University of Louisiana at Lafayette

The Tuscaloosa Marine Shale (TMS) is an unconventional play in southwestern Mississippi and southeastern Louisiana characterized by a well-developed network of natural fractures that strongly influences reservoir behavior and hydraulic fracturing performance. The play is significant to the energy industry due to its substantial hydrocarbon resources—estimated at approximately 1.5 billion barrels of oil and 4.6 TCF of gas—and its proximity to existing infrastructure. Although more than 80 wells have been hydraulically fractured in the formation, producing a total of 13.82 million barrels of oil and 9.04 BCF of gas, development remains challenging due to the shale’s high clay content, complex mineralogy, and the poorly constrained impact of natural fractures on production.This study employs an integrated workflow to characterize natural fractures in the Tuscaloosa Marine Shale using electrical borehole image logs, shear-wave splitting data, and core descriptions from seven wells distributed across the play. The analysis indicates that the natural fractures are predominantly vertical to subvertical extension fractures, commonly fully mineralized, with heights ranging from 1 to 3 feet. These fractures preferentially trend east–west, are associated with calcite-rich intervals, and are capable of transecting the entire borehole. Smaller fractures often terminate at lithological boundaries but commonly reactivate along parallel planes.The proposed methodology provides critical insight for optimizing hydraulic fracturing design by identifying stress orientation and optimal lateral placement relative to natural fracture distribution. In one lateral well alone, approximately 500 closed fractures were identified. Furthermore, the maximum horizontal stress orientation is shown to be consistent across the formation and aligned with the regional stress regime of the Gulf Coast Basin.

Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption.

We consider the impact of network connectivity on the dynamics of a population in a stream environment. The population is modeled using a graph theoretical framework, with habitats represented by isolated patches. We introduce a change in connectivity into the model through the addition of a bi-directional or one-directional edge between two patches and examine the impact of this edge modification on the metapopulation growth rate and the network biomass. Our main results indicate that adding a bi-directional edge often decreases both measures, while the effect of adding one-directional edge is more intricate and dependent on the model parameters. We establish complete analytical results for stream networks of three patches, and provide some generalizations and conjectures for more general stream networks of n patches. These conjectures are supported with numerical simulations.

Through-silicon vias (TSVs) are essential for 3D integrated circuits (ICs) and advanced chiplet packaging. The semiconductor industry is transitioning toward 3D ICs, chiplets, and system-in-package (SiP) solutions due to the slowdown of Moore’s Law and limitations in conventional silicon scaling. In this paper, we propose an optimized TSV architecture for high-frequency transmission to enhance its suitability for 6G communication chips, and develop a comprehensive equivalent circuit model for fault-free and faulty TSVs. This model accounts for open-circuit and short-circuit fault conditions while considering the effects of higher frequencies, substrate type, doping concentration, and adjacent layers. At the physical level, the TSVs are simulated using the Ansys High-Frequency Structure Simulator (HFSS), and the equivalent circuits are designed using the Cadence Virtuoso tool. An experimental evaluation is also conducted to validate the physical design. We position the TSVs in a pattern of ground-signal-ground (G-S-G) to reduce the effective inductance of the signal TSV, thereby minimizing inductive reactance at ultra-high frequencies. Consequently, the reflection coefficient remains below -10 dB across the frequency range of 0.1 to 146.3 GHz. Furthermore, we compare simulation outcomes from HFSS and Cadence for both fault-free and faulty TSVs under varying operating conditions. Additionally, the parasitic circuit components are characterized through extensive theoretical derivations for in-depth circuit verification. Collectively, the rigorous analysis, experimental validation, and thorough investigation of the proposed design and its equivalent circuit demonstrate their potential for use in creating datasets for a fault prediction machine learning model.

This study investigated the effects of dietary supplementation with a red macroalga, identified as Laurencia snackeyi (LS), on lactational performance and enteric gas emission in lactating dairy cows. In preliminary in vitro experiments, LS decreased CH4 production up to 97% dependent on dose. In the current in vivo experiment, 8 Holstein cows (2 primiparous and 6 multiparous) averaging (±SD) 79 ± 11 DIM and 48 ± 5.6 kg/d milk yield (MY) were enrolled in a crossover design experiment with two 28-d periods with a 10-d washout period between the experimental periods. Treatments were the control (basal diet) or basal diet + 1.5% LS (diet DM basis). Dry matter intake and MY data were collected daily, and milk composition samples were collected 4 times during the final week of each period. Ruminal contents were sampled via ororuminal tubing at 4 h after feeding on 2 consecutive days in the final week of each period. Spot fecal and urine samples were collected across 8 time points during the final week of each period for determination of nutrient digestibility and N utilization. Blood samples were collected at 4 time points for quantification of blood cell counts. Enteric CH4 emission was measured using the GreenFeed system (C-Lock Inc.) during the final week of each period. Dry matter intake was decreased 12% by LS compared with control. Milk composition was largely unaffected, except tendencies for decreased milk true protein and lactose yields (7% for both) in cows fed LS. Milk bromide concentration increased 4.6-fold with LS supplementation relative to control, and iodine concentration was decreased by 11%. Daily CH4 production, yield (CH4 g/kg DMI), and intensity (CH4 g/kg ECM) were decreased by LS compared with control by 37, 26, and 27%, respectively. Conversely, daily H2 emission was increased 2.2-fold by LS. The concentration of total VFA was unaffected by treatment, but molar proportions of valerate and butyrate increased by 18% and tended to increase by 13%, respectively, in cows fed LS. Apparent total-tract digestibility was unaffected, except for a tendency for LS to increase ADF and starch digestibility. As a percentage of N intake, LS increased fecal N excretion and tended to increase milk N secretion. As a proportion of total white blood cells, lymphocytes were decreased by 7% and eosinophils increased 51% by LS. In conclusion, despite the decrease in DMI, LS can be an effective enteric CH4 inhibitor, at least during short-term supplementation. Long-term efficacy and impacts on animal health and performance need to be investigated. Additionally, the inhibitory mechanism of LS on methanogenesis remains to be identified.