Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Recent advances in large language models (LLMs) have enabled new forms of software creation through natural-language interaction. However, many AI-assisted coding tools continue to assume familiarity with development environments, programming workflows, and technical conventions, which may limit accessibility for early-stage learners and communities historically underrepresented in digital participation. This challenge is particularly relevant in Aotearoa New Zealand, where M¯aori and Pacific peoples remain underrepresented across STEM and technology pathways. This paper introduces TechTahi, a browser-based, syntax-free AI-assisted platform designed to support low-barrier digital creation through natural-language prompts and immediate in-browser previews. The study had two aims: to describe the design rationale and workflow of TechTahi and to explore early learner perceptions following initial use of the platform. An exploratory pilot design was employed. Five participants completed a post-use survey after hands-on interaction with TechTahi. Responses were analysed descriptively, with open-ended feedback reviewed for recurring themes. Findings suggested generally positive perceptions of accessibility and ease of use, particularly the ability to create working applications without prior coding knowledge. Participants also identified opportunities for culturally relevant features, including language support and locally meaningful design elements, alongside areas for improvement such as clearer onboarding guidance and reduced information density. These preliminary findings suggest that syntax-free, culturally responsive AI creation tools may offer promising pathways for widening participation in digital learning. Further research with larger and more diverse samples is needed to evaluate longer-term educational impact....
Accurate wheat yield prediction is critical for global food security, yet existing forecasting models often struggle to balance highdimensional genomic data with dynamic environmental variables. This study developed an automated framework based on genetic algorithms (GAs) to simultaneously optimize phenotypic selection, climatic feature engineering, and machine learning hyperparameters. The framework was evaluated across two contrasting cultivation environments: irrigated (Mexico) and nonirrigated (Middle East). For the irrigated dataset, the model achieved a peak performance of coe6cient of determination (R2) = 0.8363 and root mean squared error (RMSE) = 38.59, demonstrating that the proposed methodology is capable of predicting wheat yield with a R2 exceeding 0.80 under irrigated conditions. Meanwhile, in the nonirrigated environment, the system maintained robust predictive power with R2 = 0.6199 and RMSE = 721.67. To ensure the statistical reliability and reproducibility of these ;ndings, a bootstrapping validation (1000 iterations) was performed on the top-performing individuals. This process yielded narrow 95% con;dence intervals, con;rming that while the GA-optimized features provide higher stability in irrigated systems, the framework e=ectively captures genotype–environment interactions even under water-limited conditions. This dual-environment validation, underpinned by robust resampling techniques, demonstrates the scalability of the proposed soft computing approach for precision breeding across diverse agroclimatic zones....
CONTEXT: High-tech solutions are potential tools to benefit fruit and vegetable (F&V) food systems' firm-level economic performance and engineering resilience. However, their high energy demand and the complexity of integrating high-tech throughout the food system stages may hinder achieving these benefits. OBJECTIVE: We conducted a systematic literature review on the impact of high-techs on firm-level economic performance and engineering resilience of F&V food systems. METHODS: For the period 2016–2024, following the PRISMA protocol, we identified a total of 52 primary studies, yielding 103 estimated effects. Based on predefined criteria, we included high-tech solutions ranging from artificial intelligence (AI), internet of things (IoT), machine learning (ML), deep learning (DL), sensors, blockchain, robotics, data analytics, drones, model predictive control, to decision support systems. RESULTS AND CONCLUSION: We found that the indicators only partially reflect high-tech solutions' economic performance such as water use reduction (8–95%), increased productivity (5–29%), and reduced food waste (7–25%). The predictive capabilities of high-tech innovations highlight their potential to strengthen the food system's ability to anticipate and mitigate shocks. In addition, we found consumer impacts remain unexamined,including whether high-tech enhances F&V affordability and accessibility. Finally, we developed a conceptual framework illustrating high-techs potential to integrate all F&V system stages. We recommend undertaking more quantitative impact research at the consumer and system-level to identify the potential of high-tech solutions to transform food systems. SIGNIFICANCE: Our results inform policy debates on fostering the use of high-techs in designing F&V systems, and guide firm-level actors who decide on the adoption of high-techs in F&V systems....
Real-time pressure field measurement in aerospace vehicles is challenging because flexible sensor arrays must operate on curved surfaces under coupled thermal and pressure conditions. In this study, a temperature-compensated flexible capacitive pressure sensing system was developed for aerospace applications by integrating an 8 × 8 flexible sensor array, a multi-channel readout circuit based on time-division multiplexing and synchronous detection, and a Particle Swarm Optimization–Backpropagation (PSO-BP) neural network model. Calibration results showed high linearity, with a correlation coefficient of 0.9998 and a maximum relative error of 2.23%. Under coupled temperature–pressure conditions over 5–150 kPa and 10–110 ◦C, the average measurement error remained below 6%. Flight experiments further demonstrated valid in-flight data acquisition and trend-level pressure variations during key flight events, verifying the feasibility of the proposed approach for distributed aerospace pressure monitoring....
Portable and mobile biomedical devices (MBD) are an important part of modern healthcare. This is due to their ability to perform continuous monitoring, early diagnosis, and personalized treatment even outside the conventional clinical settings. Advancements in the healthcare domain are driven by high-tech sensor technology, wireless communication, and data analytics, which help identify, analyze, and interpret these devices’ applications in telemedicine and remote patient monitoring. A systematic literature review was conducted in accordance with established protocols. Peer-reviewed articles published in English during 2015–2025 were identified through a Boolean search string using the related keywords across leading scientific databases. The article selection procedure was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Relevant data were extracted and thematically synthesized. The study is also supported by manual keyword analysis. The review finds four major types of dominant devices, which include wearable sensors, handheld diagnostic devices, smartphone-based medical devices, and implantable portable monitors. They have been used for cardiovascular monitoring, diabetic treatment, neurology, respiratory, and rehabilitative purposes. Besides, there is an improvement in patient engagement, as demonstrated in this research. The barrier to healthcare services access, on the other hand, is reduced. Indeed, portable and mobile biomedical devices are a welcome change that will bring the healthcare system to decentralized and patient-centered care. It should be developed in the future using standardized validation procedures, secure data infrastructures, and regulatory balance. The current review also emphasizes some of the measures that can be adopted to ensure that mobile biomedical technologies can be integrated into digital health ecosystems to benefit the researchers, device designers, and doctors....
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