The new generation of wireless body area networks (WBAN) for the internet of things (IoT) is emerging in a fast-paced. Today patients can be tested using remote clinical nanosensors. WBAN involves small interconnected sensors for the collection of ongoing medical data and transmitted through the networks for further processing. However, the protection of healthcare data is very important and difficult because of the various active and passive numbers of attacks. Although there exist several literatures on data security techniques such as digital signatures, Elliptic curve cryptography, and Advanced Encryption Standards (AES) they contain stumbling blocks for the security methodologies. Thus, in this paper, a novel data protection genetic-based encryption scheme is proposed for higher performance.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In Wireless Body Area Network (WBAN), for example, the patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity rendering the data transmission slower. Cognitive Radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors rendering the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Introduction: Early Alzheimer's disease (AD) risk assessment requires accessible alternatives to invasive biomarkers. We developed a multi‐modal machine learning framework using questionnaire metadata from participants with concurrent microbiome sequencing data. Methods: We analyzed 9832 participants with 120 metadata features across five categories (demographic, dietary, lifestyle, nutritional, medical). Features were selected via Pearson correlation and chi‐squared tests. Four algorithms were trained using 10‐fold cross‐validation with synthetic minority oversampling technique (SMOTE), validated on 1967 samples. The 16S rRNA sequencing data from the same cohort with 2000 samples enabled microbiome composition analysis. Results: Medical history (area under the curve [AUC] = 0.871) and dietary patterns (AUC = 0.874) achieved best performance, outperforming demographic (0.795), lifestyle (0.660), and nutritional (0.569) domains ( p < 0.001). Microbiome analysis revealed dysbiosis markers ( Prevotella/Bacteroides ratio: 1.921) linking dietary factors to potential neuroinflammatory pathways. Discussion: These findings support non‐invasive, multi‐modal screening combining medical and dietary evaluation for AD risk stratification, with preliminary microbiome evidence suggesting gut–brain axis dysbiosis as a mechanistic pathway warranting validation in larger cohorts.

This study examines how socio-economic and demographic factors influence electric vehicle (EV) adoption in New South Wales (NSW), Australia. Using 2021 Australian Bureau of Statistics (ABS) census data and EV registration records, a cross-sectional analysis was conducted at the postcode level. Ordinary Least Squares (OLS) regression was applied to identify key determinants of EV uptake. The final model demonstrates strong explanatory power (R² = 0.819%). Results indicate that economic status, captured through a combined income and mortgage indicator, is the strongest predictor, associated with an approximately 101.7% increase in EV registrations for each standard deviation increase. Vehicle ownership density also shows a strong positive effect (an 80.9% increase). In contrast, areas with higher reliance on active transport exhibit a statistically significant negative association with EV adoption. Additional factors, including age, education, and occupational composition (managers and professionals), show moderate positive effects. These findings highlight persistent socio-economic disparities in EV uptake and suggest that targeted policy interventions are required to support a more equitable transition to sustainable transport.