Marathon Oil (United States)

The thermal performance of photovoltaic (PV) modules is a critical factor influencing their electrical efficiency, energy yield, and long-term reliability, particularly in hot climate regions characterized by high ambient temperatures and intense solar irradiance. This study presents a comparative thermal performance analysis of mono-crystalline, poly-crystalline, and thin-film photovoltaic modules under real operating conditions. The experimental framework included continuous monitoring of module temperature using thermocouples and infrared thermal imaging, along with simultaneous measurement of key environmental parameters such as solar irradiance, ambient temperature, wind speed, and relative humidity. The objective was to quantify thermal behavior, evaluate temperature-induced electrical performance degradation, and compare long-term thermal efficiency and energy yield across different PV technologies. The results demonstrated that module operating temperature has a significant impact on electrical performance, with increased temperature leading to reductions in open-circuit voltage, maximum power output, and overall conversion efficiency. Mono-crystalline and poly-crystalline modules exhibited higher temperature coefficients, resulting in greater performance degradation under elevated temperature conditions. In contrast, thin-film modules showed lower temperature sensitivity and improved thermal dissipation, leading to more stable electrical performance. Regression analysis confirmed a strong negative correlation between module temperature and power output for all PV technologies, with thin-film modules exhibiting the lowest thermal derating. Long-term performance evaluation revealed that thermal effects significantly influence energy yield and performance ratio in hot climate environments. Although mono-crystalline modules provided higher nominal efficiency under standard test conditions, thin-film modules maintained more stable energy production and thermal efficiency under sustained high-temperature operation. The findings indicate that thermal resilience plays a crucial role in determining real-world PV system performance and technology suitability for hot regions. Overall, this study provides a comprehensive assessment of the thermal characteristics and energy performance of different PV technologies, offering valuable insights for optimizing photovoltaic system design and technology selection in hot climate environments. The results support the adoption of thermally resilient PV technologies and improved thermal management strategies to enhance system efficiency, reliability, and long-term sustainability in high-temperature regions.

The coronavirus disease 2019 (COVID-19) pandemic, first identified in late 2019, has caused extensive global morbidity and mortality. While most individuals recover from the acute illness, a considerable proportion develop post-COVID-19 syndrome, characterized by persistent symptoms lasting weeks to months. This study aimed to characterize the prevalence, type, and duration of persistent symptoms following COVID-19 recovery, and to examine their association with the severity of the acute infection. A cross-sectional, analytical observational study was conducted among 515 COVID-19 survivors in western Libya. Data were collected via face-to-face interviews and online questionnaires (Google Forms) comprising demographic information, COVID-19 illness details, comorbidities, and post-COVID-19 symptoms. Associations between symptom persistence and clinical/demographic factors were assessed using Chi-square tests. Persistent symptoms were reported by 72.0% of participants, with over 70% experiencing three or more symptoms. Recovery occurred within three months in 75.5% of respondents, within three to six months in 22.1%, and after more than six months in 2.4%. During acute illness, 78.8% required only home isolation, 21.2% were hospitalized, and 8.3% required intensive care. Most participants (91.7%) experienced mild-to-moderate acute disease, and 81.6% reported no comorbidities. Common persistent symptoms included fatigue, cough, anosmia, ageusia, joint pain, anxiety, and headache; less frequent but more severe sequelae included chest pain, pulmonary fibrosis, and myocarditis. A significant proportion of COVID-19 survivors experienced prolonged symptoms lasting up to 12 months post-recovery, independent of gender, smoking status, or vaccination history. Symptom severity correlated with the severity of the initial illness and the presence of comorbidities. Post-COVID-19 follow-up and targeted rehabilitation are recommended to address these long-term health effects.

The use of artificial intelligence (AI) alongside medical skills has witnessed significant growth in recent years, leading to impressive results in classification and processing, while facilitating the work of medical staff. Moreover, there has been an urgent need for software tools to assist in the early classification of diseases. In response to this need, this paper aims to develop a bidirectional neural network model with a web application interface for diagnosing Autism Spectrum Disorder (ASD) in children .The methodology employed relies on machine learning techniques for the early detection of this disorder, which includes collecting a comprehensive dataset containing 6,000 images, adjusting the image dimensions to focus on the facial area, and extracting morphological features associated with the disorder's symptoms. Edge detection techniques were applied, and images were segmented into recognizable parts. Subsequently, the model was trained using these features, and its performance was evaluated using metrics such as accuracy and F1 score. The proposed model achieved an accuracy ranging from 70% to 76% and an F1 score ranging from 75% to 79%, indicating its ability to classify cases of Autism Spectrum Disorder with high precision . The web application provides a user-friendly and accessible interface to utilize the diagnostic model, facilitating the screening and evaluation process. However, it is important to emphasize that the Convolutional Neural Network (CNN) model is a supportive tool for diagnosis and not a definitive solution. While it can assist in identifying potential cases of Autism Spectrum Disorder, the findings highlight the importance of expert involvement in diagnosis. Recommendations include expanding the dataset, applying data augmentation techniques, and training specialists to use the model and interpret its results correctly.

Summary Solvent huff ’n’ puff (HnP) is becoming a common enhanced oil recovery (EOR) practice in unconventional tight and ultratight reservoirs. For an effective HnP operation, achieving miscibility is essential for promoting solvent transport into the reservoir matrix and subsequent oil production. This is typically achieved by either increasing the injection pressure or enriching the solvent. However, injection pressure is constrained by compressor capacity, formation fracture pressure, and lateral/vertical containment. In this study, we experimentally assess the feasibility of using natural gas liquid (NGL) for HnP in an ultratight Eagle Ford (EF) shale sample, providing insights into extreme solvent enrichment scenarios in an HnP process. We hypothesize that NGL extracts oil from an oil-saturated shale core through a counterdiffusion process, primarily governed by first-contact miscibility (FCM) between NGL and oil. In this study, we explore the impact of solvent injection on the phase envelope of both dead oil and live oil during the HnP process. We present a critical comparison between C1 HnP, representing the lower limit of solvent enrichment, and NGL HnP, representing the upper limit, focusing on their respective oil recovery mechanisms and in-situ solvent-oil interactions. Using a high-pressure and high-temperature (HPHT) visualization apparatus, we investigate the interactions between NGL and oil, as well as their compositional variations, under bulk-phase conditions and in the core during the HnP process. We propose an analytical theory for the transport of NGL and oil into and out of an ultratight porous medium, explaining the experimental oil recoveries observed from the shale core. NGL and oil transport is modeled under a diffusion-dominated scenario, with FCM playing a crucial role in enhancing diffusion. Compositional analysis indicates that, contrary to C1, NGL extracts heavier oil components during the soaking stage. Core visualization demonstrates a gradual color change of NGL from clear to amber during soaking, indicating oil production via counterdiffusion. NGL expands the two-phase envelope of the dead oil, making it more volatile, while suppressing the phase envelope of the live oil. This potentially extends the duration of single-phase oil flow during the depletion stage in a live-oil system and enhances the oil production through diffusion. NGL achieves significantly lower FCM pressure (FCMP) with oil compared with C1, C1/C2 (70/30), C2, and separator gas, explaining its higher diffusion into the oil-saturated core. The analytical model demonstrates that NGL diffuses to the end of the core by the end of soaking. NGL recovers significantly more oil than C1 in the HnP process. Most of the oil is produced during soaking due to counterdiffusion, with solution-gas drive contributing additional recovery at later stages of depletion, though not as markedly as in C1 HnP.

Parking infrastructure is an essential urban facility that must be adequately provided in all cities. However, the increasing number of vehicles has led to significant congestion, making it challenging for drivers to locate available parking spaces, particularly during peak hours. This situation underscores the growing need for fully automated parking systems, which have become a priority for modern cities globally. The primary objective of this paper is to propose and develop a fully automated parking system that minimizes human intervention while addressing these challenges and ensuring real-time parking availability in everyday life. A prototype system was designed using a programmable logic controller (PLC), incorporating a display screen located outside the parking area to monitor vacant spaces and provide drivers with real-time information on their locations. The system features two gates—one for entry and another for exit—along with two sensors to detect the presence of vehicles and multiple sensors to identify available parking spots. When space is available, the system triggers the gate to open, and if the parking facility is full, the PLC signals the gate to remain closed, marking the lot as fully occupied. This automated process significantly reduces the time and effort involved in searching for vacant parking spaces. The prototype underwent extensive testing in various scenarios and demonstrated robust performance, effectively addressing the issues associated with manual parking and congestion.

Abstract One of the mitigation methods for a pipeline affected by landslides is stress-relief excavation. In this mitigation approach, the soil above and around the pipeline in the affected area is excavated to allow the pipeline to rebound and relieve some of the accumulated elastic stresses on the pipeline. This paper presents the data collected before, during, and after stress-relief excavations at four landslide sites in the USA. These case studies include a variety of pipe strain condition, geometry, and ground conditions in which the stress-relief excavations were performed. The collected information includes pipe rebound measurements using survey laths, strain change measurements during and/or after stress-relief excavations using strain gauges when available, and pipeline rebound and strain change comparison before and after the stress-relief excavations using inertial measurement unit (IMU) measurements, when available.

A key risk in refinery capital projects is executing the work during a planned turnaround (TAR). To minimize risk, requires a project approach that shifts work to a non-TAR time frame. A recent successful project conducted in a Southern California refinery, reduced the project’s TAR activities from 37 to 12 days all by defining new, additional non-TAR activities. This paper discusses the work performed, the decisions made, and the challenges faced in that project, and proposes opportunities to minimize the TAR scope in electrical substation replacement and upgrade projects.