This work presents a control strategy designed to reduce the energy consumption of direct current motors by implementing smooth motion trajectories in a point-to-point control system, utilizing a fuzzy logic controller based on the Tsukamoto inference method. The proposed controller’s energy performance was experimentally compared to that of a conventional PID controller, considering three motion profiles: parabolic, trapezoidal, and S-curve. The results demonstrate that the combination of the fuzzy controller with smooth trajectories effectively reduces energy consumption without compromising motion accuracy. Under no-load conditions, average energy savings of 11.77% for the parabolic profile, 9.27% for the trapezoidal profile, and 3.45% for the S-curve profile were achieved. This improvement remained consistent even when a load was introduced to the system. To validate these findings, the coefficient of variation was calculated, revealing lower dispersion in the fuzzy controller’s results, indicating greater consistency in energy efficiency. Furthermore, Welch’s t-tests were conducted for each profile and load condition, with all p-values falling below the 0.05 significance threshold, confirming the statistical relevance of the observed differences.

Uncertain masses, dimensions, and friction coefficients can cause a robot’s motion to differ from predictions based on nominal parameters. This study examines how these uncertainties affect joint trajectories and which parameters deserve closer characterization. Ten physical parameters of a planar two-degree-of-freedom manipulator are represented by triangular fuzzy numbers, defined by lower, nominal, and upper values. Their $\alpha$-cuts form nested parameter intervals, whose vertices are propagated through the nonlinear rigid-body equations to approximate the range of joint responses. Numerical tests use sinusoidal and step torques, different initial conditions, Monte Carlo sampling, and sensitivity analysis. Verification with 50,000 samples per tested domain reveals a small second-joint exceedance under baseline sinusoidal excitation and larger exceedances under step torques; the alternative sinusoidal configuration retains complete sampled containment. Under the baseline sinusoidal excitation, the individual parameter ranking remains unchanged at the four nondegenerate $\alpha$ levels tested; the link length $l_1$, second-joint friction $b_2$, and first-link mass $m_1$ have the largest effects. A reduced-order surrogate also lowers the cost of repeated model evaluation within the tested domain. The contribution is an integrated workflow for assessing response dispersion and prioritizing parameter characterization before controller design or hardware testing. Its envelopes support preliminary model assessment rather than guaranteed response bounds.

Reliable deployment of predictive emission monitoring systems (PEMSs) requires evaluation under future operating conditions and explicit attention to high-emission events. This study presents a pollutant-specific, risk-constrained framework for predicting Gas Turbine CO and ${\mathrm{NO}}_x$ emissions under multiyear distribution shift. A dataset containing 36,733 observations was divided chronologically: 2011–2013 were used for model development, 2014 for configuration selection and calibration, and 2015 as a locked final test. Matched random within-prefix and forward temporal validation were compared, and Extra Trees was selected for both pollutants. Conventional, tail-weighted, covariate-shift-weighted, and combined configurations were evaluated under global-RMSE budgets of 2%, 5%, and 10%. Random within-prefix validation produced RMSE estimates 25.0% lower for CO and 34.0% lower for ${\mathrm{NO}}_x$ than matched temporal validation. In 2015, CO tail weighting produced modest changes, reducing global RMSE by 0.9% and ${\mathrm{Q}}_{99}$ mean underprediction loss by 2.0%, without statistically resolved ${\mathrm{Q}}_{99}$ improvement. For ${\mathrm{NO}}_x$, tail weighting reduced ${\mathrm{Q}}_{99}$ RMSE by up to 5.5% while increasing global RMSE by 5.8%. Linear calibration reduced ${\mathrm{NO}}_x$ global RMSE from 13.246 to 6.333 mg/m³ but worsened ${\mathrm{Q}}_{99}$ RMSE. Overall, temporal generalization, global calibration, and high-emission performance were distinct criteria, and no adaptation strategy was uniformly preferable.

Emerging mid-sized cities in the Global South face growing challenges in aligning transport infrastructure with equity and safety objectives under conditions of rapid urban expansion. While accessibility metrics are widely used in transport planning, their ability to capture functional inequalities and safety-related dynamics remains limited, particularly in corridor-level assessments. This study examines structural and functional accessibility patterns along two planned BRT corridors in Querétaro, Mexico, an emerging mid-sized Latin American city. The analysis integrates spatial accessibility indicators, selected urban process proxies related to inequality and road safety, and AI-based modeling to explore non-linear spatial associations across fine-grained corridor segments. Structural accessibility is evaluated using network-based indicators, while functional accessibility reflects observed service dynamics and operational conditions. Spatial correlations and artificial neural networks are employed as exploratory tools to identify co-occurring patterns rather than causal relationships. Results reveal pronounced spatial mismatches between structural and functional accessibility, socio-spatial marginalization, and crash concentration, highlighting corridor segments where future BRT implementation may either reinforce or mitigate existing inequalities. By framing BRT corridors as test cases, the study contributes a transferable diagnostic framework for assessing accessibility–equity–safety tensions in emerging mid-sized cities.