bluuebunny/crossref_metadata_2025 huggingface dataset:
Effective means of physical education of students in self-isolation conditions
Authors: Kabanova I.A. , Chicherin V.P. , Terekhova N.V. , Grebneva L.V.
Published: November 2023

The article considers the substantiation of the effectiveness of the distance learning format on the subject of physical culture on the basis of the positive impact of the means and methods of physical education used by students during the pandemic.
For the scientific experiment of this study, various means of physical culture have been selected, aimed at exerting a positive effect on the functional systems of the body of students in conditions of self-isolation using modern digital technologies. The following methods were used: a questionnaire to determine the sources of stress, ways to adapt to emerging difficulties in organizing independent physical education classes in a distance learning format, determining the general working capacity and the level of functional condition of students of a special medical group (the Barbell and Genchi test), a set of general developmental exercises based on the complex of breathing gymnastics by A. N. Strelnikova.
During the experiment, there was a significant improvement in the indicators of the Barbell and Gencha samples, the level of flexibility of the subjects, indicating the effectiveness of work in the distance learning format when using specially designed sets of physical exercises.
The results of the experiment and the experience gained in remote work can be used in the future as a factor in the development of human capital.
Game indicators as a basis for optimizing the training process
and the preparation of the basketball club team
Authors: Kovrigin V.E.
Published: November 2023

The game indicators of the “Chelbasket” basketball club have been identified, having the greatest relationship with the game effectiveness in attack and with that defense. Based on the results obtained, practical recommendations for the team are formulated.
For a detailed analysis of the basketball team’s game, a number of game indicators are used characterizing the team’s actions both in attack and defense. It allows optimizing the training process and the team’s preparation in order to increase the game effectiveness.
The research aim is to study the main game indicators of the “Chelbasket” basketball club to identify those having a great relationship with the game effectiveness in attack and that in defense.
Correlation analysis revealed the relationship of individual game indicators of the “Chelbasket” basketball club with the number of points scored and missed.
The highest correlation with the number of points scored was found in the following indicators: offensive passes, two-point shots, three-point shots and ball tackling. The greatest impact on reducing the missed points number is provided by such indicators as defensive rebounds, offensive rebounds and the total number of rebounds. At the same time, the number of the team’s own fouls has a significant impact on increasing the number of missed points.
The training process and tactical preparation of the “Chelbasket” basketball club should be planned taking into account the results obtained, paying special attention to improving precisely those indicators that have a great relationship with the game effectiveness and the number of points missed.
TEACHERS’ NONVERBAL IMMEDIACY BEHAVIORS AND LEARNERS’ ACADEMIC PERFORMANCE IN MATHEMATICS
Authors: DAMAYO MA. TERESA M. , SIAT GREGG O.
Published: November 2023

This study aimed to determine teachers’ nonverbal immediacy behaviors and learners’ academic performance in Mathematics subject in the elementary schools of Calubian Districts, Calubian, Leyte. The study utilized retrospective research design with a survey questionnaire as the main data gathering tool to the Grade II teachers teaching Mathematics. Majority of the teachers belong to the age range 31-50 years old; as regard to their sex, 30 out of 34 are female; as for the civil status, majority of them are married; most of the teachers are receiving a salary ranging from 26,000-30.00; 16 of them holding a Teacher III position, and 23 of them are holder MA units. Result implies that teachers in Calubian North and South Districts possess qualities needed to actively perform the tasks expected from them. The highest mean of the teachers’ nonverbal immediacy behaviour as to instructional context is 4.60 inferred as always and the lowest mean is 3.94 interpreted as highly observed. The average mean is 3.09 described as observed. As to instructional context, the highest mean of the teachers’ nonverbal immediacy behaviour as to interpersonal context is 3.59 inferred as Highly Observed and the lowest mean is 1.97 interpreted as moderately observed. The average mean is 2.94 described as observed. As to mathematics teachers teaching practices, the highest mean of mathematics teachers teaching practices is 3.71 described as highly practiced and the lowest mean is 2.74 interpreted as practiced. The average mean is 3.42 inferred as practiced. There was no significant relationship between the profile of the teacher respondents and their nonverbal immediacy behaviors; the significant relationship only exist between sex and teachers’ teaching practices in mathematics subject; and the hypothesis which states that there is no significant relationship between the profile of the teachers and their teaching practices” was rejected.
The Gendered Contribution of Neria to the Repertoire of African Filmmaking
Authors: Witness Roya , Sandiso Ngcobo
Published: November 2023

In an epoch-changing moment in Zimbabwean film history, Neria was produced by Media for Development (MFD) in 1991. It was the first locally produced film to portray relatively strong black women as shown by the titular character who resolutely fights her greedy in-law for her late husband’s estate. Neria remains Zimbabwe’s most popular film and has elicited analyses in books, films and reviews among other commentaries. Though they note empowerment of women in the film, most of these studies tend to pit women against men yet male and female characters support Neria’s cause. Hence, the purpose of this paper is to analyze the contribution made by the Neria film in promoting gender equality in an African cultural context. Womanism which asserts that abuse of women is an aberration from African culture provided theoretical grounding to the paper. Research questions that guided the study are: (i) What is the representation of male and female relationships in Neria?  (ii) To what extent does this reflect Zimbabwean society? (iii) What are the factors that influence this portrayal? The research methodology adopted for this study is qualitative and purposeful sampling since it involves visual analysis of the film that is reported in words. Research findings are discussed thematically using eight themes that emerged from collected visual data that is presented qualitatively. The findings revealed that Neria exposes gender inequality in Shona traditional culture and calls for its integration with western culture albeit at times it seems too moralistic. Apparently due to the film’s external funding, it fails to link moral decadence to materialism brought by western cultures. This paper recommends concerted efforts from the government, individuals and organizations in training film personnel and funding films without undue interference. Moreover, films like Neria ought to increase the usage of indigenous languages such as Shona and Ndebele with English subtitles especially in emotional scenes for them to be more comprehensible.
Retracted article
Authors: Molikevych Roman
Published: November 2023

Retracted article
Multi-Access Edge Computing Architecture and Smart Agriculture Application in Ubiquitous Power Internet of Things
Authors: Hoang Nguyen Van
Published: November 2023

The Ubiquitous Power Internet of Things (UPIoT) is a deep integration of the interconnected power network and communication network, enabling full perception of the system status and business operations for power production, transmission, and consumption. To address the challenges of real-time perception, rapid response, and privacy protection, UPIoT can benefit from the use of edge computing technology. Edge computing is a new and innovative computing architecture that enables quick and efficient processing of data close to the source, bypassing network latency and bandwidth issues. By shifting computing power to the edge of the network, edge computing reduces the strain on cloud computing centers and decreases input response time for users. However, access latency can still be a bottleneck, which may overshadow the benefits of edge computing, particularly for data-intensive services. While edge computing offers promising solutions for the IoT network, there are still some issues to address, such as security, incomplete data, and investment and maintenance costs. In this paper, researcher conducts a comprehensive survey of edge computing and how edge device placement can improve performance in IoT networks. The paper includes a comparative use case of smart agriculture edge computing implementations and discusses the various challenges faced in implementing edge computing in the UPIoT context. The results also aim to inspire new edgebased IoT security designs by providing a complete review of IoT security solutions at the edge layer in UPIoT
Enhancing Clinical Trial Access and Diversity: An Innovative App for Personalized Trial Recommendations and Equitable Healthcare Participation
Authors: Xing Jasmine , Li Ang
Published: November 2023

This project focuses on creating an app to recommend clinical trials to users based on their profiles, improving access and diversity in trial participation. It addresses disparities in trial representation and aims to provide easy access to trial information. The app utilizes web scraping, a recommendation system, and a saving mechanism for trials. An experiment evaluates the recommendation system's accuracy, highlighting the challenges of ethnicity clustering. The app aims to promote equitable healthcare access and expand participation in clinical trials. Shortcomings of existing solutions include limited scope, such as being specific to cancer trials or certain patient groups. In contrast, the app aims to provide recommendations for a broader range of diseases and profiles, improving accessibility and diversity. Future improvements include refining the recommendation model, increasing the number of clusters, adding location-based recommendations, and expanding the trial database. The ultimate goal is to enhance the diversity of trial participants and improve healthcare equity.
Integrating Iot and Blockchain Technology for Securing Sensors Communication
Authors: B. Wankhede Sonali , Patel Dhiren , Shirole Mahesh
Published: November 2023

Blockchain offers new approaches to manage and store data. The technical trajectory of blockchain makes it beneficial in various industries because of its benefits for tamperproof and immutable data security. The increasing use of IoT devices will surely result in a smarter world. A growing number of applications, including environment monitoring, building automation, smart metering, surveillance, and asset tracking, are turning to networks of wireless sensors, each of which is capable of a combination of processing, communication, and sensing. The Internet of Things (IoT) has facilitated the transition to a connected and intelligent digital environment, allowing smart manufacturing, improved decision-making and data analytics. In this study, we review the various IoT and Blockchain frameworks and discuss how integration of IoT and Blockchain resolves existing gaps.
Chinese Named Entity Recognition based on Knowledge-Based Question
Authors: Chandrasekaran Arvind
Published: November 2023

The knowledge-Based Question Answering (KBQA) system is an essential part of the customer service system aiming to answer natural language questions by recovering the structured data stored under the knowledge base. KBQA answers the natural language questions by recovering the structured data stored under the knowledge base.KBQA receives the user’s query and first needs to recognize the topic for the query entities like the location, name,organization, etc., The process is Named Entity Recognition (NER) using the Bidirectional Long Short-Term Memory Conditional Random Field model, and the SoftLexicon method is introduced as the Chinese NER tasks. A fuzzy matching module is proposed to analyze the application scenario characteristics using multiple methods. The module efficiently modifies the error recognition results, improving entity recognition performance. The fuzzy matching and the NER model are combined into the NER system. The power grid-related original data is collected to improvise the system following the power grid data characteristics.
Image Smoothing and Edge Enhancement as Neuromorphic Decoding based Onsampling Representation Partitioning
Authors: Antsiperov Viacheslav
Published: November 2023

This work discusses the image neuromorphic encoding/decoding issues inspired by the mechanisms of visual perception encoding/decoding. The importance of this topic is directly related to the current problems of perceptual quality and perceptual reconstruction of images today. Therefore, to obtain reliable results in these directions, it was natural to turn to the most adequate mechanisms of perception. As a result, we propose a new approach to image processing, which uses the most realistic representation of the input data in the form of a stream of events or counts. Such events / counts simulate the firing of retinal receptors in response to the action of radiation recorded. The statistical model of the counts stream is chosen in the form of the two-dimensional inhomogeneous Poisson point processes, considered as a convenient representation of the input data. In the current paper such a representation is referred to as sampling representation. To adequately model the mechanisms of neural encoding of input data, we consistently use the concept of receptive fields. This general model implements well-known features of neural processing, including central/lateral inhibition. Decoding issues are considered in the context of Retinex paradigm of contrast detection. It is shown that the model of coupled ON-OFF receptive fields allows to restore sharp image details in the form of local edges. At the end of the work, we demonstrate an interpretation of synthesised encoding/decoding as classical smoothing and edge outlining of encoded images.
Towards Optimizing Performance of Machine Learning Algorithms on Unbalanced Dataset
Authors: Thumpati Asitha , Zhang Yan
Published: November 2023

Imbalanced data, a common occurrence in real-world datasets, presents a challenge for machine learning classification models. These models are typically designed with the assumption of balanced class distributions, leading to lower predictive performance when faced with imbalanced data. To address this issue, this paper employs data preprocessing techniques, including Synthetic Minority Oversampling Technique (SMOTE) for oversampling and random undersampling, on unbalanced datasets. Additionally, genetic programming is utilized for feature selection to enhance both performance and efficiency. In our experiment, we leverage an imbalanced bank marketing dataset sourced from the UCI Machine Learning Repository. To evaluate the effectiveness of our techniques, we implement it on four different classification algorithms: Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). We compare various evaluation metrics, such as accuracy, balanced accuracy, recall, F-score, Receiver Operating Characteristics (ROC) curve, and Precision-Recall (PR) curve, across different scenarios: unbalanced data, oversampled data, undersampled data, and data cleaned with Tomek-Links. Our findings reveal that all four algorithms demonstrate improved performance when the minority class is oversampled to half the size of the majority class and the majority class is undersampled to match the minority class. Subsequently, applying Tomek-Links on the balanced dataset further enhances performance.
Unlocking Efficiency and Sustainability in Enzyme Optimization: A Machine Learning-Driven Approach for Industrial Applications
Authors: Zhang Yijia , Revilla Ivan
Published: November 2023

This paper addresses the challenge of optimizing enzyme activity and production in various industries by leveraging machine learning models [9]. Traditionally, enzyme optimization has been resource-intensive and costly [10]. Our proposed solution involves collecting diverse enzymatic reaction data, generating synthetic data, and using cross-validation and ensemble methods for model selection. Challenges such as data availability and negative value generation in dummy data were addressed creatively. Experimentation revealed that ensemble methods like Random Forest and Decision Tree Regressor outperformed linear models, highlighting the potential of machine learning in enzyme optimization [11]. This research offers a data-driven approach that promises efficiency and resource conservation, with significant implications for biotechnologists, industrial manufacturers, and the scientific community [12]. The application of machine learning in enzyme optimization not only streamlines processes but also paves the way for sustainability and innovation in enzyme-related industries, making it a compelling solution for widespread adoption.
Democratizing Personal Website Creation: An AI- Driven Approach for Effortless, Cost-efficient, and High-Quality Web Page Generation
Authors: Yang Jason , Xu Tiancheng
Published: November 2023

As the world becomes increasingly digitized, the demand for personalized websites is also gradually growing [1]. However, creating a personal website is not an easy task. Generally speaking, people can invest time and effort to learn programming languages for website development, or they can spend money to purchase services fromprofessional web development companies. Both of these methods require significant investment in terms of time or money. To democratize the creation of personal websites, we have developed a program that automatically generates personalized web pages. Users simply need to input instructions in natural language, and the program will generate a personalized website based on the information provided. Websites produced in this manner usually have high quality while simultaneously saving substantial amounts of time and money [2].
Improve Navigation of the Webmail Interface using Arabic Voice Commands for Elderly and Disabled Employees: A Computer Application
Authors: Alkhattali Mokhtar , Dow Mostafa
Published: November 2023

The development of humanitarian assistance applications has revolutionized business efficiency and daily convenience. Voice recognition technology (VRT), with its improved accuracy, has found extensive use in various fields, including assistance programs for individuals with disabilities or limited mobility in vehicles, homes, and websites. The authors have developed "Asis_Webmail," a computer application (PC-App) written in Python, to enhance the accessibility and usability of Webmail for Arabic-speaking seniors and physically disabled employees. The application allows users to navigate the webmail interface using Arabic Voice Commands (AVC), promoting independence and functionality in their daily lives. The effectiveness of "Asis_Webmail" was assessed through a survey of disabled employees, who reported finding the application useful and mentioning improved interaction with their email interface. Ultimately, this application aims to empower Arabic-speaking individuals, regardless of mobility disability levels, to independently use the Webmail interface using AVC, thereby promoting independence in both social and functional aspects of their lives.
Forest Mixing: Investigating the Impact of Multiple Search Trees and a Shared Refinements Pool on Ontology Learning
Authors: Pop-Mihali Marco , Groza Adrian
Published: November 2023

We aim at development white-box machine learning algorithms. We focus here on algorithms for learning axioms in description logic. We extend the Class Expression Learning for Ontology Engineering (CELOE) algorithm contained in the DL-Learner tool. The approach uses multiple search trees and a shared pool of refinements in order to split the search space in smaller subspaces. We introduce the conjunction operation of best class expressions from each tree, keeping the results which give the most information. The aim is to foster exploration from a diverse set of starting classes and to streamline the process of finding class expressions in ontologies. The current implementation and settings indicated that the Forest Mixing approach did not outperform the traditional CELOE. Despite these results, the conceptual proposal brought forward by this approach may stimulate future improvements in class expression finding in ontologies.
Find Drivable Segments from Road Image using Depth and RGB Image
Authors: Li Xuemei
Published: November 2023

Perception is a very critical and challenging task in the realm of autonomous driving. The current approach relies on a sophisticated model pipeline built upon various deep learning models, each tasked with solving distinct challenges. This leads to a large model size. The proposed approach dissects an entire driving scene into two distinct elements: driving backgrounds that are not for vehicles to drive onto and road segments that are for vehicles driving. It performs the pointwise fusion using disparity image and RGB image. It uses pointwise and depthwise convolution to reduce multiplication times. It integrates image segmentation neural networks Deeplab V3 as backbone and significantly reduces the model size using ResNet-18. The efficacy of the proposed neural network is substantiated through validation using the Cityscape dataset, yielding an impressive 0.979 accuracy, 0.948 precision, and a 0.947 F1-score. Furthermore, it boasts a training speed that is five times faster compared to conventional UNetbased models, and the model size is eight times smaller than UNet-based models.
Addressing the Knapsack Challenge through Cultural Algorithm Optimization
Authors: Vahdatpour Mohammad Saleh
Published: November 2023

The "0-1 knapsack problem" stands as a classical combinatorial optimization conundrum, necessitating the selection of a subset of items from a given set. Each item possesses inherent values and weights, and the primary objective is to formulate a selection strategy that maximizes the total value while adhering to a predefined capacity constraint. In this research paper, we introduce a novel variant of Cultural Algorithms tailored specifically for solving 0-1 knapsack problems, a well-known combinatorial optimization challenge. Our proposed algorithm incorporates a belief space to refine the population and introduces two vital functions for dynamically adjusting the crossover and mutation rates during the evolutionary process. Through extensive experimentation, we provide compelling evidence of the algorithm's remarkable efficiency in consistently locating the global optimum, even in knapsack problems characterized by high dimensions and intricate constraints.
Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning
Authors: Guedes Gustavo Bartz , Silva Ana Estela Antunes da
Published: November 2023

Scientific articles are long text documents organized into sections, each describing aspects of the research. Analyzing scientific production has become progressively challenging due to the increase in the number of available articles. Within this scenario, our approach consisted of fine-tuning transformer language models to generate sentence-level embeddings from scientific articles, considering the following labels: background, objective, methods, results, and conclusion. We trained our models on three datasets with contrastive learning. Two datasets are from the article’s abstracts in the computer science and medical domains. Also, we introduce PMC-Sents-FULL, a novel dataset of sentences extracted from the full texts of medical articles. We compare the fine-tuned and baseline models in clustering and classification tasks to evaluate our approach. On average, clustering agreement measures values were five times higher. For the classification measures, in the best-case scenario, we had an average improvement in F1-micro of 30.73%. Results show that fine-tuning sentence transformers with contrastive learning and using the generated embeddings in downstream tasks is a feasible approach to sentence classification in scientific articles. Our experiment codes are available on GitHub.
Enhancing Transfer Learning Across Annotation Schemes with Minerr: A Novel Metric
Authors: Guilluy Samuel , Méhats Florian , Chouli Billal
Published: November 2023

This paper introduces MINERR (MINimal ERRor evaluation metric between consecutive tasks), a novel metric designed to enhance the efficiency of transfer learning in the context of argument structure identification. One of the principal challenges in the Argument Mining field pertains to the need for high- quality training data, which requires achieving a high level of interannotator agreement for argument constituents. Therefore, datasets within this domain tend to be smaller compared to those in other domains. To address this issue, we propose the consolidation of different datasets and employ the classical two-step method for argument identification, encompassing the identification of argumentative spans and the categorization of labels. An issue related to the separation of these two tasks is the errors interconnectedness between them. To tackle this problem, we introduce a new metric that distinguishes errors stemming from incorrect labelling and errors arising from span misidentification. Our approach incorporates a novel method for dissecting the prediction error of an argument component labelling task into two distinct categories: errors caused by misidentifying the component and errors resulting from assigning incorrect labels. Subsequently, we evaluate our method using a corpus including four distinct argumentation datasets. Overall, this work facilitates the development of a new transfer learning methodology for the application of diverse argument annotation schemes.
Saving Endangered Languages with a Novel Three-Way Cycle Cross-Lingual Zero-Shot Sentence Alignment
Authors: Hwang Eugene
Published: November 2023

Sentence classification, including sentiment analysis, hate speech detection, tagging, and urgency detection is one of the most prospective and important subjects in the Natural Language processing field. With the advent of artificial neural networks, researchers usually take advantage of models favorable for processing natural languages including RNN, LSTM and BERT. However, these models require huge amount of language corpus data to attain satisfactory accuracy. Typically this is not a big deal for researchers who are using major languages including English and Chinese because there are a myriad of other researchers and data in the Internet. However, other languages like Korean have a problem of scarcity of corpus data, and there are even more unnoticed languages in the world. One could try transfer learning for those languages but using a model trained on English corpus without any modification can be sub-optimal for other languages. This paper presents the way to align cross-lingual sentence embedding in general embedding space using additional projection layer and bilignual parallel data, which means this layer can be reused for other sentence classification tasks without further fine-tuning. To validate power of the method, further experiment was done on one of endangered languages, Jeju language. To the best of my knowledge, it is the first attempt to apply zero-shot inference on not just minor, but endangered language so far.
A Discourse-Driven Intervention Recommendation Framework for United Nations Peacekeeping in Post-Colonial Africa
Authors: Zhou Brian , Arulandu Alvan Caleb
Published: November 2023

The United Nations (UN) is the foremost international body helping uphold world peace through peacekeeping missions, ranging from deployments that enforce peace treaties, monitor conflicts, and protect civilians; However, determining when and how to intervene is complex. The updated UN General Debate Corpus (UNGDC), cataloging every speech from the UN's inception in 1946 to 2022, is a treasure trove of national policy, as the UNGD is the only body where every country can speak. We propose a discourse-driven intervention recommendation framework that categorizes ongoing conflicts based on UN precedent and recommends the magnitude of funds and forces that should be committed to addressing a conflict. We employ natural language processing techniques to tokenize, preprocess, and analyze word stem frequencies in the UNGDC, generating a timeseries of the number of UN mentions for any given country. Paired with historical analysis, we show that debate in the UNGDC is a potent indicator to determine UN intervention and response mechanisms for conflicts in Africa; further, by aggregating mention statistics across periods of active conflict, we provide quantitative backing for the correlation of mention dynamics and the presence of an active conflict, for a given country. Finally, we present and test an interpretable, shallow decision tree model that can perform intervention type classification and response magnitude recommendation with 91.7% accuracy. Our results, established by computational experiments and statistical testing, suggest that corpus analysis and broader computational diplomacy methods can drive intervention recommendations to improve the UN’s decision-making.
Lightweight Public Key Encryption in Post-Quantum Computing Era
Authors: Hillmann Peter
Published: November 2023

Confidentiality in our digital world is based on the security of cryptographic algorithms. These are usually executed transparently in the background, with people often relying on them without further knowledge. In the course of technological progress with quantum computers, the protective function of common encryption algorithms is threatened. This particularly affects public-key methods such as RSA and DH based on discrete logarithms and prime factorization. Our concept describes the transformation of a classical asymmetric encryption method to a modern complexity class. Thereby the approach of CramerShoup is put on the new basis of elliptic curves. The system is provable cryptographically strong, especially against adaptive chosen-ciphertext attacks. In addition, the new method features small key lengths, making it suitable for Internet-of-Things. It represents an intermediate step towards an encryption scheme based on isogeny elliptic curves. This approach shows a way to a secure encryption scheme for the post-quantum computing era.
Trust-Based Approaches Towards Enhancing IoT Security: A Systematic Literature Review
Authors: Okporokpo Oghenetejiri , Olajide Funminiyi , Ajienka Nemitari , Ma Xiaoqi
Published: November 2023

The continuous rise in the adoption of emerging technologies such as Internet of Things (IoT) by businesses has brought unprecedented opportunities for innovation and growth. However, due to the distinct characteristics of these emerging IoT technologies like real-time data processing, Self-configuration, interoperability, and scalability, they have also introduced some unique cybersecurity challenges, such as malware attacks, advanced persistent threats (APTs), DoS /DDoS (Denial of Service & Distributed Denial of Service attacks) and insider threats. As a result of these challenges, there is an increased need for improved cybersecurity approaches and efficient management solutions to ensure the privacy and security of communication within IoT networks. One proposed security approach is the utilization of trust-based systems and is the focus of this study. This research paper presents a systematic literature review on the Trustbased cybersecurity security approaches for IoT. A total of 23 articles were identified that satisfy the review criteria. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats and grouped them into three major categories, namely: Observation-Based, Knowledge-Based & Cluster-Based systems. Finally, several open issues were highlighted, and future research directions presented.
Children Facing Earthquakes in Mexico City: An Educational Strategy to Promote Prevention Awareness
Authors: Pérez-Sosa Daniela , Luna-Ramírez Wulfrano Arturo , Bustamante-Loya Sara Margarita
Published: November 2023

Mexico is a country with high seismic activity, and its capital, Mexico City, is considered especially vulnerable due to its geographical characteristics, urbanization and dense population. In this context, risk awareness coupled with education focused on emergency and prevention management is key in minimizing the negative effects of such disasters. Increasing seismic preparation in the Mexican population requires disseminating solid theoretical knowledge in addition to actionable and practical recommendations, i.e. life-saving action protocols, as early as childhood. We focus on an educational strategy co-designed with children, through an agile development process, to promote preparedness via a meaningful communication system that is relevant and efficient. Our contribution is two-fold, namely: a workshop involving children, teachers, and emergency staff to encourage interest in risk prevention during earthquakes; and an autonomous and self-managing workshop manual that allows an iterative improvement each time it is performed as required by everyone involved.
Patrón ultrasonográfico pulmonar en gestantes sanas en el tercer trimestre de embarazo
Authors:   Maternidad Concepción Palacios. Universidad Central de Venezuela, Berroterán Albino Yoisis De Jesús , Yépez Contreras Franlex , Noguera Millán Luisauri del Valle , Rivero Fraute Alexandra , González Blanco Mireya
Published: November 2023

Objective: To characterize the pulmonary ultrasonographic pattern in healthy pregnant women in the third trimester of pregnancy,entered in the Maternity Conception Palaces in the period September-December 2020. Methods: Prospective, descriptive, cross-sectional study that included 386 pregnant patients who were evaluated for the ultrasonographic lung pattern using the 8 quadrant technique. Results: All 384 patients had an aerated lung pattern: 244 (63.5%) 57 (14.8%) 37 (9.6%) they had two B lines. There was no relationship between the number of lines B and the age (p-0.391) or parity (p-0.444). Absence of B lines was more common in pregnancies of lower gestational age: between 289 pregnant people aged 37 weeks or more, 167 (57.8%) they had no B-lines; 56 pregnant people with pregnancy of 33-36 weeks, 43 (76.8%) they did not have lines B. Thirty-four of the 39 (87.2%) 28 to 32 weeks had no B-lines (p-0.003). One hundred and thirty-nine patients were in labor; 75 of them (54%) they had no B-lines. Among the 245 who were not in labor, 169 (69.0%) they had no lines B (p-0.013). The duration of the procedure averaged 1.83 ± 0.62 minutes (1.04-5.45). 384 (100%) referred to good tolerance; the difficulty to carry out the study was low 376 (97,9%) of the patients. Conclusion: In healthy pregnant women in the third trimester of pregnancy, the pulmonary ultrasonographic pattern is aerated.
Ultrasonido pulmonar: patrón pulmonar en preeclampsia con criterios de gravedad
Authors:   Maternidad Concepción Palacios. Universidad Central de Venezuela, Noguera Millán Luisauri del Valle , Padilla Leidenz Hilgrys Bethania , Rivero Fraute Alexandra , González Blanco Mireya
Published: November 2023

Objective: To evaluate the pulmonary ultrasonographic pattern in pregnant women with a diagnosis of preeclampsia with severity criteria admitted to the Maternal Fetal Medicine Service of the Maternidad Concepción Palacios Hospital in the period between February and September 2020. Methods: The study will be prospective, descriptive, correlational and cross sectional, where patients with a diagnosis of severe preeclampsia will be included and lung ultrasound will be performed with convex transducer using the 8 quadrant technique to evaluate the B lines pattern. The pattern will be correlated with risk factors for preeclampsia and with the development of complications. The association between the number of B-lines and the diagnosis of pulmonary edema will be evaluated. Results: A group of 55 patients with preeclampsia with severity criteria was studied, 56,4 % had an airy lung pattern and 43,6 % a wet pattern. The relationship between the number of B lines and the development of pulmonary edema was statistically significant (p = 0.000) with an average of B lines of 23,3 ± 5,0. All patients who developed acute lung edema were postpartum. Conclusions: the predominant pulmonary pattern was the aerated or A-line pattern. HELLP syndrome may condition the development of a wet pattern and an increased risk of acute lung edema during the puerperium. The number of B lines is directly correlated with an increased risk of developing acute lung edema.
Autopercepción sobre la apariencia genital en pacientes con diferentes grados de protrusión de labios menores
Authors:   Hospital Central de Maracay, Hernández Herllurys , Gómez Maryory , Hernández Oriana
Published: November 2023

Objective: To evaluate the self-perception of genital appearance, in patients with different degrees of protrusion of the labia minora in the Gynecology Service of the Central Hospital of Maracay, between June-November 2021. Methods: This is a descriptive and correlational study, with a field design, cross section and prospective. The sample was deliberate non-probabilistic, made up of 132 patients whose gynecological examination showed protrusion of the labia minora. Self-perception was measured using the Modified Lip Cosmetic Procedures Screening Scale (COPS-L). Results: a mean age of 36.67 years ± 1.02 was recorded. Mostly housewives (59.09%); with symmetrical vaginal lips (78.03%) and with protrusion of the labia minora grade II (70.45%). A maximum lip length of less than 3.22 cm ± 0.07 was recorded. A moderate disagreement was found with the appearance of the genitals (42.42%); the sample did not consider a surgical treatment necessary to correct your labia minora (41.67%). Functional (16.67%) and aesthetic (15.91%) reasons are among the reasons why surgical treatment is justified. Conclusion: A statistically significant association was found between conformity with the appearance of the genitalia and symmetry, with the degree of protrusion and with the need for surgical treatment (p < 0.05).
Aspectos bioéticos del uso de redes sociales en obstetricia y ginecología
Authors:   Universidad Central de Venezuela, Gómez Marín Jeiv , Robayo Rodríguez Yeyderli , Cabrera Lozada Carlos
Published: November 2023

The proliferation of professional use of social networks in obstetrics and gynecology by specialists and residents in training entails a series of bioethical aspects that should not be neglected. These go hand in hand with the emergence of the networked society and homo digitalis, taking into account the ontology of the digital being. The doctor-patient relationship has a series of characteristics and functions that should not be lost due to the emergence of e-professionalism in interaction on social networks, especially in the face of the high incidence of lack of professionalism in them. The review of the literature on its use in the specialty allows us to develop a series of recommendations whose guidelines allow for appropriate professional behavior in online interactions. Following these recommendations will help avoid online professional exposure and subsequent legal liability
Estudio morfológico macroscópico de un feto acardio
Authors:   Hospital de Maracaibo, Maternidad Dr. Armando Castillo Plaza, Villalobos Noren , Vargas Ramón , Villalobos Manuel , López Carol
Published: November 2023

The morphological study of the acardio anceps fetus presented, product of a controlled twin pregnancy, primiparous for 17 years, which was obtained by eutocic delivery. The normal twin did not present alterations and its subsequent development has been normal. Among the characteristics of the acardium, sketches of the skull, thorax, abdomen and limbs were presented, the body cavities being occupied by a substance formed by loose connective tissue. The cord insertion was velamentous.
Miasis vulvovaginal. Reporte de caso
Authors:   Hospital Central “Dr. Urquinaona”, Maracaibo, Venezuela., Reyna-Villasmil Eduardo
Published: November 2023

Myiasis is brought on by the larval infection of numerous species of Diptera in humans and other vertebrate animals. The larvae are typically discovered on exposed body parts. On covered body parts, such as the external genitalia, it is uncommon. Low socioeconomic status and poor personal cleanliness are associated with a higher risk of this infestation. Myiasis seldom affects the urogenital system, and vulvovaginal myiasis, which makes up just 0.7% of all cases, is far less frequent. Only a few cases have been documented. Most diagnoses are made on the basis of clinical findings. Localized larval removal and topical or systemic antiparasitic drugs are available as treatments. A case of vulvovaginal myiasis is presented.
ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE IN IT AUDIT: CURRENT PRACTICES AND FUTURE PROSPECTS
Authors: Uzoamaka Iwuanyanwu  , Apeh Jonathan Apeh  , Olubukola Rhoda Adaramodu  , Evelyn Chinedu Okeleke  , Ololade Gilbert Fakeyede
Published: November 2023

This research paper explores the integration of Artificial Intelligence (AI) into Information Technology (IT) audits, analyzing current practices, training requirements, and prospects. The literature review traces the historical evolution of IT audits, emphasizing the transformative impact of AI. The discussion on training and skill requirements outlines the evolving role of auditors and the strategies for equipping them with essential competencies. Recommendations emphasize continuous learning, ethical considerations, and collaboration, envisioning a future where auditors adeptly leverage AI to enhance the efficiency and strategic value of IT audits within organizations

Keywords: Artificial Intelligence, Information Technology Audits, IT Governance, Machine Learning, Auditing Practices, Skill Development
GRC STRATEGIES IN MODERN CLOUD INFRASTRUCTURES: A REVIEW OF COMPLIANCE CHALLENGES
Authors: Apeh Jonathan Apeh  , Azeez Olanipekun Hassan  , Olajumoke Omotola Oyewole  , Ololade Gilbert Fakeyede  , Patrick Azuka Okeleke  , Olubukola Rhoda Adaramodu
Published: November 2023

This research paper delves into the intricate landscape of Governance, Risk, and Compliance (GRC) in modern cloud infrastructures. As organisations increasingly migrate critical operations to the cloud, they encounter data security, legal compliance, and effective vendor management challenges. The paper explores a comprehensive range of GRC strategies, technological solutions, and best practices to address these challenges. It investigates the evolving regulatory landscape, the shared responsibility model, and the dynamic nature of cloud environments. Technological solutions, including cloud-native GRC platforms, AI and ML for threat detection, and automated security tools, emerge as pivotal components for fortifying GRC in the cloud. The paper concludes with strategic recommendations for organisations seeking to enhance their GRC strategies and navigate the complexities of modern cloud computing.

Keywords: Governance, Risk Management, Compliance Challenges, Automated Security Tools, CSPM Solutions.
Emergency Department Overcrowding in Canada
Authors: CADTH
Published: November 2023

What Is the Issue?

Emergency departments (EDs) across Canada are under strain and experiencing overcrowding, a situation that arises when the demand for health services in the ED exceeds the capacity of the health system — which includes the ED, hospital, and community — to provide quality care in a reasonable amount of time.

ED overcrowding is contributing to a deteriorating standard of care as health care providers and staff become overworked and burned out, is putting health and lives at risk, and is placing additional strain on an already overwhelmed health care system.

What Did We Do?

The CADTH Health Technology Expert Review Panel (HTERP) convened to develop objective, impartial, trusted pan-Canadian guidance to inform decisions about which evidence-informed solutions should be considered to help alleviate ED overcrowding in Canada.

What Is HTERP’s Position on ED Overcrowding?

ED overcrowding is a complex health system issue. EDs operate within hospitals and broader health and social systems, which means that accountability for causes, impacts, and solutions do not lie solely within the ED and its operations.

Output factors (e.g., misalignment between acute care bed capacity within the hospital and population needs) and input factors (e.g., misalignment between care available in the community and population needs, including care outside of regular business hours) respectively, are the main contributors to ED overcrowding in Canada. Health system capacity is not aligned with, nor has kept pace with, the growing and changing health care needs of the population, which results in overcrowded EDs.

Health systems will observe better results by implementing strategies that improve patient flow and focus on output and input factors relative to throughput factors.

What Is HTERP’s Guidance to Help Alleviate ED Overcrowding?

Interventions to alleviate overcrowding need to align with the main contributing factors to ED overcrowding in the particular context in which they will be implemented.

Understanding the context in which ED overcrowding is occurring, with attention to bottlenecks to patient flow, should be the first step to identify evidence-informed solutions.

Transparency and accountability should be key principles in ED, hospital, and health system operations. HTERP recommends identifying and ensuring clear roles, responsibilities, and reporting relationships embedded within an accountability framework for ED overcrowding across health system partners, including a commitment to act on data.

Each province and territory should mandate consistent and comprehensive reporting by all hospitals to the Canadian Institute of Health Information’s National Ambulatory Care Reporting System (NACRS) database.

HTERP’s guidance includes an Evidence Navigation Guide to support identification of evidence-informed interventions to help alleviate ED overcrowding.
Fault Tolerance Conceptual Strategy for a Quadcopter Drone with Rotor Failure
Authors: Zaludin Zairil A.
Published: November 2023

The ability of a quadcopter drone to maintain its attitude relies solely on its four rotors. If even one motor fails, the drone loses its ability to hold attitude and altitude. This paper explores a new fault tolerance solution to enhance attitude control for quadcopter drones following the complete loss of a single rotor. By following the fundamental principle of balancing forces and moments on a quadrotor drone, the paper demonstrates that it is feasible to land the drone safely by minimizing roll, pitch, and yaw when a rotor fails. The concept centers around thrust vectoring, which allows an opposite motor to tilt independently. The results indicate that tilting the opposite rotor by 45o provides better management of the drone’s roll, pitch, and yaw, enabling the incapacitated drone to land in a more controlled and manageable manner. The paper includes simulation results and a summary table of the novel idea’s performance enhancements.
Wanda Malczewska (1822-1896)
Authors: Czermińska Serafina
Published: November 2023

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