Vellore Institute of Technology University

The Neurocultural Remake Reflex Model (NRRM) is an interdisciplinary qualitative and neuroscience-informed analytical method that integrates heuristic insights from intersubject correlation (ISC) literature, cultural memory theory and affect theory to make reception-centered, testable inferences about how remakes trigger recognition, emotion and reinterpretation. The six-steps involved in the framework, Attention Mapping, Cognitive Processing, Cultural Recall, Cultural Reframing, Emotional Resonance and Emotional Dissonanceare operationalized through systematic qualitative scene analysis rather than empirical neuroimaging. Most existing research on remakes has primarily focused on comparative textual analysis, often overlooking the neurocognitive and emotional aspects of audience reception. The NRRM addresses this gap by explaining how viewers attend to, interpret, remember, emotionally connect with, and sometimes resist or contest a remake's narrative and aesthetic cues. The paper demonstrates the method through a scene-level application of two case studies to show how remakes function simultaneously as mnemonic devices and affective tools. In doing so, NRRM reveals how remakes preserve cultural legacies while generating new meanings, offering film scholars, media psychologists and cultural analysts a reproducible framework for tracing how remakes conserve, reframe or disrupt cultural memory and affective experience. This study proposes a six-step systematic qualitative methodology for analyzing remakes. By integrating cognitive and cultural approaches, it introduces an interdisciplinary framework for remake analysis. This framework demonstrates how remakes function as neurocultural reflexes, triggering nostalgia through both resonance and disruption.

• Novel 1,2,4-triazole sulfonic acid porphyrin photocatalyst synthesized. • TBSAFPc displayed a 97% degradation efficiency of Rhodamine B. • Optimized parameters revealed: 10 ppm, 0.2 g/L TBSAFPc, pH 5 in 180 min. • TOC of the degraded RhB sample was 82.92% attained. • The optimized parameter effectively degraded 250mL RhB with 84.26% efficiency. The increasing discharge of synthetic dyes into aquatic systems poses serious environmental concern due to their toxicity and resistance to conventional treatment methods. In this context, the development of efficient, metal-free, and sustainable photocatalysts is highly desirable. Herein, a novel 1,2,4-triazole-based sulfonic acid functionalized porphyrin (TBSAFPc) was successfully synthesized via the reaction of triazole ionic liquid and pyrrole under reflux conditions in the presence of glacial acetic acid. The prepared TBSAFPc was confirmed by 1 H NMR, 13 C NMR, PXRD, FT-IR, SEM techniques. The Hammett acidity, and energy band gap (DRS) were determined by UV-Visible Spectrophotometer while surface area was studied by BET method. The photocatalytic performance of TBSAFPc was investigated for the degradation of Rhodamine B (RhB) under irradiation with a low-power 5 W LED light source in a homemade photo-chamber under ambient conditions. The catalyst exhibited excellent photodegradation efficiency of 97 % for a 10 ppm RhB solution without the use of any additive under optimized conditions. Scavenger experiments confirmed the formation of active species responsible for degradation. The degradation process was further supported by UV–Visible analysis, FT-IR identification of intermediates, and a significant reduction in total organic carbon (TOC) up to 82.92%. A plausible photocatalytic mechanism was proposed based on experimental findings. Importantly, the heterogeneous TBSAFPc catalyst demonstrated good stability and recyclability for five successive runs, as confirmed by 1 H NMR and FT-IR analysis. Furthermore, under optimized conditions, mass-scale degradation (250 mL) achieved 84.26% degradation efficiency, highlighting its potential for practical wastewater treatment applications. Visible-Light Photocatalytic RhB Degradation

The discomfort, invasiveness, and inconvenience associated with traditional glucose monitoring methods continue to hinder patient compliance and the effectiveness of diabetes management. This simulation-driven feasibility study presents the design of the Photoacoustic Glucose Monitoring Device (PAGMD), a non-invasive glucose sensing system that integrates photoacoustic spectroscopy, machine learning, and IoT connectivity to enable real-time glucose estimation. PAGMD employs pulsed near-infrared light to induce glucose-dependent acoustic signals, which are detected by ultrasonic transducers and analyzed by machine learning algorithms for concentration prediction. The device features a hybrid energy harvesting system, combining thermoelectric and piezoelectric elements, to support continuous, battery-assisted operation and long-term usability. The system was theoretically validated using a multi-stage simulation pipeline that included Monte Carlo photon modeling, finite element acoustic simulation, and synthetic glucose dataset generation across a range of physiological conditions. The machine learning models achieved a coefficient of determination of 0.98 and a mean absolute relative difference of 6.97%, with 98.2% of predictions falling within clinically acceptable error zones. While current findings are based on simulated environments, a future validation roadmap encompassing in vitro, ex vivo, and in vivo studies is proposed to support clinical translation. This work should be regarded as a simulation-driven feasibility study; the reported benchmarks represent design targets for future prototyping rather than experimentally validated hardware results. By prioritizing non-invasiveness, ease of use, and adaptive intelligence, PAGMD represents a promising step toward inclusive, personalized metabolic health monitoring, especially for individuals with disabilities or limited dexterity.