Jiwaji University

This chapter explores how gamification offers a transformative approach to communicating Intangible Cultural Heritage in the digital marketing era. As consumers increasingly adopt participatory and immersive cultures, gamification serves as a bridge between heritage and contemporary brand communication. By employing mechanics such as quests, challenges, and interactive storytelling, brands can foster engagement, cultural resonance, and differentiation in competitive markets. These strategies extend across various contexts, including tourism marketing, cultural branding, education, and cross-generational outreach, while also raising ethical considerations related to authenticity and community participation. Emerging technologies such as AI, extended reality, and the metaverse further open pathways for gamified cultural transmission that remain globally relevant and locally grounded. Finally, the chapter explores how gamifying ICH provides a means to preserve cultural significance while advancing innovative marketing strategies.

Voice recordings are the most widely used data type for identifying Parkinson's disease (PD) using machine learning (ML) methods, as they are non-invasive, cost-effective, and easily accessible. Auditory information obtained through telephone or mobile devices can facilitate large-scale screening, but variations in data collection methods and analysis have led to inconsistent results in existing research, hindering their direct application in clinical practice. Parkinson's disease is a common neurodegenerative disorder that affects a significant number of people after the age of 50 and is associated with movement and speech difficulties. Timely diagnosis is crucial for proper disease management and maintaining patients' quality of life, but the challenge of early diagnosis is compounded by a shortage of specialist neurologists. In this context, machine learning offers a promising model for accurate and timely PD diagnosis by uncovering complex patterns in biomedical data. Various ML classifiers, such as Decision Trees (DT), Random Forests, k-Nearest Neighbors (kNN) classifier, AdaBoost, and Support Vector Machines(SVM), have been employed to improve diagnostic performance. Experiments conducted on the UCI Parkinson's dataset demonstrate that ML models can differentiate between PD patients and healthy individuals and aid in early clinical decision-making. In the current work, seven machine learning models were tested for PD classification, but the ensemble model (Random Forest) was found to be more accurate than the other models.

Noisy measurements can make a control system overreact, wearing out its parts and making it less effective. Filters help clean up the signal, but if they're too strong, they can slow down the system and make it less stable. This study presents a new version of the Indirect Internal Model Control-Proportional-Integral-Derivative (IIMC-PID) controller that includes a noise filter, which is the key innovation. The new control schemes handle both problems (phase lag and noise) effectively. Its strength is tested through sensitivity analysis, and its settings are fine-tuned using Teaching Learning Based Optimization (TLBO). The efficacy of the offered approach is evaluated using two benchmark problems. The design is clear and easy to understand, making it a good fit for real-world industrial use.

Abstract The urgent demand for sustainable energy conversion technologies has stimulated intensive research on thermoelectric materials capable of directly converting waste heat into electricity. In this work, the Bi-based half-Heusler alloys RhNbBi and IrNbBi are investigated through density functional theory to elucidate the interplay between their structural, electronic, mechanical, phonon, thermoelectric, thermodynamic and optical properties relevant to semiconductor and energy applications. Structural optimization and elastic analyses performed using WIEN2k and Quantum ESPRESSO confirm dynamically and mechanically stable non-magnetic metallic ground states with strong 4d/5d-6p hybridization and ductile behaviour satisfying the Born-Huang criteria. Phonon dispersions free from imaginary frequencies verify dynamical stability, while quasi-harmonic Debye modeling reveals excellent thermal robustness up to 1200 K with smoothly varying heat capacity and entropy. The optical spectra, derived from the complex dielectric function, exhibit large static dielectric constants ε₁(0) =70 for RhNbBi and 90 for IrNbBi, high refractive indices n(0) ≈ 7.8–9.0, and strong absorption extending to 12 eV, consistent with experimentally reported optical data for CoTiSb and YbPtBi analogues, thereby validating the present theoretical predictions and establishing their reliability for Bi-based half-Heusler systems. Transport coefficients, evaluated using BoltzTraP2, indicate positive Seebeck coefficients and p-type conduction, suggesting their potential for thermoelectric energy conversion at elevated temperatures. Overall, this study provides comprehensive first-principles insights into Bi-based 4d/5d half-Heusler frameworks, establishing RhNbBi and IrNbBi as promising thermally robust, optically active and mechanically stable candidates for next-generation thermoelectric and optoelectronic devices.

Plasmodium vivax poses a persistent obstacle to global malaria elimination efforts. While no detectable chloroquine (CQ) resistance has been found in patient samples from India, this observation awaits confirmation. The mechanism of action of CQ continues to be debated. Hemoglobin degradation within the parasite's food vacuole (FV) is integral to its survival and serves as a promising target for antimalarial drug development. This study investigates the molecular and structural characteristics of three key FV enzymes-plasmepsin IV (PM_IV), heme detoxification protein (HDP), and falcilysin (FLN)-in P. vivax isolates from India. Genomic DNA from 30 clinical isolates and three chloroquine (CQ)-resistant reference strains was analyzed to identify mutations and assess structural implications through homology modelling. Several nonsynonymous mutations were detected, including c.493G>A (Val165Ile) in PM_IV, c.1537A>G (Asn513Asp), and c.2027G>A (Gly676Asp) in FLN, and a novel in-frame duplication c.28_33dup (ATCGCC) in HDP. Structural modelling revealed that these mutations did not affect the active binding sites of the enzymes. The genes were highly conserved across isolates, underscoring their important (essential) roles in parasite survival and their potential as drug targets. These are the first findings from the Indian subcontinent that provide critical insights into the mechanisms of chloroquine (CQ) action and resistance, paving the way for novel therapeutic strategies against Plasmodium vivax malaria.

Anti-aging tips for naturally minded folks can help you stay young and healthy. We’d all love to live to be healthy and wrinkle-free in our 90s, but the truth is, nothing can stop us from aging. This is particularly true in today’s world, which is plagued with aging catalysts like environmental toxins, foods filled with chemicals, poor nutritional values, and dangerous temptations. However, we can keep the aging process from moving at an accelerated pace by making better choices. Several successful aging studies have shown that lifestyle choices are two-thirds what predicts how well we age. We’ve all heard the common anti-aging tips: like-Eat tons of fruits and vegetables rich in vitamins, lower your alcohol intake, stop smoking, exercise regularly, stay out of the sun, drink a LOT of water, Meditate etc. These are all great, but there’s even more we can do. And you don’t have to take synthetic supplements or smear unpronounceable artificial ingredients on your skin to do it. Many of the prominent anti-aging products on the market are packed with harmful chemicals that ultimately make you age faster. Take advantage of nature’s best ingredients and prove that you can age successfully. Keywords: wrinkle free, aging catalysts, environmental toxins, vitamins, etc.