bluuebunny/crossref_metadata_2025 huggingface dataset:
Inflation Measurement in China under the Coronavirus Pandemic
Authors: Liu Yuqi
Published: April 2023

The consumer price index (CPI) and core consumer price index (core-CPI) are the main indicators reflecting the price level and inflation of a country or region. The two indicators and their transmission relationship are also widely concerned by the economic community. Theoretically, these two indexes will not fluctuate suddenly. However, in recent years, due to the uncertainty of the Corona Virus Disease 2019, the trends of both indicators have declined to vary degrees. This paper will start with the CPI, CORE-CPI, and inflation rate from 2017 to the present, and analyze the reasons behind their abnormal phenomenon. The results showed that the pork sales volume was the main reason for causing the fluctuations. Understanding the reasons for the decline will help China quickly formulate the most effective plan and help the inflation index to return to the right track. At the same time, it timely leads the country out of difficulties.
Research on the analysis of financial risk contribution within stocks based on artificial neural networks
Authors: Zhao Wendi , Yu Jiabeizi , Huang Suijia
Published: April 2023

Since the late 1980s, as stock markets have faced a variety of challenges, the assessment and management of financial risk within stocks has become increasingly important. The management of financial risk within stocks is an important part of modern portfolio investment theory. In order to study the internal influencing factors and risk transmission mechanism of stock price in China, this paper selects a system of 13 indicators from 4 aspects, conducts contribution analysis by constructing ANN (Artificial Neural Network) algorithm, and uses R2 ,MSE indicators to test the indicators, and the results show that the growth ability and profitability of enterprises have the most influencing factors on stock price. The research in this paper is beneficial to further explore the internal influencing factors and their intrinsic relationships of Chinese firms' stocks.
Research on Finance Reducing the Cost of Real Businesses
Authors: Wang Jiawen
Published: April 2023

In the context of financial development tending to be digital and technological, exploring the relationship between fintech and the cost of real enterprises is significant for the high-quality development of real enterprises. Using provincial panel data from 2011-2020, this study empirically investigates the impact, heterogeneity characteristics and transmission mechanism of fintech on the cost of real enterprises using a two-way fixed effects model. This study demonstrates that fintech significantly lowers the cost of real businesses; in terms of different regions, the impact of fintech development on the cost of real enterprises is more significant in eastern regions than in central and western regions; corporate gearing, innovation capacity, financing constraints and financial regulation are the moderating variables of fintech impact on the cost of real firms. Based on this, suggestions are made in this paper related to the high-quality development of fintech, this is an important study to promote the deep integration of fintech and real businesses and to promote high quality development of the real economy.
Exploring the Impact of Virtual Anchor Features and Live Content on Viewers' Willingness to Pay for “Superchat” in Live Entertainment Scenarios
Authors: Zhan Jinming , Zhang Nan
Published: April 2023

With the Internet, the Virtual anchor industry has flourished, gaining a wide fan base and creating eye-catching economic benefits. The new form of reward “Superchat”, which is born with the development of Virtual anchors, has not been studied yet. Therefore, this study aims to investigate the influence of the features of Virtual anchors and live content on viewers' willingness to pay for Superchat in the live entertainment scene. The results showed that: (1) Virtual anchor features are significant, and viewers' willingness to pay for Superchat is strong, and different features will have different effects on willingness. (2) Viewers' perceived recognition and internalization mechanisms mediate the process from virtual anchor features to the willingness to pay for Superchat. Different features correspond to different mechanisms to influence the willingness to pay for Superchat. (3) The type of live content will have a moderating effect on the mechanism of viewers' perception. (4) In summary, in the hedonistic live broadcast, the attractiveness, technical capability and character property of the virtual anchor influence the willingness to pay for Superchat through the mediating effect of the recognition mechanism; in the utilitarian live broadcast, the professionalism of the virtual anchor influence the willingness to pay for Superchat through the mediating effect of the internalization mechanism.
Study on the Technical Model Design of a Tourist Attraction from the Metaverse Perspective
Authors: Tian Shuang , Song Qinghua
Published: April 2023

With the development of digital technology and the epidemic's impact in recent years, the public's demand for "cloud tourism" using various virtual technologies has gradually increased. The concept of building a tourist attraction in a metaverse is studied based on the metaverse perspective and the current development of metaverse technology. The rationality and feasibility of building a metaverse tourist attraction are analyzed. The application of high technology in various fields in recent years in constructing metaverse tourism scenery is studied and analyzed. A feasible technical model of metaverse tourism scenery is proposed based on this research.
A study of gold and bitcoin trading strategies based on quantitative trading decision models
Authors: Zhang Yu , Xu Shulin , Hou Liwei
Published: April 2023

In the computer era, high frequency trading is one of the hottest topics in the financial investment market. In this paper, a quantitative trading decision analysis is conducted for a portfolio consisting of gold and bitcoin, applying gray prediction to get the future asset price, applying time series to predict the risk of purchasing the asset, and combining the two to make a reasonable buying and selling operation. To prove the accuracy of the prediction, the MSE indicator is used to measure the error of the gray prediction model, and the results show that the error is small. Finally this paper also proves that the model is well robust by varying the transaction cost.
Unraveling the Global Financial Crisis
Authors: Ma Yike
Published: April 2023

The primary focus of this paper is on the causes of the financial crisis of 2008 and how it affected the world economy. The three main causes of the financial crisis are the Fed's monetary policy, subprime mortgage and credit default swaps. Irrational lending methods, the bubble economy in the housing market and unregulated insurance schemes became the inducement of the global financial crisis, which began with the bankruptcy of Lehman Brothers, the merger and acquisition of JPMorgan Chase and more and more people losing their jobs. This is not only an explosion in the American market, but also in the Asian and European markets are facing a depression. Therefore, the US government learned from the experience after 2008 and issued two corresponding important laws to prevent such incidents from happening again. The two proposals are the Dodd-Frank Act and Basel III. Therefore, this paper will introduce the two plans in detail and analyze their impact on the American economy.
A Comparative Study of Transfer Learning based Models for Lung Cancer Histopathology Classification
Authors: Chen Mo
Published: April 2023

Lung cancer is the deadliest form of cancer, which attracted a lot of attention in the past. Transfer learning is a very popular approach in deep learning as it can apply the knowledge obtained from a previous task to improve the performance in another. In this research, four transfer learning models with different complexity are utilized to detect lung cancer, which are AlexNet, VGG16, ResNet50 and Inception-v3. Since the early detection and histopathological diagnosis can considerably decrease the likelihood of mortality, the lung cancer histopathological images dataset is considered. This dataset contains histopathological images of 3 classes, all of them are considered in this study. Firstly, the four models are trained on the histopathological database from random initialization for 10 epochs. Next, the four models are first pre-trained on ImageNet and then trained on the histopathological dataset for 10 epochs. For each epoch, the testing accuracy is recorded so as to find the optimal number of epochs and determine whether transfer learning models are capable in lung cancer detection. Then, various evaluation metrics e.g., accuracy and precision are used to measure and compare the four models’ performance. The study’s finding shows that AlexNet, VGG16, ResNet50 and Inception-v3 pre-trained on ImageNet are adequate in lung cancer detection. There corresponding accuracy rates are 99.367%, 99.800%, 100% and 100% respectively, which are much higher than that trained from random initialization. Among the four transfer learning models, ResNet50 and Inception-v3 perform the best on lung cancer classification.
Investigations on the Performance of Pre-established CNN Model in Music Emotion Detection
Authors: Yu Yifan
Published: April 2023

Music is a medium for emotional artistic expression. Different people have different understandings of music. Music emotion recognition (MER) has thus become a novel branch in computer music. The goal of this essay is to investigate in the performance of established CNN architectures, such as AlexNet and VGG16, to recognize emotions contained in a song. CAL500 dataset is used as it covers a variety of genres. The dataset is transformed to spectrograms, which can be understood by computers through image recognition. The result of this investigation turned out to be that previous architectures would lead to overfitting within the training of a few batches. Possible explanations for this are that the parameters used in the model are too large for a simple regression task. This research provides some understanding of how CNN works as a network initially designed for image classification. Understanding emotions using spectrograms might require less complex CNN models or new models that are specialized in such tasks.
Prediction and Visualization Analysis of Lung Cancer Risk by Machine Learning
Authors: Wan Ziqi
Published: April 2023

One of the most lethal cancer types in the world is lung cancer. It gravely threats human health and causes more than one million deaths annually. Current research demonstrate that an early and accurate determination of lung cancer is the key element in reducing the mortality rate, which motivates us to predict cancer at an early stage by exploiting the capacity of machine learning. In this work, both qualitative visualization analysis and quantitative experiments are conducted to reveal the relevant factors and the effectiveness of lung cancer risk prediction. Visual analyses are implemented to display the distribution and correlation between characteristics of patients and lung cancer risk. The visualizations demonstrate that smoking has a relatively large impact on lung cancer. Moreover, various types of machine learning algorithms are implemented and compared, including the logistic regression, K-nearest neighbors, random forest, and decision tree. These methods achieve satisfactory prediction results, with an accuracy of about 84% to 90%.
Synthesizing Human Characteristics based on String Instruments
Authors: Zhang Vincent
Published: April 2023

As music technology continues to evolve, new software enables us to digitally produce audio. Although plenty of progresses have been made in music genres (e.g., EDM or hip hop), many string instruments have yet to be thoroughly synthesized. Instruments, such as the cello, require incorporating human characteristics to evoke emotion. On this basis, Nyquist and Audacity are utilized in this paper to analyze and create the synthesized pitch of the cello. Some may argue that computers are incapable of human characteristics in music, but the results of this study clarify the feasibilities at some extent. Based on both visualizing and listening, the output creates a similar sound hardly indistinguishable from a human. It presents the possibility of expanding onto other string instruments including the violin, viola, etc. Instead of the necessity for an entire orchestra, anyone can have their own symphony at their fingertips. From movies, video games, or any form of entertainment, the advancements in this field of research could revolutionize the music industry. These results shed light on guiding further exploration of synthesizing human characteristics in context of string instruments.
The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China
Authors: Zou Chengke
Published: April 2023

House prices increase substantially in China from 1998. Because of expensive house prices, most Chinese people have only one chance to select suitable houses. Therefore, building a house price prediction model based on housing conditions is significant for customers to make decisions. This paper collects the estate market data of Jinan city from the HomeLink website and performs several feature selection algorithms to get critical features for house price prediction. The paper compares the classical machine learning methods for the problem, including Multiple Linear Regression, Random Forest, and Catboost. After cross-validation tests, the CatBoost, algorithm with the lowest Mean Square Error (MSE) is regarded as the most accurate algorithm to predict house prices. The analytic results show that the house price is dominated by the location features such as area and block.
Research and Analysis of VR in the Field of Education
Authors: Chen Yuxin
Published: April 2023

In recent years, virtual reality has become a hot spot for applications in several fields. As a relatively new technology, every breakthrough it makes in a particular field is closely watched. The viability of this novel technology, has even begun to be applied in education. This paper mainly analyzes the teaching problems faced by some professions (medical education, science experimentation, and art education) and the application and help of virtual reality for these professions. First, this paper examines real-world uses of virtual reality in medical education, science experimentation, and art instruction. The benefits and drawbacks of these cases are presented and explored. Besides, this research also covers the professionalism of virtual reality in education, which not only boosts students' learning motivation but also improves classroom teaching quality. Finaly, this article examines VR's future uses in a variety of sectors, as well as summarizing and addressing some of the most crucial issues of its usage in education.
Crucial Processing of Detect Depression with EEG
Authors: Xiang Xingyu
Published: April 2023

Depression is a common mental disease that causes varying degrees of social impact. With the COVID-19 pandemic increasing the prevalence of depression, researchers are trying to develop better methods to diagnose and treat this disease. EEG, an emerging technology belonging to Brain-Computer Interface has been widely used in mental illnesses like Parkinson's and epilepsy monitoring. In recent years, many attempts have been made to apply EEG technology to the field of depression research. In this review, we focus on some major methods utilized in Depression detection with EEG technology as well as machine learning. We will talk about some mainstream methods in preprocessing, feature extraction, and classification, their features, advantages, drawbacks, and the parameters preferred in depression detection. Provide an overview for relevant researchers to promote their studies. Finally, the pipeline’s drawbacks and defects, what can we do, how to make it better, and its potential in relative areas are discussed.
Analysis of POW in Bitcoin and POS in Peercoin
Authors: Yu Yihua
Published: April 2023

The technology of blockchain, which is the underlying technology of cryptocurrency like Bitcoin, has changed the world dramatically and it has fascinated researchers from all over the world. Originating from the Byzantine General Problem, the consensus mechanism is utilized to reach an agreement in a group where the channel for communication could be unreliable. Among all the consensus mechanisms in the blockchain, proof of stake (POS) and proof of work (POW) are the most widely used. POW was first used in the Bitcoin blockchain and soon became its central consensus mechanism of it and the earliest implementation of POS for cryptocurrency was Peercoin. Numerous research has been done in this field, but they failed to explain the features of the two mechanisms comprehensively. In this paper, the author did a brief introduction to the hash function and consensus mechanism as well as how POW and POS work, then made detailed analysis and comparisons of a POW in Bitcoin and POS in Peercoin in terms of fairness, security, environmental influence, penalty, negotiability and sustainability, and future trend, and declared that both of POS and POS had some advantages and drawbacks. The author also listed some risks and potential problems of POW and POS and then gave possible solutions for them.
Predicting Stock Trend Using GNN
Authors: Chen Zhiluo , Huang Zeyu , Zhou Yukang
Published: April 2023

In stock market, numerous ways have been used to predict the future asset return. Traditional time series models and neural network models are both popular in practice. Many of them have been shown that they do not make full use of the property of long-term dependency while with the help of graph structure, one can turn time series into complex network and keep the long-term dependency property. To make use of the property of the long memory of financial market in prediction, our framework first applies the visibility method to turn stock price data into graph structure, and then applies graph neural network to make graph classification task to let the network learn the overall topological structure of the graphs. The result shows that with the use of such property, our model can successfully forecast the future stock trend.
Cryptocurrency Security Study based on Static Taint Analysis
Authors: Yang Anyu
Published: April 2023

Cryptocurrency represented by Bitcoin is a very popular topic in recent years. However, the prosperity of cryptocurrency drives an increasing number of applications published. Some malicious or vulnerable programs are also detected and reported these years. To do a deeper study into security of cryptocurrency application, this paper learns common vulnerabilities, threat models inside normal applications, and taint analysis, a useful vulnerability-detecting tool, concludes a common and useful methodology for threat detection in application programs, especially Android apps. This approach uses static taint analysis to detect vulnerabilities inside a given Android application, classify them into common vulnerability categories and then make conclusions. This paper does research in analyzing statistics of threats in common cryptocurrency apps in Google play store and draw conclusions on the status of cryptocurrency software as well. Finally, some suggestions are provided at the end of this paper. These recommendations apply to application programmers, app store administrators, scholars and experts in related area, government officer and users. This set of analysis process can be applied to analyze any type of application programs.
Heart Disease Prediction Model based on Prophet
Authors: Lu Yi
Published: April 2023

Heart disease is one of the major causes of death for people of all races, genders, and nationalities. In the United States, for instance, heart disease causes more than 600,000 deaths every year and is the largest leading cause of death in 2020. A reliable heart diseases mortality prediction model could acknowledge the patients’ medical professionals that the heart disease risk level of the specific group. This approach is significant in preventing further increases in heart disease mortality rates worldwide. Nowadays, multiple Machine Learning (ML) models, including hybrid models produced impressive predictions and realized that newly developed ML models might provide new perspectives on heart disease predictions. In this paper, we introduced the Facebook Prophet model (FB Prophet model), a time series prediction tool that could present seasonality in its result, since studies point out that heart disease mortality rate also shows seasonality. We produced an accuracy of approximately 94 % in predicting weekly heart disease mortality numbers in specific states. Furthermore, we explored the effects that external factors, ambient temperature, have on heart disease, and utilize this relationship in improving model accuracy.
The Advance of Cryptocurrency Wallet with Digital Signature
Authors: Ji Pu
Published: April 2023

This paper explains the composition of the cryptocurrency wallet by analyzing a wallet’s components, functions, categories, the algorithm of signing, and security features as three major components used in the constitute of the cryptocurrency wallet, private key, public key, and address are introduced from their generation process with specific Elliptic Curve Digital Signature Algorithm secp256k1 and Keccak hash function. A variety of crypto wallets are divided into different categories by their purpose of use, the form of display, and Internet connectivity, and multiple crypto wallets are explained in the second part of this paper. The elliptic curve secp256k1 is selected in Ethereum for both calculating the public key information and performing signing transactions. This paper details secp256k1 and Keystore used behind the signing process and security of cryptocurrency wallet and outlines potential risks due to the development of computing power and transfer of validation system in Ethereum.
Application Effect Analysis for Helmet Detection Algorithm based on YOLOV5
Authors: Jia Churui
Published: April 2023

Safety helmets can provide better safety protection for the human head in industrial production, which is one of the important protective tools to protect the lives of workers. In the on-site environment, checking whether construction workers are wearing safety helmets mainly depends on manual inspection, which is time-consuming and labor-intensive. In order to solve the problem of hard hat detection and recognition in the complex environment of the construction site, in this paper, we propose a YOLOV5 hard hat detection method. Specifically, a pre-trained object model is used to locate the area of the worker and the face detection module is used to mark the face. Then, the helmet recognition module is used to extract the sub area of the worker's head. Finally, the extracted image is classified into two categories to judge the situation of the worker wearing the helmet. Focusing only on workers can reduce training time and improve the efficiency of the model, so the training label only uses the label of people. The model refines and extracts the head, inputs the separated head area into the subsequent network for subsequent analysis, and then uses the binary classification method to judge whether the helmet is worn. We compare the results of different models such as YOLOV5s, YOLOV5m and YOLOV5l, where YOLOV5l can achieve a best mAP of 91.9%. We also analyze the effect of light to the performance, where the detection effect will be affected by low visibility through the comparative detection of samples in the day and at night.
Studies Advanced in Image Super-resolution Reconstruction based on Deep Learning
Authors: Cao Hongjie
Published: April 2023

Image super-resolution reconstruction (SR) has always been a research hotspot in the computer vision community, whose purpose is to convert low-resolution (LR) images into high-resolution (HR) images by algorithms without changing the hardware. Early SR works were mainly based on manual features and mathematical statistical models, relying on interpolation to achieve high-resolution image reconstruction. Thanks to the rapid development of convolutional neural networks, SR based on deep learning has achieved breakthroughs in both accuracy and speed. This paper first introduces the basic principles of image super-resolution reconstruction, including the basic framework and loss function of SR. Then, we introduce the representative super-resolution reconstruction methods such as SRCNN, SRGAN, and TTSR. We also introduce the common public datasets and evaluation indexes, and compare the performance of several SR methods to analyze their advantages and disadvantages. Finally, we summarize the existing challenges in the image super-resolution reconstruction, and give a look out for the future development direction of SR.
Preface: 2nd International Conference on Biochemical Engineering and Environmentally Friendly Material (BEEFM 2023)
Authors: Gunther Miranda , Pan Disheng
Published: April 2023

2023 2nd International Conference on Biochemical Engineering and Environmentally Friendly Material (BEEFM 2023) was successfully held online during February 11-12, 2023 in Xi'an, China. The conference invited scholars and experts in the fields of chemistry, biology, environmental engineering and materials application from various universities and institutes, to participate in the review and guidance of this conference. The conference focuses on the latest research fields, and provides an international cooperation and exchange platform for experts, scholars and business managers in these fields.

The aim of BEEFM 2023 is to share the research achievements, discuss the key challenges and research directions of the development, and jointly promote the industrialization cooperation and continuous innovation of international academic achievements. This collection of proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the special committee.

Due to travel restrictions and indoor activities related to COVID-19, we were forced to move BEEFM 2023 online. Attendees were notified one month in advance that the on-site conference was changed to an online conference. The online conference was held on the video conferencing software. Because of the wide coverage of this conference and the large number of attendees, we divided the conference into main session and sub-sessions. The opening ceremony was held in the main session and a speech was delivered by the invited speaker. After the main session, we divided the participants into three sub-sessions. Each author spoke for about 15-20 minutes on detailed material in the slides, with the same amount of time set aside for questions and discussion. The format of questioning and discussion was that after each author's introduction, the conference chairman and committee members asked and answered questions. At least one attendee could ask questions that will be answered by the speaker.

BEEFM 2023 had brought together many excellent works, latest ideas and concepts. New ideas have been provided in the fields of chemistry, biology, environmental engineering and materials application. Look forward to meeting again next year.

BEEFM 2023 Conference Scientific Committees

Xi'an, China
Study on Removal of Heavy Metal Pollution from Water by Biochar and Its Composite Materials
Authors: Cheung Sinming , Cao Yalin , Jiang Quansheng
Published: April 2023

Heavy metal pollution is a long-term pollution problem that is difficult to control. Water microorganisms cannot directly degrade heavy metals, and heavy metals can produce mutual transformation, dispersion and enrichment in various forms in water. Adsorption is the most effective techniques to remove harmful heavy metals from water. Biochar (BC) and its composite materials, because of their simple and abundant source, abundant pore structure and unique surface chemical properties, are a new type of efficient adsorption material. Their technical development offers a new idea for solving the heavy metal pollution condition in water and preparing adsorbent required by adsorption method. In this paper, the preparation method of biochar and its composite materials, the mechanism and principle of heavy metals adsorption in water are reviewed, also development prospect of the biochar for application composite materials in the heavy metal pollution for treatment in the future is discussed.
Mechanism of Cross-Linking, Self-Assembly, Controlled Release, and Applications of Nanogels in Drug Delivery System
Authors: Jiao Fengqian
Published: April 2023

Drug delivery system has been very important for achieving the last goal of precision medicine, also, among various drug carrier materials, nanogel is considered a promising one. Nanogels are a kind of material that have been proven very effective and specialized on targeted controlled release of drugs in medical therapies. Many researches and applications have used nanogels as an efficient drug carrier through its properties of loading and high capacity, and very high future expectations has been given to this material as a future idealized material in nano medical areas. This paper mainly discusses the key features of the nanogels including degradability and high drug capacity, as well as the mechanism of cross- linking mechanism and self assembly of nanogels in both physical and chemical ways. In addition, this paper explains how nanogels can carry different drugs and control-release the drug under different stimuli to aim the goal at targeted drugs delivery. Finally current research situations and future expectations of this topic are also proposed.
Property Graph Schema contributions to WG3
Authors:   LDBC Property Graph Schema Working Group
Published: April 2023

Five papers and three accompanying presentations contributed to the ISO/IEC JTC 1/SC 32 WG3 (Database Languages) standards committee relating to property graph schema, including meta-properties and keys
A Survey on Traffic Modeling and Mitigation Techniques
Authors: Buwa Pallavi , Taneja Varun , Chaumal Aarya , Abraham Jibi , Matange Ashwini
Published: April 2023

Abstract—The growing road traffic congestion in India is becoming more prevalent nowadays, the worst hit being the metropolitan and developed cities. Its ever-increasing nature poses a threat to development and requires quick action to help ease the troubles faced by people on a day-to-day basis. Modern technology offers a great aid to these efforts by providing mechanisms to model the travel patterns of a region and provide quick congestion relief by introducing adaptive traffic signals that operate based on the current traffic density of the road. In this paper, we present a survey of the most popular techniques involving travel demand modeling and adaptive signal control and compare the various methods employed by researchers to tackle both of these issues separately. The survey focuses on analyzing these methods from the perspective of implementation in an Indian scenario and offering a theoretical solution that involves the use of both these techniques to ease the problem of traffic congestion. Index Terms—road traffic congestion mitigation, adaptive traf- fic signal, travel demand modeling,
Estrategias proyectuales de las conductas emanada en los grupos de adolescentes para la recuperación y reinserción social, Chiclayo, Lambayeque
Authors: Barba Gordillo Cristhian Bryan , Alarcon Purihuaman Lila Marleny , Vargas Salazar Mario Uldarico , Itabashi Montenegro Eduardo Alfredo , Rivadeneyra Céspedes Carlos Omar
Published: April 2023

Se propone la proyección de un espacio denominado centro de servicios de orientación adolescente para la reinserción social por faltas a la justicia, Chiclayo, Lambayeque, cuyo objetivo es la resocialización del adolescente infractor al Código Penal, dentro de las diversas teorías que el carácter punitivo de un estado como el nuestro ha recogido como base de su expresión, la investigación toma como base los estudios diversos de Instituciones Internacionales abocadas a la protección de la Niñez y Adolescencia y de sus derechos fundamentales; asimismo, los estudios diversos de profesionales abocados a la investigación científica para determinar las causas, factores y consecuencias que afectan a la niñez y adolescencia y en su rol ético profesional el planteamiento de sus propuestas de alternativas de solución para que tengan oportunidad de desarrollo sin discriminación. La metodología es de tipo cualitativa-crítica-proyectiva, finalmente, por conclusiones se plantearon en base las especificaciones de este proyecto y el desarrollo arquitectónico propuesto.
Tipologías proyectuales para la educación técnica industrial afectada
Authors: Castillo Ccaulla Diana Daniella , Guerrero Pais Claudia Cecilia , Vargas Salazar Mario Uldarico , Rivadeneyra Céspedes Carlos Omar
Published: April 2023

La investigación realizada nos permitió identificar como objetivo general la elaboración de estrategias de las tipologías proyectuales para la educación técnica industrial afectada. Permitió identificar la concepción tipológica existente, que actualmente se ha ido modificando y alterando de acuerdo a las necesidades y actividades a realizar dentro del ámbito técnico industrial por los pobladores, identificándose la simplicidad en el funcionamiento interno, generando informalidad y déficit de criterio espacial, funcional y profesional en los sectores industriales, dentro de los talleres automotrices, perdiendo interés en la educación técnica y en la valoración de los espacios. Las nuevas modalidades tipológicas que se proyectan, crean un orden dentro de la clasificación generando la posibilidad de los talleres formales y aptos para la empleabilidad. El modelo teórico aplicado se fundamenta en la teoría de la clasificación de los talleres, para determinar los tipos de centros formativos improvisados, siendo una investigación, cualitativa, crítica y proyectiva.
Monitoring and Evaluation Budgetary Practices on Project Service Delivery
Authors: Ngacha Njeri. S. , McDonald Julie
Published: April 2023

Successful execution of projects is critical to the development and strategic variations within an institution. However, there have been setbacks due to the deficiencies in monitoring and evaluation (M&E) practices. M&E is an important part of any project’s success story because they oversee project effectiveness, efficiency, transparency, and accountability, among other things. A background review discloses the insufficiency of M&E practices within the institution. The purpose of the study was to examine how M&E budgetary practices affect project service delivery. The study’s theoretical foundation was based on resource-based theory. A descriptive study design was employed targeting administrators, project managers, nurses, chief of centers, and data collection staff. The study focused on a target population of 140 people. The study employed a sample size of 103 respondents using Fisher’s exact formula. The researcher exploited the purposive sampling technique during the study. Data collection instruments administered were questionnaires and key informant interviews. Reliability of instruments was through internal consistency using Cronbach’s Alpha Coefficient of 0.70 or greater while the validity of these instruments was done using content, face, and constructs validity. Quantitative data from questionnaires were analyzed using descriptive statistics used including frequencies, standard deviation, mean, and percentages with the aid of a statistical package for social science (SPSS). Pearson Coefficient Correlation and Multiple regression models were used to make inferences and generalizations. The hypotheses test was graded at the 0.05 level of significance. The study findings indicated that there was a statistically strong significant positive effect of M&E budgetary practices on project service delivery (r=0.622; p<0.05). These findings indicated the alternate hypotheses was accepted and the null hypotheses was rejected. M&E budget serves as a cost and revenue indicator for project managers’ daily operational activities. M&E budget is used to provide information and support management decisions, as well as monitor and control the organization throughout the year. This study recommends that the CBCHS institute budgetary adjustments in the budgetary processes and practices. This will help to predict the future expenses, and costs and accordingly work towards the expected revenues as well as cater adequately on the allocated resources.
A Matemática na prática dos professores de Educação Infantil
Authors: Belo Cibelli Batista , Zimer Tania Teresinha Bruns
Published: April 2023

Este artigo aborda o trabalho do professor de Educação Infantil com a matemática nessa fase de ensino. A questão norteadora é: como os professores de Educação Infantil trabalham com a Matemática nessa fase de ensino? Seu objetivo é perceber de que forma a Matemática é trabalhada na Educação Infantil. Trata-se de um recorte de uma pesquisa de abordagem qualitativa, cujos dados analisados foram captados a partir de respostas a um questionário contendo perguntas voltadas às práticas desenvolvidas com as turmas dos professores participantes de um curso de extensão de forma online. A análise ocorreu por meio da Análise Textual Discursiva (ATD). Dentre os resultados obtidos, destaca-se a predominância de práticas que contemplam jogos diversos e o conhecimento matemático relativo à contagem, valorizando-se a oralidade.
COVID-19 and pneumothorax, pneumomediastinum, subcutaneous emphysema: Analysis of risk factors
Authors: Büyükkarabacak Yasemin , Gökhan Pirzirenli Mehmet , Gurz Selçuk , Abacı Hasan , Taslak Şengül Ayşen , Çelik Burçin , Basoğlu Ahmet
Published: April 2023

Background: In this study, we aimed to analyze the risk factors of barotrauma in patients who were followed in the intensive care unit due to novel coronavirus disease 2019 (COVID-19) pneumonia.

Methods: Between March 2020 and January 2021, a total of 261 patients (155 males, 106 females; mean age: 63.3±15.3 years; range, 11 to 91 years) who were followed in the intensive care unit due to COVID-19 pneumonia and were diagnosed with pneumothorax, pneumomediastinum, and subcutaneous emphysema were retrospectively analyzed. Demographics data of the patients, past and current medical history, clinical management, patient progress, and survival data were obtained from medical records of our hospital.

Results: Twenty-seven of the patients were diagnosed with barotrauma. A total of 88.8% of the patients were followed with intubation. The development of pneumothorax, pneumomediastinum, and subcutaneous emphysema due to barotrauma was not dependent on sex, smoking/non-smoking status, using/not using corticosteroids, or comorbid diseases. There was a significant correlation between pneumothorax, pneumomediastinum, and subcutaneous emphysema development in intubated patients with different ventilator modes. Changing the ventilator mode from synchronized intermittent mandatory ventilation to airway pressure release ventilation increased the possibility of barotrauma by 15 times.

Conclusion: Despite all lung-protective applications, barotrauma is a common complication, particularly in mechanically ventilated patients who have COVID-19 pneumonia with severe acute respiratory distress syndrome. Mechanical ventilator pressure modes should be patientspecific and followed carefully and frequently for the risk of barotrauma.
Producción científica en revistas estudiantiles cubanas sobre traumatismo craneoencefálico, 2015 - junio del 2021
Authors: Garces Ginarte Mario Javier , Pérez Ortiz Letier , Sarabia Aguila Erennys Carema , Quintero Alvarez Pablo Enrique , Vitón-Castillo Adrián Alejandro
Published: April 2023

Introducción: los traumatismos craneoencefálicos constituyen un reto para los servicios de salud. Recibir una formación desde pregrado en su atención constituye una necesidad y un reto. Objetivo: caracterizar la producción científica sobre traumatismo craneoencefálico publicada en las revistas científicas estudiantiles cubanas entre enero de 2015 y junio del 2021.Métodos: se realizó un estudio bibliométrico, descriptivo y retrospectivo. La muestra estuvo constituida por 9 artículos; de los que se analizó tipología, año de publicación, cantidad de autores, provincia y centro de procedencia de los autores, cantidad de referencias, cantidad de artículos por revista y el número de citas recibidas. Resultados: el 77,8 % de los artículos fueron originales donde el 2020 fue el año más productivo (55,6 %). El 44,4 % de los artículos fue firmado por 5 autores, el 32,4 % de los autores pertenecían a la provincia de Pinar del Río. La media de referencias por artículos fue de 19,9, el Índice de Price se encontró entre 0,45 y 0,82; con una media de 0,75. El 55,6 % de las publicaciones se realizaron en la revista Universidad Médica Pinareña y los artículos publicados en dicha revista también recibieron la mayor cantidad de citas (85 %). Conclusiones: la producción científica estudiantil sobre trauma craneoencefálico es baja, y la influencia y visibilidad de los artículos disponibles atendiendo a la cantidad de citas es pequeña.
Monitoreo y registro de parámetros, un reto para la enfermería de atención al enfermo grave
Authors: Hernández Nápoles Albert , Rodríguez Curbelo Miguel , González Reyes Misael , Cabrera Benítez Leticia , Díaz López Ramona , Escalona Arzuaga Alberto
Published: April 2023

Introducción: La Historia Clínica (HC) constituye un documento de alto valor médico; su correcta gestión contribuye a mejorar la calidad de atención, proteger intereses legales del paciente, trabajadores y entidades; además de proporcionar información con fines de investigación y docencia. A criterio del autor es importante que los datos registrados en la HC, sean veraces, abarcadores, adecuados y pertinentemente actualizados; ya que será la fuente primaria de obtención de estos de manera cronológica a la cual se remiten todos los involucrados en el tratamiento del paciente. Objetivos: Realizar un diagnóstico sobre los vacíos de conocimientos de Enfermería en la monitorización y registro de parámetros del paciente grave.Método: Se realizó un estudio analítico observacional, descriptivo y transversal. El universo estuvo conformado por 50 enfermeros y la muestra por los que se encontraban de manera física laborando en el servicio en el periodo de estudio, para un total de 31. Resultados: SE Pudo diagnosticar con nuestro estudio, grandes deficiencias a la hora de registrar el peso del paciente, los modos de ventilación mecánica y otros parámetros ventilatorios, la formación de agua endógena y las pérdidas insensibles y extraordinarias de agua.Conclusiones: se concuerda en la necesidad que tiene la profesión de Enfermería en unificar sus métodos y estrategias de registrar su proceso, se visualiza la problemática existente a nivel mundial en cuanto a la temática abordada y creemos oportuno desarrollar proyectos de investigación sobre monitoreo y registro de Enfermería del paciente grave.
Caracterización de la morbimortalidad de la materna extremadamente grave. Hospital Abel Santamaria. 2014-2019
Authors: Cecilia-Paredes Eduardo Enrique , Echevarria-Cruz Ángel , Cecilia-Paredes Elizabeth
Published: April 2023

Introducción: Como evento natural, la maternidad debería estar libre de complicaciones y, por lo tanto, de muerte. Los fallecimientos derivados del embarazo, parto y puerperio se consideran, en su mayoría, evitables con los recursos de la medicina actual, no obstante, cada año fallecen más de medio millón de mujeres en todo el mundo por estas causas.Objetivo: Caracterizar la morbimortalidad de las maternas extremadamente graves ingresadas en el Servicio de Cuidados Intensivos del Hospital General Docente “Abel Santamaría Cuadrado” en el periodo comprendido entre enero de 2014 y diciembre de 2019.Diseño metodológico: Se realizó un estudio observacional, descriptivo y transversal. El universo quedó constituido por 290 casos. Los datos se obtuvieron revisando el las historias clínicas de los pacientes. Las variables, se analizaron mediante la estadística descriptiva e inferencial. Resultados: La mayoría las maternas pertenecieron al grupo etario de 20-29 años con el 47.9% del total, la mortalidad fue de 1.3%. Las hemorragias puerperales fue la principal causa de ingreso seguida de las infecciones. El shock hipovolémico y las infecciones respiratorias fueron las complicaciones más frecuentes. El 58.1% de las pacientes fueron ventiladas por menos de 2 días.Conclusiones: Predominaron las pacientes de 20 a 29 años y fallecieron 3 pacientes. Las hemorragias puerperales fueron la principal causa de ingreso. El shock hipovolémico y las infecciones respiratorias fueron las complicaciones más frecuentes. La mayoría de las pacientes fueron ventiladas por menos de 2 días.
IMPORTANCE OF NON-COGNITIVE FACTORS FOR HUMAN CAPITAL DEVELOPMENT AND INNOVATION
Authors:   Sofia University „St. Kliment Ohridski“, Bulgaria, Misheva Jasmina
Published: April 2023

This study analyzes the non-cognitive factors of human capital development and their impact on innovation as an organizational created value. Hypotheses are presented on the direct, positive influence of six behavioral and psychological factors on innovation. A quantitative method was used to analyze the data collected through a survey, including 204 respondents working in the IT field in Bulgaria and other countries. The results of the proposed regression model show a statistically significant relationship between non-cognitive influence factors and innovation, revealing the importance and need to consider these elements when measuring, analyzing, and investing in human capital. This research provides a clear direction for further analysis of the importance of non-cognitive factors for human capital value creation and can be of practical use to organizations in their human capital development strategies and planning.
Uçuş Mekaniği Hesaplamalarinda MATLAB/Simulink Kullanımı: İstanbul dan Kahire ye Uçuş Planlamasi Örneği
Authors: Özcan Abdurrahim Bilal , Kaval Berkay
Published: April 2023

Benzetim araçları gerçek bir sistemin üretimi ve/veya yönetimi öncesinde büyük maliyetleri önlemek, hızlı prototipleme ile AR-GE çalışması gerçekleştirmek, farklı senaryolarda sınırsız test imkanı sağlamak gibi bir çok avantajı kullanıcısına sunmaktadır. Bu çalışmada MATLAB/Simulink benzetim ortamı kullanılarak uçuş mekaniği kapsamında, İstanbul’dan Kahire’ye bir uçuşun planlaması yapılmıştır. Uçuş planına dair hesaplamalardan önce Euler’in diferansiyel denklem çözücü metodundan (ode1) ve benzetim aracındaki kullanımından bahsedilmişdir. Ardından uçağın değişkenlerinden ( X ve Y eksenlerindeki yatay konum, irtifa, gerçek hava hızı, baş açısı, uçağın kütlesi), kontrol işaretlerinden (motor itkisi, yalpa açısı, uçuş yolu açısı), hava trafiği kurallarından ve gerçek bir uçağın rotasından bahsedilerek benzetim aracının kullanım kapsamı ortaya konmuştur. Çalışmanın kapsamı belirtildikten sonra İstanbul Havalimanından (IST) Kahire Havalimanına (CAI) gerçek bir Boeing 737-700 uçağının uçak performansı operasyonel verileri kullanılarak uçuş rotası ve bu rotadaki kalkış, tırmanma, seyir uçuşu, dönme, iniş gibi hareket değişimleri belirtilmiştir. Tüm uçuşun benzetimi aşamasında Eurocontrol Experimental Centre’ın BADA (Base of Aircraft Data) projesi kapsamında yayınladığı hesaplamalara dayanarak uçağın değişkenleri, 3 boyutta rotası, yataydaki hızı, yakıt tüketimi, uçağın rotasının değişimi vb. gibi veriler görselleştirilerek yorumlanmıştır. Son kısımda ise elde edilen sonuçlar ile birlikte benzetim araçlarının uçuş mekaniği özelinde sağladığı faydalardan bahsedilmiştir.