Recent publications
This study addresses a question that has not been researched much previously, namely, does the unavailability of health insurance act as an incentive for persons to enlist in the military in the U.S.? This relationship is proffered as the “Military Health Care Magnet Hypothesis.” The present study endeavors to provide insight into this issue within a cost-benefit framework. The empirical analysis uses annual data for the years 1974 through 2007, the only years to date for which all of the variables in the model are dependable after the end of military conscription in the U.S. in 1973. Both OLS and 2SLS results demonstrate, among other things, that the greater the percentage of the civilian population without health insurance, the greater the rate of enlistment in the U.S. Army.
Measurements of the pressure developed in the load-carrying film of a grease-lubricated journal bearing show that grease can operate under hydrodynamic conditions. In a testing fixture, constructed for the purpose, the magnitude and the distribution of that pressure is investigated using a cup grease under conditions of copious feed. The graphical analysis of the results shows a grease dome similar to that of oil, but flatter and extending over a larger arc.
Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals.
This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world.
The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take?
The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?
The most common challenge facing the oil industry in the Age of AI is talent scarcity. As digital transformation continues to redefine what it takes to work in the industry, staying relevant in the industry will require knowledge and understanding of the underlying technologies driving this transformation. It also requires a re-evaluation of how next generation petrotechnical professionals are nurtured, educated, and trained. The human talent that is needed in the Age of AI is different, and simply obtaining a science or engineering degree will no longer suffice to survive and thrive in the industry. While it is vitally important that students continue to take fundamental engineering and science courses and learn industry-specific skills, we must recognize when an existing curriculum or way of teaching and learning has either run its course or has evolved.
This paper examines how artificial intelligence will impact the training and development of the industry's future workforce and what organizations must do to retain existing talents while at the same time developing new ones, so they are not rendered irrelevant by AI. It proposes novel ways by which practical digital transformation and energy transition technologies can be integrated into core oil and gas education and training curriculum. It also outlines various innovative ways that academic institutions can join forces with industry to educate and train technical professionals, who, right out of college are sufficiently grounded to analyze, evaluate, and communicate data findings to drive better business decisions. For students and young professionals, it lays out the roadmap to readiness, and how to thrive in a digitally transformed world, as well as several ways to robot-proof their career and stay ahead of the curve.
The task of training industry leaders of the future is enormous, sensitive, and demanding. The ability of next generation petrotechnical professionals to succeed in the digital age, and compete in a data-centric world, depends on their ability to develop, adopt, and apply next generation skills. Having the right mix of skills is not only essential to their success, it is critical to the survival of the industry. In the Age of AI, classroom learning needs to be deemphasized and experiential learning needs to be emphasized.
The workforce of the future will be dominated by people with analytics skills and capabilities. Preparing next generation professionals for the future of work calls for a re-evaluation, re-design and recasting of the synergy between academia and industry. Universities and industry will need to routinely intersect to create symbiosis and enhance our educational system. Success will depend on sustained partnership and collaboration, not merely shifting the problem to one another.
The Age of AI is defining a new set of challenges for leaders and the integration of digitalization and analytics into management decision-making is now a strategic priority for the oil industry. The fundamental challenge currently confronting the industry is to find leaders who can lead in the digital age. As the industry grapples with the AI revolution, pressure is mounting on leaders to react swiftly to the disruption that comes in its wake. Leadership and management methodologies currently employed by most organizations will not suffice in the digital age because leadership in this new age requires a different set of skills and organizational alignment. Yet, many organizations continue to struggle to put leaders in place with the knowledge and expertise to take on the challenges of leading in an AI-enabled world.
This paper addresses the challenges and responsibilities that the AI revolution presents to oil industry leaders and provides practical insights to confront them. It details the concept of ambidexterity and why it is difficult for oil industry managers to achieve. It also outlines what it takes to implement an ambidextrous strategy in the industry and presents a framework for leaders as they drive transformation and explore strategies that will shape the industry's transition to net-zero energy. With social media now shaping business decision-making, the paper also discusses its impact and presents a unique approach for leadership to be strategically positioned to reconfigure their organizations to ensure they survive and thrive in the social age.
Artificial Intelligence in the oil industry is not just about managing operations and reducing operating cost. It is also about developing a completely new way of doing business. Leadership in the digital age will be held accountable to a different standard. They would not only be judged by their ability to drive strategy and deliver financial results; they would also be judged on their ability to leverage AI resources and drive deep analytics mindset across their organization, while dealing with energy transition and social media. The workforce of the future will be dominated by technologically sophisticated people connected to multiple platforms. Managing this workforce will require a new kind of managerial wisdom.
The big gains from digital transformation will not be realized unless industry executives rethink the criteria with which leadership and management success is judged. Becoming a transformational digital leader requires the ability to define a strategic vision for transformation, understand the promise and peril of social media, cultivate employees to succeed with AI, and use AI responsibly. The future belongs to leaders with these abilities and capabilities.
Background: The association between heart rate variability (HRV), training load (TL),
and performance is poorly understood. Methods: A middle-aged recreational female runner was monitored during a competitive 20-wk macrocycle divided into first (M1) and second mesocycle (M2) in which best performances over 10 km and 21 km were recorded. Volume (km), session rating of perceived exertion (sRPE), TL, and monotony (mean TL/SD TL) were the workload parameters recorded. The root mean square of the successive differences in R-R intervals (RMSSD), its coefficient of variation (RMSSDcv), and the RMSSD:RR ratio were the HRV parameters monitored. Results: During M2, RMSSD (p = 0.006) and RMSSD:RR (p = 0.002) were significantly increased, while RR was significantly reduced (p = 0.017). Significant correlations were identified between monotony and volume (r = 0.552; p = 0.012), RR (r = 0.447; p = 0.048), and RMSSD:RR (r = -0.458; p = 0.042). A sudden reduction in RMSSD (from 40.31 to 24.34 ms) was observed the day before the first symptoms of an influenza. Conclusions: The current results confirm the practicality of concurrent HRV and sRPE monitoring in recreational runners, with the RMSSD:RR ratio indicative of specific adaptations. Excessive training volume may be associated to both elevated monotony and reduced RMSSD:RR. Identification of mesocycle patterns is recommended for better individualization of the periodization used.
Oil and Gas operations are now being "datafied." Datafication in the oil industry refers to systematically extracting data from the various oilfield activities that are naturally occurring. Successful digital transformation hinges critically on an organization's ability to extract value from data. Extracting and analyzing data is getting harder as the volume, variety, and velocity of data continues to increase. Analytics can help us make better decisions, only if we can trust the integrity of the data going into the system. As digital technology continues to play a pivotal role in the oil industry, the role of reliable data and analytics has never been more consequential.
This paper is an empirical analysis of how Artificial Intelligence (AI), big data and analytics has redefined oil and gas operations. It takes a deep dive into various AI and analytics technologies reshaping the industry, specifically as it relates to exploration and production operations, as well as other sectors of the industry. Several illustrative examples of transformative technologies reshaping the oil and gas value chain along with their innovative applications in real-time decision making are highlighted. It also describes the significant challenges that AI presents in the oil industry including algorithmic bias, cybersecurity, and trust. With digital transformation poised to re-invent the oil & gas industry, the paper also discusses energy transition, and makes some bold predictions about the oil industry of the future and the role of AI in that future.
Big data lays the foundation for the broad adoption and application of artificial intelligence. Analytics and AI are going to be very powerful tools for making predictions with a precision that was previously impossible. Analysis of some of the AI and analytics tools studied shows that there is a huge gap between the people who use the data and the metadata. AI is as good as the ecosystem that supports it. Trusting AI and feeling confident with its decisions starts with trustworthy data. The data needs to be clean, accurate, devoid of bias, and protected. As the relationship between man and machine continues to evolve, and organizations continue to rely on data analytics to provide decision support services, it is imperative that we safeguard against making important technical and management decisions based on invalid or biased data and algorithm. The variegated outcomes observed from some of the AI and analytics tools studied in this research shows that, when it comes to adopting AI and analytics, the worm remains buried in the apple.
Decision‐framing describes how choices are presented or framed.¹ A choice can be worded either positively (gain‐framed) to explain the benefits of a therapy, or negatively (loss‐framed) to explain the risks of not taking a therapy. In the management of chronic diseases, patients’ agreement with clinician recommendations during the shared decision making process predicts long‐term adherence to therapies.2‐5 In this randomized controlled trial, we examined the effects of gain‐ versus loss‐framing on patient preferences for a proposed therapy among psoriasis patients with and without psoriatic arthritis (PsA).
Introduction:
Manual ventilations during cardiac arrest are frequently performed outside of recommended guidelines. Real-time feedback has been shown to improve chest compression quality, but the use of feedback to guide ventilation volume and rate has not been studied. The purpose of this study was to determine whether the use of a real-time visual feedback system for ventilation volume and rate improves manual ventilation quality during simulated cardiac arrest.
Methods:
Teams of 2 emergency medical technicians (EMTs) performed two 8-min rounds of cardiopulmonary resuscitation (CPR) on a manikin during a simulated cardiac arrest scenario with one EMT performing ventilations while the other performed compressions. The EMTs switched roles every 2 min. During the first round of CPR, ventilation and chest compression feedback was disabled on a monitor/defibrillator. Following a 20-min rest period and a brief session to familiarize the EMTs with the feedback technology, the trial was repeated with feedback enabled. The primary outcome variables for the study were ventilations and chest compressions within target. Ventilation rate (target, 8-10 breaths/minute) and tidal volume (target, 425-575 ml) were measured using a novel differential pressure-based flow sensor. Data were analyzed using paired t tests.
Results:
Ten teams of 2 EMTs completed the study. Mean percentages of ventilations performed in target for rate (41% vs. 71%, p < 0.01), for volume (31% vs. 79%, p < 0.01), and for rate and volume together (10% vs. 63%, p < 0.01) were significantly greater with feedback.
Conclusion:
The use of a novel visual feedback system for ventilation quality increased the percentage of ventilations in target for rate and volume during simulated CPR. Real-time feedback to perform ventilations within recommended guidelines during cardiac arrest should be further investigated in human resuscitation.
Background
Endothelium‐derived prostacyclin and nitric oxide elevate platelet cyclic nucleotide levels and maintain quiescence. We previously demonstrated a synergistic relationship exists between cyclic nucleotides and P2Y12 receptor inhibition. A number of clinically approved drug classes can modulate cyclic nucleotide tone in platelets including activators of NO‐sensitive guanylyl cyclase (GC) and phosphodiesterase (PDE) inhibitors. However, the doses required to inhibit platelets produce numerous side effects including headache.
Objective
We investigated using GC‐activators in combination with P2Y12 receptor antagonists as a way to selectively amplify the anti‐thrombotic effect of both drugs.
Methods
In vitro light transmission aggregation and platelet adhesion under flow were performed on washed platelets and platelet rich plasma. Aggregation in whole blood and a ferric chloride‐induced arterial thrombosis model were also performed.
Results
The GC‐activator BAY‐70 potentiated the action of the P2Y12 receptor inhibitor prasugrel active metabolite in aggregation and adhesion studies and was associated with raised intra‐platelet cyclic nucleotide levels. Furthermore, mice administered sub‐maximal doses of the GC activator cinaciguat together with the PDE inhibitor dipyridamole and prasugrel, showed significant inhibition of ex vivo platelet aggregation and significantly reduced in vivo arterial thrombosis in response to injury without alteration in basal carotid artery blood flow.
Conclusions
Using in vitro, ex vivo and in vivo functional studies, we show that low dose GC activators synergise with P2Y12 inhibition to produce powerful anti‐platelet effects without altering blood flow. Therefore modulation of intra‐platelet cyclic nucleotide levels alongside P2Y12 inhibition can provide a strong, focused anti‐thrombotic regimen whilst minimising vasodilator side effects.
A new model for the location and distribution of carbonate ions in carbonated apatite was used to assign the IR spectra of A- and AB-carbonated apatites. The percentage of total carbonate as measured by the mass loss in the TGA of these compounds is in good agreement with the percentage obtained by combustion analysis. The decomposition of pure A-type carbonate appears at temperatures of 985–1123 °C, whereas the decomposition of AB-type carbonated apatites occurs in the range of 600–800 °C. This difference is attributed to changes in the environment of channel carbonate brought about by B-type substitution of carbonate for phosphate. In the presence of sodium ions, the channel is changed by substitution of sodium for calcium in order to accommodate the difference between the charge of the carbonate and phosphate ions. A thermodynamic cycle is introduced to rationalize the differences in decomposition temperatures of A- and B-type carbonate. Preferential loss of B-type carbonate upon heating to 600 °C also suggests the migration of B-type carbonate to A-sites.
Background: Developing a health promotion program plan requires attention to the links between objectives, activities, and overall program goals. Instructors developed the “Connecting the Dots” worksheet to help students establish these linkages. Methods: The “Connecting the Dots” worksheet included six questions pertinent to the students’ health promotion program plans. The worksheet was given to the students in a flipped classroom setting. Evaluation of the effectiveness of the tool was based upon group presentations at the end of the semester. Results: Students developed more viable program plans that included stronger links between objectives and corresponding program activities. Conclusions: The “Connecting the Dots” worksheet is a promising tool for engaging public health students in the process of developing health promotion program plans. Keywords: Teamwork, personality, leadership
Purpose:
To survey the use of Pearson's correlation coefficient (r) and related statistical methods in the ophthalmic literature, to consider the limitations of r, and to suggest suitable alternative methods of analysis.
Recent findings:
Searching Ophthalmic and Physiological Optics (OPO), Optometry and Vision Science (OVS), and Clinical and Experimental Optometry (CXO) online archives using correlation and Pearson's r as search terms resulted in 4057 and 281 hits respectively. Coefficient of determination, r square, or r squared received fewer hits (65, 8, and 22 hits respectively). The assumption that r follows a bivariate normal distribution was rarely encountered (3 hits) although several studies applied Spearman's rank correlation (70 hits). The intra-class correlation coefficient (ICC) was widely used (178 hits), but fewer hits were recorded for partial correlation (43 hits) and multiple correlation (13) hits. There was little evidence that the problem of sample size was addressed in correlation studies.
Summary:
Investigators should be alert to whether: (1) the relationship between two variables could be non-linear, (2) the data are bivariate normal, (3) r accounts for a significant proportion of the variance in Y, (4) outliers are present, the data are clustered, or have a restricted range, (5) the sample size is appropriate, and (6) a significant correlation indicates causality. In addition, the number of significant digits used to express r and the problems of multiple testing should be addressed. The problems and limitations of r suggest a more cautious approach regarding its use and the application of alternative methods where appropriate.
Objective: This study aims to examine the relationship between different levels of cognitive impairment (CI) and the frequency of hospital admission (HA). Method: Data from the National Health and Aging Trend Study, Round 1 (2011), with 8,245 respondents from Medicare beneficiaries were used. The data account for the number of hospital admissions for one year before the data collection. Clock Drawing Test and delayed word recall were employed to measure CI. Results: The severity of CI is one of the factors significantly associated with HA. Controlling for the level of function, the likelihood of HA increased among respondents with moderate, mild-to-moderate, and mild CI. Counterintuitively, HA was reduced when CI is severe. Discussion: People with CI are at more risk of frequent HA and the severity of impairment can increase this risk subsequently. Screening for CI at admission can open up the possibility of interventions, hence reducing complications during and after hospitalization.
Core Ideas
Manure application method and timing affected ryelage but not rye cover biomass.
Manure injection compared to broadcast conserved manure‐N in ryelage.
Manure injection compared to broadcast with rye cover resulted in more corn silage.
Late injection compared to early injection conserved more manure‐N in ryelage.
Late versus early injection resulted in more manure‐N in soil and more corn after cover crops.
Fall manure applications can lead to nutrient losses prior to spring planting. This 2‐yr study evaluated effects of winter cereal rye (Secale cereal L.) crop management (cover crop versus ryelage), and fall, dairy slurry manure application method (broadcasted versus injected) and timing (September vs. November) on manure–N conservation. Nitrogen conservation was calculated from NH3 volatilization, percentage of manure–N in aboveground rye (%ManureN‐R) and soil (%ManureN‐S) in spring prior to corn (Zea mays L.) planting, and subsequent corn yield. Ryelage compared to cover crop conserved more %ManureN‐R (2.6‐fold) and despite reduced corn yield, ryelage‐corn treatments with 3.6‐fold greater rye biomass produced 20% greater total harvested forage than cover crops. Compared to broadcasted manure (BM), injected manure (IM) reduced NH3 losses and increased ryelage biomass (42%) after September (EARLY) applications, %ManureN‐R (50%), %ManureN‐S at multiple depths, and total harvested forage (20%). Corn silage yield with IM compared to BM was also greater after all cover crop treatments (23%), all ryelage treatments in 2015 (35%), and ryelage with a November application (LATE) in 2014 (30%). Compared to EARLY, LATE applications increased %ManureN‐R in ryelage after BM (44%) and IM (50%), %ManureN‐S at multiple depths in both rye treatments, and corn silage yields with IM following cover crops (21%). Following ryelage, corn yields were not larger following greater %ManureN‐S in LATE IM versus BM, suggesting potential for more soil NO3 leaching loss with LATE IM. However, multiple management options reduced fall manure–N losses and conserved manure–N for crop utilization, allowing for farm management flexibility.
According to Gallup, 57% of people age 50 and older are sports fans. Television audiences across most professional sports boast older demographics in increasing numbers. For example, the average age of a baseball viewer is 57, up from 52 in 2006. This is a huge market opportunity for the sports industry, although we cannot ignore older adults in longterm care, especially those who are bedridden or immobile or those who feel lonely and/or are socially isolated.
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