Texas Southern University

This study presents an integrated framework combining computational simulations and machine learning (ML) to design and optimize supercritical carbon dioxide (CO₂) pipeline networks for large-scale carbon capture and storage (CCS) applications. Using ANSYS fluent, the fluid dynamics of supercritical CO₂ transport were analyzed under various operational conditions to generate synthetic insights and complement real-world datasets collected from CO₂ sequestration activities and industrial sources. Key variables such as pressure, temperature, and pipeline diameter were extracted, engineered into time-lagged and rolling features, and used to train a random forest regressor model. Feature importance analysis revealed that short-term flow trends, volatility, and geographic segmentation are dominant predictors of CO₂ flow behavior. The ML model achieved high performance, with an R² score of 0.9897 and a low RMSE of 0.0335, indicating strong predictive reliability. Hyperparameter tuning further optimized model accuracy, while visual analyses including actual vs. predicted comparisons, residual distributions, and feature interaction heatmaps validated the model’s robustness. The results demonstrate that data-driven approaches can complement simulation-based methods to improve the design, monitoring, and operational efficiency of CO₂ transport systems. This work provides a scalable, intelligent solution for CCS pipeline optimization, supporting the global transition toward low-carbon infrastructure.

Chemotherapy-induced neuropathic pain (CINP) can be caused by several chemotherapeutic drugs, including paclitaxel, oxaliplatin, and vincristine, which is difficult to treat with several drugs, including antidepressants and anticonvulsants. The patho-mechanisms of CINP are not completely understood. However, they showed oxidative stress, mitochondrial damage, ion channel damage, and immunological dysfunction. Acting as a key regulator of antioxidant responses, nuclear factor erythroid 2-related factor 2 (Nrf2) decreased oxidative stress and mitochondrial damage. In addition, it plays a role in inhibiting nuclear factor kappa B (NF-κB). A systematic, English-only search of MEDLINE (PubMed) was performed for studies on Nrf2, chemotherapy, and neuropathic pain from database inception through 1 December 2024. Several Nrf2 activators, including tempol, oltipraz, rosiglitazone, pristimerin, cannabidiol, daidzein, bardoxolone methyl, curcumin, resveratrol, and mitoquinone, demonstrated analgesic effects in CINP animal models. Furthermore, in clinical studies, curcumin demonstrated significant efficacy in reducing vincristine-induced neuropathy in pediatric leukemia patients, while the combined administration of alpha-lipoic acid with ipidacrin hydrochloride prevented paclitaxel-induced motor neuropathy and improved axonal function in breast cancer patients. Thus, the purposes of our review article were to summarize the analgesic effects of Nrf2 activators and the patho-mechanisms of Nrf2 in CINP animal, and then the consequences for clinical trials were presented.

Constipation and gastrointestinal (GI) dysmotility are prevalent disorders often associated with impaired gut–brain axis regulation and limited therapeutic options. This study investigated the safety and gastrokinetic efficacy of CL18100F4 (Digexin®), a standardized polyherbal formulation comprising extracts of Withania somnifera (Ashwagandha) root and Abelmoschus esculentus (Okra) pods. Ex vivo assays using isolated rat ileum demonstrated that CL18100F4 significantly enhanced intestinal contractility in a dose-dependent manner, with muscarinic receptor involvement confirmed by atropine inhibition. In vivo administration of CL18100F4 (200- and 300 mg/kg body weight for eight days) to Wistar rats showed significant improvements in gastric emptying (73.97 ± 15.12%, p = 0.0410), intestinal transit (80.43 ± 4.76%, p = 0.0184), and fecal moisture content (57.42 ± 5.05%, p = 0.0101), without affecting fecal pellet count. Serum corticosterone levels were significantly reduced, while serotonin levels increased, suggesting modulation of the gut–brain axis. No adverse alterations were observed in liver, kidney, or metabolic biochemical parameters, indicating systemic safety. Collectively, these findings demonstrate that CL18100F4 exerts synergistic gastrokinetic and neuroendocrine regulatory effects while maintaining a good safety profile. The formulation shows promise as a functional food or nutraceutical for managing GI motility disorders by modulating the gut–brain axis–associated biomarkers.

In recent years, Healthcare Internet of Things (HC-IoT) applications have enabled remote patient monitoring, realtime diagnostics, and personalized treatment. As these systems start to use agentic AI, AI that can perceive, reason, and act on its own,

This study presents a comprehensive analysis of global methane (CH<sub>4</sub>) emissions using advanced data exploration and machine learning techniques, with an emphasis on identifying key sectoral contributors, geographic emission hotspots, and the performance of mitigation technologies. Employing methods such as random forest regression, geospatial mapping, and multi-dimensional visual analytics, the research highlights the energy sector’s dominant role in methane output and reveals detailed emission patterns across U.S. states. The analytical framework includes time-series feature engineering, synthetic data augmentation for localized insights, and 3D surface modeling to examine the relationships between energy production levels, temporal trends, and emission intensities. The results provide actionable insights for policymakers by identifying critical points of intervention and advocating for the integration of artificial intelligence-driven exhaust after-treatment systems to reduce methane emissions. This work offers a scalable, reproducible approach for environmental monitoring and supports global decarbonization efforts in line with U.S. clean energy objectives. The random forest model used in this study achieved a mean absolute error of 2.71 and an R² score of 0.81, demonstrating strong predictive accuracy for methane emissions trends based on regional and sectoral data.

Internet of Audio Things (IoAuT) is an emerging paradigm that integrates intelligent audio processing, ubiquitous connectivity, and edge–cloud computing resources to enable a network of devices capable of sensing, analyzing, and exchanging sound-based information seamlessly across distributed environments. This technology supports a wide range of applications, ranging from interactive musical performances to intelligent environmental monitoring, that play a significant role in everyday life. Although this technology holds great potential and could be highly useful across many domains in the future, its reliance on real-time audio streaming and distributed sensing introduces several communication and Quality of Service (QoS) challenges, such as latency, bandwidth limitations, packet loss during audio transmission, resource management, and energy-efficient networking, all of which directly affect its usability and scalability. In this paper, we provide the first holistic analysis of IoAuT applications from a QoS and communication perspective. We systematically review artistic, functional, and industrial use cases to explore how performance and sustainability trade-offs shape system design. Unlike prior reviews, our study introduces a unified architecture taxonomy and sustainability-enabled QoS metrics to evaluate network efficiency, interoperability, and energy use. We further examine the roles of edge computing, adaptive streaming, and dynamic resource allocation in achieving reliable and low-latency audio transmission. Finally, the paper highlights open challenges and suggests future research directions to help build IoAuT applications that can scale, work well with other systems, and reduce their impact on the environment.

Abstract We present a comprehensive comparison of PHENIX measurements of transverse energy production (d E T /d η ) and charged-particle multiplicity (d N /d η ) at midrapidity to simulations from PYTHIA-8, AMPT, HIJING, and SMASH. These comparisons span both small systems (d + Au, 3 He + Au) and large systems (Cu + Cu, Cu + Au, Au + Au, and U + U) at s N N ∼ 200 GeV and Au + Au over a range of beam energies s N N = 7.7 –200 GeV. Using the Rivet framework, we assess the performance of these models. While general trends are captured, significant deviations persist, particularly in low-energy and peripheral collisions, underscoring the need for improved modeling of baryon stopping and energy deposition mechanisms.