bluuebunny/crossref_metadata_2025_split huggingface dataset:
EL FLAMENCO EN LA INVESTIGACIÓN Y LA EDUCACIÓN INTERDISCIPLINAR
Authors: Morales Martínez Daniel 
Published: March 2023

Desde su declaración como Patrimonio Inmaterial de la Humanidad, el flamenco ha ido adquiriendo cada vez más importancia dentro del panorama investigador y educativo. En este artículo realizaremos un repaso por las principales líneas de investigación que el género ha experimentado, destacaremos su papel dentro del ámbito educativo como herramienta interdisciplinar para el aprendizaje de competencias emocionales y concluiremos con una reflexión acerca de los retos futuros del género en este terreno.
EL UNIVERSO EN UN BOTÓN
Authors: García Aguilera Juan de Dios 
Published: March 2023

Progresar no es simplemente aumentar la capacidad de acceso al conocimiento, sino llegar a experimentarlo; solo la experiencia de ese conocimiento es capaz de afectar a tu carácter, tu pensamiento y tus emociones. O lo que es lo mismo, tener un mundo de posibilidades al alcance de un botón (como dice Stravinski) no garantiza nada, porque entender las cosas, acceder a la comprensión de un cuadro, una pintura, una sinfonía o un concierto, no es fácil sin un esfuerzo previo. Pero para quien está dispuesto a realizarlo y lo consigue, ese esfuerzo produce una recompensa única e incomparable, una emoción extraordinaria.
"EL REPERTORIO SEGOVIANO".
Authors: Villafuerte Jiménez Javier 
Published: March 2023

La figura de Andrés Segovia fue clave en la historia de la guitarra, además de por su im- portancia como intérprete, por el enriquecimientodel repertorio del instrumento que llevó a cabo mediante diversas facetas. El título hace referencia a las obras que fueron creadas para Segovia, no sólo como dedicatario sino también como intérprete.
LA MEMORIA MUSICAL: ALGUNOS APUNTES SOBRE LA MEMORIZACIÓN EN LA INTERPRETACIÓN PIANÍSTICA (I)
Authors: Gallardo Lorenzo Ángeles 
Published: March 2023

La capacidad de hacer y comprender música a través de la ejecución instrumental es un proceso de gran complejidad física y psíquica que exige el desarrollo y la integración de diferentes destrezas técnicas y el uso de la memoria sensomotora y auditiva. Nos adentraremos en este mundo de vital importancia para el músico.
LA PASIÓN SEGÚN SAN JUAN, DE JOHANN SEBASTIAN BACH
Authors: Millán Julián 
Published: March 2023

La estética pone todo su esfuerzo en el estudio o la observación de los fenómenos que, en mayor o menor medida y desde orígenes que se remontan al mundo clásico, tienen una relación directa con la belleza. La música brinda la posibilidad de escoger a la carta un intérprete que optimice la relación que debe establecerse entre oyente y creador. la distancia que tercia entrecreador y espectador no es una fácil salvedad. Es precisamente ése el ámbito donde ha de moverse el intérprete con versatilidad y sutileza, a fin de procurar una recreación de lo que el autor quiso decir pero se supo falto de ubicuidad. Procuraremos ofrendar la experiencia estética de la Pasión según san Juan, de J. S. Bach, desde la posición del intérprete.
SOBRE EL MODO FLAMENCO Y SUS FUNCIONES TONALES. PUBLICACIÓN DE LOS TIENTOS PARA GUITARRA FLAMENCA "DE VIENTO Y SAL".
Authors: Cera Vera Manuel 
Published: March 2023

Cualquier pieza compuesta para guitarra flamenca es susceptible deser analizada con las herramientas adecuadas. En el ámbito de la música clásica donde se han desarrollado mejor los métodos de análisis para conseguir un acercamiento a la música con el necesario nivel de concreción y objetividad. Al tratarse de música paraguitarra flamenca, con sus consabidas peculiaridades, la pregunta que cabría hacerse es si el análisis musical clásico o tradicional nos podría seguir siendo útil. Daremos respuesta y un enfoque.
FRÉDÉRIC CHOPIN: SCHERZO Nº 1, OP. 20. ESTUDIO COMPARATIVO DE EDICIONES: WESSEL & CO. Y PADEREWSKI
Authors: Tello Sánchez Isaac 
Published: March 2023

En este artículo se analizan las diferencias existentes entre las dos ediciones objeto de estudio, manifestando la importancia de in-vestigar a partir de las fuentes, con el objetivo de acercarse a la intención primigenia del compositor.
A través de los parámetros contemplados, trata de mostrar razonadamente que las resoluciones planteadas por cada editor no son axiomáticas, pudiendo en cada caso alcanzarse un nivel de acierto distinto. Asimismo, se plantea que todo intérprete debe tomar parte activa en la toma de decisiones partiendo de la comprensión, y no solo entendimiento, del texto impreso, si pretende verdaderamente mostrar la obra a través de su prisma y de su propia visión artística.
Application of Computer Vision in Machine Learning-Based Diagnosis of Water Production Mechanisms in Oil Wells
Authors: Abdelaziem O. E. , Gawish A. , Farrag S. F. 
Published: March 2023

Summary
Diagnostic plots, introduced by K. S. Chan, are widely used to determine excessive water production mechanisms. In this paper, we introduce a computer vision model that is capable of segmenting and identifying multiple Chan signatures per plot, for the sake of surveillance and early screening, given that wells could exhibit diverse mechanisms throughout their life cycle. As deep learning (DL) demands a vast amount of information, we start our workflow by building a data set of 10,000 publicly available oil wells that have experienced varying water production mechanisms. Next, we perform preprocessing and remove anomalies from production data, which could be deceptive in analysis. Then, we visualize Chan plots as images and annotate them. Thereafter, we split data set, carry out augmentation, and put data together to be used as input for a convolutional neural network (CNN) layer. Eventually, data are trained utilizing you only look once (YOLO)—a one-stage object detector, hyperparameters are tuned, and model performance is evaluated using mean average precision (mAP). The collected data from fields in Alaska and North Dakota represent oil wells that have been producing for decades. When working with wells that possess noisy production data, we recognized challenge, bias, and tedium in human interpretation of Chan plots. Subsequently, we observed the inevitability of cleaning well production data before constructing the plots and thoroughly revealed its effect on enhancing model potentiality to get a fair score. In addition, we concluded that following a systematic approach of active learning, a technique that allows user to analyze mistakes of algorithm predictions and label data accordingly, accomplished a significant boost in model performance, especially with underrepresented classes. The proposed CNN model, which uses automatic feature extraction and expresses data in detail, is presumed to be robust as it successfully predicted multiple mechanisms of excessive water production, with confidence scores higher than 80%, in wells that exhibit different production conditions such as horizontal trajectories, artificial lift, waterflooding, stimulation, and other well intervention events. In this work, we introduce a novel computer vision model, which combines image processing and DL techniques to identify multiple water production signatures that a well can undergo and eliminate subjectivity of human interpretation. This approach has the potential to be effective, as a part of workflow automation, in expeditious surveillance of large oil fields. Source code is available on GitHub for public use.
Trends of antihypertensive, antidiabetic, and non-steroidal anti-inflammatory drugs use among the Health Workers Cohort Study, Mexico 2004 to 2018.
Authors: Ortega-Montiel Janinne , Montoya Alejandra , Soria-Saucedo René , Gallegos-Carrillo Katia , Ramírez-Palacios Paula , Salmerón Jorge , Salazar-Martinez Eduardo 
Published: March 2023

Abstract
Purpose
Hypertension and Type 2 Diabetes (T2D) are the most prevalent non-communicable diseases in Mexico and worldwide. According to international practice management guidelines, the principal chronic management therapy is a daily oral medication. We aim to describe the trends of antihypertensive, antidiabetic, and non-steroidal anti-inflammatory (NSAID) drugs use among the Mexican adult population from 2004-2018.
Methods
We analyzed data from the Health Workers Cohort Study (HWCS) for males and females aged > 18 years. The prevalence of chronic diseases and utilization for every kind of antihypertensive, antidiabetic, and NSAIDs (measured by self-reported utilization) were calculated at baseline and two follow up (2004-2010-2017). Trends were analyzed using Fisher’s exact test.
Results
Hypertension prevalence increased from 19.8 to 30.3%, higher than T2D prevalence from 7.0 to 12.8% through fourteen years of follow-up. The proportion of patients using beta-blockers and angiotensin II receptor blockers increased, like the self-reported dual therapy. Regarding T2D, the prevalence of metformin utilization increased to 83.9%. The utilization of common NSAIDs, mainly for muscular pain, remained around 13 to 16%.
Conclusions
Our findings showed a clearly changing prevalence of drug utilization for hypertension and T2D between 2004 and 2018 and consistent use of NSAIDs (acetaminophen mainly) in the adult Mexican population.
Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement
Authors: Kim Juyoung , Ock Minsu , Oh In-Hwan , Jo Min-Woo , Kim Yoon , Lee Moo-Song , Lee Sang-il 
Published: March 2023

Abstract
Background
The recent rising health spending intrigued efficiency and cost-based performance measures. However, mortality risk adjustment methods are still under consideration in cost estimation, though methods specific to cost estimate have been developed. Therefore, we aimed to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in efficiency measurement.
Methods
We used the Health Insurance Review and Assessment Service–National Patient Sample as the data source. A separate linear regression model was constructed within each major diagnostic category (MDC). Individual models included explanatory (demographics, insurance type, institutional type, diagnosis-based risk adjustment methods) and response variables (episode-based costs). The following risk adjustment methods were used: Refined Diagnosis Related Group (RDRG), Charlson Comorbidity Index (CCI), National Health Insurance Service Hierarchical Condition Categories (NHIS-HCC), and Department of Health and Human Service-HCC (HHS-HCC). The model accuracy was compared using R-squared (R2), mean absolute error, and predictive ratio. For external validity, we used the 2017 dataset.
Results
The model including RDRG improved mean R2 from 34.2–38.5% compared to the adjacent DRG. RDRG was inferior to both HCCs (RDRG, 38.5%; NHIS-HCC, 40.6%; HHS-HCC, 41.4%) and superior to CCI. Model performance varied depending on the MDC groups. While both HCCs had the highest explanatory power in 11 MDCs, including MDC P (Newborns), RDRG showed the highest adjusted R2 in 6 MDCs, such as MDC O (pregnancy, childbirth, and puerperium). The average mean absolute errors were the lowest in the model with RDRG ($1,241). The predictive ratios showed similar patterns among models regardless of subgroups according to age, sex, insurance type, institutional type, and upper and lower 10th percentiles of actual costs. External validity also showed a similar pattern in the model performance.
Conclusions
Both NHIS-HCC and HHS-HCC were useful in adjusting comorbidities, excluding complications, for episode-based costs in the process of efficiency measurement.
Medical Intelligent System Application in Schizophrenia Treatment Satisfaction Based on Neural Networks
Authors: Ren Xiajin , Wang Xin 
Published: March 2023

Abstract
Aiming at the problems of poor convergence and uneven energy consumption of heed routing protocol, this paper proposes an opfh routing protocol based on options clustering algorithm for the first time. Firstly, the protocol divides the network into several top-level groups by using options clustering algorithm, then elects each top-level group at the same time, and designs and develops the medical intelligent system through wireless sensor. The medical intelligence system developed in this paper mainly includes the patient end associated with the WeChat official account, an intelligent voice phone that supplements and enhances patient care services, and an information management platform for handling aftercare services. Follow up service is mainly supplemented by the follow-up procedure and linked to the official WeChat official account. The follow-up medical intelligent system and intelligent voice telephone have replaced the traditional way of follow-up, which not only facilitates the completion of rehabilitation work, but also facilitates the management of hospital rehabilitation, and greatly reduces the manpower and material resources. It plays an important role in the treatment of schizophrenia. However, patients with schizophrenia have high inner sensitivity, often accompanied by anxiety and depression. Nurses need to take the initiative to communicate with patients, understand and meet the needs of patients, and use the system to manage the negative emotions of patients, so as to improve the confidence and compliance of patients. In order to improve the treatment satisfaction of patients, the large-scale data set without marked target task was used for domain transfer learning of small-scale data set. In this paper, the research of wireless sensor is applied to medical intelligent system, so as to promote the vigorous development of medical intelligent system.
The effects of socioeconomic activities on water quality in Hainan Island, south China
Authors: Ma Jianrong , Chen Qiao , Wu Xiangliang , Paerl Hans W , Brookes Justin D , Li Guangyu , Zeng Yan , Wang Jingfu , Chen Jingan , Qin Boqiang 
Published: March 2023

Abstract
Water quality is intimately related to the livelihood of the numerous people, and affects the development and operating benefits of various industries in society. This study clarifies the effects of human-driven economic activities on inland water quality in Hainan Island, and reveals relationships between water quality and tourism on the island. Based on previously monitored data, this study uses a static Bayesian network and radial basis function neural network (RBFNN) to model and predict the future water quality. From 2012 to 2015, water quality in the Nandu, Wanquan and Changhua Rivers was good (at level II, GB3838-2002). The static Bayesian network demonstrated that Gross Output Value (GOV) of agriculture, GOV of fishery, GOV of animal husbandy and chemical oxygen demand (COD) discharge will significantly affect water quality in the Nandu and Changhua Rivers. The effect of tourism on water quality in Wanquan River was significantly higher than that on the Nandu and Changhua Rivers. In the Wanquan River, the DO content fluctuated greatly in comparison to the other two rivers, and unexpectedly, increased tourism led to higher DO values. However, it remains necessary to closely monitor negative changes in water quality due to tourism, especially in Wanquan River and eastern Hainan province. The developed RBFNN showed that the changes in water quality were predicted accurately in comparison with experimental values in the present study and the water quality also is continuously improving. Overall, results suggest that current anthropogenic socioeconomic activities had a modest effect on water quality in Hainan Island. Agriculture, fishery, animal husbandy and COD discharge were relatively important factors affecting water quality, while tourism had a perceptible effect in eastern Hainan. Our findings provide a reference for the balance of water quality, people’s livelihood and economic development (tourism and port construction) in Hainan province.
Spectral graph modeling reveals global slowing of neurophysiological network transmission in Alzheimer's disease
Authors: Verma Parul , Ranasinghe Kamalini , Prasad Janani , Cai Chang , Xie Xihe , Lerner Hannah , Mizuiri Danielle , Miller Bruce , Rankin Katherine , Vossel Keith , Cheung Steven , Nagarajan Srikantan , Raj Ashish 
Published: March 2023

Abstract
Alzheimer's disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. We employed a spectral graph-theory model (SGM) to identify abnormal biophysical markers of neuronal activity in AD. SGM is an analytic model that describes how long-range fiber projections in the brain mediate excitatory and inhibitory activity of local neuronal subpopulations. We estimated SGM parameters that captured the regional power spectra obtained from magnetoencephalography imaging of a well-characterized population of patients with AD and controls. The long-range excitatory time constant was the most important feature for the accurate classification of AD and controls and was associated with global cognitive deficits in AD. These results indicate that a global impairment in the long-range excitatory neurons might be a sufficient factor underlying spatiotemporal alterations of neuronal activity in AD.
Predictors and Influence of Postoperative Moderate-to-severe Pain of PACU in the Patients with Malignancy
Authors: Zhang Yu , Dai Qinxue , Xu Kaiwei , Fu Haifeng , Zhang Anqi , Du Wenwen 
Published: March 2023

Abstract
Background

This study was identied the risk factors for and designed to investigate influence of postoperative moderate-to-severe pain of post anaesthesia care unit (PACU) in patients with malignancy.

Methods

A retrospective study was performed on 22600 cancer patients with malignancy who underwent elective radical surgery in the Department of Anesthesiology, The First Affiliated Hospital of Wenzhou Medical University, between October 2020 and December 2021. All patients were transferred to the PACU after tracheal extubation. Patients were divided into two groups according to a visual analogue scale (VAS) score of > 3: the no-moderate-severe-pain group and moderate-to-severe-pain group. Data pertaining to demographic, surgical, anaesthetic, and other factors were recorded. Logistic regression analysis was performed to explore the risk factors associated with postoperative moderate-to-severe pain of PACU in patients with malignancy.

Results

The incidence of postoperative moderate-to-severe pain of PACU in patients with malignancy was 1.42%. Multivariate logistic regression analysis showed that male sex, chest surgery, abdominal surgery, intraoperative haemorrhage, intraoperative use of non-steroidal anti-inflammatory drugs (NSAIDs), duration of anaesthesia, postoperative vomiting, and hypothermia were risk factors for postoperative moderate-to-severe pain of PACU in patients with malignancy. Female sex and intraoperative NSAID use were protective factors. The area under the curve was 0.85. Moderate-to-severe pain in the PACU correlated with hypertension, hyperglycaemia, dysphoria, and hypoxemia (P < 0.05).

Conclusions

Clinicians should actively provide preoperative pain education for men, patients undergoing chest or abdominal surgery, and patients who may experience massive haemorrhage during surgery. Active temperature monitoring and heat preservation, assisted analgesia with NSAIDS, and anti-nausea drugs were performed during surgery. Pain assessment and management should be conducted in time to reduce the occurrence of postoperative moderate-to-severe pain of PACU in patients with malignancy.
Trial registration
The study was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (No.KY2021-097) and registered in the Chictr.org.cn registration system on 06/12/2021(ChiCTR2100054013).
Detection Mechanism in IoT framework using Artificial Neural Networks
Authors: Jain Rachna , Dhand Geetika , Sheoran Kavita , Kaur Amandeep , Puri Navyah , Jatana Nishtha , Jain Kashika , Bansal Hiti , Jain Panav 
Published: March 2023

Abstract
Internet of Things (IoT) applications are now used more frequently due to the rapid expansion of wireless networking and the digital revolution. IoT helps in user-to-machine and machine-to-machine interaction. IoT objects have gained popularity because they can be accessed from anywhere. Healthcare, agriculture, smart cities, and the military are different domains where IoT objects are communicating with each other. The goal of anomaly-based techniques is to figure out which patterns are normal and which are aberrant. This approach of intrusion detection has the benefit of detecting original works of authorship intrusions. However, this technique has the drawback of frequently producing false positive results. To increase the effectiveness of anomaly-based intrusion detection methods, machine learning techniques are being evaluated. Anomaly-based intrusion detection techniques can be used by machine learning algorithms to watch active behavior and compare it to known intrusion footprints in order to stay aware of potential future attacks. In a hybrid approach, different identifying methods are combined in the same scheme. This technique will eliminate the weaknesses of a particular operation while improving the overall IoT system's reliability. In this research, we study intrusion-based systems using comparative analysis of several machine learning and deep learning algorithms. In the proposed work one hot encoding technique is used to deal with the categorical data. Different parameters like accuracy, F-1 score, precision, and recall value have been calculated. Experimental results prove that ANN yields 99.61% accuracy over other hybrid models. However, in Machine Learning, RandomForestClassifier yields the best results.
Comparison of metagenomic next-generation sequencing and blood culture in patients with suspected bloodstream infection
Authors: Shi Wen , Zhou Yuhua , Wen Yi , Mao Enqiang , Ni Tongtian 
Published: March 2023

Abstract
Background
The application of metagenomic next-generation sequencing (mNGS) has gradually been carried out by clinical practitioner. However, few studies have compared it with blood cultures in patients suffering from suspected bloodstream infections. The purpose of this study was to compare the detection of pathogenic microorganisms by these two assays in patients with suspected bloodstream infection.
Methods
We retrospectively studied patients with fever, chills, antibiotic use for more than 3 days, suspected bloodstream infection, and admission to the emergency department of Ruijin Hospital from January 2020 to June 2022. All patients had blood drawn on the same day for blood mNGS and blood cultures. Clinical and laboratory parameters were collected on the day blood was drawn. The detection of pathogenic microorganisms by the two methods was compared. Risk factors and in-hospital mortality in patients with bloodstream infections were analysed separately for these two assays.
Results
In all 99 patients, the pathogenic microorganisms detection rate in blood mNGS was significantly higher than that in blood culture. Blood mNGS was consistent with blood culture in only 12.00% of all positive bacterial and fungal test results. The level of CRP is related to bacteraemia, fungaemia and viraemia detected by blood mNGS. No clear risk factors could be found in patients with a positive blood culture. In critically ill patients, both tests failed to improve patient outcomes.
Conclusion
In patients with suspected bloodstream infection, mNGS is not yet a complete replacement for blood cultures. The results of both tests could not improve in-hospital mortality.