bluuebunny/crossref_metadata_2025_split huggingface dataset:
Life, Lawfulness, and Contingency: Kant and Schelling on Organic Nature
Authors: Fisher Naomi 
Published: October 2021

AbstractIn theCritique of the Power of Judgment,Kant calls purposiveness the “lawfulness of the contingent”. I argue that this should be interpreted not as lawfulness assumed in order to remove unacceptable mechanical indeterminacy, but rather as an additional kind of lawfulness which, in the case of organisms, inexplicably coincides with mechanical determination. Schelling adapts Kant’s notion of natural purposiveness in his own conception of the relation between mechanism and organism. He states in his 1798 work,On the World Soul, that nature is “lawless in its lawfulness, and lawful in its lawlessness”. This should be interpreted similarly: there is a coincidence of two orders of lawfulness in nature. However, while Kant maintains that the coincidence or unity of these two orders is inexplicable for beings like us, Schelling explains the unity by assigning these laws to distinct levels of operation in nature. Organic organization is generated by a second-order operation of an organic principle on the first-order mechanical forces that are characteristic of matter. Schelling thereby builds on Kant’s thirdCritiqueframework in a creative way in order to offer a more fully unified account of nature. In this account, mechanism and organism maintain distinct roles but are both grounded in a further, higher principle.
Clarkson inequalities related to convex and concave functions
Authors: Gao Fugen , Li Meng 
Published: October 2021

Abstract
In this paper, we obtain some norm inequalities involving convex and concave functions, which are the generalizations of the classical Clarkson inequalities. Let A
1, …, A

n
be bounded linear operators on a complex separable Hilbert space

H

$\mathcal{H}$

and let α
1, …, α

n
be positive real numbers such that

∑

j
=
1

n

α

j

=
1

$\sum\limits^{n}_{j=1}\alpha_{j}=1$

. We show that for every unitarily invariant norm,

If f is a non-negative function on [0, ∞) such that f(0) = 0 and

g
(
t
)
=
f
(

t

)

$g(t)=f(\sqrt{t})$

is convex, then

|

|

|

∑

j
=
1

n

α

j

f
(

|

A

j

|

)

|

|

|

≥

|

|

|

∑

j
,
k
∈

S

ℓ

(

f

(

α

j

α

k

4

α

ℓ

(
1
−

α

ℓ

)

|

A

j

+

A

k

−
2

∑

j
=
1

n

α

j

A

j

|

)

+
f

(

α

j

α

k

(
2

α

ℓ

−
1
)

4

α

ℓ

(
1
−

α

ℓ

)

|

A

j

−

A

k

|

)

)

+
f

(

|

∑

j
=
1

n

α

j

A

j

|

)

|

|

|

$$\begin{align*}\bigg|\bigg|\bigg|\sum\limits^{n}_{j=1}\alpha_{j}f(|A_{j}|)\bigg|\bigg|\bigg|&\geq\bigg|\bigg|\bigg|\sum\limits_{j,k\in S_{\ell}}\bigg(f\bigg(\sqrt{\frac{\alpha_{j}\alpha_{k}}{4\alpha_{\ell}(1-\alpha_{\ell})}}\;\bigg|A_{j}+A_{k}-2\sum\limits^{n}_{j=1}\alpha_{j}A_{j}\bigg|\bigg)\\&\qquad+f\bigg(\sqrt{\frac{\alpha_{j}\alpha_{k}(2\alpha_{\ell}-1)}{4\alpha_{\ell}(1-\alpha_{\ell})}}|A_{j}-A_{k}|\bigg)\bigg)+f\bigg(\bigg|\sum\limits^{n}_{j=1}\alpha_{j}A_{j}\bigg|\bigg)\bigg|\bigg|\bigg|\end{align*}$$

for ℓ = 1, …, n.

If f is a non-negative function on [0, ∞) such that

g
(
t
)
=
f
(

t

)

$g(t)=f(\sqrt{t})$

is concave, then the inverse inequality holds. Here, the symbol S
ℓ = {1, …, n} ∖ {ℓ} for ℓ ∈ {1, …, n}.

In addition, we provide some applications of the above inequalities.
Reducing Benzodiazepine Exposure by Instituting a Guideline for Dexmedetomidine Usage in the NICU
Authors: Morton Sarah U. , Labrecque Michelle , Moline Mark , Hansen Anne , Leeman Kristen 
Published: October 2021

BACKGROUND
Midazolam is a benzodiazepine sedative used in NICUs. Because benzodiazepine’s effects include respiratory depression and potential detrimental developmental effects, minimizing exposure could benefit neonates. Dexmedetomidine is routinely used for sedation in older pediatric populations. We implemented a quality improvement initiative with the aim of decreasing midazolam infusions by 20% through use of dexmedetomidine.

METHODS
A multidisciplinary committee created a sedation guideline that included standardized dexmedetomidine dosing escalation and weaning. Baseline data collection occurred from January 2015 to February 2018, with intervention from March 2018 to December 2019. Percentage of sedation episodes with dexmedetomidine initiated was followed as a process measure. Outcomes measures were percentage of eligible infants receiving midazolam infusions and midazolam-free days per sedation episode. Bradycardia with dexmedetomidine, unplanned extubation rates, and morphine dosage were monitored as balancing measures.

RESULTS
Our study included 434 episodes of sedation in 386 patients. Dexmedetomidine initiation increased from 18% to 49%. The intervention was associated with a significant reduction in midazolam initiation by 30%, from 95% to 65%, with special cause variation on statistical process control chart analysis. Midazolam-free days per sedation episode increased from 0.3 to 2.2 days, and patients receiving dexmedetomidine had lower midazolam doses (1.3 mg/kg per day versus 2.2 mg/kg per day, P = 5.97 × 10−04). Bradycardia requiring discontinuation of dexmedetomidine, unplanned extubation rates, and morphine doses were unchanged.

CONCLUSIONS
Implementation of a quality improvement initiative was successful in reducing the percentage of patients receiving midazolam infusions and increased midazolam-free days per sedation episode, revealing an overall reduction in benzodiazepine exposure while maintaining adequate sedation.
Academic stress and sleep quality during the COVID-19 pandemic among medical students from a university of Peru
Authors: Allende-Rayme Fiorella Rosemary , Acuña-Vila Jeremi Harold , Correa-López Lucy Elena , De La Cruz-Vargas Jhony Alberto 
Published: October 2021

Introduction: Sleep quality is an important factor for both the physical and mental health of medical students.
Objective: To evaluate the association between academic stress and sleep quality among medical students enrolled in a university of Lima (Peru) during the COVID-19 pandemic.
Materials and methods: Analytical cross-sectional study carried out in 410 medical students from the School of Human Medicine of Universidad Ricardo Palma, who, in October and November 2020, were administered the Spanish version of the Pittsburgh Sleep Quality Index, the SISCO SV-21 Academic Stress Inventory, and a sociodemographic and habits questionnaire via Google Forms. Bivariate and multivariate analyzes were performed to identify factors associated with poor sleep quality and academic stress, using prevalence ratios (PR) with a 95% confidence interval and a significance level of p≤0.05.
Results: 97.32% of students presented with “academic stress” and 90.48% had “poor quality of sleep”. Factors associated with “poor quality of sleep” were “high levels of academic stress” (aPR: 2.433; 95%CI: 1.619-3.657; p=0.000), “not living with relatives” (aPR: 1.264; 95%CI: 1.107-1.443; p=0.001), and “working in addition to studying” (aPR: 1.106; 95%CI: 1.012-1.209; p=0.026). Additionally, “female biological sex” (aPR: 1.178; 95%CI: 1.090-1.273; p=0.000) and “place of birth Lima” (aPR: 0.929; 95%CI: 0.882-0.979; p=0.006) were significantly associated with “academic stress.”
Conclusion: Having high levels of academic stress, not living with relatives, and working in addition to studying were factors associated with poor sleep quality among the study population during the COVID-19 pandemic.
Functional and environmental performance of plant-produced crumb rubber asphalt mixtures using the dry process
Authors: Bueno M. , Haag R. , Heeb N. , Mikhailenko P. , Boesiger L. , Poulikakos L. D. 
Published: October 2021

AbstractIncorporating crumb rubber (CR) using the dry process, directly in the asphalt mixture rather than into the bituminous binder requires no plant retrofitting, and therefore is the most practical industrial method for CR incorporation into asphalt mixtures. Nevertheless, very few large scale studies have been conducted. This work uses a holistic approach and reports on the functional and environmental performance of asphalt mixtures with different concentrations of CR fabricated employing the dry process in asphalt plants. Gaseous emissions were monitored during the production and laboratory leaching tests simulating the release of pollutants during rain, was conducted to evaluate the toxicology of both the CR material alone and the modified asphalt mixtures. In addition, laboratory compacted samples were tested to assess their fatigue behavior. Furthermore, noise relevant surface properties of large roller compacted slabs were evaluated before and after being subjected to a load simulator (MMLS3) to evaluate their resistance to permanent deformation. The results confirm that comparable performance can be achieved with the incorporation of CR using the dry process for high performance surfaces such as semi-dense asphalt, which usually require the use of polymer modified binders. Environmental performance improvement can be achieved by a washing step of the CR material that could remove polar CR additives which have commonly been used as vulcanization accelerator during rubber production.
Examining patterns and drivers of variability in playa water status on the High Plains of western Kansas, 2016–2019
Authors: Bowen Mark W. , Lepe Luis 
Published: October 2021

Playa wetlands are widely distributed across the High Plains of the central United States, providing a range of ecosystem services, such as groundwater recharge, surface water storage, and wetland habitat. Although playas are essential resources, few studies have examined the variability and controls on playa water storage. The purpose of this project is to determine how playa and watershed morphology, watershed land cover, and precipitation patterns affect timing and duration of water storage in playas. This project focuses on 92 playas distributed throughout a 10-county region in western Kansas.
Playa and watershed morphology were calculated in a GIS environment and classified into quartiles based on playa and watershed surface area. Watershed tilled index (i.e., percent cropland versus grassland) was determined using 2016, 2017, 2018, and 2019 Cropland Data Layers available from the National Agricultural Statistics Service and classified as either cropland (more than 75% cropland), grassland (more than 75% grassland), or mixed. Monthly precipitation data for 2016–2019 were compiled from the Oakley 22S High Plains Regional Climate Center weather station. Playa water status for 2016–2019 was classified monthly as either standing water or dry (i.e., no visible standing water) by visually examining four-band satellite imagery with 3.7 m resolution available from Planet Explorer (www.planet.com).
Playa water status is influenced by a combination of factors, including playa and watershed morphology, watershed land cover, and precipitation patterns. Larger playas have larger watersheds and standing water more frequently and for longer periods than smaller playas. Playas in cropland watersheds store water more frequently and for longer periods than playas in grassland watersheds, though differences are not statistically significant. Standing water within playas is positively correlated with monthly precipitation and reflects a short-term response to precipitation patterns, regardless of playa size or watershed land cover. The strongest controls on playa water status are playa area, monthly precipitation, and watershed area.
Playas are critical resources for the High Plains, providing a range of ecosystem services that are dependent upon the playa’s ability to store water. Playa functions are under continued threat from cropland expansion, climate change, and playa and watershed modifications. To sustain playa functions in Kansas, efforts should focus on conserving larger grassland playas and reducing sediment inputs to playas in cropland watersheds.
Synthesis of Optimal Information and Energy Schemes of Measuring and Converting Devices
Authors: Bushuev A. B. , Boikov V. I. , Bystrov S. V. , Grigoriev V. V. , Mansurova O. K. 
Published: October 2021

The synthesis of information and energy schemes is posed as an extreme problem, the purpose of which is a weighted directed graph of the minimum length from the input value to the output value of the device. The nodes of the graph are the physical effects included in the given database, and the branches are the input and output values of the effects. Nodes and branches are mathematically defined by diagonal multidimensional matrices, whose elements are determined by the dimensions of the quantities in the selected system of physical coordinates with a given number of basic units of measurement. The weight or resource intensity of the graph elements is determined by the norm of the corresponding matrices. The resulting circuit is suitable for use in technical documentation to explain the operating principle of the device, as well as for patent protection.In the enhanced formulation of the extreme problem, restrictions are introduced on the numerical values of the input and output values of the effect and its dynamic properties in the form of the transfer function of the effect. In this case, the size of the transfer matrices of nodes and branches is expanded by one. As a result, the transfer matrix of the effect contains information not only about the dynamic properties of the effect, but also about the dimensions of the physical quantities at its input and output.In a detailed example, the case of searching for the operating principle of a measuring-converting device of a pressure sensor with an electric current output is considered. To simplify the geometric representation of graph vectors on a plane, the problem is considered for a two-dimensional system of physical quantities with basic units of length and time. The calculation of the resource capacity is carried out according to the scheme of dimensional simulation, in which the phase variables of the differential equation enter with their physical dimensions. According to the numerical value of the resource capacity, you can compare different versions of the implementation of the operating physical principle of the device.
Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques
Authors: Suleymanov Vagif , Gamal Hany , Glatz Guenther , Elkatatny Salaheldin , Abdulraheem Abdulazeez 
Published: October 2021

Abstract
Acoustic data obtained from sonic logging tools plays an important role in formation evaluation. Given the associated costs, however, the industry clearly stands to benefit from cheaper technologies to obtain compressional and shear wave slowness data. Therefore, this paper delineates an alternative solution for the prediction of sonic log data by means of Machine Learning (ML).
This study takes advantage of an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) ML techniques to predict compressional and shear wave slowness from drilling data only. In particular, the network is trained utilizing 2000 data points such as weight on bit (WOB), rate of penetration (ROP), standpipe pressure (SPP), torque (T), drill pipe rotation (RPM), and mud flow rate (GPM). Consequently, acoustic properties of the rock can be estimated solely from readily available parameters thereby saving both costs and time associated with sonic logs.
The obtained results are promising and supportive of both ANFIS and SVM model as viable alternatives to obtain sonic data without the need for running sonic logs. The developed ANFIS model was able to predict compressional and shear wave slowness with correlation coefficients of 0.94 and 0.98 and average absolute percentage errors (AAPE) of 1.87% and 2.61%, respectively. Similarly, the SVM model predicted sonic logs with high accuracy yielding to correlation coefficients of more than 0.98 and AAPE of 0.74% and 0.84% for both compressional and shear logs, respectively. Once a network is trained, the approach naturally lends itself to be integrated as a real time service.
This study outlines a novel and cost-effective solution to estimate rock compressional and shear-wave slowness solely from readily available drilling parameters. Importantly, the model has been verified for wells drilled in different formations with complex lithology substantiating the effectiveness of the approach.
Use of Machine Learning Methods for Gas Export Volume Predictions
Authors: Ibragimov Abdulmalik 
Published: October 2021

Abstract
Gas sales volume have a significant impact on oil production in the Karachagank field due to gas injection constraints. Ambient temperature is one of the variables influencing gas sales volume. As global warming takes a toll on a climate, extreme weather conditions become frequent in the region hindering hydrocarbon production. The author used machine-learning techniques to predict gas sales volume based on weather forecast data. The results of prediction allow foreseeing potential possible drops in export volumes that can help field staff in proactive planning for opportunity maintenance on wells, the surface network, and a gas plant thus helping in avoiding large negative impacts caused by high ambient temperature.
Expand Excellence: Business Intelligence Tools for Drilling Data Analysis as Enhanced Efficiency Approach
Authors: Rakhmangulov Farit , Garipov Andrey , Chertenkov Mikhail 
Published: October 2021

Abstract
The business intelligence tools allow you to work with databases containing historical drilling records. This gives a possibility to collect data into a single visualized shell, to see patterns, and, most importantly, to identify the main issues. A database among Yamal region projects was taken as an example. This process automation allows you to reduce the time for collecting information and makes it possible to conduct a more detailed and thoughtful analysis of project indicators. In turn, this results in making quick and effective decisions.
At the initial stage, historical drilling data over three years was analyzed, namely information on more than 800 drilling runs. A model was formed from various sources in reliance on the database, and a visual working dashboard of the project was compiled. The dashboard allowed us to recognize the main issues that were plain to see. Awareness of plain-to-see issues gives the possibility to find out what information needs to be added to the data model in order to perform more thoughtful analysis. Collecting all the data in one place is not enough. In order to bring the data together, they were collated and visualized in the most explicit way.
The bulk data collected in a single model allowed us to see the whole picture of what was happening on the project and to concentrate on solving the root problem. Major issues resulted in equipment failures due to vibrations in the horizontal section. A more detailed vibration analysis showed that the bit was the main driver of vibrations. Hence, the data on rock strength and bottom hole assembly (BHA) vibrations were added to the model for further analysis. However, the complete package of data does not guarantee success. It becomes problematic to see the patterns in some situations, since analogous wells are not always available. While business intelligence tools make it easy to manage filters and find analogy, that is only fair if you have enough offset data. Based on the analysis, the most efficient bit designs were selected and proposed. The updated range of bits allowed to increase ROP in the horizontal section and reduced failures due to vibrations, so the well construction cycle was shortened.
These tools are accessible for technicians, which means there is no need-to-know programming languages. Suggested approach allows to accelerate decision-making and identification of key issues. In addition, existing tools give the possibility to monitor key performance indicators continuously within a single project as well as throughout the country. In contrast to the well-known program with tables, the main advantage is the automatic update of the already built data model. This fact speeds up the analytical process and report generation by times.
Well Performance Monitoring Using Management by Exception Rules and Alerts
Authors: Nadirov Maruan , Sadyrbakiyev Renat , Skopich Anton 
Published: October 2021

Abstract
Wells in Tengiz and Korolev oil fields are equipped with data transmitting devices, which provide real-time process data used by Production engineers for continuous production monitoring and identification of unusual process conditions. Monitoring and analysis of each well performance becomes a tedious process with growing well inventory. Up until recently, real-time data from wellsite transmitters was not used to its full potential to simplify and automate well performance analysis. To improve the quality of daily well performance monitoring and detection of abnormal process conditions, sets of data rules have been developed to create alerts and screens with real-time process data managed by exception. These alerts and screens help to identify malfunctioning equipment and changes in operating conditions. Timely evaluation of critical conditions helps to proactively prepare a mitigation plan and prevent unscheduled well shutdowns.
Data management by exception allows automatic filtering of big data sets and draws attention only to wells with deviations from the stable operating regime. Detailed review of highlighted well conditions helps to differentiate between malfunctioning equipment and actual changes in operating conditions. Fast identification of the issues allows taking preventative actions to maintain process stability of each producing well. Implementation of these tools significantly reduced number of unscheduled well shutdowns due to leaks in Surface Controlled Subsurface Safety Valve (SCSSV) hydraulic system and pneumatic valves control system. The screens also help to identify malfunctioning equipment including pressure and temperature gauges, pressure downhole gauges (PDHGs) and multiphase flow meters (MPFMs), as well as flow assurance issues such as hydrate formation.
Developed data rules can be useful for any field equipped with data transmitting devices. This paper aims to share the best practices of using real-time operational data analytics to identify malfunctioning equipment, changing operating conditions and other process related issues to maintain stable production process.
Mitigating Well Construction Challenges of a Caspian Deepwater Exploratory Well: A Cementing Perspective
Authors: Kurawle Irfan , Dieker Ansgar , Soltero Adriana , Nafikova Svetlana 
Published: October 2021

Abstract
BP returned to Caspian deepwater exploratory drilling in 2019. The exploration well was drilled on the Shafag-Asiman structure in water depths greater than 2,000 ft. Well challenges included high shallow water flow (SWF) risk with multiple re-spuds on the nearest offset, lost circulation due to complex wellbore geometry combined with a narrow pore and fracture gradient window, and uncertainty in pore pressure prediction in abnormally pressured formations with a new depositional model. In addition, a well total depth more than 23,000 ft, eight string casing design and bottom-hole pressures greater than 20,000 psi presented a truly modern-day challenge to well integrity. A six-month planning phase for the cementing basis of design concluded by delivering slurry designs capable of combating SWF, qualified by variable-speed rotational gel strength measurement. Engineered lost circulation with selective placement of wellbore strengthening materials in combination with cement and mechanical barriers to provide isolation and integrity for the life of the well. Exhaustive pilot testing to account for changes required a cement design based on pore pressure variation and comprehensive modeling for hydraulics, centralizer placement, and mud displacement. This was complemented by a custom centralizer testing process specifically designed to simulate forces exerted in wells with similar complexity. Long-term effects on cement were evaluated, not only for placement but also for future operations including pressure and temperature cycles during wellbore construction or abandonment.