Programmable hydrogels have emerged as a new generation of intelligent biomaterials capable of integrating stimuli-responsiveness, biocompatibility, and regenerative functions for precise drug delivery. Unlike traditional passive hydrogels, these systems utilize dynamic covalent and supramolecular crosslinking to achieve reversible adaptability and spatiotemporal regulation of therapeutic release. This review systematically summarizes the molecular design principles, stimuli-responsive mechanisms (pH, redox, enzyme, thermal, mechanical, and light), and crosslinking strategies that enable programmability and biodegradability within hydrogel networks. The crucial design approaches, including hybrid network architectures, molecular imprinting, and bioresponsive linkers, are highlighted as central to achieving selective and adaptive functionality. Particular focus is placed on multi-stimuli and feedback-controlled systems that coordinate drug release with biological signals to enable autonomous, context-specific therapy. Recent progress shows the integration of molecular imprinting, bioresponsive linkers, and hybrid structures that improve structural stability while maintaining responsiveness. Applications in wound healing, angiogenesis, and tissue regeneration emphasize the role of programmable hydrogels as bio-instructive matrices that modulate the immune response, preserve redox balance, and promote scar-free healing. Furthermore, emerging technologies such as AI-guided material design, 4D bioprinting, and bioelectronic integration are accelerating the development of closed-loop and patient-specific therapeutic platforms. Despite these advances, significant challenges remain, particularly regarding scalability, reproducibility, long-term stability, and the predictability of in vivo performance, which continue to limit clinical translation. Overall, programmable hydrogels mark a significant shift from static carriers to dynamic, self-regulating biomaterials for advanced regenerative medicine.
Purpose-The study ascertains the mothers' empowerment level and investigates its influence on the intergenerational transmission of educational achievements. Design/methodology/approach-The study uses primary data from 432 households, and the mothers' empowerment index is constructed with six distinct dimensions using principal component analysis. The impact of mothers' empowerment on the educational achievement of children is evaluated using multinomial logistic regression. Findings-Women in the sample show a moderately high progressive attitude, and there is positive cooperation between husband and wife; the empowerment index indicates a modest level of empowerment. Results indicate that empowered women positively influence their children's education; however, a daughter's educational accomplishments are more impacted by maternal empowerment than a son's. Originality/value-This study introduces two important novel aspects of women's empowerment that are absent in existing literature, i.e. husband-wife cooperation and women's progressive attitudes; both aspects have a substantial influence on the overall level of empowerment.
Early distinction between oral squamous cell carcinoma (OSCC) and possibly malignant disorders (PMDs) has been one of the most important diagnostic issues that may involve invasive biopsies. Therefore, it is of interest to assess the usefulness of salivary lactate dehydrogenase (LDH) and matrix metalloproteinase-9 (MMP-9) as non-invasive biomarkers of early detection. Spectrophotometry and ELISA were used in analyzing saliva samples of 135 participants (50 OSCC, 45 PMDs, 40 controls). The level of LDH and MMP-9 was much greater in OSCC than in PMDs and controls and demonstrated very high diagnostic accuracy (AUC > 0.89). Simultaneous biomarker measurement had better sensitivity and specificity indicating their possible use as well-grounded, non-invasive measures of screening oral cancer in its early stages.
Abstract Latency management has been identified as one of the most important performance metrics in wireless sensor networks (WSNs). Many sensor applications are latency sensitive; however, they are resource constrained. This combination of constraints creates difficulties for traditional machine learning (ML) methods to find complex relationships between various traffic attributes. In addition, although deep learning (DL) methods have shown promise for modeling relationships in WSN data, many require significant amounts of memory and are often difficult to interpret. This paper introduces an explainable artificial intelligence (XAI) based attention-fusion gated DL architecture (AFG-TabNet) for WSN latency classification. AFG-TabNet captures feature level interactions through a feature-wise attention mechanism and improves feature representation using a gated residual feature mechanism. The proposed architecture was tested using a stratified 70%–15%–15% split on a WSN latency testing dataset, with 5-fold and 10-fold cross validation on the training set. The proposed AFG-TabNet architecture is compared to other architectures which are similar but without the use of XAI (TabNet and TabNet variant) and to traditional ML algorithms such as decision tree, bagging, boosting, support vector machines, XGBoost, Catboost, and LightGBM. Accuracy, precision, recall, f 1-score, confusion matrix and roc curve were used to evaluate the performance of each architecture. Computational time analysis is also performed. Results show that the AFG-TabNet consistently outperforms the comparison architectures, with accuracy-98.67%, precision-98.69%, recall-98.67% and f 1-score-98.66%, thereby achieving the best results in all cases. The discriminant ability of AFG-TabNet is also demonstrated through its significantly better macro average AU-ROC (>0.97) compared to ensemble and boosting based architectures. Additionally, SHAP, LIME and t-SNE analysis provide the first global and local interpretation of the AFG-TabNet’s results demonstrating that key congestion, routing and traffic attributes are associated with the predicted latencies. These results confirm that the AFG-TabNet represents an efficient and transparent method for WSN latency classification, and thus suitable for future generation smart and latency-aware sensor network applications.
Conventional dyslexia interventions, limited by their static, one-size-fits-all nature, fail to address learner heterogeneity, thus reducing efficacy and engagement. We introduce the Emotion-based Generative Cognitive Imprinting (EGCI) framework, a personalized, emotionally adaptive therapeutic paradigm. EGCI's closed-loop architecture uses WebSockets to connect a Python-based Emotion Sensor (DeepFace, MediaPipe) for realtime affective analysis, a core logic server, and a web interface. Its foundation is a Bayesian Dynamic Affective-Cognitive Learner Model (DACLM) that fuses a learner's cognitive profile with real-time emotion data. The DACLM's state functions as a highdimensional prompt for a Generative Content Engine (GCE) that leverages a Large Language Model (LLM) to dynamically synthesize therapeutic exercises. An optimal intervention policy is derived by a Q-learning agent, which solves a state-aware reinforcement learning problem to co-optimize for skill acquisition and learner well-being. This integration of probabilistic modeling, affective sensing, generative AI, and reinforcement learning yields a scalable, dynamic, and deeply personalized solution poised to advance assistive educational technology.
