the rGO layer. This work establishes a cost-effective, real-time monitoring of multiple heavy metal ions and highlights the potential of graded ZnO-rGO FETs for next-generation environmental water quality assessment.

In this paper, we extend the classical Hastings–Powell tri-trophic food chain model by incorporating two ecologically realistic mechanisms: predator-induced fear in prey and a weak Allee effect. A rigorous mathematical analysis is carried out, including positivity, boundedness, dissipativeness, and persistence of solutions, followed by a detailed investigation of all equilibrium points and their local and global stability properties. Through bifurcation analysis, we identify that govern system transitions. Sensitivity analysis using Partial Rank Correlation Coefficients (PRCC) highlights the most influential parameters for prey, predator, and top predator dynamics. Numerical simulations, conducted via MATLAB and MATCONT, further reveal complex dynamical regimes. In particular, variation of the prey-handling time parameter [Formula: see text] drives the system into chaos, as confirmed by a positive maximum Lyapunov exponent (MLE). Importantly, we demonstrate that chaos can be suppressed by strengthening the weak Allee effect ([Formula: see text]) and predator-induced fear ([Formula: see text]). Two-parameter bifurcation diagrams for [Formula: see text], [Formula: see text], and [Formula: see text] provide deeper insights into the combined ecological effects of these parameters. The results, supported with ecological interpretations, show how behavioral responses and Allee effects can regulate chaotic oscillations and promote system stability, offering useful theoretical insights for ecological management and conservation.