bluuebunny/crossref_metadata_2025_split huggingface dataset:
Karakter Pelajar Pancasila sebagai Penanggulangan Dekandensi Moral dalam Pembelajaran Bahasa Indonesia di Era Merdeka Belajar
Authors: Wachidah Liana , Putikadyanto Agus Purnomo Ahmad , Kusumawati Hesty , Adebia Ika Cahya 
Published: January 2023

Dekandensi moral merupakan penurunan moral dalam diri seseorang yang disebabkan adanya faktor-faktor tertentu. Tingkat moralitas masyarakat menjadi faktor yang mendominasi problematika di Indonesia. Upaya yang dilakukan untuk menanggulani problematika tersebut yakni penanaman karakter dalam dunia pendidikan. Tujuan penelitian ini untuk mendeskripsikan karakter Pelajar Pancasila sebagai penaggulangan dekandensi moral dalam pembelajaran Bahasa Indonesia. Jenis penelitian kualitatif melalui studi pustaka dipilih dalam penelitian ini. Data berupa informasi yang diperoleh dari hasil observasi, studi pustaka, dan transkrip wawancara. Sumber data dalam penelitian ada tiga yaitu buku, jurnal, dan hasil penelitian (baik melalui media cetak maupun internet), serta informan yakni guru Bahasa Indonesia dan kepala SMPN 3 Pamekasan. Data dikumpulkan melalui observasi, studi literatur, dan wawancara. Lalu dianalisis dengan pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Solusi untuk menanggulangi dekadensi moral dalam pembelajaran Bahasa Indonesia yakni dengan menanamkan pendidikan karakter dan didukung proyek Pelajar Pancasila. Selain itu perlu dilakukan tiga kesiapan berikut, yakni (1) mengadakan program pembiasaan, keteladanan, tutorial serta pendampingan bagi siswa oleh guru bidang pelajaran, wali kelas, serta guru bimbingan konseling; (2) melakukan koordinasi dengan elemen pendukung seperti orang tua dan masyrakat; dan (3) menjaga kedisiplinan siswa untuk memanfaatkan waktu dengan efektif.
A method to position a simple strip trial to improve trial efficiency and maximise the value of vineyard variability for decision-making
Authors: Song Xinxin , Bramley Robert G.V. , Evans Katherine J. 
Published: January 2023

The main difficulties grapegrowers and consultants face in obtaining robust trial results include time and labour to collect data and land variability that confounds trial results. Spatial approaches that use whole-field designs, sensing technologies and geostatistical analysis enable more efficient data collection and account for the impact of spatial variation on crop responses while generating statistically robust results. However, the practical application of these approaches for vineyard trials requires affordable automation of measurements of viticultural variables and access to skills for geostatistics. A strip approach has been developed to simplify experimentation by allowing the farmer to use a single crop row to trial and analyse data in a spreadsheet. However, guidance is needed as to how to position trial strips in a vineyard block to reveal likely treatment effects across the entire block. Here, we investigated using a covariate to a response variable of interest to position a strip trial to infer treatment effects beyond the trial strip. Strip trials were simulated for two experiments: one comparing three treatments for vineyard floor management on grape yield and another comparing two spray programs for powdery mildew control. Useful covariates for yield or mildew severity were determined using correlation analyses. Trial results were analysed using a moving pairwise comparison of treatments and a moving average of the covariates. Simulated trial strips that incorporated a range of variation in a useful covariate close to that encountered in the whole block showed how yield or mildew severity varied with the covariates along the strips. Importantly, such results provided information about likely crop responses in other parts of the block according to variation in the covariates, thus contributing to better-informed decision-making. Compared to whole-field approaches, this strip approach is more efficient and simpler for growers to implement.
Pengaruh Trichoderma harzianum dan Nano Silika Terhadap Penyakit Moler dan Produksi Bawang Merah
Authors: Ishlah Muhammad Akhlishil , Kristanto Budi Adi , Kusmiyati Florentina 
Published: January 2023

<p>Twisted disease is one of the important diseases that can decrease shallot yield.The purpose of this experiment was to examine the effect of the interaction <em>Trichoderma harzianum</em> and nano silica on twisted disease and shallot yield. The research was conducted in Platar, Mangunjiwan, Demak and the Laboratory of Ecology and Plant Production, Faculty of Animal and Agriculture Science, Undip, on April – Agustus 2022. The experimental design used factorial completely randomized design with three replications. The first factor was <em>T. harzianum</em> consisted of three levels (P0 = 0 (control), P1 = 10, and P2 = 20 g plant<sup>-1</sup>). The second factor was nano silica concentration with four levels (S0 = 0, S1 = 1000, S2 = 2000, and S3 = 3000 ppm). The results showed that application of <em>T. harzianum </em>20 g plant<sup>-1</sup> or nano silica 3000 ppm had the lowest disease intensity 2,74 % and 2,48 % observed at 20 days after planting and 11,81 % and 10,81 % observed at 40 DAP. Combination <em>T. harzianum </em>20 g plant<sup>-1</sup> and nano silica 3000 ppm resulted in the highest yield parameters which were tuber diameter (2,370 cm), number of tubers (6 tubers), tuber fresh weight (36,2 g), and tuber dry weight (28,7 g). Treatments of <em>T. harzianum</em> and nano silica did not significantly affect the infection rate and number of leaves, but application of <em>T. harzianum</em> 20 g plant<sup>-1</sup> and nano silica 3000 ppm showed a decrease in twisted disease intensity and the highest shallot yield.</p>
Optimization Trading Strategy Model for Gold and Bitcoin Based on Market Fluctuation
Authors: Xie Hong-Xia , Feng Yan , Yu Xue-Yong , Hu Yu-Ning 
Published: January 2023

As a new type of digital currency, Bitcoin is considered as “future	gold” by various scholars. Therefore, this study considers Bitcoin and gold	as a group of hedging assets to conduct investment research and it also	discusses the investment rules between Bitcoin and gold: prediction of the	rise and fall of Bitcoin, comparison of the characteristics of Bitcoin and	gold, and the impact of the transaction procedures of Bitcoin and gold on	the final trading results, and formulates trading strategies through	optimization algorithms. Then, four machine learning algorithms, i.e., LSTM, BP neural	network, Adaboost, and Bagging, are introduced to predict the rise and fall	of gold and Bitcoin the next day, and then, the entropy weight method is	used to synthesize four predicted results to ensure the robustness of the	predicted results. To establish the optimal trading strategy, this study	considers the maximum expected return as the goal to develop a	single-objective optimization model and historical five-day price volatility	as a risk factor. In this study, ant colony, simulated annealing, and	genetic algorithms are used to solve the single-objective optimization	model. Finally, we conclude that Bitcoin, similar to other financial assets,	e.g., gold, is sensitive to shocks and volatile and possesses a relatively	quiet cycle. When Bitcoin has an asymmetric impact, Bitcoin and gold can	equally treat transactions.
Physics-Informed Neural Networks and Capacitance-Resistance Model: Fast and Accurate Oil and Water Production Forecast Using End-to-End Architecture
Authors: Gladchenko Elizaveta S. , Illarionov Egor A. , Orlov Denis M. , Koroteev Dmitry A. 
Published: January 2023

Abstract
Capacitance-Resistance model (CRM) has been a useful tool for fast production forecasts for decades. The unique combination of simplicity and physics-based nature in this data-driven approach allowed it to stay as an object of scientific interest and get its own place among other types of models capable of giving predictions on flow rates, such as full-scale 3D reservoir models. However, the model simplicity, assumptions, and limitations does not allow wide application of a conventional CRM in complex field cases. A vast majority of studies on CRM are about overcoming its limitations by introducing new coefficients, modifying the analytical form of the equation, etc. Integrating CRM with rapidly developing artificial intelligence (AI) methods seems to be a logical continuation of model evolution.
Recently introduced Physics-Informed Neural Networks (PINN) can preserve CRM's governing equations and coefficients that gives some insights about wells and formations standing out from other popular machine learning and deep learning methods. Moreover, PINN type models give certain flexibility in the choice of architectures – it means that the model architecture can be changed in a way that may assist in solving different problems. Thus, we introduce end-to-end learning of neural networks (NN) while implying some physical constraints. It is intended to overcome one of the major limitations of CRM, which is obtaining predictions for oil and water production rates from total liquid. This way, the additional training of rough approximation fractional flow models that are either not suitable for the case or may require the knowledge of reservoir properties is not needed.
In this work, the well-known concept of Capacitance-Resistance models appears in a new form, which allows performing history matching rather rapidly, achieving robustness and forecasting liquid, oil and water production rates simultaneously. To test this new approach, several datasets (both synthetic and real) were used. The results obtained by PINN are compared to those obtained by a conventional CRM. By conventional we mean the analytical solution, which was modified by our research group to take into consideration common real field cases such as shut-in wells, workover operations, etc. by introducing dynamic characteristic coefficients [1].
Advisory System for Drilling Optimization Using Artificial Intelligence and In-Cutter Sensing Data
Authors: Koulidis Alexis , Ahmed Shehab 
Published: January 2023

AbstractThe drilling optimization concept corresponds to enhancing the drilling efficiency by identifying the optimum drilling parameters with respect to bit wear. On many occasions, human interference can reduce the scale of a problem and enhance efficiency, but it has limitations, especially when working with big data and complex machines. This work utilizes in-cutter force sensing data and a scaled-drilling rig to apply an AI-based solution and facilitate drilling optimization.There are several experimental challenges to building an advisory system: tuning the PID controller, continuous communication and feedback between hardware and software, data synchronization, and system efficiency. The core capability that enables drilling optimization is in-cutter sensing, which allows evaluating the forces acting on a PDC cutter. Initially, an extensive experimental study is conducted to record and process the drilling data with sharp cutters. Weight on bit, torque, rotational speed and rate of penetration are measured and sampled at the same frequency as the in-cutter force sensing. Rock samples with different mechanical properties are utilized and tested in atmospheric conditions.The AI-based solution utilizes the data from the PDC cutter and the scaled-drilling rig structure to identify the optimum range of the drilling parameters depending on the mechanical properties of the rock samples. Artificial Neural Network (ANN) is utilized to predict the rate of penetration for samples with different uniaxial compressive strength. The supervised machine-learning models are trained on input variables such as weight on bit, torque, rotational speed, uniaxial compressive strength, vibrations, and more importantly the measured force at the PDC cutter.A physics constraint is applied for torque, weight on bit and vibrations to guarantee that the output of the ML model is within the operating ranges. For each sample, approximately 100 data points are extracted per variable, with 70% for training, 15% for validation and 15% for testing. Cross-validation is used to enhance the robustness of the ML model. The results show that prediction performance is enhanced when the in-cutter sensing measurement is implemented as an input variable. The reliability of the ML models is tested on samples with different mechanical properties, with the results indicating an accurate prediction of the rate of penetration.The paper demonstrates an innovative workflow that combines AI and in-cutter sensing data to enhance the drilling efficiency with respect to system's mechanical limitations. The model provides a reliable and rapid decision to identify the optimum drilling parameters with respect to physics constraints.
Optimization of Architecture and Operation of Intelligent Wells Under Geological and Economic Uncertainties
Authors: Khan Zurwa , Tafreshi Reza , Wahid MD , Retnanto Albertus 
Published: January 2023

Abstract
Intelligent Wells (IWs) actively manage oil production and water breakthrough. IWs comprise Inflow Control Valves (ICVs), which are automated valves with multiple openings. In reservoirs with geological uncertainties, ICVs can be used to optimize production in different segments of IWs. However, economic uncertainties related to the varying oil prices and the high capital cost of IWs also need to be examined. The objective is to maximize the Net Present Value (NPV) of a horizontal Intelligent Well (IW), undergoing geological and economic uncertainties, by changing IW architecture and number, flow control position, and placement of Inflow Control Valves (ICVs), while satisfying safety standards, equipment capacity constraints and minimizing water breakthrough. To simulate the pressure drops for different scenarios within the IW for the optimization problem, a mechanistic model of a Conventional Wellbore (CW) and ICVs has been considered for oil-water flow. Nested loops were employed to maximize the objective function. The inner loop optimized the placement and flow control position with a genetic algorithm. The outer loop optimized the number of ICVs and IW's physical dimensions with a gradient-based method. Validating with experimental data resulted in 30.1% root mean squared error. The Pressure, Volume and Temperature (PVT) properties for oil and water were determined with empirical correlations. Sensitivity analysis indicated that the number of ICVs is the most essential variable among all variables for maximizing NPVs. The presented optimization strategy is successful at increasing NPVs relative to conventional wells. Unlike conventional literature, current optimization strategy not only focuses on design or production of IW, but it also considers both, the design and the IW's production optimization simultaneously.
Digitally Augmented Subsurface History Match (DASH-Sim): A New Frontier in Reservoir Simulation History Matching
Authors: Baig Raheel , Nooruddin Hasan 
Published: January 2023

Abstract
History matching is a long-standing industry challenge and resource-intensive process. In this paper, we introduce a new method in the area of automated history matching algorithms, called Digitally Augmented Subsurface History Match (DASH-Sim), that focuses on calibrating the permeability property at the field level. The new approach captures the global displacement mechanism as observed in the field by capitalizing on time-lapse surveillance data.
Time-lapse surveillance data is used to derive an accurate representation of subsurface saturation as a function of time. We refer to this dynamic saturation distribution as the 4D saturation model. The DASH-Sim method minimizes the difference in saturation between the 4D model and the simulation model at all timesteps by adjusting the 3D grid-cell permeability values. A novel mathematical relationship is introduced that calculates the required permeability calibration in the simulation model as a function of the underlying rock quality and the difference in water saturation between the 4D saturation model and the simulation model. The algorithm considers the dynamic nature of saturation by successively updating the static permeability property at each timestep of the corresponding observed 4D saturation.
A synthetic case study is presented in which the new algorithm is applied. First, a truth case simulation model was run using a permeability distribution from which water saturations were recorded as a function of time. The simulated water saturation over time using the truth case permeability was treated as the observed 4D saturation model. Next, a significantly modified permeability property was replaced in the simulation model resulting in a new water saturation distribution over time. The DASH-sim algorithm was then used as an inverse history match calibration process to generate permeability modifications guided by the objective function to minimize mismatch in saturations between the observed 4D saturation model and the numerical simulation model. Results indicate a very good history match is achieved in an automated manner, reducing the turnaround time for such tasks by orders of magnitude.
The inverse problem of history matching subsurface reservoir models is a very challenging and time-consuming task. The DASH-Sim offers a new approach to automate this tedious task consistently by considering both surface-level production and subsurface time-lapse measured surveillance data.
A Novel High-Speed AI-Physics Hybrid Model to Predict the Reservoir Behavior; A Case Study
Authors: Qubian Ali , Zekraoui Mohammed Ahmad , Mohajeri Sina , Mortezazadeh Emad , Eslahi Reza , Bakhtiari Maryam , Al Dabbous Abrar , Al Sagheer Asma , Alizadeh Ali , Zeinali Mostafa 
Published: January 2023

Abstract
Reservoir simulation is widely applied to model and manage subsurface flow operations. However, due to the nonlinear nature of the governing equations and the multiscale character of the geological description, executing numerical simulations can be expensive and increasingly slow. In this study, we propose an innovative high-speed simulation method to build a model that is capable to predict the field's behavior. In this study, a novel AI-Physics hybrid model was proposed for reservoir simulation. Classical numerical simulation and deep learning neural networks are coupled together to create an AI-Physics hybrid reservoir simulation. As a result, a model was obtained with predictive capabilities to forecast the field's behavior. Then, we combined AI-Physics history training with blind test prediction calculation of remaining oil maps. Finally, forecast scenario definitions based on the remaining oil map were created by the AI-Physic model. The proposed high-speed simulation model can reduce the history matching and scenario assessment time by 90 to 95%. According to its capabilities, three improved forecast scenarios were created based on a predefined scenario. These improved scenarios can produce a significant million standard barrels more oil than the original development scenario within three years. This technology eliminates limitations for multiple scenario assessments. In our AI hybrid model, the power of dynamic reservoir simulation is combined with a modern machine learning approach to "Evergreen" forecasts in reservoir assets. Consequently, the simulation resulted in a sub-optimal shortcut between model updates and inconsistencies in production forecasting. Moreover, applying deep learning methods to focus on the critical reservoir properties intelligently leads to tremendous time-saving in the static model update life cycle. In fact, with this novel simulation that we implemented, the new production data could be incorporated within minutes to regenerate more reliable and up-to-date forecasts. This simulation generates ‘up-to-date remaining hydrocarbon maps interactively, so the operator can continuously optimize the infill drilling locations between Field Development Plan cycles.
Standardizing and Automating Well Log Data Sources in Subsurface Applications
Authors: Kumar Pranav , Krishnapillai Anish 
Published: January 2023

Abstract

Objectives/Scope
Well operations team deals with series of Well log data through real time system and any other data exchange system. This data is streaming through different channels and with a naming convention implemented by the respective originator of the data or the system. The goal of the data is to be used for Studies and Operations. This paper mentions the implemented data workflow to deal with Well log data sources, channels, mnemonics and sharing through a standardized workflow.

Methods, Procedures, Process
The Real time data streaming through WITSML servers is connected directly into Subsurface application by proper authorization and access. The data naming conventions was collected from all the specific service provider, and it was than mapped with standard naming that is used in the organization. The data consists of several different type of mnemonics. Example could be XXX_P10H, XXX_ROBB, XXX_MCIA, XXX_GRM2, where XXX stands for vendor specific name. The frequency of Real time data depends on the streaming and some time it takes time to update data. In parallel, memory data for the same wells are made available to the Operations team through a defined exchange system. A mapping was developed between Log mnemonics from the vendors and the standard naming convention proposed by the Operations team. Python scripts were used to automatically rename and map any log mnemonics and aliases coming to the subsurface application. For the memory data, other scripts work, which goes to the individual LAS files and check the mnemonics and changes them back to standard naming convention.

Results, Observations, Conclusions
Implementation of the mapping and python scripting has helped engineers achieve their objectives in efficient way. They do not have to worry about individually renaming the specific log and channel names. Data quality has improved by comparing the real time and memory data sources. User experience has enriched and enhanced with the implemented data mapping and standardization workflow. For example, in case of Neutron log sometimes, users must change units from % to Fraction for using it for other Petrophysical calculations. This process is now automated, and data gets converted with the defined standard.

Novel/Additive Information
By mapping and standardizing the way of comparing data sources, logs and channels has improvised the data quality, data sharing and data governance related issues. This is a continuous process which requires further improvements and result oriented approach.
Whole exome sequencing and RNA sequencing analyses of PDAC samples
Authors: Hyeon Do Young , Nam Dowoon , Han Youngmin , Kim Duk Ki , Kim Gibeom , Kim Daeun , Bae Jingi , Back Seunghoon , Mun Dong-Gi , Madar Inamul Hasan , Lee Hangyeore , Kim Su-Jin , Kim Hokeun , Hyun Sangyeop , Kim Chang Rok , Choi Seon Ah , Kim Yong Ryoul , Jeong Juhee , Jeon Suwan , Choo Yeon Woong , Lee Kyung Bun , Kwon Wooil , Choi Seunghyuk , Goo Taewan , Park Taesung , Suh Young-Ah , Kim Hongbeom , Ku Ja-Lok , Kim Min-Sik , Paek Eunok , Park Daechan , Jung Keehoon , Baek Sung Hee , Jang Jin-Young , Hwang Daehee , Lee Sang-Won 
Published: January 2023

Abstract
The associated publication reports proteogenomic analysis of human pancreatic ductal adenocarcinoma (PDAC), where we provided significantly mutated genes (SMGs)/biomarkers, cellular pathways, and cell types as potential therapeutic targets to improve stratification of patients with PDAC. This protocol describes the detailed methods for whole exome sequencing (WES) and RNA sequencing analyses of PDAC samples, including tissue processing for proteogenomic analysis, DNA and RNA extraction, experimental procedures for WES and RNA sequencing, and WES and RNA sequencing data analyses.