bluuebunny/crossref_metadata_2025 huggingface dataset:
The Concept of an Information System to Support Decision-Making to Improve the Efficiency of the Forest Complex
Authors:   Petrozavodsk State University, Petrozavodsk, Karelia, 185910, Russian Federation, Sokolov A. P. , Pitukhin E. A. ,   Petrozavodsk State University, Petrozavodsk, Karelia, 185910, Russian Federation, Shabaev A. I. ,   Petrozavodsk State University, Petrozavodsk, Karelia, 185910, Russian Federation, Galaktionov O. N. ,   Petrozavodsk State University, Petrozavodsk, Karelia, 185910, Russian Federation, Zyateva O. A. ,   Petrozavodsk State University, Petrozavodsk, Karelia, 185910, Russian Federation
Published: September 2023

The article is devoted to the discussion of the possibilities of improving the efficiency of the forest industry of the northern and arctic border regions of the Russian Federation in the new geopolitical and economic conditions. This goal is planned to be achieved through the use of a new type of regional information system of scenario modeling and support of strategic management decisions of the upper level. The need to develop such a system is due to the changes taking place in the Russian economy in recent years. They include the course on wide import substitution, development of deep processing of natural resources inside the country, reduction of export of goods with low degree of processing, sanctions policy of some Western countries, etc. These changes require the development of science-based effective solutions for the use of wood for industrial and consumer purposes. This problem is especially relevant for the border areas, for a long time largely focused on the export of many types of products. Describes the concept of the regional information system of scenario modeling and support for strategic management decisions of the upper level, its functions and structural and functional model scheme of the subject area. The requirements for the system are formulated and its top-level architecture is proposed. The structural model of integration of this information system into the management scheme of the system "forest complex — environment — regional economy" was developed. It is emphasized that the use of the system will allow one tochoose the most effective options for the development of the forest complex, taking into account all the requirements for the provision of rational and sustainable forest management and sustainable development, taking into account the characteristics of the border regions of the North and the Arctic.
Implementation of Transmission Line Fault Detection System using Long Range Wireless Sensor Networks
Authors: Raja P. Dinesh Anton , Thapa K. Naresh Kumar , Harsha Kotti Sai Sri , Naidu Javvadi Harish Mohana Swami , Krishna Kollipara Sai , Sivakumar A.
Published: September 2023

This paper proposes a fault detection system designed for transmission lines using Long-Range Wireless Sensor Network (LoRAWSN). The system is designed to detect and locate faults across transmission lines in real-time, which can significantly improve the reliability and efficiency of power transmission systems. A WSN will be built across transmission lines over an area. The faults identified by these sensor nodes is then transmitted to a central control unit, which analyses and displays the data. The LoRaWAN technology enables the WSN to cover long distances while consuming minimal power, making it ideal for monitoring transmission lines. The proposed fault detection system is evaluated through real world experiments, which demonstrate the feasibility and effectiveness of the proposed system. Overall, this paper presents a novel and practical approach for fault detection on transmission lines, which has the potential to improve the reliability and efficiency of power transmission systems.
Sharing Secret Colour Images with Embedded Visual Cryptography Using the Stamping Algorithm and OTP Procedure
Authors: Thorat Nilesh N. , Singla Amit , Dhaigude Tanaji
Published: September 2023

Finding a way to ensure the safety of media is becoming increasingly common in the modern world as digital media usage increases. Visual cryptography (VC) offers an efficient method for sending images securely. Images that have been protected using visual encryption can be decoded using features of human vision. Emails are not a highly safe method of exchanging private data because someone else can quickly weaken the content. In the visual cryptography technique, we presented for colour pictures, the divided shares are enclosed in additional pictures using stamping. Using a random number generator, the shares are created. Visual cryptography schemes (VCS) are a method of encoding pictures that conceals the secret information which is present in images. A secret image is encrypted using a straightforward visual cryptography technique by splitting it into n shares, and the stamping operation is carried out by overlapping k shares. It can be beneficial for hiding a secret image. There is a chance that employing cryptography for information exchange could cause security problems because the process of decryption of simple visual cryptographic algorithms can be completed by the human eye. To address this issue, we are using the OTP procedure. In the past, static ID and passwords were employed, making them susceptible to replay and eavesdropping attacks. One Time Password technology, which generates a unique password each time, is utilized to solve this issue. The suggested approach strengthens the security of the created transparencies by applying an envelope to each share and employing a stamping technique to address security vulnerabilities that the previous methods had, such as pixel expansion and noise.
Restricted Hidden Markov Model based Innovation in Physical Learning Education with Computer Internet Technology
Authors: Zhao Ang
Published: September 2023

The integration of computer internet technology into college physical education teaching has greatly enriched the teaching methods and forms and has promoted the innovation of the physical education teaching mode. The integration of computer internet technology into college physical education teaching mode has brought about significant innovation and improvement, enabling teachers to provide richer, more interactive, personalized, and flexible teaching. The integration of computer internet technology in physical education teaching has opened up new opportunities for enhancing the learning experience and improving student outcomes. This research focuses on the development of a technological innovation that leverages deep learning techniques, specifically the Restricted Hidden Markov Model (RHMMDL), to optimize physical education instruction. By harnessing the power of deep learning algorithms, the RHMMDL model aims to analyze and interpret movement patterns, providing personalized feedback and guidance to students. This paper presents the conceptual framework and methodology of the RHMMDL model, highlighting its potential benefits and implications for physical education teaching. The study also discusses the challenges and limitations associated with the implementation of this innovative technology.
A Comprehensive Review of Image Restoration and Noise Reduction Techniques
Authors: Agrawal Rashmi , Gupta Neha
Published: September 2023

Images play a crucial role in modern life and find applications in diverse fields, ranging from preserving memories to conducting scientific research. However, images often suffer from various forms of degradation such as blur, noise, and contrast loss. These degradations make images difficult to interpret, reduce their visual quality, and limit their practical applications.

To overcome these challenges, image restoration and noise reduction techniques have been developed to recover degraded images and enhance their quality. These techniques have gained significant importance in recent years, especially with the increasing use of digital imaging in various fields such as medical imaging, surveillance, satellite imaging, and many others.

This paper presents a comprehensive review of image restoration and noise reduction techniques, encompassing spatial and frequency domain methods, and deep learning-based techniques. The paper also discusses the evaluation metrics utilized to assess the effectiveness of these techniques and explores future research directions in this field. The primary objective of this paper is to offer a comprehensive understanding of the concepts and methods involved in image restoration and noise reduction.
Energy Optimization Efficiency in Wireless Sensor Networks for Forest Fire Detection:
Authors: Ibraheem Manar Khalid , Mohamed Mbarka Belhaj , Fakhfakh Ahmed
Published: September 2023

Wireless Sensor Networks (WSNs) have the potential to play a significant role in forest fire detection and prevention. However, limited resources, such as short battery life pose challenges for the energy efficiency and longevity of WSN-based IoT networks. This paper focused on the energy efficiency aspect and proposed the ECP-LEACH protocol to optimize energy consumption in forest fire detection cases. The proposed protocol consists of two main components: a threshold monitoring module and a sleep scheduling module. The threshold monitoring module continuously monitors energy consumption and triggers sleep mode for nodes surpassing the predetermined threshold. The ECP-LEACH protocol offers a promising solution for improving energy efficiency in WSN-based IoT networks for forest fire detection. By optimizing sleep scheduling and duty cycles, the ECP-LEACH protocol enables significant energy savings and extended network lifetime
A Hybrid Optimization Approach for Neural Machine Translation Using LSTM+RNN with MFO for Under Resource Language (Telugu)
Authors: Garugu Srisudha , Bhaskari D. Lalitha
Published: September 2023

NMT (Neural Machine Translation) is an innovative approach in the field of machine translation, in contrast to SMT (statistical machine translation) and Rule-based techniques which has resulted annotable improvements. This is because NMT is able to overcome many of the shortcomings that are inherent in the traditional approaches. The Development of NMT has grown tremendously in the recent years but NMT performance remain under optimal when applied to low resource language pairs like Telugu, Tamil and Hindi. In this work a proposedmethod fortranslating pairs (Telugu to English) is attempted, an optimal approach which enhancesthe accuracy and execution time period.A hybrid method approach utilizing Long short-term memory (LSTM) and traditional Recurrent Neural Network (RNN) are used for testing and training of the dataset. In the event of long-range dependencies, LSTM will generate more accurate results than a standard RNN would endure and the hybrid technique enhances the performance of LSTM. LSTM is used during the encoding and RNN is used in decoding phases of NMT. Moth Flame Optimization (MFO) is utilized in the proposed system for the purpose of providing the encoder and decoder model with the best ideal points for training the data.
An Enhanced CNN-based ELM Classification for Disease Prediction in the Rice Crop
Authors: Parasa Gayatri , Arulselvi M. , Razia Shaik
Published: September 2023

To meet the demands of a constantly expanding population, intensive farming is becoming more popular in the modern day. This strategy, meanwhile, increases the possibility of a wider range of plant illnesses. By reducing crop productivity in terms of both quantity and quality, these infections represent a threat to food production and ultimately result in a fall in the economy. Fortunately, new opportunities for early diagnosis of such epidemics have emerged because of technological improvements, which are advantageous for society as a whole. The difficulties created by technology and bio-mutations create a potential for additional breakthroughs, notwithstanding the significant contributions made by researchers in the field of agricultural disease diagnosis. The suggested framework comprises three key phases: preprocessing, feature extraction, and the classification of leaf diseases. To optimize computational resources and memory utilization, the input image undergoes pre-processing as a preliminary step. Afterward, a Convolutional Neural Network (CNN) is utilized on an extensive dataset of labeled images to capture pertinent features for the diagnosis of rice leaf diseases. The suggested model utilizes an Efficient Selective Pruning of Hidden Nodes (ELM) classifier based on the RBF kernel to classify the input data.
Deep Neural Network Solution for Detecting Intrusion in Network
Authors: Khan Zakiya Manzoor , Singh Harjit
Published: September 2023

In our experiment, we found that deep learning surpassed machine learning when utilizing the DSSTE algorithm to sample imbalanced training set samples. These methods excel in terms of throughput due to their complex structure and ability to autonomously acquire relevant features from a dataset. The current study focuses on employing deep learning techniques such as RNN and Deep-NN, as well as algorithm design, to aid network IDS designers. Since public datasets already preprocess the data features, deep learning is unable to leverage its automatic feature extraction capability, limiting its ability to learn from preprocessed features. To harness the advantages of deep learning in feature extraction, mitigate the impact of imbalanced data, and enhance classification accuracy, our approach involves directly applying the deep learning model for feature extraction and model training on the existing network traffic data. By doing so, we aim to capitalize on deep learning's benefits, improving feature extraction, reducing the influence of imbalanced data, and enhancing classification accuracy.
A Study of Techniques and Challenges in Text Recognition Systems
Authors: Kaur Gurvir , Kumar Ajit
Published: September 2023

The core system for Natural Language Processing (NLP) and digitalization is Text Recognition. These systems are critical in bridging the gaps in digitization produced by non-editable documents, as well as contributing to finance, health care, machine translation, digital libraries, and a variety of other fields. In addition, as a result of the pandemic, the amount of digital information in the education sector has increased, necessitating the deployment of text recognition systems to deal with it. Text Recognition systems worked on three different categories of text: (a) Machine Printed, (b) Offline Handwritten, and (c) Online Handwritten Texts. The major goal of this research is to examine the process of typewritten text recognition systems. The availability of historical documents and other traditional materials in many types of texts is another major challenge for convergence. Despite the fact that this research examines a variety of languages, the Gurmukhi language receives the most focus. This paper shows an analysis of all prior text recognition algorithms for the Gurmukhi language. In addition, work on degraded texts in various languages is evaluated based on accuracy and F-measure.
Zero-day Network Intrusion Detection using Machine Learning Approach
Authors: Alam Naushad , Ahmed Muqeem
Published: September 2023

Zero-day network attacks are a growing global cybersecurity concern. Hackers exploit vulnerabilities in network systems, making network traffic analysis crucial in detecting and mitigating unauthorized attacks. However, inadequate and ineffective network traffic analysis can lead to prolonged network compromises. To address this, machine learning-based zero-day network intrusion detection systems (ZDNIDS) rely on monitoring and collecting relevant information from network traffic data. The selection of pertinent features is essential for optimal ZDNIDS performance given the voluminous nature of network traffic data, characterized by attributes. Unfortunately, current machine learning models utilized in this field exhibit inefficiency in detecting zero-day network attacks, resulting in a high false alarm rate and overall performance degradation. To overcome these limitations, this paper introduces a novel approach combining the anomaly-based extended isolation forest algorithm with the BAT algorithm and Nevergrad. Furthermore, the proposed model was evaluated using 5G network traffic, showcasing its effectiveness in efficiently detecting both known and unknown attacks, thereby reducing false alarms when compared to existing systems. This advancement contributes to improved internet security.
Congestion Detection and Mitigation Technique for Multi-Hop Communication in WSN
Authors: Krishna K. Phani Rama , Mohammad Habibulla , Gangadhar Ch , Mohammed Riazuddin
Published: September 2023

The primary function of a network system is to gather information from the observation region and transmit it to the base station. The network life span and congestion are the two major concerns in wireless networks. To enhance the lifespan of the sensor system; multi-hopping has been proved as best in class. Congestion is an important factor to be taken, where multiple nodes forward data to one another in the process of communication. Hence to overcome the issue of congestion in WSN, we proposed a congestion detection and mitigation method along with the multi-hop concept. In this technique, we have considered different routes among communication units that were classified on distance, relative attainment rate (RAR) and node storage occupancy. A utility function (U) has been proposed and calculated using the above illustrated factors for every node that acts as a neighbour to the transmitting node. Neighbour node with highest U-valued will be considered as the packet forwarding node's next hop. In this manner congestion free nodes are selected for data transmission.
Exploration of Deep Learning Models for Video Based Multiple Human Activity Recognition
Authors: Janardhanan Jitha , Umamaheswari S.
Published: September 2023

Human Activity Recognition (HAR) with Deep Learning is a challenging and a highly demanding classification task. Complexity of the activity detection and the number of subjects are the main issues. Data mining approaches improved decision-making performance. This work presents one such model for Human activity recognition for multiple subjects carrying out multiple activities. Involving real time datasets, the work developed a rapid algorithm for minimizing the problems of neural networks classifier. An optimal feature extraction happens and develops a multi-modal classification technique and predicts solutions with better accuracy when compared to other traditional methods. This paper discussing on HAR prediction in four phases namely (i) Depthwise Separable Convolution with BiLSTM (DSC-BLSTM); (ii) Enhanced Bidirectional Grated Recurrent Unit with Long Short Term Memory (BGRU-LSTM); (iii) Enhanced TimeSformer Model with Multi-Layer Perceptron Neural Networks classification and (iv) Filtering Single Activity Recognition are described.In comparison to previous efforts like the DSC-BLSTM and BGRU-LSTM classifications, the experimental result of the ETMLP classification attained 98.90%, which was more efficient. The end outcome revealed that the new model performed better in terms of accuracy than the other models.
GBJOF: Gradient Boosting Integrated with Jaya Algorithm to Optimize the Features in Malware Analysis
Authors: Rani D. Sandhya , Krishna G Bala , Radha M , K L S Sirisha , Kumar V Pradeep , Anupama Meduri
Published: September 2023

Malware analysis is used to identify suspicious file transferring in the network. It can be identified efficiently by using the reverse engineering hybrid approach. Implementing a hybrid approach depends on the feature selection because the dataset contains static and dynamic parameters. The given dataset contains 85 attributes with 10 different class labels. Since it has high dimensional and multi-classification data, existing approaches of ML could be more efficient in reducing the features. The model combines the enhanced JAYA genetic algorithm with a gradient boosting technique to identify the efficiency and a smaller number of features. Many existing approaches for feature selection either implement correlation analysis or wrapper techniques. The major disadvantages of these issues are that they are facing fitting problems with a very small number of features. With the Usage of the genetic approach, this paper has achieved 95% accuracy with 12 features, approximately 7% greater than ML approaches.
Graph Neural Networks for E-Learning Recommendation Systems
Authors: Tolety Venkata Bhanu Prasad , Prasad Evani Venkateswara
Published: September 2023

This paper presents a novel recommendation system for e-learning platforms. Recent years have seen the emergence of graph neural networks (GNNs) for learning representations over graph-structured data. Due to their promising performance in semi-supervised learning over graphs and in recommendation systems, we employ them in e-learning platforms for user profiling and content profiling. Affinity graphs between users and learning resources are constructed in this study, and GNNs are employed to generate recommendations over these affinity graphs. In the context of e-learning, our proposed approach outperforms multiple different content-based and collaborative filtering baselines.
Heart Disease Prediction using Different Machine Learning Algorithms
Authors: P. K. Rajani , Patil Kalyani , Marathe Bhagyashree , Mhaisane Prerna , Tundalwar Atharva
Published: September 2023

Identifying a person's potential for developing heart disease is one of the most challenging tasks medical professionals faces today. With nearly one death from heart disease every minute, it is the leading cause of death in the modern era [4]. The database is taken from Kaggle. Various machine learning algorithms are used for prediction of heart disease detection here are Random Forest, XG-Boost, K- Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM). All these algorithms are implemented using Python programming with Google collab.  The performance evaluation parameters used here are Accuracy, precision, recall and Fi-score. Training and testing are implemented for different ratios such as 60:40, 70:30 and 80:20. From the analysis and comparisons of evaluation parameters of all the above algorithms, XG-Boost is having the highest accuracy and recall value. KNN having worst accuracy and recall amongst all. XG-Boost is having a training accuracy of 98.86, 98.74 and 97.68 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. XG-Boost is having a testing accuracy of 95.85, 95.45 and 96.09 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. So, XG-Boost algorithm can be used for obtaining the best prediction for heart disease.  This type of heart disease prediction can be used as a secondary diagnostic tool for doctors, for best and fast prediction. This can help the early prediction of heart disease thus increasing the chances of the saving the life heart patient.
Implementation of Deep CNN Model for the Detection of Plant Leaf Disease
Authors: Deepa S. , Vijayanand J. , Danesh K. , Gomathi M. , Subramani Kavitha
Published: September 2023

The potato is the most important tuber crop in the world, and it is grown in about 125 different nations. Potato is the crop that is most commonly consumed by a billion people worldwide, virtually every day, behind rice and wheat. However, a number of bacterial and fungal diseases are causing the potato crop's quality and yield to decline. Potato Leaf diseases must be promptly identified and prevented to increase production. Various researchers look for solutions to protect plants instead of   traditional processes which take more time. Recent technological developments have thrown up many alternates to traditional methods which are labour intensive. The application of AlexNet model Deep Convolutional Neural Network(CNN) to recognise diseases in potato plants avoids the disadvantages of selecting disease spot features artificially and makes more objective the plant disease feature extraction. It improves research efficiency and speeds up technology transformation. Accuracies ranging from 85% - to 95% were obtained using AlexNet model Deep.
The Human Side of Accountability: Dilemmas of Reaching All Learners
Authors: KIM TAEYEON
Published: September 2023

This portraiture-informed study by Taeyeon Kim challenges the dominant discourse of accountability, which often focuses on high-stakes policies at the expense of relational aspects of accountability in schools. Building on working theories of accountability, humanizing leadership, and paradox theory, Kim theorizes the “human side of accountability,” where leaders simultaneously address the tensions of multiple demands and implement policy mandates in ways that attempt to mitigate their unintended harm to students, particularly minoritized students. Using interviews, shadowing, photo-elicited focus groups, and artifacts generated from a yearlong qualitative study of three US elementary school principals, Kim explores how school principals make meaning of accountability in their daily practices and how they address dilemmas created by competing demands. The analysis suggests that leaders’ enactment of accountability can be understood as a daily balancing act of promoting equity to provide missing and overlooked support in policy man dates. This article thus challenges existing policy approaches and provides strategies for rethinking how processes of accountability can be imagined in school settings.
Ethical Decision-Making in the “Crowded Classroom”
Authors: GERON TATIANA
Published: September 2023

In this essay Tatiana Geron argues that classroom “crowdedness”—the spatial, temporal, and group dynamics of many students interacting in a shared space—shapes teachers’ every day ethical decision-making and should be essential to an ethical theory of teaching. Drawing from Philip K. Jackson’s ethnographic work and her own teaching experience, Geron identifies features of the classroom that contribute to its ethical complexity—size, compulsory closeness, diversity, temporal pressures, and group dynamics—and lead to three types of challenges for teachers: linear challenges, or ethical difficulties that scale up with classroom size; second-order challenges that arise from the unintended consequences of teacher decisions; and integral challenges that arise from the group dynamic as teachers and students interact as ethical agents. Geron uses a case study of teacher decision-making to explore these features and the potential they create for novel resolutions to ethical dilemmas in the classroom.
A EUROPEAN ANTIPOPULIST MOVEMENT? THE EMERGENCE AND DIFFUSION OF THE ITALIAN SARDINES AND FINNISH HERRINGS*
Authors: Caiani Manuela , Eren Batuhan
Published: September 2023

This article investigates the emergence and diffusion of antipopulist mobilizations. Resembling the iconic image depicting a school of small fish chasing a big one, the Italian 6000 Sardine and the Finnish Silakkaliike (Herrings) emerged as two movements with antipopulist claims. Although the scholarship on populism is abundant, antipopulism remains mostly neglected, especially its mobilization from below. Drawing on extensive fieldwork including a groundedtheory approach applied to twenty-seven interviews with activists from these two movements, plus the analysis of offline and online organizational documents, this study shows the mechanisms—cognitive, affective and relational—of their national and crossnational diffusion, relating them to the opportunities of the context. Exploring the internal movement dynamics and actors’ perceptions and motivations, this study also contributes to the conceptualization of antipopulism from below, defining the main characteristics and the ideological underpinnings of these two antipopulist movements.
Violência Armada e Saúde na América Latina: revisão de literatura
Authors: Ribeiro Fernanda Mendes Lages , Andrade Cristiane Batista , Dias Camila Athayde de Oliveira , Almeida Brena Costa de
Published: September 2023

The article aimed to analyze the presence of Armed Violence (AV) in Latin America (LA), in vulnerable spaces and its impacts on the health of the population. As a methodology, the authors adopted an integrative literature review with an analysis of 33 articles. AV in LA is associated with the colonization process, social inequalities, structural violence, military conflicts between organized crime and the State and homicides. It imposes difficulties in the performance of health professionals, interruption of care and therapeutic bonds, feelings of fear and frustration, psychological distress, stress and depression. It is concluded that the historical weight of Latin American colonization reifies the vulnerabilities in certain territories affected by the AV; this must be seen as a public health problem that requires the development of public policies to face it.
Perfil del postulante a Pedagogía y atracción de estudiantes académicamente talentosos en un contexto de mayores incentivos a la demanda
Authors: Espinosa-Valenzuela Denisse , Rodríguez-Garcés Carlos , Padilla-Fuentes Geraldo
Published: September 2023

Based on the information from the 2020 admission process to Chilean Universities, this article explores, through descriptive and inferential statistical analyses, the profiles of students who apply and enroll in Pedagogy programs, according to their socioeconomic attributes and school trajectory. Among the main findings, together with a historical female presence, there is a predominance of students who even showing a good school performance during High School, do not achieve good results in the university admission tests. The conclusions highlight that, despite the new provisions and incentive policies, pedagogy programs are still not able to attract academically talented students.
Constitucionalismo dialógico y justicia constitucional. Una vuelta larga para volver a las virtudes deliberativas de la Acción Pública de Inconstitucionalidad
Authors: Rodríguez-Peñaranda María Luisa
Published: September 2023

Through Dialogical Constitutionalism (DC), some theorists believe they have found an escape valve for the global discussion on the legitimacy of judicial control over the law. With the situated contextual methodology, this article analyzes the challenges that DC poses in Latin America, particularly in Colombia, in four sections: i) Why is an attractive theory; ii) Its differences from deliberative democracy (DD); iii) The arguments of Roberto Gargarella in defense of DD, DC, and “A Dialogue between Peers”. This invites us to question whether it is merely new packaging for old discussions. Lastly, iv) I will reflect on the advantages of the Public Action of Unconstitutionality (PA) as a participatory and deliberative legal challenge mechanism, which dialogical potential is not properly appreciated due to a preference bias. Additionally, I will address DC in its two versions: the Canadian institutional version and the conversational version, ultimately proposing a third nuestramerican approach.
Performances solidarias. Apoyo público al Paro Nacional Agrario del 2013 en Colombia
Authors: Portela-García Juan Camilo
Published: September 2023

This article aims to explain how a favorable interpretation of the National Agrarian Strike was positioned in the public sphere. The category of solidarity performance is proposed to account for the support of actors who actively engaged in the course of conflict due to their moral positions, even if they do not belong to the offended group. A documentary archive is built to record those performances, to do so, several sources and interviews to participants in the protest were reviewed. It is proposed that the support was possible thanks to a symbolic perforcoalition that resulted in a common interpretation: the striking peasantry was a victim that shared ancestral and historical ties with the other sectors of Colombian society. The classification of the protest as an insurgent action gave way to its codification as a democratic action thanks to the commitment shown in the solidarity performances.
Métricas sin control: el caso de la ingeniería en Colombia
Authors: Ramírez-Ajiaco Catalina , Aguilera-Prado Marco
Published: September 2023

This text proposes an approach to the use of metrics and bibliometrics in research management scenarios as an instrument to control the audit culture in Colombian higher education institutions. For this purpose, the article analyzed the scientific production on engineering between 2000 and 2019 and obtained a context that connects additional criteria such as collaborations, institutional affiliations, and topics to citation. These elements enabled to determine the limitations of using only bibliometric indicators to measure scientific work, since it significantly reduces the framework in which knowledge production occurs in Colombia.
Co-authorship Networks and Scholarly Books: A Methodological Approach from a University Press Case Study
Authors: Abadía Adolfo A.
Published: September 2023

This article contributes to bibliometric studies of scholarly book publishing by showing the co-authorship networks in the case of a university press. It uses social network analysis attributes associated with integrity and centrality metrics to, on the one hand, characterize the interaction in the network as a whole, and on the other hand, to identify the authors who play an important role within it. For this purpose, a database with 989 authors was built, it structures a network of 9656 links between co-authors distributed in 188 books published between 2007 and 2021 by Editorial Universidad Icesi. The main results are of two types: methodological ones that seek to present this study as a replicable model to study the production of books by other publishers; and another to understand this case study.
A combined treatment for patients with dry eye and associated laryngopharyngeal reflux: a real-life approach
Authors:   Allergy Clinic, Casa di Cura Villa Montallegro, Genoa 16145, Italy, Ciprandi Giorgio , Schiavetti Irene ,   Health Science Department, University of Genoa, Genoa 16142, Italy, Damiani Valerio ,   Medical Department, DMG, Pomezia 00070, Italy
Published: September 2023

AIM: To evaluate the efficacy and tolerability of administering a combined therapy in patients with dry eye syndrome (DES) and associated laryngopharyngeal reflux (LPR).
METHODS: The study was retrospective, open, observational, and conducted in a real-life setting. Patients had pathological symptom assessment in dry eye (SANDE) and reflux symptom index (RSI) at baseline. Patients were re-assessed after 1mo and at the end of treatment. The treatment consisted of a three-month course based on the combined therapy: Gastroftal eye drops, one drop three times a day, and Gastroftal tablets, two tablets after lunch and two tablets after dinner. Tear break-up-time (TBUT) test, Schirmer test, RSI, and SANDE questionnaire were evaluated.
RESULTS: The study included 253 patients. The mean age was 58±11.19y. TBUT test score and Schirmer’s test significantly increased (both P<0.001) after 1mo and at the end of treatment. The RSI score and SANDE scores significantly decreased (both P<0.001) after 1mo and at the end of treatment.
CONCLUSION: The current, retrospective, and open study shows that combined therapy using Gastroftal eye drops and tablets could represent a valuable option in managing patients with DES associated with LPR.
Segregação racial e vieses algorítmicos: máquinas racistas no âmbito do controle penal
Authors: Chaves Junior Airto , Guasque Bárbara , Padua Thiago Santos Aguiar de
Published: September 2023

A pesquisa tem por objetivo verificar se o emprego de Sistemas de Inteligência Artificial poderia abrandar a altíssima carga de seletividade do Controle Penal, notadamente aquelas fundadas no racismo e na discriminação. Para tanto, num primeiro momento, o texto trata da seletividade do poder punitivo como característica estrutural. Na sequência, da atenção aos dados que são utilizados na construção de modelos de IA. Depois disso, elenca possíveis soluções para reduzir a incidência de algoritmos enviesados e mitigar a reprodução do racismo a partir do emprego da máquina. Por fim, propõe-se identificar algumas externalidades negativas oriundas da não observância de determinados padrões éticos em modelos de IA. Justifica-se a pesquisa em face da necessidade de se conhecer os perversos reflexos que algoritmos enviesados podem produzir nos segmentos de Controle Penal, catalisando preconceitos e segregando setores sociais eleitos pela cor da pele. Quanto ao método de pesquisa, revela-se o indutivo, subsidiado pela pesquisa bibliográfica e números oficiais extraídos de bases oficiais.
Examining the Application of Al-'Urf in Islamic Financial Contracts: Insights and Implications for Modern Banking Products
Authors: Haisyi Noorwahidah
Published: September 2023

This study aims to investigate various examples of the implementation of al-`urf as a source of law in Islamic economy. The study used qualitative approach, which involves a review of relevant literature and an analysis of primary sources. The findings of this study suggest that the use of `urf in various practices of Islamic financial contracts including bai’ mu’atah, istishna, and profit-sharing arrangements in mudharabah or musharakah contracts is a long-lasting application that has become staple in modern banking products. However, we also discovered some challenges in these practices such as the lack of contract standardization, the absence of clear guidelines for the calculation of profits and losses, and the lack of awareness among the general public about the contracts. Future research could explore these issues to offer new standards and guidelines, or suggest ways to arise awareness of Islamic contracts. Penelitian ini bertujuan untuk menyelidiki berbagai contoh implementasi al-‘urf sebagai sumber hukum dalam ekonomi Islam. Pendekatan yang digunakan dalam studi ini adalah kualitatif, yang melibatkan tinjauan terhadap literatur yang relevan dan analisis terhadap sumber-sumber utama. Temuan dari penelitian ini menunjukkan bahwa penggunaan ‘urf dalam berbagai praktik kontrak keuangan Islam, seperti bai' mu'atah, istishna, dan pengaturan bagi hasil dalam kontrak mudharabah atau musyarakah, merupakan aplikasi yang sudah lama digunakan dan menjadi bagian penting dalam produk perbankan modern. Namun, kami juga menemukan beberapa tantangan dalam praktik-praktik ini, seperti kurangnya standarisasi kontrak, ketidakjelasan panduan untuk perhitungan keuntungan dan kerugian, dan kurangnya kesadaran di kalangan masyarakat umum tentang kontrak-kontrak tersebut. Penelitian yang akan datang dapat mengeksplorasi isu-isu ini untuk memberikan standar dan panduan baru, atau memberikan saran tentang cara meningkatkan pengetahuan publik tentang kontrak-kontrak Islam.
Al-Ghazali's Sufistic Economy Concept; From Homo Islamicus To Economic Progress
Authors: Aravik Havis , Hamzani Achmad Irwan , Khasanah Nur
Published: September 2023

Islamic economics is present as a solution to the failure of capitalist and socialist economies in producing prosperity. Islamic economics is a new solution in answering various economic problems. For this reason, it is necessary to have the ability to access Islamic economic thought, especially from previous scholars to answer all existing problems. One such scholar is al-Ghazali. In Islamic economics, al-Ghazali offers a new perspective in the form of a Sufistic economic concept. This research is library research (library research) and is qualitative, which is sourced from primary and secondary data, and analyzed using content analysis both descriptively and inductively. The result is that Al-Ghazali no longer sees economic problems only in one field of knowledge, but is compared in various other fields of science to produce a comprehensive solution in which all human economic activity is part of worship to Allah Swt. For this reason, it must produce maslahah and falah in its activities. The state is obliged to play a role in ensuring that all of this is achieved by issuing various policies that help Muslims carry out their religious obligations as well as possible, including in economic matters and economic progress can only be achieved if justice, peace, and stability are fulfilled, in which the state plays an important role. inside it.
Improved Methodology for Testing the Compliance of Residual Current Detection of Non-Isolated Grid-Connected Photovoltaic Inverters
Authors: Morari Henrique Bizzi , Bortolini Ricardo Jochann F. , Bellinaso Lucas Vizzotto , Michels Leandro
Published: September 2023

Ensuring user safety in transformerless photovoltaic (PV) inverters is crucial due to the high leakage current caused mainly by the PV modules’ capacitance. Compliance with safety standards IEC 62109-2:2011 and IEC 63112:2021 demands leakage current tests utilizing a variable resistive-capacitive (RC) load. However, limited research has been conducted to assess the impact of test parameters on result conclusiveness. To address this gap, this paper investigates the influence of AC mains voltage phase angle on trip time results in the continuous leakage current test as per these standards. Our experiments, performed with five commercial transformerless PV inverters, reveal significant sensitivity of the results to this specific parameter. Supported by RMANOVA analysis, our findings substantiate this observation. Subsequently, we discuss potential strategies to enhance the accuracy of test outcomes and propose a well-defined methodology to ensure repeatability and reliability of results.
Hormonal therapy in women, thromboembolic complications and laboratory characteristics of the hemostasis system for personalized solutions
Authors: Startseva O. N. , Vavilova T. V. , Zybina N. N.
Published: September 2023

The use of hormonal drugs for contraception and menopausal replacement therapy is widespread throughout the world. However, the use of these drugs is associated with an increased  risk of thromboembolic complications (TEO). At present, it has been established that the most  important cause of feasibility study is the initial, sometimes hidden, violations of the hemostasis system, predisposing to increased blood clotting and thrombosis. Their timely identification helps to personalize the use of the necessary hormonal therapy.
K.D. Ushinsky – harbringer of personal-value approach in education
Authors:   Russian Federal Research Institute of Fisheries and Oceanography, Knyazev Eugene A.
Published: September 2023

The analyzed basic pedagogical ideas of the great pedagogue are significant in the modern conditions of the development of Russian education. The considered main principles of K.D. Ushinsky's pedagogical theory were created in the era of Great reforms and are still relevant, such as the ideology of democratization of the education system and the commitment to the people in education. The central idea of K.D. Ushinsky's pedagogical theory is the recognition of the creative power of the working people in the historical process and their right to a full-fledged education. The struggle of the Russian progressive public for the creation of public education in the era of Great Reforms contributed to the formation of the social and pedagogical movement. Contrasting the policy of “unenlightened enlightenment” with the scientifically grounded concept of the democratization of the education system, K.D. Ushinsky began to develop an anthropological paradigm. Denying universal pedagogy, he declared the need for a national school and national pedagogy, which implied the rejection of blind copying of foreign educational systems, the approval of real education that prepares students for life. K.D. Ushinsky proclaimed the democratically interpreted concept of nationality as the basis of education; asserts the dominant role of the native language in education. The great scientist declared the connection of theory and practice in the pedagogical process; discovered the inseparable connection of pedagogy with other humanities; developed a unique concept of anthropological education and the formation of the creative personality of the teacher. The obtained results can be used in the educational process of students of pedagogical directions in the study of the discipline “History and Philosophy of Education”.
Effective forms of mentoring in an educational organization
Authors:   Chechen State Pedagogical University, Muskhadzhieva Tamara A.
Published: September 2023

The purpose of the study is to identify certain aspects of mentoring activities on the example of the Chechen State Pedagogical University. Mentoring is the most economical and effective method available for personal and professional development of the individual. We consider the necessity and importance of returning the institute of mentoring to the educational environment, the requirements for the personal and professional characteristics of the mentor. Considered tactics of pedagogical support (O.S. Gazman) should contain four tactical areas of activity: protection, support, assistance and cooperation. The study focuses on the successful mentoring activities of a junior year and senior student. This form of mentoring activity seems to us effective due to the proximity of age, biological rhythm, similarity of interests, goals, aspirations, physical capabilities. The study presents the results of a survey among students of 1–2 courses of the Chechen State Pedagogical University on mentoring. The survey indicators showed that the largest part of the respondents (about 60 %) would like to have a mentor for the duration of their studies; about 47 % of respondents want to see a senior student as their mentor; about 38 % believe that a lecturer will be the best mentor for them. The experience of a lecturer and senior students in the role of a mentor can be successfully applied in any educational organization.
Seagrass ecosystem biodiversity mapping in part of Rote Island using multi-generation PlanetScope imagery
Authors: Wicaksono Pramaditya , Harahap Setiawan Djody , Hafizt Muhammad , Maishella Amanda , Yuwono Doddy Mendro
Published: September 2023

Remote sensing offers an effective and efficient solution to provide information on the biodiversity of seagrass ecosystems, which is currently lacking in most parts of the world. Therefore, this study aimed to map the biodiversity of seagrass ecosystems in parts of Rote Island, which is one of the seagrass biodiversity hotspots, using multi-generation PlanetScope magery to see how they compare. The most frequently used biodiversity indicators were identified, including the major benthic habitat (coral, seagrass, macroalgae, bare substrate) and the composition of seagrass species based on life forms. We also aim to understand the actual biodiversity indicators of seagrass ecosystems captured by PlanetScope imagery. To achieve this, field data was integrated with the resulting ISODATA classification results to assess what ISODATA class clusters represent in the field, and new classification schemes are developed accordingly. The random forest algorithm was used to carry out the classification, with seagrass field data serving as training data. Independent field data was subsequently used to assess the accuracy. The results showed that the accuracy of benthic habitat and seagrass mapping ranged from 60%-70%. However, through the use of a classification scheme built on ISODATA clustering, the spatial distribution of classes and accuracy of all PlanetScope images was significantly improved to > 90%. This highlighted the importance of understanding which indicators of seagrass biodiversity were effectively captured by PlanetScope images to achieve higher mapping accuracy. Overall, this approach optimized the ability of PlanetScope images to map seagrass biodiversity while obtaining a higher number of biodiversity indicator classes and mapping accuracy than the commonly used biodiversity indicator classification scheme.