Pilot Design Research Articles

Comparison of Analgesia Methods Through a Web Platform in Patients Undergoing Thoracic Surgery: Pilot Design, Implementation, and Validation Study.

Pain management is a vital and essential part of postoperative pectus excavatum (PE) care. Given the lack of an international consensus on guidelines for postoperative handling and evaluation, further research is necessary to compare the efficacy of existing pain management methods regarding pain relief, side effects, and long-term outcomes. In this context, the use of eHealth solutions for data mining can enhance data collection efficiency, reduce errors, and improve patient engagement. However, these digital health care frameworks are currently underused in the context of pain management for PE. This research is part of the broader Cryoanalgesia for Pain Management After Pectus Excavatum Repair (COPPER) study conducted by Giannina Gaslini Children's Hospital to address postoperative pain and recovery in PE patients treated with either standard thoracic epidural analgesia or cryoanalgesia, which is considered its innovative alternative approach. Specifically, this work is aimed at introducing a valuable tool for a comprehensive and quantitative comparison of the 2 analgesia strategies. The tool is a web and mobile app designed to facilitate data collection, management, and analysis of clinical data for pain assessment. The adopted approach involves a careful design based on clinician input, resulting in an intuitive app structure with 3 main screens. Digital surveys are borrowed from paper surveys, including medical history and preoperative, postoperative, and follow-up evaluations. XTENS 2.0 was used to manage the data, and Ionic facilitated cross-platform app development, ensuring secure and adaptable data handling. Preliminary analysis on a pilot cohort of 72 patients (36 treated with standard therapy and 36 treated with cryoanalgesia) indicated successful patient enrollment and balanced representation across treatment groups and genders. Notably, hospital stay was significantly shorter with cryoanalgesia than with standard therapy (Mann-Whitney-Wilcoxon 2-sided test with Bonferroni correction; P<.001; U statistic=287.5), validating its treatment efficacy. This work is a step toward modernizing health care through digital transformation and patient-centered models. The app shows promise in streamlined data collection and patient engagement, although improvements in multilingual support, data validation, and incentivization of questionnaire completion are warranted. Overall, this study highlights the potential of digital health solutions in revolutionizing health care practices, fostering patient involvement, and improving care quality.

Three-outcome designs for external pilot trials with progression criteria

BackgroundWhether or not to progress from a pilot study to a definitive trial is often guided by pre-specified quantitative progression criteria with three possible outcomes. Although the choice of these progression criteria will help to determine the statistical properties of the pilot trial, there is a lack of research examining how they, or the pilot sample size, should be determined.MethodsWe review three-outcome trial designs originally proposed in the phase II oncology setting and extend these to the case of external pilots, proposing a unified framework based on univariate hypothesis tests and the control of frequentist error rates. We apply this framework to an example and compare against a simple two-outcome alternative.ResultsWe find that three-outcome designs can be used in the pilot setting, although they are not generally more efficient than simpler two-outcome alternatives. We show that three-outcome designs can help allow for other sources of information or other stakeholders to feed into progression decisions in the event of a borderline result, but this will come at the cost of a larger pilot sample size than the two-outcome case. We also show that three-outcome designs can be used to allow adjustments to be made to the intervention or trial design before commencing the definitive trial, providing the effect of the adjustment can be accurately predicted at the pilot design stage. An R package, tout, is provided to optimise progression criteria and pilot sample size.ConclusionsThe proposed three-outcome framework provides a way to optimise pilot trial progression criteria and sample size in a way that leads to desired operating characteristics. It can be applied whether or not an adjustment following the pilot trial is anticipated, but will generally lead to larger sample size requirements than simpler two-outcome alternatives.

Sparse channel estimation for underwater acoustic OFDM systems with super-nested pilot design

Underwater acoustic channels are usually sparse and have large delay spread. In this paper, super-nested array structure in the field of array signal processing is borrowed to be the pilot design of underwater acoustic OFDM systems, in order to better estimate large delay spread channels with limited number of pilots. Specifically, by constructing the pilot subcarriers’ covariance matrix and the pilot position difference, the virtual pilot on the differential co-array are employed for sparse channel estimation. In order to reduce the error between the estimated pilot subcarriers’ covariance matrix and the ideal covariance matrix, the cross-correlation matrix of pilot subcarriers is estimated in advance for interference cancellation. Then the sparse iterative covariance estimation algorithm (SPICE) is adopted to further refine the covariance matrix and improve the channel estimation performance. Simulation, pool and sea experimental results show that the proposed method can effectively estimate the large delay spread sparse channels and improve the performance of underwater acoustic OFDM systems.

Telehealth-delivered recovery-orientated well-being plan group program for bipolar disorder: a pilot randomised feasibility and acceptability study.

Psychological interventions may assist in the management of bipolar disorder, but few studies have assessed the use of group therapy programs using telehealth. The present study aimed to assess the feasibility and acceptability of a well-being group program for people living with bipolar disorder designed to be delivered via telehealth (Zoom platform) using a randomised controlled pilot design. Participants were randomly assigned to either the 8-week well-being plan treatment condition or the wait-list control condition. They were administered a structured diagnostic instrument to confirm bipolar disorder diagnosis followed by a set of self-report questionnaires relating to mood, quality of life, personal recovery, and stigma. A total of 32 participants (16 treatment; 16 control) were randomised with 12 participants completing the intervention, and 13 the control condition. The program appeared acceptable and feasible (75% retention rate) with a mean attendance being reported of 7.25 sessions attended out of a possible 8 sessions. Participants reported high levels of satisfaction overall with the intervention, with a mean score of 9.18 out of 10. Preliminary evidence suggests that delivery of the group program online is feasible and acceptable for participants living with bipolar disorder. As the program was designed to prevent relapse over time, further research is needed to determine if the program may be helpful in improving symptom outcomes over a longer follow-up period.

Carbon-efficient Nutrients removal from real municipal wastewater under conditions of highly variable influent quality and low temperature

In order to maximize carbon efficient nutrient removal of real primary effluent under the conditions of highly variable influent quality and low temperature, this study integrated four advanced bioprocesses, namely partial nitrification, endogenous denitrification, partial denitrification-anammox, enhanced biological phosphorus removal, into one advanced pilot design. The pilot system has been on-site operated for 212 days and was able to handle highly variable conditions with the aid of automatic feedback and feedforward controllers nested in secondary and tertiary treatment. Comparing to the case when full nitrification and full dentification technologies had to be used to meet the effluent requirement of total inorganic nitrogen (TIN) ≤ 3 mg/L and orthophosphate phosphorus ≤ 0.1 mg/L, the pilot results showed that 100 wt% carbon and 55.5 wt% oxygen could be saved in the secondary process and another 45.2 wt% carbon could be saved in the tertiary process. 44.5 wt% influent TIN was removed through endogenous denitrification in the secondary treatment with another 39.5 wt% TIN polished by partial denitrification-anammox in tertiary treatment. Because post-anoxic endogenous denitrification has removed majority of the NOX-N, it was concluded that the energy intensive mixed liquor recirculation that has been popular in traditional design for pre-anoxic zone denitrification can be eliminated.

Searching for application areas for using AI and VR in the Higher Education classroom in Ireland

The changing landscape of education for all, internationally, has led to education policy and operationalisation mis-matches, we advocate for active learning and critical pedagogy in our modern society but our society and resources are not sufficiently welcoming to adopting emerging hardware and software in the classroom. Resource constraints restrict the move from experiential learning to implementation where resistance is in-built within our culture to hold on to traditional teaching and learning methods and delivery mechanisms. We are familiar and open to science requiring apparatus, but using tools such as virtual reality and artificial intelligence in our everyday teaching and learning is uncommon and warrants further evidence to support its implementation to becoming the norm. Education is the keystone for progress, we promote education as a sustainable, complementary activity to economic social and caring aspects of our society. As an integral part of the UN Sustainable developmental goals how we meet the objectives are contextual and in Ireland we currently adopt a traditional in-person educational experience with many online programmes affiliated with options for lifelong learning rather than mainstream education. Thus the experience of the authors through SEERLab (South East Education Research Lab) to design and develop teaching and learning artefacts is presented in this paper. SEERLab focuses on strategies to build a sense of community in HE (virtually) of educationalists who through their own learning experiences gain empowerment to dramatically change the classroom composition, using both physical hardware (VR headsets) and software (AI) to accentuate critical thinking and immersive learning in the classroom. There is evidence to support the use of VR in the classroom toward enhanced engagement of students (Kavanagh et al., 2017 and Campos et al., 2022). However, the emergence of new education policies based on data, learning analytics, tertiary market demand and access is not easily understood in our communities. We purport that educationalists need to embrace change at HE to administer and deliver curriculum using non-standard applications. This research initiated by lecturers in the design of pilots to meet learning outcomes through using VR is gathering evidence to further develop HE policy using VR in the classroom. There are tensions in global societal research discourses in relation to emerging technologies such as AI and scant social scientific research on societal attitudes towards AI; its value and limitations in education, fears and emotions about AI technology which requires redress. Without guidance lecturers advocating for the use of VR and AI in the classroom are challenged to demonstrate understanding the socio-cultural legitimacy of AI in Irish society. We present the cases of SEERLab that has been established to scaffold educationalists toward enhancing their attitudes towards VR and AI. These challenges can be overcome using an innovative pedagogical approach to link the needs of teachers and lecturers and students to untangle the complexity of emerging technology and deliver educational solutions globally and nationally. The pilot cases presented include modules from Nursing, Business and PhD postgraduate programmes. The piloting of this technology is moving to implementation stage and along with evidence from open educational communities we present the potential benefits and limitations of VR and AI in HE classrooms, which can lead to engaged students, innovative pedagogies, misperceptions, heightened sense of risk, inertia and resistance to its adoption. Universities, governments and organizations can work together to raise awareness about VR and AI through education and outreach initiatives aimed at different student cohorts but this necessitates greater knowledge in relation to accessibility, usability and educational ‘buy in’ and exposure to VR and AI technologies that combine scientific expertise with pedagogical know-how.

Joint AP Selection and Grey Wolf Optimization Based Pilot Design for Cell-Free Massive MIMO Systems

Joint AP Selection and Grey Wolf Optimization Based Pilot Design for Cell-Free Massive MIMO Systems

Learn and Lead: Implementation of a Leadership Development Pilot Program at the Centers for Disease Control and Prevention.

The Centers for Disease Control and Prevention (CDC) needs leaders at all levels who can address technical and adaptive challenges in a changing public health landscape. We assessed the feasibility of implementing an enterprise-wide leadership development model. In June 2023, we launched a pilot program, Learn and Lead, for nonsupervisory staff in early and mid-career levels. One hundred sixty-nine participants registered, and 149 completed at least 6 weeks of the 8-week program. We gathered quantitative and qualitative data through weekly electronic surveys and in-depth interviews. We calculated frequencies for closed-ended Likert-scale items and performed content analysis of open-ended items to assess most frequently mentioned themes. We based the pilot's design on CDC's leadership development framework to assess application of the framework and alignment of curriculum to the framework. Three themes emerged: logistics and facilitation, leadership development curriculum, and making connections with self and others. Findings for logistics and facilitation highlighted preferences for course length, small breakout groups, mixed weeks, and value of staff support. Findings for the leadership development curriculum underscored the relevance of the US Office of Personnel Management's fundamental competencies to leadership development. Findings for making connections with self and others supported the cohort model and the importance of networking. The pilot curriculum aligned well with the CDC leadership development framework. Feedback provided by pilot participants is being used to help shape CDC's ongoing leadership development efforts.

MDB-48. A SINGLE INSTITUTION EXPERIENCE OF CIPN MEASURED BY THE PED-MTNS IN CHILDREN AND ADOLESCENTS WITH MEDULLOBLASTOMA TREATED ON A TRIAL USING SIMILAR DOSING OF VINCRISTINE AND CISPLATIN FOR ALL PATIENTS

Abstract BACKGROUND Standard treatment for medulloblastoma in patients &amp;gt;3 years of age consists of tumor resection, radiation and chemotherapy using vincristine, cisplatin, cyclophosphamide +/- CCNU. Vincristine and Cisplatin are known to cause chemotherapy induced peripheral neuropathy (CIPN). The prevalence of CIPN in children with non-CNS cancers can be measured using the pediatric-modified Total Neuropathy Score (ped-mTNS). The purpose of this observational, retrospective pilot design is twofold: to investigate the prevalence of CIPN in children with medulloblastoma receiving neurotoxic chemotherapy, and to examine the feasibility of using the ped-mTNS as a measurement tool in this population. METHODS Data was manually extracted from medical records and entered into a REDCap database for analysis. The percent of patients successfully tested and clinical inferences made from ped-mTNS testing results were used to determine feasibility and barriers to use. A total of 33 medulloblastoma patients &amp;gt;3 years of age were treated from 6/2013 - 6/2021 at Children’s Minnesota. Of those, 18 received testing using the ped-mTNS during chemotherapy treatment. RESULTS During cancer treatment, 54.5% of the cohort of patients received ped-mTNS testing. The average age of children tested was 10.26 (4.39) years with a majority being male (79%), and non-Hispanic white race/ethnicity (89%). Of the tested participants, 61.1% had more than one measurement. For those patients tested at multiple timepoints, 72.7% demonstrated worsening ped-mTNS scores while on chemotherapy treatment. Therapists recorded inferences relating to neuropathy in 88.1% of testing occurrences with questions of accuracy recorded in 31.3% of testing occurrences. Barriers to use of results included confounding impairments that may occur as a result of the primary tumor, post-surgical changes, and radiation. CONCLUSIONS Using the ped-mTNS is feasible in children and adolescents with medulloblastoma. The ped-mTNS scores indicated that children with medulloblastoma, treated with Vincristine and Cisplatin, experience worsening neuropathy while undergoing chemotherapy treatment.

Telehealth Engaged Music for Pain Outcomes: A Music and Imagery Proof-of-concept Study with Veterans.

Music therapy interventions target biopsychosocial outcomes and are a non-pharmacological option for integrated pain management. To date, most music and pain studies have focused on acute pain, passive music experiences, and in-person delivery. The purpose of this study was to examine feasibility and acceptability and determine proof-of-concept for a newly developed telehealth music imagery (MI) intervention for Veterans with chronic pain. A single-group proof-of-concept pilot study was conducted with Veterans with chronic pain (n = 8). Feasibility was assessed through examination of recruitment, retention, and session/measure completion rates; acceptability through participant interviews; and whether the intervention resulted in clinically meaningful change scores (pre- to post-intervention) on measures of pain, anxiety, and depression at the individual level. For Veterans who passed eligibility screening, we had an enrollment rate of 89%, with good retention (75%). Overall, participating Veterans found the intervention acceptable, identified specific challenges with technology, and recommended an increased number of sessions. Preliminary outcome data for pain, anxiety, and depression were mixed, with some Veterans reporting clinically meaningful improvements and others reporting no change or worsening symptoms. Findings informed modifications to the telehealth MI intervention and the design of a larger pilot randomized controlled trial to assess feasibility and acceptability of the modified intervention in a larger population of Veterans with chronic pain using additional measures and a control condition.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study.

Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Pilot Design and Signal Detection for Symbiotic Radio Over OFDM Carriers

Pilot Design and Signal Detection for Symbiotic Radio Over OFDM Carriers

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

P184 Challenging Barriers in Inflammatory Bowel Disease: First Observational Analysis of Double Biological Therapy in Mexico

Abstract Background Inflammatory bowel disease is a chronic inflammation of the GI tract, despite advances in treatment with biologic therapy, some patients may develop an inadequate response, known as refractory IBD (rIBD).Given this reality, the approach of double biological therapy (DBT) has emerged to effectively address and improve control of inflammation, providing them with an additional therapeutic option Methods Pilot prospective observational study evaluateing response to DBT in patients with refractory IBD Results A total of 7 patients with rIBD who received DBT were included, the average age was 29 ± 9, all female. The average duration of illness before starting dual therapy was 8 ± 2years. Patients had received an average of 2 previous biological treatments, 4 (57%) had a history of surgery related to IBD, 5 (71%) and 2(29%) had a history of stenosis and fistulas respectively and 100% used steroids and immunomodulators before starting DBT. Four DBT combinations were evaluated: CERTOLIZUMAB+VEDOLIZUMAB (group 1), USTEKINUMAB+VEDOLIZUMAB (group 2), INFLIXIMAB+USTEKINUMAB (group 3) and INFLIXIMAB+VEDOLIZUMAB (group 4).Group 1 included 1 patient with improvement in clinical activity, with a decrease in endoscopic activity from i4 to i2 by Rutgeerts criteria and Nancy index 1.Group 2 included 4 patients showed improvement in clinical and endoscopic activity, there was a reduction in clinical activity by CDAI from 225 to 66, and improvement in endoscopic activity from L1 B2 to L1 B1, i0 L2 B3P to i0 L1 B1 and i3 L3 B2 to i2 L3 B1 with different activity rates, Nancy's histology that varied from 0 to 3.Group 3 included 1 patient showed a reduction in clinical activity by CDAI from 210 to 166, endoscopic activity also decreased from i0 L2B3P to i0 L1 B1 with Nancy index of 2.Group 4 includes 1 with a decrease in clinical activity by Truelove-Witts from 2 to 2, endoscopic activity also decreased from 8 to 5 accordingto UCEIS with Nancy index of 1.In all groups, a reduction in the use of steroids was observed and no cases of infections associated with dual biological therapy were reported Conclusion This prospective observational pilot study represents the first analysis in Mexico and, to our knowledge, in Latin America, on the use of DBT in patients with rIBD who showed clinical, endoscopic and histopathological improvement in the majority of cases. It is important to recognize the limitations of this study, such as its pilot design and sample size. This pioneering study in refractory IBD presents encouraging results that suggest clinical and endoscopic benefits in the treated patients. These results justify the conduct of larger studies to strengthen the evidence and provide a solid basis for thepotential use of this therapy in clinical practice

Being a "Good Parent" to a NICU Infant With a Major Congenital Anomaly.

In the United States, up to one-third of infants with a congenital anomaly require neonatal intensive care unit (NICU) hospitalization. Parents of these infants may have different decision-making priorities, which may be influenced by the timing of the infant's diagnosis. (1) To compare the ranked importance of decision-making beliefs for parents of infants who received a prenatal versus postnatal congenital diagnosis and (2) explore how parents describe their decision-making beliefs. A cross-sectional, sequential mixed-methods pilot design was applied to collect quantitative data using the Good Parent Ranking Exercise and further explore parents' decision-making beliefs through qualitative interviews. Maximum difference scaling/hierarchical Bayes estimation and content analysis were used to analyze the quantitative and qualitative data, respectively. Forty mothers completed the Good Parent Ranking Exercise and 20 mothers completed qualitative interviews. Four of the top 5 ranked parenting beliefs were shared by mothers in the prenatal and postnatal groups. Mothers in the postnatal group ranked "focusing on my child's quality of life" higher. Qualitative interviews revealed that previously identified decision-making beliefs were consistent in this NICU parent population, with 1 additional belief identified. Mixed-methods analysis revealed high concordance between the prenatal and postnatal groups. NICU nurses need to know that decision-making beliefs for parents who receive a prenatal versus postnatal congenital diagnosis, while largely similar, may have differences. Future research should explore decision-making beliefs in demographically diverse parent groups (ie, fathers, partnered vs nonpartnered couples) and effective strategies for promoting NICU parents' decision-making beliefs.

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

Longitudinal Functional Safety Analysis and Algorithm Design of Traffic Jam Pilot

Longitudinal Functional Safety Analysis and Algorithm Design of Traffic Jam Pilot

Integrated Sensing and Communication: Joint Pilot and Transmission Design

Integrated Sensing and Communication: Joint Pilot and Transmission Design

Technology Focus: EOR Modeling (January 2024)

The oil and gas industry is at a critical juncture in addressing the pressing need to balance operational efficiency with environmental stewardship. With the world’s growing emphasis on climate change and sustainability, the industry is tasked with the dual objectives of refining operational efficiency and simultaneously diminishing its carbon footprint. This balancing act has become a strategic imperative crucial for the long-term viability of the industry. In light of the recent COP28 agreement, which emphasizes a “swift, just, and equitable energy transition,” the role of low-emission enhanced oil recovery (EOR) and operational optimization becomes increasingly significant. This period of transition, which may span several decades, demands a pragmatic approach where the existing hydrocarbon resources are used as efficiently and responsibly as possible. In the quest to optimize operations and reduce carbon footprint, three innovative technical papers present a cohesive story of advancement through modeling and machine learning. These papers underscore the industry’s shift toward more efficient practices, each contributing a crucial piece to the larger puzzle of EOR.The first paper introduces an advanced modeling approach for fractured reservoirs, a significant development in tight rock simulations that simplifies fracture heterogeneity characterization and enhances computational efficiency, crucial for optimizing asset development decisions in shale and tight oil resources. Building on the theme of efficiency, the second paper pivots to the realm of surfactants, exploring their role in wettability alteration within unconventional liquid-rich reservoirs. This research provides a systematic approach for screening thermally stable surfactants under reservoir conditions, crucial for improving hydrocarbon recovery without unnecessary field trials. The final piece of this narrative is a study using machine learning to predict gas-injection parameters in EOR processes. This approach addresses the lack of extensive gas-injection laboratory data, demonstrating the power of data-driven predictions in enhancing operational efficiency and decision-making. These papers weave a compelling narrative of technological innovation, highlighting the role of advanced modeling and machine learning. As the oil and gas industry progresses in its vital mission to supply the world’s energy needs, the pivotal role of technology innovation becomes increasingly evident. Advanced technologies focused on operational optimization and reducing carbon emissions are essential in reinforcing the industry’s adaptability and resilience in the context of this rapidly evolving energy landscape. Recommended additional reading at OnePetro: www.onepetro.org. SPE 215083 An Analytical Tool to Predict Fracture Extension and Elastic Desaturation for Polymer Field Projects by M.B. Abdullah, The University of Texas at Austin, et al. SPE 216822 First Pilot Design of Low-Tension-Gas (LTG) Flooding in Carbonate Field in North Oman by Mohammed Al-Abri, Petroleum Development Oman, et al. SPE 213037 Field-Scale Multistage and Multiobjective Optimization of Rate and Concentration for Polymer Flooding by Ruxin Zhang, Texas A&amp;M University, et al.

Non-Orthogonal Pilot Design Exploiting Spatial Non-Stationarity for TDD XL-MIMO Transmission

Non-Orthogonal Pilot Design Exploiting Spatial Non-Stationarity for TDD XL-MIMO Transmission