bluuebunny/crossref_metadata_2025_split huggingface dataset:
Kryminologiczne aspekty bezdomności. Wybrane zagadnienia
Authors: Mikołajczuk Krzysztof 
Published: December 2021

Bezdomność jest niewątpliwie trudnym problemem społecznym, który generuje wiele trudności w funkcjonowaniu jednostek i zbiorowości i to nie tylko w Polsce czy Europie, ale i w całym świecie. Zmiany społeczno-ekonomiczne, który pojawiły się w Polsce po 1989 r., przyczyniły się do podziału społeczeństwa na płaszczyźnie ekonomicznej. Doprowadziło to niewątpliwie do pojawienia się większej liczby osób bezdomnych, które nie były w stanie poradzić sobie w nowych warunkach życia. Codzienne życie takich osób zaczęło ulegać degradacji na wielu płaszczyznach ich egzystencji. Niniejsze rozważania oscylują wokół zagadnień problemu bezdomności w aspekcie kryminologicznym. Istnieje bowiem związek między bezdomnością a przestępczością. Osoby bezdomne popełniają przestępstwa, ale często są też ich ofiarami.
Zakładanie spółdzielni socjalnych przez kościelne osoby prawne
Authors: Poniatowski Michał 
Published: December 2021

W przedmiotowym artykule zostało przedstawione zagadnienie zakładania spółdzielni socjalnych przez kościelne osoby prawne. W pierwszej kolejności został scharakteryzowany zarys historyczny działalności charytatywnej i działalności spółdzielczej, a następnie zagadnienie pojęcia spółdzielni socjalnej w rozumieniu prawa polskiego. W dalszej kolejności przedstawiono podstawy prawa kościelnych osób prawnych do założenia spółdzielni socjalnych oraz specyfikę statutu takich spółdzielni. Ostatnia część artykułu zawiera wnioski końcowe.
Implementation of Classification Algorithms in Neo4j using IPL data
Authors: Medhi Surajit , Baruah Hemanta K. 
Published: December 2021

The main objective of this paper is to implement the classifications algorithms in Neo4j graph database using cypher query language. For implementing the classification algorithm, we have used Indian Premier League (IPL) dataset to predict the winner of the matches using some different features. The IPL is the most popular T20 cricket league in the world. The prediction models are based on the city where the matches were played, winner of the toss and decision of the toss. In this paper we have implemented Naïve Bayes and K-Nearest Neighbors (KNN) classification algorithms using cypher query language. Different classifiers are used to predict the outcome of different games like football, volleyball, cricket etc, using python and R. In this paper we shall use cypher query language. We shall also compare and analysis the results which are given by Naïve Bayes and K-Nearest Neighbors algorithms to predict the winner of the matches.
Color and Organoleptic Changes of the Golek Mango Fruit (Mangifera indica L.) On Zero Energy Cool Chamber (ZECC) Storage Method with Packaging Combination
Authors: Dirpan Andi , Nurfaidah Rahman Andi , Tahir Sapsal Muhammad , M. Tahir Mulyati , Dewitara Singgang 
Published: December 2021

The increase in the amount of mango fruit production is due to their high nutritional content affordable, and relatively easy to find. However, like other horticultural crops, Mango can be easily bruised or damaged. The fruit damage can be caused by improper post-harvest handling, causing mango quality and has a short shelf life. The study aimed to determine the shelf life and quality of mango Golek stored in Zero Energy Cool Chamber (ZECC) using LDPE packaging and LDPE packaging with additional perforations. The first stage was observing the physical quality of the fruit and then post-harvest treatment such as washing, packaging and storing mangoes at ZECC temperature (±26°C). The second stage was the mango quality test after storage. Tests in the form of skin color and organoleptic. The results obtained in this study were mangoes packed with LDPE and LDPE with perforations. Mango packaged in LDPE packaging were able to retain L* color, b* color, organoleptic color, aroma, texture and taste. Mangoes packed with perforated LDPE packaging can retail, L * color, b * color, organoleptic color, aroma, texture and taste
RESPON PERTUMBUHAN DAN BIOKIMIA MICROGREENS TANAMAN BASIL (Ocimum basilicum L.) TERHADAP KOMBINASI WARNA LED DAN LAMA PENYINARAN YANG BERBEDA
Authors: Nugraheni Endah , Karno Karno , Sutarno Sutarno 
Published: December 2021

Microgreen merupakan tanaman pangan fungsional yang bertujuan untuk menciptakan diversifikasi pangan yang berkelanjutan secara global dengan nilai gizi yang tinggi dibandingkan dengan tanaman biasa. Kemangi merupakan salah satu jenis tanaman yang sering dijadikan sebagai microgreen karena kandungan nutrisinya yang lengkap. Peningkatan produksi dan kualitas microgreens dapat dilakukan dengan memberikan pencahayaan LED dengan spektrum cahaya yang dibutuhkan dan meningkatkan durasi penyinaran. Penelitian ini bertujuan untuk mengetahui pertumbuhan dan respon biokimia microgreen basil terhadap kombinasi LED merah, biru, dan merah:biru serta lama penyinaran selama 12, 16 dan 20 jam. Penelitian dilaksanakan pada bulan April 2021 – Mei 2021 di Laboratorium Ekologi dan Produksi Tumbuhan dan analisis klorofil, karotenoid dan fenol dilakukan di Laboratorium Fisiologi dan Pemuliaan Tanaman Fakultas Peternakan dan Pertanian (FPP) Universitas Diponegoro. Rancangan percobaan yang digunakan dalam penelitian ini adalah Rancangan Acak Lengkap (RAL) dengan pola faktorial 5 x 3 dengan 3 ulangan, sehingga terdapat 45 satuan percobaan. Faktor pertama adalah kombinasi warna LED yang meliputi L1: 100% Merah: 0% Biru, L2: 0% Merah: 100% Biru, L3: 70% Merah: 30% Biru, L4: 50% Merah: 50% Biru, L5: 30% Merah: 70% Biru. Faktor kedua adalah lama penyinaran meliputi T1 (12 jam), T2 (16 jam), T3 (20 jam). Berdasarkan penelitian yang dilakukan, dapat disimpulkan terdapat interaksi antara kombinasi warna cahaya dengan lama penyinaran terhadap kandungan biokimia microgreen basil. Penyinaran dengan kombinasi warna cahaya merah 100% dengan penyinaran selama 20 jam dapat meningkatkan kandungan klorofil dan karotenoid microgreen basil.
POTENSI ANTIBAKTERI EKSTRAK DAUN PEDADA (SONNERATIA CASEOLARIS) SEBAGAI PENGAWET ALAMI IKAN KEMBUNG (RASTRELLIGER SP) SEGAR
Authors: Hasibuan Nirmala Efri , Sumartini Sumartini 
Published: December 2021

Pengawetan merupakan salah satu cara untuk menghambat kemunduran mutu ikan. Penelitian ini bertujuan untuk memanfaatkan ekstrak daun pedada (Sonneratia caseolaris) sebagai bahan pengawet alami pada ikan kembung segar. Penelitian ini dibagi menjadi tiga tahap yaitu persiapan sampel, ekstraksi daun pedada dan aplikasi ekstrak pada ikan. Penelitian ini menggunakan Rancangan Acak Lengkap Faktorial yang terdiri dari tiga faktor. Faktor pertama adalah penambahan ekstrak daun pedada. Faktor kedua adalah suhu penyimpanan ikan yaitu suhu ruang (23º-27ºC), suhu dingin (10ºC), suhu beku (0ºC). Faktor ketiga adalah lama penyimpanan yaitu 1, 7 dan 14 hari. Masing-masing perlakuan dilakukan pengulangan sebanyak dua kali. Parameter yang diuji meliputi nilai pH, kadar air, kadar protein, angka lempeng total (ALT) dan E-coli. Hasil penelitian menunjukkan bahwa penambahan ekstrak daun pedada, perbedaan suhu penyimpanan, dan lama penyimpanan berpengaruh signifikan (sig<0.05) terhadap nilai pH, kadar air, kadar protein dan angka lempeng total (ALT). Selanjutnya hasil uji E-coli diperoleh bahwa perlakuan suhu dingin dan suhu beku pada ikan dengan penambahan ekstrak daun pedada menunjukkan kandungan E-coli negatif.
Attachment of a Myoelectric Prosthesis After Transulnar Osseointegration Implantation
Authors: Jaime Kayla M. , Reif Taylor J. , Kafedzic Haris , Garrison Glenn , Dhawan Jennifer , Rozbruch S. Robert 
Published: December 2021

Case:
This report discusses 2 successful cases of traumatic transulnar amputees who underwent osseointegration implantation. After surgery, a myoelectric prosthetic equipped with Coapt (Chicago, IL) recognition software was attached directly to the implant. Patients underwent training with pattern recognition software to learn to control the myoelectric prosthetic with the multiarticulating hand and wrist. Both implants osseointegrated without signs of loosening at the most recent follow-up of 18 months and 2 years, respectively. Prosthetic control gradually improved to allow activities of daily living.

Conclusion:
These cases demonstrate what can be achieved with interdisciplinary coordination between surgeons, prosthetists, and emerging technologies.
Prediction of Adverse Perinatal Outcomes and Preeclampsia in Pregnant Women with Chronic Arterial Hypertension
Authors: Radzinsky Victor , Gasanova Bakhtykei , Polina Miroslava , Douglas Natalya , Zakharova Praskovya , Dedy Tatyana 
Published: December 2021

The objective of this study was to determine predictors and develop a prognostic model for preeclampsia (PE) and adverse perinatal outcomes in pregnant women with chronic arterial hypertension (CAH).	Methods and Results: The study cohort included pregnant women (n=376) with hypertensive disorders: Group 1 –pregnant women with CAH (n=134), Group 2 – with PE on the background of CAH (n=242). Healthy pregnant women made up the control group (n=34). The diagnosis of pregnant women with CAH was made on the basis of existing national and foreign recommendations that an increase in SBP ≥140 mmHg and/or DBP ≥90 mmHg indicates CAH. All patients underwent the following examinations: a survey on a special questionnaire; an anthropometric examination; physical examination; an assessment of the content of uric acid (UA) in the blood serum and microalbuminuria (MAU) in the urine at 6-8 and 16-17 weeks of pregnancy; 12-lead ECG, echocardiography, 24-hour ambulatory blood pressure monitoring, sonography of the uterus, fetus, and placenta at 6-8 and 16-18 weeks. It was found that an inappropriate left ventricular mass (LVM) in pregnant women with CAH, a certain “phenotype” of 24-hour ABPM and indicators of metabolic disorders demonstrate the systemic nature of organ damage and appear to be a predictor of adverse perinatal outcomes and the development of PE. The revealed changes in the LV structure, which are more significant in PE on the background of CAH, suggest the association of concentric left ventricular hypertrophy and disorders of uteroplacental blood flow. Detecting abnormal blood flow from early pregnancy will reduce not only perinatal morbidity and premature birth, but also the probability of organ (LV myocardium, kidney) damage in women with CAH. To predict the risks of adverse perinatal outcomes in pregnant women with CAH and PE, a number of factors were identified that have a statistically significant relationship with the studied complications. The developed model makes it possible to predict the probability of PE and unfavorable perinatal outcomes in pregnant women suffering from CAH with high efficiency (91.1%).
High-Resolution Micro-Continuum Approach to Model Matrix-Fracture Interaction and Fluid Leakage
Authors: He Xupeng , Alsinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: December 2021

Abstract
Understanding the fundamental mechanism of fracture-matrix fluid exchange is crucial for the modeling of fractured reservoirs. Traditionally, high-resolution simulations for flow in fractures often neglect the matrix-fracture leakage influence on the fracture hydraulic properties, i.e., assuming impermeable fracture walls. This work introduces a micro-continuum approach to capture the matrix-fracture leakage interaction and its effect on the rock fractures’ hydraulic properties.
Because of the multiscale nature of fractured media, full physics Navier-Stokes (NS) representation everywhere in the whole domain is not feasible. We thus employ NS equations to describe the flow in the fracture, and Darcy’s law to model the flow in the surrounding porous rocks. Such hybrid modeling is achieved using the extended Darcy-Brinkman-Stokes (DBS) equation. With this approach, a unified conservation equation for flow in both media is applied by choosing appropriate parameters (e.g., porosity and permeability) for the corresponding domains. We apply an accurate Mixed Finite Element approach to solve the extended DBS equation. Various sensitivity analyses are conducted to explore the leakage effects on the fracture hydraulic properties by varying surrounding matrix permeability, fracture roughness, and Reynolds number (Re).
Streamline profiles show the presence of back-flow phenomena, where in-flow and out-flow are possible between the matrix and the fractures. Further, zones of stagnant (eddy) flow are observed around locations with large asperities of sharp corners under high Re conditions. Numerical results show the significant effects of roughness and inertia on flow predictions in fractures for both impermeable and leaky wall cases. Besides, the side-leakage effect can create non-uniform flow behavior within the fracture that may differ significantly from the case with impermeable wall conditions. And this matrix-fracture leakage influence on hydraulic properties of rock fractures matters especially for cases with high matrix permeability, high fracture roughness, and low Re values. In summary, we present a high-resolution micro-continuum approach to explore the flow exchange behavior between the fracture and rock matrix, and further investigate the static and dynamic effects, including variable Reynold numbers, mimicking flow near and away from the wellbore. The approach and results provide significant insights into the flow of fluids through fractures within permeable rocks and can be readily applied in field-scale reservoir simulations.
Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques
Authors: Al Gharbi S. H. , Al-Majed A. A. , Abdulraheem A. , Patil S. , Elkatatny S. M. 
Published: December 2021

Abstract
Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations.
To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality.
To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.
Uncertainty in Geomechanical Modeling
Authors: Tatur Olga , Ocheretyanyy Anton , Petrakov Yuriy , Sobolev Alexey 
Published: December 2021

Abstract
The active application of geomechanical modeling in the oil and gas industry began in the early 2000s. Geomechanics is used in all life cycles of the field-from exploration drilling to field development models, construction of production wells and monitoring of the development process. The success of geomechanical modeling directly depends on the quantity and quality of input information. In contrast to the geological and hydrodynamic models in geomechanics, there is still no common approach and algorithm for the quantitative determination of the error of the model. This paper presents an algorithm for determining the 1D error of a geomechanical model, taking into account the measurement errors of the devices and the correlation dependencies used. This is the first step towards the formation of a unified approach to the quantitative determination of the error of 1D and 3D geomechanical models and the construction of uncertainty corridors.
In this paper, we propose a mechanism for estimating the error of correlation dependencies, which includes not only the error of the measuring equipment, but also the resulting convergence of the dependence and the rate of growth or decline of this convergence. The confidence parameter is used to estimate the contribution of a particular dimension to the calculations. The sensitivity of the resulting error to the quality of the source information is analyzed. In the application of this technique, it is possible at the initial stage to make a conclusion about the quality of the simulation results and to take measures to improve the reliability of the calculations.
Deep Similarity Learning for Well Test Model Identification
Authors: Nagaraj Guru , Pillai Prashanth , Kulkarni Mandar 
Published: December 2021

Abstract
Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction.
Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses.
For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates.
Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis.
This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction.
This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.
Relative Permeability Modifiers as a Chemical Means to Control Water Production in Oil and Gas Reservoirs
Authors: Al-Taq Ali , Alrustum Abdullah , Alfakher Basil , Al-Ibrahim Hussain 
Published: December 2021

Abstract
It is challenging to control water production in horizontal wells or in vertical wells having oil and water produced from the same zone using conventional methods such as through-tubing bridge plugs or other mechanical means. Relative permeability modifiers (RPMs), known to selectively reduce the relative permeability to water with minimum impact on the relative permeability to oil or gas, are considered a promising technology for solving this problem. The current generation of RPMs, unlike the old ones, can tolerate high hardness and so have higher success rates.
An extensive experimental work was carried out to evaluate three RPMs for water control in gas and oil wells. Test conditions included gas flow in sandstone cores with temperatures of up to 300°F, and oil flow in carbonate cores with temperatures as high as 220°F. The effect of initial core permeability to brine, RPM concentration, flow rate, and water-wetting surfactants on the effectiveness of RPM to reduce water production was investigated using sandstone and carbonate cores.
Coreflood experiments were undertaken at downhole conditions. The end-point relative permeabilities to various phases were measured. A back pressure of 500 psi, an overburden pressure of 3,500 to 5,000 psi and flow rates of 0.1 to 5 cm3/min were used. The concentration of RPM polymers was monitored in the core effluent using total organic carbon (TOC) analyzer to determine polymer retention in the core.
The results revealed that temperature adversely affected the effectiveness of all RPMs evaluated. A better reduction in permeability to water was obtained at 220°F compared to that obtained at 300°F. The use of RPM at the right concentrations was found to significantly reduce permeability to water. A better water reduction was obtained at higher polymer injection rates, which was attributed to flow-induced polymer retention. Adsorption of RPM polymer tended to alter wettability of a carbonate rock to more water-wet. This paper discusses the effects of the above parameters on the performance of RPM in sandstone and carbonate reservoirs, and it gives some recommendations for improving the success rate of these chemical applications in the field.