J. Exp. Theor. Anal., Volume 4, Issue 2 (June 2026) – 4 articles

This study demonstrates the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the elemental analysis of agricultural soils in South Punjab, Pakistan. Soil degradation due to intensive farming, imbalanced fertilizer use, and declining organic matter has reduced crop productivity in the region. To address this, rapid and accurate soil diagnostics are essential. LIBS, coupled with Calibration-Free analysis (CF-LIBS), was employed to quantitatively determine the concentrations of major and trace elements—including calcium, silicon, iron, aluminum, magnesium, titanium, potassium, sodium, lithium, and barium—without requiring chemical standards. Plasma characterization was performed using the Boltzmann plot method, yielding temperatures between 7750 and 9000 K, and electron number densities were derived from Stark-broadened spectral profiles. The results reveal significant spatial variability in elemental composition, reflecting differences in land use and irrigation sources. This work confirms LIBS as a versatile, efficient, and reliable tool for soil health assessment, offering a practical solution for monitoring soil nutrients and supporting sustainable agricultural management in resource-limited settings. Full article

The TWC Sigma model, part of the Topological Weighted Centroid (TWC) family, is introduced as a spatial framework for source localization in systems where network information is incomplete or unavailable. Its architecture relies on two alternative approaches: one based on nonlinear correlation, capable of capturing complex spatial dependencies among observed signals, and another based on supervised neural networks, which use adaptive learning on a discretized spatial grid to estimate the probability of hidden source localization. In both cases, TWC Sigma provides a robust and consistent mechanism to estimate the probable positions of hidden sources using only spatial coordinates and signal intensity. Applications on both synthetic and real-world datasets—such as those collected by Minna-no Data Site on post-Fukushima radiocesium contamination—confirm the model’s ability to identify both primary and secondary emission zones with strong spatial coherence. These results highlight TWC Sigma as an efficient and interpretable model that can be used both independently and as a complementary tool to more complex network-based frameworks, offering rapid and reliable localization even in the presence of sparse, noisy, or heterogeneous data. Full article

Robust control, adaptive control, and adaptive control allocation methods can create resilient systems that are able to handle uncertainties as well as unknown deficiencies in actuator effectiveness. The capabilities of these methods can further enable advanced missions for autonomous space systems. Thus, in this paper, a resilient control with an adaptive control allocation method is proposed and implemented on a vehicle with 3 degrees of freedom (DoF) that operates with eight thrusters to reduce the impact of external uncertainties as well as unknown effects of the actuator. Specifically, the method includes a combination of sliding mode and novel adaptive control design elements to ensure trajectory tracking in the presence of uncertainties. Moreover, an adaptive control allocation method is also introduced to obtain the desired forces and moments in the presence of unknown effects of the actuator. The boundedness of the closed-loop system is proven with Lyapunov stability analysis. The proposed controller results are compared to a baseline sliding mode controller without adaptive control and adaptive control allocation enhancement, where different uncertainties and unknown actuator degradation, as well as failure cases, are considered within several experimental cases under external fan-induced disturbances. The experimental metrics, including integral squared tracking error, maximum tracking error, actuator effort, actuator impulse, and settling time, are provided. Across all cases, the proposed method reduces the integral squared tracking error, improves settling time, and significantly improves yaw regulation compared to a baseline sliding mode controller. This, in turn, yields a slightly increased control effort for the proposed method. Full article

Electrocardiogram (ECG)-based biometrics have emerged as a promising solution for continuous and intrinsic human identification; nevertheless, the robustness of these systems under realistic noise conditions remains a critical challenge for practical deployment. This work presents a theoretical and experimental analysis of how different noise types and levels affect ECG biometric recognition by comparing three methodological families: fiducial-based approaches using morphological features with traditional classifiers such as SVM and k-NN, non-fiducial methods based on signal compression and global descriptors, and Deep Learning models. Controlled distortions and additive noise injection into public ECG databases enable systematic quantification of feature degradation. Experimental validation is performed using the CardioWheel system, a real-world in-vehicle ECG acquisition platform, to evaluate performance under realistic motion and noise conditions. The methodological framework proposed for robustness evaluation and noise-aware training is inherently generic and can be extended to other biometric tasks subject to noise. Results show that different algorithmic families exhibit distinct resilience profiles under noise contamination and reveal a practical signal quality boundary for reliable ECG biometric recognition, with performance deteriorating under severe noise conditions. Noise-aware training improves robustness, particularly for Deep Learning and SVM-based classifiers, highlighting the trade-off between interpretability and robustness. By bridging theoretical analysis and applied experimentation, this work provides practical signal quality guidelines for real-world ECG biometric systems. Full article