Renewable Energy and Energy Efficiency Organization

Tumor microenvironment modulators have produced durable effects in cancer treatment. Targeting immune checkpoint receptors, such as PD-L1, has demonstrated efficacy in eliciting antitumor responses. However, resistance to immune checkpoint blockers (ICBs) has constrained the efficacy of these therapies. Previous studies showed a link between the expression of AXL receptor tyrosine kinase and resistance to ICBs. Therefore, designing combination treatments with synergistic mechanisms to overcome ICB-based resistance is needed. In addition to antibody-based therapies, gene silencing with siRNAs has recently been explored to alter the cancer environment to enhance the immune response. In this study, we targeted PD-L1 using an siRNA and AXL using a blocker (R428) in OVACAR-3 and CaSki cells, ovarian and cervical cancer cell lines, respectively, in the following groups: Scramble-siRNA, PD-L1-siRNA, Scramble-siRNA in conjunction with R428, PD-L1-siRNA in conjunction with R428, R428 monotherapy and untreated controls. Cell viability was assessed by MTT assay after 48 hours of treatment, and cisplatin sensitization was evaluated in resistant OVACAR-3 cells. Gene expression was analyzed by qRT-PCR, while flow cytometry quantified CD44+PD-L1+ populations, apoptosis (Annexin V/PI), and cell cycle distribution. The results showed a significant decrease in cell proliferation, suppression of EMT-regulating genes, reduction of stemness in cancer cells, increased apoptosis and disruption of the cell cycle in the studied cell lines. These findings suggest that simultaneous blockade of PD-L1 and AXL could serve as a novel tumor-suppressive strategy, especially for cancer patients resistant to ICBs.

Image logs aid in accurate fracture network characterization by providing detailed information about natural fractures in reservoirs. Moreover, larger-scale fracture mapping and detection can be accomplished by extracting seismic attributes offering important insights into the distribution of reservoir fractures. Through the extraction of seismic attributes from image logs the current study offers a novel method for locating naturally occurring fractures in a carbonate reservoir of southwest Iran. Extending the application of seismic attributes to image log data is made possible for the first time in this study by using data from electrical and ultrasonic image logs converted from DLIS to SEGY format. After converting the format of the image logs, seismic attributes were extracted and assessed for possibility of enhanced image log interpretation. To examine and compare the results of various attributes, a total of 23 structural, stratigraphic, and signal processing attributes were derived. The newly tested attributes, including the iso-frequency component, RMS amplitude, and variance (edge method), showed improved performance in identifying fractures. However, the iso-frequency component attribute was found to be very powerful in emphasizing the fractures among the different attributes examined. The findings of this study show how seismic attributes can be used to detect the natural fractures based on image logs in a reservoir resulting in more accurate fracture detection and modeling.

Background: Phubbing refers to the act of prioritizing electronic devices over face-to-face communication. It is a growing phenomenon that negatively impacts social interactions and communication skills; therefore, a study on this phenomenon is necessary. Objectives: The present study aimed to evaluate and determine the prevalence of phubbing behavior among students of the Faculty of Rehabilitation at Shahid Beheshti University of Medical Sciences (SBMU). Methods: In this cross-sectional survey design, a set of self-reported questionnaires, including demographic data, DASS21, and the phubbing scale, were used. The Generic Scale of Phubbing (GSP) was administered to 320 students in various fields of education at the Faculty of Rehabilitation, SBMU, including optometry, audiology, physiotherapy, and occupational therapy. The scale was scored from 15 to 105, with higher scores indicating a greater degree of phubbing behavior. Results: Phubbing was observed among 49.1% of the students. The mean phubbing score was 39.55 with a standard deviation of 9.51, evaluated within the range of 16 - 72. The mean nomophobia score was 12.84, the conflict score was 8.59, the self-isolation score was 6.22, and the problem acknowledgment score was 9.66. The Kruskal-Wallis test showed statistically significant differences between different academic majors and phubbing scores (χ2 = 9.5, df = 3, P = 0.023), with a mean rank of 185.8 for optometry students, 148.03 for audiology students, 158.48 for physiotherapy students, and 150.61 for occupational therapy students. Conclusions: This study reveals a mild level of phubbing behavior among students in the Faculty of Rehabilitation, SBMU. We urge the implementation of smartphone usage training and awareness programs to mitigate such negative effects. Educational institutions should integrate such interventions into academic curricula to enhance active listening and reinforce interpersonal communication skills.

Objective: This study applied ISO 50001:2018 EMS guidelines to assess energy consumption and evaluate O&M practices’ impact on the energy efficiency of a Colombian crude oil pipeline. Methodology: A dataset comprising over 80 variables spanning a year (base period) was analyzed to define the significant energy use (SEU) of pipeline operations. An Energy Baseline (EnB) was modeled using linear regression with Non-Controllable Variables (NCVs), excluding Controllable Variables (CVs). An Energy Goal Line (EnG) model was developed through regression of average energy consumption below the EnB. O&M practices, including prioritizing efficient pumps and increasing frequency of pigging, were evaluated over an eight-month monitoring period. An Energy Performance Indicator (EnPI) quantified energy savings. Results: SEU energy consumption was influenced 80% by CVs and 20% by NCVs. The EnB and EnG models exhibited a second-order polynomial relationship involving a linear combination of the NCVs (normalized flow). At the average normalized flow, potential energy savings of 3.7% were identified, attributed to NCV fluctuations and O&M variability during the base period. During the monitoring period, the EnPI indicated energy savings of 1,003.6 MWh, representing a 27% reduction in the base period’s average consumption. Annual extrapolation of O&M estimated energy savings of 1,560.39 MWh, equivalent to 259.02 tCO2-eq and $153,529.37 USD.Conclusion: integrating energy performance monitoring with O&M practices deliver substantial energy, economic, and environmental benefits, providing a robust foundation for continuous improvement in crude oil transportation processes.

To progress toward smart grids, an automatic reconfiguration of radial distribution networks (FRC) is an inevitable necessity. Several drawbacks are associated with existing methods, such as lack of transparency in decision-making, complexity of computation, and the use of non-open source computational engines. In this regard, for the first time, a graph theory-based innovative algorithm (which is called FRANK<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>) has been proposed related to the FRC knowledge-based type methods using the concept of energy hubs that can suggest the most appropriate switching operation decisions by considering cost functions such as minimum load outage. Using the proposed algorithm, it is possible to rapidly implement self-diagnosis, self-decision, and self-recovery. Through the described flowchart, the network graph is divided into three proximity, hotline, and flow matrices. This algorithm has been applied to two feeders in the Dowlatabad city of the Khorasan Razavi province, which showed promising results serving as a model for the transition towards smart grids in Iranian SCADA companies. All the codes are developed in the MATLAB environment.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Feeder Reconfiguration Algorithm with Near Knowledge-based

With over 43,000 kilometers of aerial network, the Khorasan Razavi Province electricity distribution company (KEDC) has the longest distribution network in Iran. In its capacity as a prominent organization, this company has extended the number of tele-control RTUs from 250 to 1530, increasing by more than six times since 2019. This project has resulted in the management of 1529 MW-hours of network energy in 2023. A significant improvement was also achieved in the System Average Interruption Duration Index (SAIDI) (one of the most important reliability indicators in the electricity networks), which was reduced by 63% from 166 minutes in 2020 to 63 minutes in 2023. Although automation has been one of the contributing factors to this decrease, it is comprehensively addressed in this article. Furthermore, during the year 2023, 14565 close/trip commands for emergency load management of a total of 5226 MW have been executed. Since similar companies have invested in this area, one of the major challenges is the modeling and surveying of the effects of the increase in tele-control numbers on the SAIDI. For this purpose, an innovative model has been developed to establish the relationship between tele-control numbers and SAIDI. It serves as a useful reference for the comprehensive statistical analysis of the distribution network presented in this article. This model also has the advantage of calculating profits and predicting the SAIDI status of the network without implementing tele-control RTUs. The information provided here is essential for calculating the benefits of the tele-control projects and can be used to justify any investment in future projects. According to the proposed model, the SAIDI index would have increased to 147 minutes in 2023, instead of 63 minutes, without the implementation of the aforementioned project at the Khorasan Razavi electricity distribution company. Using the data provided by the other power companies, the model has demonstrated a high level of accuracy of 90 %.