Search Results (4,097)

This paper evaluates the macroeconomic and financial determinants of the money demand of India from 1996: Q1 to 2024: Q4. The paper utilizes the Autoregressive Distributed Lags (ARDL) bounds testing framework and an Error Correction Model (ECM) to estimate the long-run equilibrium and the short-run dynamics of monetary aggregates, narrow money (M1) and broad money (M3). The empirical findings confirm a stable, singularly cointegrated relationship between real money balances (M1, M3), real income (GDP), opportunity cost (91-Day Treasury Bill Rate), and equity wealth (BSE Sensex). Since M1’s income elasticity is 0.53 and M3’s is 0.98, the traditional transaction motives dominate both M1 and M3. The interest rate exerts a negative substitution effect on M1; however, M3 remains structurally safeguarded against short-term fluctuations. Equity market valuations exhibit statistical insignificance across all variable specifications, indicating that the equity market fluctuations do not systematically destabilize long-run money demand. The ECM results reveal a short-term adjustment speed of 17.92% and 8.37% per quarter for M1 and M3, respectively. These findings establish that M3 acts as a robust and stable measure of the Reserve Bank of India’s long-term monetary targeting, driven predominantly by the fluctuations in real fundamental macro variables. Full article

Background: Sarcopenia is an age-related condition characterized by declines in muscle strength and muscle mass associated with adverse health outcomes in older adults. Nutrition risk may contribute to sarcopenia development; however, its relationship with sarcopenia among community-dwelling older adults, especially those living in retirement homes or with subjective cognitive impairment, remains unclear. This study aimed to determine the prevalence of sarcopenia and its indicators using the EWGSOP2 criteria in community-dwelling older adults with cognitive impairment and those living in retirement homes and examine associations between nutrition risk and probable sarcopenia. Methods: A secondary analysis of cross-sectional data from 326 adults aged ≥55 years was conducted. Sarcopenia was assessed using the EWGSOP2 Find–Assess–Confirm–Severity framework and nutrition risk was measured using SCREEN-14 and the Nutrition Risk Rating Score (NRRS). Results: Overall, 37.0% of participants were at risk for sarcopenia, and 26.9%, 5.5%, and 4.3% were classified with probable, confirmed, and severe sarcopenia, respectively. Retirement home residents were more likely to meet criteria for sarcopenia risk, low grip strength, and slow gait speed (all p < 0.001) than those with subjective cognitive impairment living in the community. In adjusted analyses, walker use and higher NRRS were associated with probable sarcopenia, whereas SCREEN-14 scores were not. Conclusions: These findings suggest that sarcopenia risk is common in these populations but confirmed sarcopenia is low using the EWGSOP2 framework. Nutrition risk as assessed by a dietitian using a comprehensive assessment is associated with probable sarcopenia. Full article

Background/Objectives: Cerebral small vessel disease (CSVD) is an important contributor to motor impairment. Cortical cerebral microinfarct (CCMI) is an emerging imaging marker of CSVD. Although its association with cognitive impairment has been well established, its relationship with motor function remains unclear. We explored associations of CCMI with motor performance at baseline and 1-year follow-up in this secondary analysis of data from the China Imaging-based Biobank of Cerebral Small Vessel Diseases. Methods: CCMI was assessed on baseline brain magnetic resonance imaging. Motor function was assessed at baseline and at 1-year follow-up. Outcomes were gait speed, poor balance performance (Short Physical Performance Battery balance score ≤ 2), abnormal gait (Scale for the Assessment and Rating of Ataxia gait score ≥ 2), and repeated chair-stand time. Gait speed was the primary outcome and all other motor outcomes were secondary. Multivariable linear and logistic regression models were used, with Benjamini–Hochberg correction applied to secondary outcomes. Results: The analysis included 192 patients, of whom 74 had 1-year follow-up motor data. At baseline, 55 patients (28.65%) had CCMI. Compared with patients without CCMI, those with CCMI had lower gait speed [0.92 (0.75–1.12) vs. 1.05 (0.87–1.16) m/s]. They also had higher prevalences of poor balance performance (29.09% vs. 14.60%) and abnormal gait (63.64% vs. 36.50%). However, fully adjusted analyses provided no conclusive evidence of baseline associations. At 1-year follow-up, CCMI was associated with slower gait speed after adjustment for demographic factors, baseline gait speed, and total CSVD burden (adjusted β −0.16, 95% CI −0.24 to −0.08; p < 0.001). CCMI was also nominally associated with longer repeated chair-stand time (adjusted β 2.07, 95% CI 0.39–3.75; nominal p = 0.02; adjusted p = 0.06), but this association did not survive correction for multiple comparisons. Conclusions: CCMI was associated with slower gait speed at 1-year follow-up in patients with CSVD. This finding is hypothesis-generating and requires confirmation. Full article

Multipath routing in wireless sensor networks (WSNs) improves reliability by providing alternative forwarding paths when a route fails. However, mobile sinks make path maintenance difficult because sink movement can invalidate previously constructed source-to-sink routes. Existing protocols typically depend on either global path reconstruction, which increases control overhead, or footprint-chaining, which accumulates detours through previous sink positions and may weaken path independence. To address this problem, this paper proposes QL-LGMPRP, a reliability-aware local-grid-based multipath routing protocol that combines a sink-centered local grid, two-path delivery, link-quality-aware forwarding, and lightweight tabular Q-learning for waypoint adaptation. Mobility-related route changes are confined to the sink-centered grid, whereas a compact tabular Q-learning policy adjusts the primary-path direction using grid, link-quality, and energy-related state variables. The sink constructs a local grid around its current position, with cells sized to keep in-grid forwarding locally bounded. When an event occurs, the source computes an entry point on the grid perimeter and constructs two greedy paths: a primary path through a Q-learning-selected waypoint near the grid boundary and a backup path toward the current sink position. The Q-learning agent uses a compact tabular state representation that includes the boundary-cell index, residual-energy level, sink-grid position, and local link-quality information, and learns waypoint offsets using a reward that combines delivery success, transmission energy, and delay. This design confines routing adaptation to the sink-centered grid while allowing the waypoint policy to respond to heterogeneous link conditions. Simulation results under different sink speeds and interference conditions show that QL-LGMPRP maintains high delivery reliability while reducing detour-related forwarding costs relative to footprint-chaining and showing lower weak-link exposure than the geometric-forwarding comparison schemes. Full article

Reading speed, a core indicator of reading performance, requires efficient oculomotor behavior. While well-documented in adults, the contribution of eye movements to children’s silent reading speed and grade-related changes remain underspecified. Furthermore, the distributional properties of fixation durations have rarely been examined in children. This study investigates how changes in eye-movement parameters associate with individual differences in children’s silent reading speed. The eye movements of 194 typically developing Greek-speaking students (Grades 3–6) were recorded during silent reading of short passages. An Ex-Gaussian distributional analysis was applied to fixation durations, alongside traditional measures of mean fixation number, fixation duration, and saccade amplitude. Language and cognitive abilities were also evaluated. Silent reading speed increased with grade, driven by fewer and shorter fixations, and longer saccades. After accounting for age, vocabulary and cognitive measures, eye-movement parameters increased the explained variance in silent reading speed to 80% (adjusted R² = 0.800) in the full sample (N = 194). The Ex-Gaussian parameter τ was an independent predictor, distinct from mean fixation duration. Finally, age significantly moderated the association between forward fixations and silent reading speed. These findings indicate that oculomotor behavior is independently linked to children’s silent reading speed across primary school years, as captured both by conventional and distributional oculomotor parameters. Full article

Portable electromyography biofeedback (EMG-BFB) may support gait rehabilitation, but whole-gait responses across increasing portable auditory feedback goals are unclear. Twenty-four adults completed baseline treadmill walking and four counterbalanced right medial gastrocnemius activation-goal conditions set at 20%, 40%, 60%, and 80% above baseline; 23 contributed primary biomechanical data. Treadmill speed was adjusted within each condition to support achievement of the activation goal. Outcomes included walking speed, ground-reaction forces, force-time metrics, step length, temporal measures, and asymmetry. Repeated-measures MANOVA, outcome-specific repeated-measures ANOVAs, dose-response coefficients, bootstrap intervals, and leave-one-participant-out analyses were used. The combined gait-biomechanics outcomes differed significantly across activation-goal conditions (p < 0.001), with large condition effects for walking speed, propulsion, braking magnitude, and step length. From baseline to the highest goal, treadmill speed increased from 1.07 to 1.44 m/s, mean propulsion by 0.086 N/BW, braking magnitude by 0.109 N/BW, and mean step length by 0.150 m. Exploratory speed-adjusted models retained anterior–posterior and vertical loading-response associations but not peak propulsion; activation goal and achieved speed were strongly collinear. Unilateral feedback was not associated with systematic step-length or step-time asymmetry. Portable auditory EMG-BFB at increasing activation goals was accompanied by coordinated changes across gait mechanics. Full article

This pilot in vitro study explored the tensile retention of lithium disilicate and monolithic zirconia crowns cemented onto zirconia one-piece implants using two resin cements and two cementation protocols. In addition, the relationship between micro-computed tomography (micro-CT)-derived cement layer characteristics and retention was explored for lithium disilicate crowns. Thirty-two implant–crown assemblies were prepared using 16 lithium disilicate and 16 zirconia crowns. Specimens were cemented with either an adhesive resin cement (Panavia V5) or a self-adhesive resin cement (SpeedCem Plus) using two protocols: conventional apical-half cementation (AH) and an abutment-assisted apical-half protocol (A-AH). Cement thickness and porosity for lithium disilicate crowns were obtained from a previously published micro-CT analysis of the same specimens; no micro-CT measurements were available for the zirconia specimens. Tensile pull-out testing was performed using a universal testing machine. The primary outcome was the maximum recorded force at the first observed mechanical failure, irrespective of the mode of that failure, so that all 32 specimens contributed a value. Failure occurred by crown debonding in 27 specimens, by crown fracture in 4 and by implant fracture in 1. For the primary outcome, the maximum recorded force was lower for lithium disilicate than for zirconia crowns (medians 347.20 versus 596.05 N; exact Mann–Whitney p = 0.017) and lower with the A-AH than with the AH protocol (medians 304.24 versus 614.38 N; p < 0.001), whereas the difference between the two resin cements was not statistically significant (medians 438.88 versus 550.83 N; p = 0.210). The highest observed mean maximum load was recorded for zirconia crowns cemented with Panavia V5 using the AH protocol (729.9 ± 237.7 N), whereas the lowest observed mean maximum load was recorded for lithium disilicate crowns cemented with Panavia V5 using the A-AH protocol (219.7 ± 105.1 N). In a secondary, cause-specific exploratory analysis restricted to crown debonding (27 events, 5 specimens censored at fracture), Cox proportional hazards regression on the applied-force scale gave hazard ratios of 3.75 (95% CI 1.40–10.01) for lithium disilicate versus zirconia, 6.47 (2.39–17.53) for A-AH versus AH and 1.82 (0.76–4.39) for Panavia V5 versus SpeedCem Plus. For lithium disilicate crowns, exploratory factorial ANOVA indicated that cementation protocol was associated with differences in cement thickness (p = 0.035), while cement type was associated with differences in porosity (p < 0.001). All 16 lithium disilicate cement thickness observations lay between 253.29 and 254.96 µm, a total span of 1.67 µm. Within that extremely restricted range, a univariable exploratory Cox model expressed per 0.1 µm gave a hazard ratio for debonding of 1.24 (95% CI 1.03–1.48; p = 0.024); this is an unadjusted association across a range that is itself associated with cementation protocol, and it does not demonstrate a clinically meaningful or independent effect of cement thickness. No association was detected for total porosity (0.959 per percentage point, 0.717–1.283); that interval is wide and indicates absence of evidence rather than evidence of no association. Within the limitations of this pilot in vitro study—four specimens per subgroup, wide confidence intervals and no adjustment for multiplicity—the findings suggest that crown material and cementation protocol may be associated with retention patterns. They are exploratory and hypothesis-generating and require confirmation in larger, independently powered studies. Full article

Large-scale maritime activity, persistent shipping incidents, and complex marine environments continue to place substantial demands on maritime search and rescue (MSAR). Current MSAR systems do not fully capitalize on the complementary strengths of unmanned aerial vehicles (UAVs) and satellites for collaborative tracking and rescue support. Existing air-space collaboration technologies suffer from two critical limitations: (1) rigid processes, including fixed task allocation, pre-determined path planning without real-time environmental adaptation, and isolated satellite–UAV decision-making, and (2) long task completion cycles, mainly because many methods are adapted to wide-area, long-duration military tracking scenarios. They therefore provide limited support for the dynamic flexibility required in MSAR. This study proposes a Proximal Policy Optimization (PPO)-based air-space collaborative tracking method for maritime moving targets to address these shortcomings and enhance air-space cooperation in MSAR operations. The core implementation of the method includes: (1) integration of target drift forecasting, satellite orbit prediction, UAV task allocation, and path planning into a unified reinforcement learning framework to reduce isolated single-platform decision-making; (2) the adoption of PPO to generate dynamic and flexible air-space collaborative tracking strategies that adjust satellite observation angles and scanning ranges, as well as UAV altitude, speed, and heading according to real-time target, environmental, and platform states; and (3) the design of a multi-dimensional reward function that balances target proximity, energy efficiency, coverage overlap, and inter-platform cooperation to guide strategy optimization. Simulation experiments include system-feasibility verification, baseline-controller comparison, PPO hyperparameter screening, and cross-scenario evaluation. Under idealized communication and payload-matching assumptions, the method enables coordinated tracking of maritime moving targets in simulated MSAR scenarios. In the standardized evaluation, PPO achieved an 11.9% higher mean evaluation episode return, 11.2% lower aggregate UAV energy consumption, and a 9.92-percentage-point greater endurance margin than DDPG. Hyperparameter screening compared candidate learning rates, discount factors, and training budgets, informing the PPO configuration for the subsequent six-scenario evaluation. Across the six controlled scenarios, rewards stabilized after approximately 1400 steps, while action magnitudes varied among regions. These results indicate that the proposed method has potential to enhance air-space collaborative tracking for MSAR decision support. Full article

Precise and real-time posture recognition of scallions during harvesting and post-harvest processing is critical for automated conveying, orientation adjustment, bundling, and packaging. Nevertheless, their slender and flexible form, varied spatial orientations, target overlap, lighting fluctuations, background interference, and constrained computational resources of edge devices present significant obstacles to reliable visual perception. This research introduces GOPD-YOLO, a lightweight oriented object-detection network built on the YOLOv8-OBB framework. The network integrates partial-convolution-based lightweight feature extraction to minimize redundant computation, large separable-kernel attention to boost long-range structural representation, and a shared detail-enhanced detection head to improve boundary- and orientation-sensitive prediction. A custom dataset comprising 1500 conveyor-belt images under diverse scallion posture scenarios was developed for model training and assessment. GOPD-YOLO attained a precision of 90.6%, a recall of 94.5%, an mAP@0.5 of 93.2%, and an mAP@0.5:0.95 of 71.5%, with 2.38 million parameters, 6.6 GFLOPs, and a model size of 4.9 MB. Relative to YOLOv8n-OBB, GOPD-YOLO enhanced recall by 3.8 percentage points while decreasing parameter count and model size by 22.7% and 22.2%, respectively. Deployment tests were performed on the Jetson Orin NX Super platform across varying conveyor speeds, lighting conditions, and scallion stacking levels to evaluate the model’s practical utility. These results indicate GOPD-YOLO’s potential as a lightweight vision-based solution for scallion posture recognition in automated harvesting and post-harvest processing. Full article

Forecasting corporate leverage adjustments remains challenging due to persistent financing behavior, firm heterogeneity, and changing macroeconomic conditions. This study investigates whether increasing model complexity improves the forecasting of corporate leverage adjustment by comparing dynamic econometric models, machine learning algorithms, and a neuro-symbolic artificial intelligence framework. The analysis is based on an unbalanced panel of 39,226 firm-year observations from 3001 publicly listed non-financial firms across 18 countries. The empirical analysis compares Fixed Effects and two-step Difference GMM estimators with regularized regression, gradient boosting, artificial neural networks, and a theory-guided neuro-symbolic framework that incorporates economically meaningful financial constraints through a resampling-based approximation of a differentiable rule-based penalty. Model performance is evaluated using out-of-sample forecasting accuracy measured by the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results indicate that corporate leverage exhibits substantial persistence, with estimated adjustment speeds of approximately 29–36% annually. Machine learning algorithms do not improve forecasting accuracy relative to benchmark dynamic econometric models when evaluated out of sample, while incorporating symbolic financial constraints provides only limited additional predictive benefits. These findings suggest that leverage persistence dominates model complexity and that parsimonious dynamic econometric models remain highly effective for forecasting corporate leverage adjustment. The study contributes to the growing literature on explainable artificial intelligence in corporate finance by providing a comprehensive comparison of dynamic econometric, machine learning, and neuro-symbolic approaches within a unified forecasting framework for emerging economies in the MENA region. Full article

Camellia oleifera leaf disease segmentation under natural field conditions is important for precision plant protection but remains challenging because lesions often show small target areas, blurred boundaries, uneven illumination, complex backgrounds, and coexisting symptoms. To address these problems, this paper proposes BFMambaNet, a Boundary-Frequency-guided Global Semantic Mamba Network for fine-grained disease segmentation. The model adopts an encoder-decoder framework and introduces a Global Semantic Mamba-based spatial selective feature modeling block to capture long-range lesion context and reduce semantic confusion. A gated wavelet spatial enhancement block is further designed to strengthen high-frequency boundary details while suppressing noisy responses. During training, boundary-frequency auxiliary supervision guides contour localization and pathological texture recovery without additional manual boundary labels. A reinforcement-learning-guided adaptive loss controller adjusts class-wise reweighting factors and loss-component weights according to the training state, improving optimization stability. A pixel-level dataset containing 1400 images and seven disease categories was constructed for evaluation. Experimental results show that BFMambaNet achieves 92.39% Precision, 91.43% Recall, 91.26% Dice, and 85.46% mIoU, outperforming representative CNN-based, Transformer-based, and Mamba-based models. Evaluations on environmental subsets confirm superior robustness, outperforming VMamba by 3.70% mIoU under uneven illumination, 3.55% mIoU under complex backgrounds, and 5.10% mIoU under coexisting symptoms. Cross-dataset validation on Apple leaf diseases further proves its generalization with 3.39% mIoU and 3.84% Dice improvements over U-Mamba, while maintaining a competitive inference speed of 30 FPS. Qualitative results also show clearer boundaries, fewer missed small lesions, and more stable predictions in complex field scenarios. Full article

The pursuit of greater efficiency in agricultural operations, particularly in input application, has led producers to adopt strategies aimed at minimizing production costs. Aerial fertilizer application has emerged as a viable alternative due to its high operational efficiency and its ability to operate in conditions where ground-based application is not feasible. However, few studies have evaluated its efficiency, resulting in limited technical guidelines for calibration and adjustment. In this context, the present study aimed to evaluate the quality of broadcast application of solid potassium fertilizer via aircraft. The experiment was conducted using a split-plot design in a factorial arrangement with three flight altitudes (10, 15 and 20 m) and four application rates (50, 75, 100 and 125 kg ha⁻¹), each with three replications. Longitudinal and transverse distributions were evaluated, as well as the correlation between wind speed and applied doses. The longitudinal analysis showed that flight altitude influenced both the uniformity of distribution and the effective dose applied, with a significant interaction between factors. In the transverse analysis, the overall transverse deposition pattern was predominantly governed by the 4.0–2.0 mm particle-size fraction, which represented approximately 90% of the recovered fertilizer mass across all flight heights. Although the granulometric composition remained consistent, the spatial distribution of individual particle-size classes varied with flight height, particularly for the finer fractions. Overall performance was achieved and the best results were observed at a 25 m swath width with flight heights between altitudes of 10 and 15 m. While the 10 m flight height resulted in lower eccentricity and greater fertilizer deposition, the 15 m flight height provided lower coefficients of variation after overlap simulation across most application rates, indicating more uniform transverse distribution. Full article

Background/Objectives: Phase angle (PhA), derived from bioelectrical impedance analysis (BIA), has been proposed as an indicator of cellular health, nutritional status, and physical function. This study aimed to investigate the association between PhA, physical function, and sarcopenia defined according to the Asian Working Group for Sarcopenia (AWGS) 2025 criteria in community-dwelling older adults. In addition, we explored a preliminary PhA cutoff for discriminating women with prevalent sarcopenia. Methods: This cross-sectional study included 317 participants (81 men, 236 women) aged ≥65 years who participated in functional assessments conducted in three locations of Fukushima Prefecture, Japan, in 2024. PhA at 50 kHz was measured using BIA. Assessments included body composition, grip strength, knee extension strength, maximum walking speed, Timed Up and Go (TUG), the Montreal Cognitive Assessment, Japanese version (MoCA-J), and the Kihon Checklist (KCL). Sarcopenia was defined according to the AWGS 2025 criteria. Group differences were examined using the Mann–Whitney U test, and associations were assessed using Spearman’s rank correlation coefficients. Age-adjusted logistic regression analysis was performed in women to examine the association between PhA and sarcopenia. Receiver operating characteristic (ROC) analysis was performed in women to explore a preliminary PhA cutoff because the limited number of men with sarcopenia precluded a stable sex-specific analysis. Results: Sex-specific Spearman’s rank correlation analyses showed that lower PhA values were associated with lower muscle strength (grip strength and knee extension strength) and poorer physical function (maximum walking speed and Timed Up and Go [TUG] performance) in both men and women. Lower PhA values were independently associated with prevalent sarcopenia in women after adjustment for age (odds ratio, 0.26; 95% CI, 0.09–0.80). ROC analysis identified an exploratory PhA cutoff of 4.48° for discriminating women with prevalent sarcopenia (AUC = 0.67; sensitivity = 0.65; specificity = 0.71), indicating modest discriminative performance. Conclusions: Lower PhA values were cross-sectionally associated with poorer physical function and prevalent sarcopenia among community-dwelling older women. An exploratory PhA cutoff of 4.48° was identified to discriminate prevalent sarcopenia in women. However, this cutoff should be considered hypothesis-generating rather than an established clinical threshold and requires validation in larger, independent cohorts and prospective longitudinal studies before clinical application. Full article

At highway merging sections, merging vehicles must enter the mainline traffic flow within a limited acceleration section. Under high traffic demand, this process often involves rapid acceleration or deceleration. Conventional merging assistance control has mainly focused on reducing acceleration and ensuring safe inter-vehicle gaps. However, when electric vehicles (EVs) are considered, battery energy consumption is also an important evaluation perspective. This study evaluates the effects of different speed adjustment strategies for merging vehicles on EV battery energy consumption and smooth merging performance at a single highway merging section. Four cases are compared: Acceleration-Minimizing Merging Control (AMC), which minimizes the absolute value of the required acceleration; Fixed-Arrival-Time Energy-Minimizing Merging Control (FEMC), which minimizes battery energy consumption under a fixed arrival time; Variable-Arrival-Time Energy-Minimizing Merging Control (VEMC), which minimizes battery energy consumption without fixing the arrival time; and a no-control case. EV battery energy consumption is calculated by integrating battery-side power over time, considering driving resistance, inertial force, drivetrain efficiency, regenerative braking efficiency, maximum regenerative power, and auxiliary power. The simulation results show that AMC is advantageous in terms of smooth merging performance, whereas VEMC achieves the lowest overall average battery energy consumption. FEMC and VEMC reduced battery energy consumption by up to 10.7% and 39.5%, respectively, compared with AMC under the evaluated initial-speed conditions, although their smooth merging performance decreased under some conditions; however, their smooth merging performance remains lower than that of AMC, and the energy-saving effect depends on the initial speed and traffic demand conditions. These results indicate that EV-oriented merging assistance control requires a control design that considers the trade-off between energy efficiency and smooth merging performance. Full article

Lissajous figures are frequently studied and widely used objects in engineering and physics. Although these patterns are usually analysed using computer simulations or oscilloscopes, such tools may limit their educational value by covering the core physical processes that generate the curves. In order to address these problems, the design, kinematic validation, and prototyping of a dual-axis drawing robot were carried out on the LEGO Education SPIKE Prime platform. The hardware implementation centres on a LEGO-based dual Scotch yoke mechanism, which supports precise transformation of uniform circular motion into simple harmonic motion. This setup implements the superposition of two independent simple harmonic oscillations by simultaneously moving the paper tray along the x-axis and the pen along the y-axis. High-fidelity trajectories are achieved through a 40:1 worm gear reduction, which enables precise control of the parameter configuration. The phase shift can be manually set by adjustment levers. The robot’s geometry supports discrete amplitude settings of 8, 16, and 24 mm by adjusting the crankpin position. System control is managed by Python code that synchronises motor speeds and angular displacements according to frequency ratios. The research methodology used the Double Diamond design thinking framework, structuring development into four phases: identifying historical mechanical solutions, defining pedagogical and technical classroom requirements, iteratively developing the LEGO prototype, and testing the system through representative drawing experiments. Results show that the robot can reproduce a broad range of periodic Lissajous curves with high repeatability, and that its physical outputs show strong visual and mathematical correspondence to ideal trajectories simulated in the Desmos graphing calculator. The final prototype satisfies classroom constraints, providing a transparent, low-cost, modular STEAM tool that bridges the distance between abstract parametric equations and complex mechanical implementations. Full article