bluuebunny/crossref_metadata_2025 huggingface dataset:
Color and Attention for U : Modified Multi Attention U-Net for a Better Image Colorization
Authors: Nathanael Oliverio Theophilus , Prasetyo Simeon Yuda
Published: October 2024

Image colorization is a tedious task that requires creativity and understanding of the image context and semantic information. Many models have been made by harnessing various deep learning architectures to learn the plausible colorization. With the rapid discovery of new architecture and image generation techniques, more powerful options can be explored and improved for image colorization tasks. This research explores a new architecture to colorize an image by using pre-trained embeddings on U-Net combined with several attention modules across the model. Using embeddings from a pre-trained classifier provides a high-level feature extraction from the image. Conversely, multi-attention gives a little taste of image segmentation so that the model can distinguish objects in the image and further enhance the additional information given by the pre-trained embeddings. Adversarial training is also utilized as a normalization to make the generated image more realistic. This research preferred Parch GAN over base GAN as the discriminator model to ensure that the colorization has a consistent quality across all patches.  The study shows that this U-Net modification can improve the generated image quality compared to a normal U-Net. The proposed architecture reaches an FID of 48.6253, SSIM of 0.8568, and PSNR of 19.7831 by only training it for 25 epochs; hence, this research offers another view of image colorization by using modules that are often used for image segmentation tasks.
A Multi-Feature Fusion Approach for Dialect Identification using 1D CNN
Authors: Karim Sarkhel H.Taher , J. Ghafoor Karzan , O. Abdulrahman Ayub , M. Hama Rawf Karwan
Published: October 2024

The phonological variety of Kurdish, a language with several dialects, poses a distinct problem in automatically identifying dialects. This study examines and evaluates several sound criteria for identifying Kurdish dialects: Badini, Hawrami, and Sorani. We deployed a dataset including 6,000 samples and utilized a mix of 1D convolutional neural networks (CNN) and fully connected layers to conduct the identification job. Our study aimed to assess the efficacy of different sound characteristics in accurately identifying dialects. We employed the Mel-frequency Cepstral Coefficients (MFCC) and other features such as the Mel spectrogram, spectral contrast, and polynomial features to extract the sound characteristics. We conducted training and testing of our models utilizing both individual characteristics and a composite of all features. Our analysis revealed that the identification task achieved excellent accuracy rates, suggesting a promising potential for success. We achieved 95.75% accuracy using MFCC combined with a Mel spectrogram. The accuracy improved by including contrast in the MFCC feature extraction process to 91.42%. Similarly, using poly_features resulted in an accuracy of 90.83%. Remarkably, accuracy reached a maximum of 96.5% when all the attributes were combined.
Classification of Malaria Using Convolutional Neural Network Method on Microscopic Image of Blood Smear
Authors: Minarno Agus Eko , Izzah Tsabita Nurul , Munarko Yuda , Basuki Setio
Published: October 2024

Malaria, a critical global health issue, can lead to severe complications and mortality if not treated promptly. The conventional diagnostic method, involving a microscopic examination of blood smears, is time-consuming and requires extensive expertise. To address these challenges, computer-assisted diagnostic methods have been explored. Among these, Convolutional Neural Networks (CNN), a deep learning technique, has shown considerable promise for image classification tasks, including the analysis of microscopic blood smear images. In this study, we employ the NIH Malaria dataset, which consists of 27,558 images, to train a CNN model. The dataset is divided into parasitized (malaria-infected) and uninfected. The CNN architecture designed for this study includes three convolutional layers and two fully connected layers. We compare the performance of this model with that of a pre-trained VGG-16 model to determine the most effective approach for malaria diagnosis. The proposed CNN model demonstrates high accuracy, achieving a value of 96.81%. Furthermore, it records a recall of 0.97, a precision of 0.97, and an F1-score of 0.97. These metrics indicate a robust performance, outperforming previous studies and highlighting the model's potential for accurate malaria diagnosis. This study underscores the potential of CNN in medical image classification and supports its implementation in clinical settings to enhance diagnostic accuracy and efficiency. The findings suggest that with further refinement and validation, such models could significantly improve the speed and reliability of malaria diagnostics, ultimately aiding in better disease management and patient outcomes.
Structural Equations Modeling Approach: Issues in selecting a University
Authors: Simarmata Justin Eduardo , Mone Ferdinandus , Chrisinta Debora
Published: October 2024

Higher education is a continuation of secondary education that is organized to prepare students to become members of the community with academic or professional abilities. Therefore, selecting a university is essential for helping students acquire knowledge and skills and prepare themselves to be independent individuals. This study intends to determine the distribution of decision-making students who select the university based on factors of academic ability, individual characteristics, psychology, instrumental, and environmental factors. The study's methodology, which involves the collection of primary data from 474 respondents from high schools in the Indonesian-Timor Leste border region, is robust and rigorous, instilling confidence in the reader. The method used for data analysis is quantitative, which involves applying structural equation modeling to identify factors that influence students’ selection of universities. The study results showed that out of the six structural models formed, they could provide evidence that factors such as academic ability, individual characteristics, psychology, instrumental factors, and environment influence students’ selection of universities. This can be seen from the test values of the regression model formed in the structural modeling, which gives a P-value of less than 5%. The variable that provided the largest percentage, partially influencing student interest in selecting a state university, was the school environment, which was 98%. The value was based on the path coefficient of structural equation modeling. These findings have significant implications for the design of university admission processes and the development of student support programs.
Spatial-Temporal Visualization of Dengue Fever Vulnerability in Kediri Using Hierarchy Clustering Based on Mobile Devices
Authors: Hamida Silfiana Nur , Fariza Arna , Basofi Arif
Published: October 2024

In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.
Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method
Authors: Andrian Rafi , Putra Kusuma Gede
Published: October 2024

Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements
Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies
Authors: Hairani Hairani , Widiyaningtyas Triyanna , Dwi Prasetya Didik
Published: October 2024

The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline for solving unbalanced data problems. The working concept of the SMOTE method is to generate new synthetic data patterns by performing linear interpolation between minority class samples based on k-nearest neighbors. However, the SMOTE method has weaknesses, namely the problem of overgeneralization due to excessive sampling of sample noise and increased overlapping between classes in the decision boundary area, which has the potential for noise data. Based on the weaknesses of the Smote method, the purpose of this research is to conduct a systematic literature review on the Smote method modification approach in solving unbalanced data. This systematic literature review method comprises keyword identification, article search process, determination of selection criteria, and selection results based on criteria. The results of this study showed that the SMOTE modification approach was based on filtering, clustering, and distance modification to reduce the resulting noise data. The filtering approach removed the noise data before SMOTE, positively impacting resolving unbalanced data. Meanwhile, the use of a clustering approach in SMOTE can minimize the overlapping artificial minority data that has noise potential. The most used datasets are Pima 60% and Haberman 50%. The most used performance evaluation on unbalanced data is f1-measure 57%, accuracy 55%, recall 43%, and AUC 27%. The implication of the results of this literature review is to provide opportunities for further research in modifying SMOTE in addressing health data imbalances, especially handling noise and overlapping data. The thoroughness of our literature review should instill confidence in the research community.
Visual Analytic for Traffic Impact Assessment
Authors: Chan Jia Chun , Fahad Nafiz , Goh Kah Ong Michael , Tee Connie
Published: October 2024

This study strives to promote the state of traffic impact assessment through high-end visual analytics by incorporating spatial and temporal data visualization to enhance traffic management. Based on a dataset on traffic flow at three major intersections, we married data cleaning, integration, and transformation to set out for a detailed visual analysis. Thus, the critical materials comprise the traffic count in multiple lanes, vehicle types, and saturation flow rates to understand the road network's capacity. They essentially explored the traffic volume variations daily and hourly and pattern identification using heat maps, parallel coordinate charts, and bar plots. Thus, the findings expose the remarkable traffic volume and pattern differences by distinguishing peak and off-peak hours on weekdays and weekends. The level of service at each junction was determined by the volume-to-capacity ratio, identifying potential congested areas. As such, this work points to the importance of further improvements to visual analytic techniques to accurately predict traffic patterns and evaluate traffic management strategies effectively. Predictive models based on visual analytic findings can pave the way for proactive traffic control and congestion mitigation, making urban traffic management more efficient and safer. The current study provides a scaffold for additional exploration of the above-detailed methods and their penal outcomes in urban development planning and policy provision in terms of developing sustainable traffic control strategies and real-time decision-making improvements.
A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels
Authors: Latumakulita Luther Alexander , Paat Frangky J , Budiman Glenn , Tooy Dedie , Koibur Mayko Edison , Islam Noorul
Published: October 2024

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.
Modified Alexnet Architecture for Classification of Cassava Based on Leaf Images
Authors: Sholihin Miftahus , Md Fudzee Mohd Farhan , Ismail Mohd Norasri , Wati Efi Neo , Arshad Mohamad Syafwan , Gusman Taufik
Published: October 2024

The objective of this study is to address the drawbacks of conventional classification approaches through the implementation of deep learning, specifically a modified AlexNet. The primary aim of this study is to precisely categorize the four distinct varieties of cassava, namely Manggu, Gajah, Beracun, and Kapok. The cassava dataset was obtained from farmers in Lamongan, Indonesia, and was used as a source of information. Data collection on cassava leaves was carried out with agricultural research specialists. A total of 1,400 images are included in the dataset, with 350 images corresponding to each variety of cassava produced. The central focus of this research lies in a comprehensive evaluation of the modified AlexNet architecture's performance compared to the original AlexNet architecture for cassava classification. Multiple scenarios were examined, involving diverse combinations of learning rates and epochs, to thoroughly assess the robustness and adaptability of the proposed approach. Among the evaluation criteria that were rigorously examined were accuracy, recall, F1 score, and precision. These metrics were used to determine the predictive capabilities of the model as well as its potential utilization in the actual world. The results show that the modified AlexNet design has better performance than the original AlexNet for recall, accuracy, precision, and F-1 score, all achieving a rate of 87%. In situations where a learning rate of 0.0001 and an epoch count of 150 are utilized, the performance of the approach stands out significantly, displaying an excellent level of competency. Nevertheless, it is crucial to recognize that distinct fluctuations in performance were noted within particular contexts and with diverse learning rates.
Atomic Structure Simulation and Properties’ Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis
Authors: Nazrin S.N. , Zaman Halimah Badioze , Jothi Neesha , Jouay Doha , Lahrach Badreddine , Halimah M.K.
Published: October 2024

The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research.
Psychometric Properties of the Saudi Version of the Mathematics Confidence Scale using the Modern Test Theory
Authors: Alnaimi Izzeldeen Abdullah , Alenezi Abdullah Khretan , Shindi Yousef Abdelqader Abu , Al-Zboon Habis Saad , Gaberkassem Abd El. MureedAbd El
Published: October 2024

The aim of the study was to analyze the psychometric properties of the Arabic version of the Math Confidence Rating Scale (MCS), using Item Response Theory (IRT) and Exploratory Factor Analysis (EFA). The participants were (315) from Imam Mouhammad Ibn Saud Islamic University; They are distributed into (92) female and (223) male, selected through a convenience sampling. The EFA models indicated that the one-dimensional structure better represents the data, and the pairs of items were considered locally independent, IRT findings indicate that all items had a very high discrimination parameter and all items had acceptable threshold parameters, And that MCS is more informative for mid-level ability of Math confidence. The MCS in Arabic has adequate psychometric properties to be used as a short measure of Math confidence.
LIBRARY MANAGEMENT SOFTWARE: A COMPARATIVE STUDY
Authors:   Master of Library and Information Science, Swami Vivekanand Subharti University, Meerut, U.P., India., Meena Jagmohan
Published: October 2024

This article will concentrate on library management software that provides either a web interface for some of its modules or a full web-based solution for all of its modules. Additionally, this article will investigate the evolution of such software, as well as its features and trends. A secondary purpose of this article is to assess library automation software by comparing and evaluating its features, services, and capacities. The findings of a survey that was carried out among software vendors in Delhi, India, are presented in this article. The survey was carried out with the intention of obtaining information on the features of the six software packages that were selected for this study. The information was acquired via the use of a questionnaire that was sent to the firms that supply software. The software industry provides a diverse range of programs, as can be seen in the previous observation. However, there are only a handful of them that function faultlessly on the web and provide access to all of the most recent technological specifications. The only software packages that are being investigated for this research are those that are available in Delhi. It is possible that library directors who are considering automating their libraries may find the comparison of library management software systems presented in this research to be helpful. This is because it will assist them in selecting and maintaining the program that is most suitable for their needs. There has not been a comprehensive examination of the web interfaces of library management software systems across the country of India.
Transparency of Regional Budget and Expenditure (APBD) through the Optimization of E-Budgeting Implementation
Authors: Tanjung Sawaluddin F , Junaidi Junaidi , Zulgani Zulgani , Assegaf Setiawan
Published: October 2024

The implementation of budgetary reform in local government budgeting is often viewed as lacking a structured system. This is marked by low transparency and accountability in the management of local budgets. Financial management systems that contradict the principles of good governance necessitate transparency and accountability in all areas of government. The advent of e-budgeting is evidence of the utilization of e-government programs in Indonesia. E-budgeting is expected to be a solution for enhancing transparency and accountability in local budget management. With e-budgeting, the public can more easily access information about budget allocations and their usage. Public participation is also anticipated to increase, allowing for more effective oversight of local government budgets. This transparency not only supports the improvement of financial management at the local level but also contributes to the enhancement of public service quality. Therefore, the implementation of e-budgeting is expected to strengthen good governance and increase public trust in local governments. Active public participation and high transparency are key to realizing a more efficient and accountable local budget management system.
Literature Review: Analysis of the Relationship between Employer Branding, Employee Engagement, and Employee Retention
Authors: Mustika
Published: October 2024

Employer branding is a crucial concept for companies in creating a comfortable and attractive work environment for employees. This research aims to emphasize the importance of employer branding in retaining employees and increasing their engagement. The method used in this study is the Systematic Literature Review (SLR), which examines various sources from databases such as Scopus, Emerald, and Google Scholar. The search criteria include journals that are openly accessed and written in English and Indonesian. The results of the study show that employer branding has a significant influence on employee retention. When companies build a positive image as a workplace, employees tend to feel more engaged and loyal. In addition, emotional factors such as employee engagement have also been shown to have a strong impact. Employees who feel emotionally engaged with the company tend to be more productive and motivated. Thus, companies that focus on employer branding are not only able to retain employees, but also create a more passionate and dedicated team. This makes employer branding a strategy that should not be ignored by companies in facing human resource challenges in the modern era.
E-Commerce's Impact On Economic Growth
Authors: Anggraeni Lidya , Tan Syamsurijal , Junaidi Junaidi , Achmad Erni
Published: October 2024

This study aims to analyze the impact of e-commerce on economic growth in Indonesia using a Systematic Literature Review (SLR) approach. E-commerce has become a significant global phenomenon, and Indonesia is projected to have the highest e-commerce growth rate in the world in 2024. With an e-commerce trade value reaching 78%, Indonesia shows great potential in this sector. The study finds that e-commerce contributes significantly to increased productivity and efficiency, broader market access, and technology and innovation adoption by SMEs. However, challenges such as technology access, digital literacy, and infrastructure remain obstacles. The study emphasizes the need for policies that support the enhancement of digital infrastructure and training for SMEs to optimize e-commerce potential. Relevant policy implications can help the government and stakeholders maximize the benefits of e-commerce for national economic growth. By using the SLR approach, this study provides a comprehensive and integrative analysis of the impact of e-commerce on economic growth in Indonesia and presents strong empirical evidence of the causal relationship between the two. The study also offers recommendations for future research, including the use of primary data, regional analysis, international comparative studies, and attention to social and environmental impacts. Additionally, further research into policy and regulatory effectiveness, the role of technology and innovation, longitudinal studies, and collaboration with the private sector is also recommended. Thus, this study makes an important contribution to understanding and leveraging e-commerce potential for sustainable economic growth in Indonesia.
The Influence of Loan Funds and Financial Literacy on the Business Sustainability of MSMEs in East Bogor District
Authors: Widyantini Dian , Boer Adlinsyah
Published: October 2024

This research aims to analyze the influence of loan funds and financial literacy, as independent variables, on the business sustainability of MSMEs in East Bogor District. The sample size for this research was determined to be 100 respondents, who are MSME entrepreneurs in the region. The sampling method employed was non-probability sampling. Multiple linear regression analysis was used as the analysis method, which included instrument testing and classical assumption testing stages, utilizing SPSS version 25 for Windows. The results of the t-test indicate that both independent variables—loan funds (X1) and financial literacy (X2)—have a positive and significant impact on the business sustainability of MSMEs (Y) in East Bogor District. With a coefficient of determination, R Square (R²), of 0.588, it can be concluded that the business sustainability of MSMEs is explained by these two independent variables by 58.8%, while the remaining 41.2% is explained by other factors not examined in this study.
Determinants of Government Spending, Its Impact on Public Welfare, and The Role Of Economic Growth: A Literature Review
Authors: Nurkodri Moh Sahroni , Tan Syamsurijal , Junaidi Junaidi , Achmad Erni
Published: October 2024

This study delves into the multifaceted factors that influence government expenditure and its subsequent effects on public welfare, with a particular focus on its impact on economic growth. Government spending plays a pivotal role in shaping economic development, enhancing public welfare, and alleviating poverty. By conducting a systematic literature review, this research methodically evaluates a range of pertinent sources to gain a comprehensive understanding of these dynamics. The findings indicate that strategic government expenditure, particularly in areas such as infrastructure, education, and technology, is instrumental in driving economic growth and improving public welfare outcomes. Investment in infrastructure not only facilitates more efficient business operations but also creates jobs and improves living standards. Similarly, funding education ensures a more skilled workforce, which can foster innovation and productivity. Investments in technology can lead to advancements that spur economic development and enhance quality of life. However, the study also emphasizes that the effectiveness and optimal allocation of government spending are critical factors in realizing these benefits. Effective expenditure strategies, therefore, must be well-planned and targeted. The insights derived from this study provide valuable guidance for policymakers in crafting expenditure strategies that aim to achieve sustainable development and long-term economic stability.
Teacher-Turnover In the Archdiocesan School System: A Basis for Faculty Retention Program
Authors: Palomares Marissa
Published: October 2024

This study conducted an in-depth exploration into the phenomenon of teacher turnover within the nineteen member schools of the Tuguegarao Archdiocesan School System (TASS), focusing particularly on the interplay of motivation and hygiene factors and their impact on teacher retention. Utilizing a descriptive-correlational design and survey data gathered from 139 teachers who transitioned from TASS to public schools during the school years 2020-2021, 2021-2022, and 2022-2023, the research illuminated several critical aspects of the issue. There is a prevailing sense of motivation and satisfaction among TASS teachers regarding various aspects of their professional lives, particularly those linked to intrinsic rewards such as job fulfillment, recognition, and professional development opportunities. Results revealed that TASS schools generally perform well in providing hygiene factors, encompassing areas like working conditions, administrative policies, and interpersonal relationships. However, some areas are requiring significant attention, most notably concerning salary and benefits, suggesting that these factors contribute significantly to teacher attrition. There is a significant positive correlation between motivation and hygiene factors, indicating that enhancements in areas such as salary and benefits could not only elevate teacher satisfaction but also bolster their overall motivation levels. There is interconnectedness of these two factors in shaping the overall teacher experience and retention within TASS. The research proposes a comprehensive Faculty Retention Program tailored to the unique challenges and needs of TASS schools. This program underscores the importance of addressing both motivational and hygiene factors to cultivate a stable, supportive, and rewarding environment for educators within the Tuguegarao Archdiocesan School System.
ABORDAGENS ODONTOLÓGICAS INTEGRADAS NO CUIDADO E INCLUSÃO DE PESSOAS COM TRANSTORNO DO ESPECTRO AUTISTA (TEA)
Authors: Macedo Isabella Heitor , Gonçalves Rayenne Rúbia Castro , Andrade Yasmin Carlos de , Macari Karina Silva Moreira
Published: October 2024

Autism Spectrum Disorder (ASD) is characterized by difficulties in communication, social interaction, repetitive behaviors, and restricted interests, with these disorders manifesting in early childhood. These patients may also present motor coordination difficulties, poor adherence to oral hygiene, frequent use of medications, and habits such as bruxism. The behavioral development conditions of these patients require targeted approaches for dental care, which poses a challenge for the dentist. According to the literature review, methods such as TEACCH propose visual approaches like panels and schedules that help reduce anxiety and aid in dental treatment. To facilitate communication, the PECS system uses symbols, allowing individuals with autism to express their needs and emotions visually. Knowledge of these dental approaches is crucial for the dentist to gain the patient's trust and better manage the treatment.
The Role of Karnataka in the Freedom Struggle: A Historical Perspective
Authors: Annigeri Sharanappa
Published: October 2024

India and Karnataka are said to have entered a golden period during the independence struggle of the twentieth century. At the start of the 20th century, Karnataka saw the emergence of a new national consciousness. The emergence of nationalism and patriotism within the Karnataka Freedom Movement can be attributed to various factors, including the dissemination of English education, the resuscitation of Karnataka's historical narrative, the nurturing of patriotic sentiments, the influence of revolutionary newspapers, the function of political gatherings, the consequences of British policies, the advancement of transportation, and numerous others. The literature on the Karnataka freedom movement, as well as the historical significance and contributions of figures like S. Nijalingappa and Kittur's Rani Channamma, are presented in this article. The essay attempts to address the nationalist movement's rise during the Karnataka Freedom Movement. Dr. Diwakar explains that when Karnataka laborers entered Indian politics between 1885 and 1947, they saw two objectives: they imagined two ideas; they had two maps in front of them: one showed a free India, and the other showed Karnataka's unification. This illustrates how the desire for unification and the development of opportunities remained closely related in Karnataka. Furthermore, the leadership and sacrifices made by independence fighters who not only benefited the Indian country but also led to long-lasting social and economic changes that shaped its history were examined.
ChatGPT-4
Authors: Borrone Roberto
Published: October 2024

Objetivos: Evaluar el desempeño del chatGPT-4 en un examen final de la Carrera de Médico Especialista Universitario en Oftalmología de la Universidad de Buenos Aires y compararlo con el desempeño de los alumnos ante el mismo examen y con el ChatGPT-3.5.
Material y métodos: Estudio observacional, retrospectivo y analítico. Se comparó el desempeño de 7 médicos en un examen final de posgrado de 50 preguntas con 4 opciones de respuesta rendido el 8 de septiembre de 2023 con el desempeño ante el mismo examen del ChatGPT versiones 3.5 y 4.
Resultados: La mediana de las respuestas correctas de los 7 alumnos fue 39 (rango 33-45) lo que representa una exactitud del 78%. El tiempo promedio para completar el examen fue de 75 minutos. El ChatGPT 3.5 respondió correctamente 31 preguntas (31/50) logrando una exactitud del 62%. El Chat GPT 4 respondió correctamente 40 preguntas (40/50) logrando una exactitud del 80% y completó el examen en 73.49 segundos.
Conclusiones: La versión ChatGPT-4 logró un desempeño superior a la mediana de los alumnos utilizando un tiempo 61 veces inferior. El ChatGPT-4 logró una exactitud superior a la versión 3.5. La calificación obtenida por las dos versiones del ChatGPT permite aprobar el examen dado que el umbral para lograrlo es de 30 respuestas correctas.
Modificación de figuras geométricas corneales con lentes esclerales
Authors: Cano Blaires María Alessandra , Ortiz Cano María Fernanda , Vera-Duarte Guillermo Raúl , Blaires Myriam
Published: October 2024

El uso de lentes esclerales no solo tiene un impacto positivo en la corrección visual sino que también funciona como un método de protección para la córnea al mantener un depósito de líquido constante. Este enfoque no contribuye solamente a mejorar la calidad visual al minimizar irregularidades superficiales, sino que también garantiza la continua hidratación de la córnea. En este trabajo se presentan 3 casos clínicos con buenos resultados ópticos y seguimiento estrecho mediante el uso de lentes esclerales. Se concluye que las lentes de contacto esclerales se posicionan como una herramienta esencial en el conjunto de opciones para abordar astigmatismos irregulares y varias afecciones corneales. Su capacidad para brindar comodidad, mejorar la calidad visual y facilitar la funcionalidad diaria de los pacientes resalta su valía en la práctica clínica oftalmológica.
Resolución de extrusión de háptica en implante secundario con técnica de Yamane mediante retunelización y doble cobertura de esclera y conjuntiva con adhesivo tisular
Authors: Daponte Pablo L. , Álvarez Francisco , Daponte Franco , Banchero Francisco
Published: October 2024

Ante la ausencia o déficit de un adecuado soporte capsular, una opción que se ha popularizado es la técnica de Yamane, que permite fijar la lente intraocular a esclera sin necesidad de utilizar suturas. Una de las complicaciones que pueden ocurrir con el tiempo es la extrusión de las hápticas. En este trabajo se describe un modo de resolver esta complicación mediante la creación de un túnel escleral a 300 micras de profundidad. En él, luego de enhebrar el asa del háptica, se fija mediante sutura y se introduce en túnel escleral con el implante de un parche de esclera, que se recubre con conjuntiva utilizando sólo adhesivo biológico. De esta forma, si fuera necesario se puede centrar la lente, se la logra fijar y se disminuye la movilidad y la fricción de la háptica extruida para reducir futuros riesgos de exposición.
Community-Based Adaptation to Climate Change in Water Sector: Engaging Local Knowledge and Practices in Achin District, Nangarhar, Afghanistan
Authors: Shirzad Wafiullah , Behsoodi Mohammad Mukhlis , Shenwari Khan Agha , Safi Hedayat Ullah
Published: October 2024

Climate change involves persistent changes in global temperatures and weather patterns, predominantly attributed to human activities that release greenhouse gases into the atmosphere. This study specifically focused on community-based adaptation in the water sector, exploring local knowledge and practices within Achin district. Through this investigation, we gathered detailed insights into the effects of climate change within Achin district and assessed the knowledge and local practices adopted by its residents. Primary water sources in the area, such as protected wells, springs, and boreholes, are highly relied upon by residents who generally express satisfaction with their reliability and quality. However, there is a prevalent lack of awareness regarding Household Water Treatment and Safe Storage (HWTS) among most residents. Climate change effects within Achin district include drought, groundwater depletion, surface water decreasing for irrigation, and shifting precipitation patterns. Despite these challenges, the residents lack knowledge about community-based adaptation. Local strategies employed within the district encompass dams, plantation initiatives, mobilization efforts, awareness campaigns on water usage, underground water recharge, and irrigation projects. Additionally, improved irrigation practices and effective conservation methods are identified as vital adaptation strategies. This study thoroughly assesses the impact of climate change on the water sector in Achin district, emphasizing local adaptation strategies. Through field visits, interviews with the residents of Achin, and the implementation of a Google Form survey from environmental and WASH specialists, we meticulously cataloged the effects of climate change and illuminated the indigenous responses adopted by the district's residents.
The Impacts of Climate Change on Rural Areas of Afghanistan: A Comprehensive Review
Authors: Asim Khan , Muhammad Abdullah , Ullah Sajid , Rahmati Jamil , Ghaforzai Asghar
Published: October 2024

In Asia, Afghanistan is particularly vulnerable to the impacts of climate change, with frequent droughts and floods. The agricultural sector, drinking water, health, food security, hunger, and soil quality and organic matter are all negatively affected by climate change. Examining how farms are responding to variations in precipitation and temperature and the effects of climate change are the primary goals of present paper. According to recent studies, Afghani farmers use a range of adaptation techniques to cope with the impacts of climate change. These techniques include altering the type of fertilizers used, crop variety, pesticides, quality of seeds, water conservation, diversifying their farms, planting shade trees, using different methods of irrigation, moving permanently or temporarily, and selling resources. Additionally, studies conducted in the literature revealed that farmers in wetland areas felt less variation in the climate than those in dry areas. In this review, we have found severe influences of climate change on agriculture, poverty and health conditions in rural areas of Afghanistan, therefore it is important for the responsible authorities to take immediate action for climate change adaptation to overcome the severe impacts of climate change in rural areas of Afghanistan.
Climate Change Effects on Bird’s population in Afghanistan
Authors: Ariez Mujeeburrahman , Larawai Mohammad Ismail , Salari Hedayatullah
Published: October 2024

Climate change is one of the most serious  environmental problem of the centuries which has affected all ecological processes and life on the earth. Birds play important role in ecologial stability and socio econimic development of  socities. Climate change and overexpliotation  are two major chalanges against conservation of birds diversity in the world specially in Afghanistan. Given the issues, this online survey based research was undertaken during 2021 to assess the level of knowledge about climate change among people, its possible impacts on bird’s population and conservation strategies to cope with climate change in Afghanistan. Results of the study revealed that 95.1% of the respondents were aware about climate change. Source of information about climate change was also different among respondents as 50.6% had their own experience while 34.6% get information from internet and  radio and telvosion was source of information for 6.2% of respondents. 71.6% of respondets know about different impacts of climate change. Climate change negitavely affected bird’s population as 91.4/% of people said that bird’s population had drastically decreased, on the other hand 86.4% of people claim that they miss many birds species during last two decades. Based on these findings it is suggested that habitat restoration is the most accepted conservation policy in Afghanistan context.
Estimation of Land Surface Temperature of Khost Province, Afghanistan, using Satellite Remote Sensing Technologies
Authors: Haider Muhammad Sharif , Hassan Samiullah
Published: October 2024

One particularly important metric for examining the thermal flux and heat energy balance of land surfaces is land surface temperature (LST). It can be used for developing models of urban heat transmission, managing water resources, simulating climate change, and conducting environmental studies. This investigation focuses on determining the surface temperature of Khost province in Afghanistan, with an additional goal of assessing the correlation between the Normalized Difference Vegetation Index (NDVI) and the city's ground temperature. Utilizing data from the Landsat 8 Thermal Infrared Remote Sensor (TIRS), this study employs the Top of the Atmosphere Radiance method to estimate surface temperature. The findings indicate that the surface temperature exhibits a relatively low range of 10 to 48 °C in June 2023. Notably, areas abundantly covered with forest and vegetation manifest lower temperatures, while industrial or vegetation-deprived regions demonstrate higher temperatures, reaching up to 48 °C. The study leveraged NDVI to explore the relationship between thermal behavior and the extent of vegetation cover. Employing a regression technique, the investigation establishes a negative correlation between NDVI and LST, with the regression coefficient from NDVI to LST also being negative. In conclusion, the study determines that there is a negative correlation between NDVI and LST, highlighting the cooling effect of vegetation on surface temperatures.
Effect of Various Coarse Aggregates on the Compressive Strength of Concrete in Jalalabad City, Afghanistan
Authors: Irfan Irfan Ullah , Mokhlis Shah Masoud , Raheemi Elyass Khan
Published: October 2024

One of the most frequently used building materials on the globe is concrete. Concrete is composed of aggregate, water, and cement. This study investigated the influence of different coarse aggregates on the compressive strength of concrete. The coarse aggregates used in the experimental design consisted of crushed granite, crushed river stone, and natural gravel, with river sand as the fine aggregate. Sieve analysis and unite weight was performed on specimens frequently used by citizens. Fresh Portland cement and water-cement ratios of 0.55 and 0.7 were both used. To investigate the influence of various aggregates on concrete compressive strength a specimen for 20 N/mm2 concrete ratio of 1:1.5:3, and 48 concrete cubes measuring 15 cm x 15 cm x 15 cm were casted in the laboratory at Alfalah University. For fresh concrete, the slump test results were 63 mm for crushed granite, 65 mm for crushed river stone, and 50 mm for natural gravel. Crushing the concrete cubes after 7, 14, 21, and 28 days determined the compressive strength. Additionally, the highest compressive strength values achieved for crushed granite were 25.94 N/mm2, crushed river stone was 23.68 N/mm2, and natural aggregate concrete was 17.73 N/mm2. Based on these findings, crushed granite has been recommended for usage in reinforced concrete applications.
Excessive Water Usage, and Its Impact on Underground Water Depletion in Jalalabad City Zone
Authors: Fazly Mohammad Ismail
Published: October 2024

I have conducted a research study on the Underground Water Depletion (UGWD) over Excessive Water Usage in Nangarhar City Zone. Groundwater Depletion (GW) is a pressing concern in Afghanistan as well as in other countries around the world. However, in some places such as Kabul, water depletion rate is much more than its In-charge rate, which is already a serious concern. In this paper the data have been analyzed with the help of a theoretical framework. The data have been collected through normal dialog (Interview), and questionnaires distributed to the related entities including in sample frames in Nangarhar City-Zone. This research type was quantitative, and due to the large size of population, I have opted for the random sampling, and estimation method. In this research study, I have found that there is a Positive relationship between GW & In-charge rate, and Negative relationship with Discharge rate. The specific result for this research study is theoretical framework analysis. With the help of decision science tools, we examined that, due to percent change in In-charge rate upon percentage change in GW is positively changing the Groundwater in Nangarhar city zone case. Yearly based, the Groundwater Depletion Rate is 0.75 and the amount of water collectively decreased by 19.195%. The significant rate depends upon my theoretical framework, which can show reliability of our conducted research work.