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Model-based engineering (MBE) is a powerful paradigm that leverages models as essential pillars of the development process, enabling teams to clarify requirements, streamline design, specify behavior, and perform rigorous verification and validation tasks across the entire system life cycle. Digital twins (DTs) represent revolutionary software systems that mirror cyber-physical, socio-economic, or biological entities, systems, or processes. Built from robust models and data, DTs are deployed for high-impact applications such as planning, monitoring, control, and optimization of the twinned entity. The model-centric nature of DTs has naturally ignited recent exploration into harnessing MBE for the engineering and operation of DTs. However, this organic evolution has created a fragmented landscape of (partial) solutions. To confront this challenge, this article presents a rigorous and systematic literature survey on the field of model-based DT engineering (MBDTE), accompanied by a novel taxonomy for categorizing MBDTE approaches. We also introduce crisp definitions of both the field of MBDTE and the models themselves. We conclude by highlighting research gaps and outlining avenues for further exploration.

Facial Expression Analysis is essential for emotion-aware technologies such as assistive interfaces, behavioral monitoring, and intelligent surveillance, where low-latency processing is required. This work introduces a hybrid quantum-classical framework addressing key challenges in landmark precision, class imbalance, and hardware feasibility. First, a novel method is proposed for correcting facial landmark positions using edge-aware scanning guided by quantum-derived probabilities, improving spatial alignment and clarity of expressive regions. Second, quantum distance measurement uses confidence-interval–guided adaptive shot selection to stabilize estimates under noise while preserving low circuit depth. Third, quantum-computed distances are transformed into enriched representations as classifier training data, capturing relative similarity, confidence, and distributional structure to help mitigate class imbalance and improve emotion category separability. The enriched features are processed by a neural network for emotion recognition. Evaluations on diverse datasets show improvements in accuracy, generalization, robustness, and real-time feasibility while maintaining compatibility with noisy intermediate-scale quantum hardware.

A holistic investigation into the effect of chain-length in thioureas, revealed corrosion mode change (uniform-localised-uniform) phenomena, showing the populating effects and patchy coverages of various homologues at different concentrations. This work clearly demonstrates that addition of alkyl chains stabilizes the adsorbed layer. Potentiodynamic polarization and electrochemical impedance studies reveal a transition in inhibition mechanism, with increasing chain-length. Phase angle of Bode plot shows a clear signature for onset of impervious film formation, hence it can be useful in high throughput screening of molecules. Impedance response of DPTU hinted multiple equivalent circuits for Nyquist plots, however, the applicable circuit could be established using Bode plot, making it a powerful combination to model the system, in case of ambiguity. Unlike the lower homologues, DBTU being the best inhibitor, induces a negative charge on metal surface, hinting a screening parameter. Compared to lower homologues, DBTU follows a different mechanism to inhibit the corrosion. In summary, this work captures several nuances in behaviour and evaluation of thioureas i.e. small molecule substituted with increasing alkyl chain lengths. Understanding the role and contribution of alkyl chains to inhibition would take us one step closer to the easier design of efficient inhibitors.

Type 1 diabetes mellitus (T1DM) is increasingly associated with gut microbial dysbiosis. Emerging evidence suggests that short-chain fatty acids(SCFA) producing bacteria play a role in glucose regulation and immune modulation. However, interventional data in children remain limited. To evaluate the effect of an oligofructose-enriched inulin intervention on glycemic control and gut microbiome composition in Indian children with T1DM. In double-blind, randomised, placebo-controlled pilot trial, 68-children (8-18years) with established T1DM were allocated to receive either 8 g/day of oligofructose-enriched inulin or an isocaloric maltodextrin placebo for 12 weeks. Anthropometry, glycated-hemoglobin (HbA1c), and stool-microbiome-profiles (16SrRNA sequencing) were assessed at baseline and endline. Alpha and beta-diversity indices, differential abundance analyses(DESeq2), were compared between groups. Sixty-one participants (32 prebiotic, 29 placebo) completed the study with a mean compliance of 80

This study presents a non-destructive, artificial intelligence (AI)-assisted spectro-analytical system for rapid milk adulteration detection using a laboratory-developed portable reflectance spectrophotometer operating across 410–940 nm. Six adulterants—urea, formalin, hydrogen peroxide, baking soda, sucrose, and cornstarch—were evaluated individually to establish compositional–spectral relationships within a single-adulterant modelling framework. Physicochemical characterization confirmed adulterant-dependent shifts in solids-not-fat (SNF; 3.29–9.18