This paper reports on the effect of gamma-ray irradiation on single-mode optical fibers, including measurement of the relaxation process. The study was performed using gamma-irradiation facilities with 60Co sources at doses of 67.9 kGy and 170.0 kGy, with irradiation rates of 0.97 kGy/h and 1.05 kGy/h, respectively. We measured changes in optical losses for seven types of optical fibers, including those with germanium-doped silica core and pure silica glass cladding, as well as four optical fibers with pure silica glass and fluorine-doped silica cladding. Measurements of optical losses and radiation-induced attenuation were performed at four wavelengths: 1310 nm, 1490 nm, 1550 nm, and 1625 nm. The gamma irradiation of the fibers and optical loss measurements were conducted at room temperature, and our measurements showed that in harsh environments where gamma rays can irradiate optical fibers, a wavelength of 1310 nm (O-band, 1260-1360 nm) is better suited for data communications than 1550 nm (C-band, 1530-1565 nm).
Rényi transfer entropy (RTE) is a generalization of classical transfer entropy that replaces Shannon's entropy with Rényi's information measure. This, in turn, introduces a new tunable parameter α, which accounts for sensitivity to low- or high-probability events. Although RTE shows strong potential for analyzing causal relations in complex, non-Gaussian systems, its practical use is limited, primarily due to challenges related to its accurate estimation and interpretation. These difficulties are especially pronounced when working with finite, high-dimensional, or heterogeneous datasets. In this paper, we systematically study the performance of a k-nearest neighbor estimator for both Rényi entropy (RE) and RTE using various synthetic data sets with clear cause-and-effect relationships inherent to their construction. We test the estimator across a broad range of parameters, including sample size, dimensionality, memory length, and Rényi order α. In particular, we apply the estimator to a set of simulated processes with increasing structural complexity, ranging from linear dynamics to nonlinear systems with multi-source couplings. To address interpretational challenges arising from potentially negative RE and RTE values, we introduce three reliability conditions and formulate practical guidelines for tuning the estimator parameters. We show that when the reliability conditions are met and the parameters are calibrated accordingly, the resulting effective RTE estimates accurately capture directional information flow across a broad range of scenarios. Results obtained show that the explanatory power of RTE depends sensitively on the choice of the Rényi parameter α. This highlights the usefulness of the RTE framework for identifying the drivers of extreme behavior in complex systems.
This study assesses the long-term performance degradation of a Building-Integrated Photovoltaic (BIPV) system using cadmium telluride (CdTe) modules over six years of field operation. A methodology based on Gradient Boosting Regressor models was used to estimate AI-based cumulative degradation loss (LAI) with uncertainty intervals, while effective peak power analysis and laboratory I-V curve measurements were used as complementary validation methods. The AI models achieved high predictive accuracy, with normalized root-mean-square errors ranging from 1.64% to 2.92%. The calculated LAI increased from 2.91% in 2019 to 12.85% in 2024, with most losses occurring during the first three years before stabilization. The effective peak power method indicated annual degradation rates of approximately −1.7% to −1.9%/year, while I-V measurements showed a total power loss of about 10.2%. The agreement among the three methods supports the observed degradation trend. In addition, selected key performance indicators exhibited seasonal variability and long-term performance decline, consistent with the estimated degradation behavior. The results highlight the value of long-term monitoring and AI-based analysis for degradation assessment, predictive maintenance, and improved reliability of CdTe BIPV systems.
This study evaluates ten Electro-Geometric Models (EGMs) and one Leader Progression Model (LPM) for predicting lightning strikes to a 220 kV transmission line. Using 15 years of EUCLID lightning-location data (2011-2025) processed through IEC 62858 flash grouping and polarity-specific IC current filtering, we identified 7577 flash events from 18,333 stroke records along an 83.9 km line with eight confirmed fault-causing attachments. Our central finding comes from a one-factor-at-a-time Monte-Carlo analysis (200 realizations per error source): among lightning location, peak current, and stroke-type/amplitude classification, only location error materially changes whether individual confirmed events are detected-for both the geometric (EGM_Young) and physics-based (LPM) models-while peak-current and classification error change it for neither. Because the median location error (89 m) is comparable to or larger than the 43-88 m EGM attractive radii, event-level detection is bounded by geolocation accuracy rather than model choice, and 7 of 10 EGMs detect none of the confirmed attachments. We propose detection ratio and predicted attachment rate as metrics better suited to limited confirmed events with undefined true negatives. The LPM and EGM_Young attain the highest detection ratio (2/8); the LPM predicts 39% fewer total attachments (249 versus 411) at 4.8 & times; higher computational cost, reported as a model property rather than evidence that either is more accurate. This count ordering is invariant across all three error sources, whereas the detection result, resting on only two detected events, is reported as a sensitivity analysis rather than a full variance decomposition. These models can also screen candidate high-risk segments for maintenance prioritization, subject to the stated limitations.
The application of Haynes 230 (HA230) by Laser Powder Bed Fusion (LPBF) is limited by its high susceptibility to process-induced cracking. TiB2 addition offers a promising route to suppress cracking; however, its effect on the fatigue behaviour of LPBF-processed HA230 remains unclear. In this work, an LPBF-processed HA230 superalloy modified with 1.5 wt.% TiB2 was investigated in terms of its microstructure, tensile properties, and Low-Cycle Fatigue (LCF) behaviour at 23 degrees C and 850 degrees C. The addition of 1.5 wt.% TiB2 effectively suppressed LPBFrelated cracking and increased the yield strength by approximately 30 % at 23 degrees C and 20 % at 850 degrees C while maintaining high ductility. The enhanced strength is attributed to the transformation of TiB2 during LPBF, which resulted in the formation of M6(CB) carboborides and a fine dispersion of Ti-rich and La-rich nanoparticles. These nanoparticles impeded dislocation motion and contributed to strengthening. Under LCF loading, HA230 exhibited pronounced cyclic hardening at 23 degrees C, whereas cyclic softening predominated at 850 degrees C. Fatigue crack propagation was predominantly transgranular at both temperatures, although the damage mechanisms depended on the testing condition. At 23 degrees C, failure was mainly associated with brittle cracking of M6(CB) particles, whereas at 850 degrees C, oxidation-assisted surface crack initiation and propagation dominated. This study provides the first comprehensive correlation between the microstructure, tensile response, and LCF behaviour of TiB2-modified LPBF-processed HA230, demonstrating that controlled TiB2 addition can simultaneously improve LPBF processability and mechanical performance.
