This systematic literature review investigates microstrip antenna applications in image acquisition, focusing on their design characteristics, reconstruction algorithms, and application areas. We applied the PRISMA methodology for article selection. From selected studies, classifications were identified based on antenna patch geometry, substrate types, and image reconstruction algorithms. According to inclusion criteria, a significant increase in publications on this topic has been observed since 2013. Considering this trend, our study focuses on a 10-year publication range, including articles up to 2023. Results indicate that medical applications, particularly breast cancer detection, dominate this field. However, emerging areas are gaining attention, including stroke detection, bone fracture monitoring, security surveillance, avalanche radars, and weather monitoring. Our study highlights the need for more efficient algorithms, system miniaturization, and improved models to achieve precise medical imaging. Visual tools such as heatmaps and box plots are used to provide a deeper analysis, identify knowledge gaps, and offer valuable insights for future research and development in this versatile technology.

This systematic review explores recent advancements in antenna and rectifier systems for radio frequency (RF) energy harvesting within the gigahertz frequency range, aiming to support the development of sustainable and efficient low-power electronic applications. Conducted under the PRISMA methodology, our review filtered 2465 initial records down to 80 relevant studies, addressing three research questions focused on antenna design, operating frequency bands, and rectifier configurations. Key variables such as antenna type, resonant frequency, gain, efficiency, bandwidth, and physical dimensions were examined. Antenna designs including fractal, spiral, bow-tie, slot, and rectangular structures were analyzed, with fractal antennas showing the highest efficiency, while array antennas exhibited lower performance despite their compact dimensions. Frequency band analysis indicated a predominance of 2.4 GHz and 5.8 GHz applications. Evaluation of substrate materials such as FR4, Rogers, RT Duroid, textiles, and unconventional composites highlighted their impact on performance optimization. Rectifier systems including Schottky, full-wave, half-wave, microwave, multi-step, and single-step designs were assessed, with Schottky rectifiers demonstrating the highest energy conversion efficiency. Additionally, correlation analyses using boxplots explored the relationships among antenna area, efficiency, operating frequency, and gain across design variables. The findings identify current trends and design considerations crucial for enhancing RF energy harvesting technologies.

Food production systems in Ecuador’s high Andean region are pivotal for food security, rural livelihoods, and agrobiodiversity, yet they are increasingly exposed to climate stress. We assessed four representative crops (tree tomato, quinoa, potato, and maize) across three Andean zones (North, Center, South) in 2015–2022 using monthly NASA POWER (MERRA-2) climate fields. After confirming non-normality, Spearman correlations and multiple linear regressions with leave-one-year-out validation were applied to quantify the influence of maximum/minimum temperature and precipitation on cultivated and harvested area, production, sales, and loss categories. To place monthly signals in a process context, daily extreme-event diagnostics (ETCCDI-style) were also computed: heat days ($\mathrm{TX}90$), ≥5-day dry spells, and the annual maximum consecutive dry days (${\mathrm{CDD}}_{max}$). Models explained a wide range of variability across crops and zones (approx. $R^2\sim 0.55$–0.99), with quinoa showing the most consistent fits (several outcomes $R^2>0.90$). Extremes provide an eye-catching, actionable picture: the Southern zone concentrated dryness hazards, with 1–5 dry spells $\ge 5$ days per year and ${\mathrm{CDD}}_{max}$ up to ∼8 days, while heat-day frequency showed non-significant declines across zones in 2015–2022. Reanalysis frost days were virtually zero—consistent with under-detection of local valley frosts at coarse resolution—so frost risk was interpreted via monthly signals and reported losses. Overall, the results show precipitation-driven vulnerabilities in the South and support quinoa’s role as a resilient option under increasing climate stress, offering concrete guidance for water management and climate-smart planning in mountain agroecosystems.

The accurate estimation of electricity supply costs has become increasingly relevant due to growing demand, variable generation sources, and regulatory changes in emerging power systems. This study models the average unit cost of electricity supply (USD/kWh) in Ecuador using multiple linear regression techniques and ARIMAX forecasting, based on monthly data from 2018 to 2024. The regression models incorporate variables such as energy demand, generation mix, transmission costs, and regulatory indices. To enhance model robustness, we apply three variable selection strategies: correlation analysis, PCA, and expert-driven selection. Results show that all models explain over 70% of price variability, with the highest-performing regression model achieving $R^2=0.9887$. ARIMAX models were subsequently implemented using regression-based forecasts as exogenous inputs. The ARIMAX model based on highly correlated variables achieved a MAPE below 5%, showing high predictive accuracy. These findings support the use of hybrid statistical models for informed policy-making, tariff planning, and operational cost forecasting in structurally constrained energy markets.