bluuebunny/crossref_metadata_2025 huggingface dataset:
UAV hyperspectral image acquisition and processing, an application for nutrient estimation of rice in Vietnam
Authors: Minh Khanh Luong , Tong Si Son , Mai Huong , To Thi Mai Huong , Tran Son Giang , Pham-Duc Binh , Phan Hien , Le Van Canh , Pham Thi Lan , Tong Thi Huyen Ai
Published: August 2024

Hyperspectral imagery obtained from Unmanned Aerial Vehicles (UAVs) is increasingly employed to investigate nutrient concentrations in vegetation. The deployment of a hyperspectral camera on a UAV, flight planning, image acquisition, preprocessing of hyperspectral data, and the subsequent estimation of nutrient concentrations in vegetation are facing challenges. These challenges manifest as geometric, spectral distortions, and the abundance of numerous spectral bands. This study seeks to guide on mitigating the impact of issues encountered during an experiment to estimate nutrient concentrations in rice leaves using UAV hyperspectral images. An industrial hexagonal drone equipped with a push-broom hyperspectral camera featuring 122 bands within the Visible to Near-Infrared (VIS-NIR) wavelength range (400-960 nm) is employed to collect data over a 1-hectare testing rice field. Models for estimating Leaf Phosphorus Concentration (LPC) and Leaf Potassium Concentration (LKC) are developed based on the correlation between hyperspectral images, characterized by a 3 cm spatial resolution, and 162 LPC and 162 LKC reference data points. Utilizing various vegetation indices for LPC and LKC estimation, the outcomes reveal that a combination of band wavelengths at 838 nm and 734 nm is effective for LPC estimation, yielding a Root Mean Square Error (RMSE) of 27.1%. Conversely, LKC estimation exhibits an RMSE of 38.8% with an insignificant correlation between LKC and the current wavelength ranges. Above all, this study is a primary example of the utilization of UAV hyperspectral data in precision agriculture in Vietnam.
Traffic Signal Classification using Deep Learning for Enhanced Autonomous Driving Systems
Authors: Vishwakarma Prof. Zeba , Pandey Prof. Divya , Dwivedi Prof. Mallika , Sarkar Arjita , Mourya Gourav
Published: August 2024

This paper presents a deep learning-based model for the classification of traffic signals, a crucial component for the development of autonomous driving systems and driver alert mechanisms. Utilizing a convolutional neural network (CNN), the proposed model is designed to recognize 43 distinct traffic signs with high accuracy. The model is trained on a publicly available dataset, achieving an impressive accuracy of over 95% in just 50 epochs. The preprocessing steps, model architecture, and training process are discussed in detail. The model's performance indicates its potential for real-world applications in enhancing the safety and efficiency of self-driving cars. Future work includes hyper parameter tuning and the integration of the model into real-time systems for further optimization.
Enhanced Restaurant Rating Predictions: A Study of Linear and Ensemble Regression Methods
Authors: Vishwakarma Zeba , Choubey Jaya , Pandey Divya , Tiwari Aayush , Khushi Ifat
Published: August 2024

In this study, we investigate the application of various regression techniques to predict restaurant ratings based on a comprehensive Zomato dataset. The dataset encompasses 51,717 entries with features such as restaurant type, location, cuisine, and cost estimates. We employed four regression models: Linear Regression, CatBoost Regressor, Random Forest Regressor, and Decision Tree Regressor, to evaluate their performance in predicting ratings. The models were assessed using the R-squared (R²) metric to determine their effectiveness. The Random Forest Regressor achieved the highest R² score of 0.875, indicating its superior predictive capability. The CatBoost Regressor also performed well, demonstrating the effectiveness of gradient boosting methods. The study provides insights into the performance of various regression models and highlights the advantages of ensemble methods for accurate restaurant rating predictions.
Advancements and Ethical Considerations in AIDriven Mental Health Diagnosis and Treatment
Authors: Soni Prof. Kuldeep , Choubey Prof. Khusboo , Pali Prof. Pankaj , Dwivedi Harshit , Chouhan Nitin
Published: August 2024

Artificial intelligence (AI) has emerged as a transformative tool in the field of mental health, offering new avenues for diagnosis, treatment personalization, and patient care. This paper explores recent advancements in AI technologies, ethical challenges associated with their implementation, and potential future directions for integrating AI into clinical practice to enhance mental health care. Artificial intelligence (AI) technologies have shown great potential in transforming mental health diagnosis, offering novel approaches to enhance accuracy, efficiency, and accessibility of diagnostic processes. This paper reviews recent advancements in AI methodologies, explores their applications in mental health diagnosis, discusses associated challenges, and suggests future directions for research and implementation.
An Overview of Deep Learning Techniques for Road Detection in High-Resolution Satellite Images
Authors: Anjum Prof. Gulafsha , Pal Muskaan , Verma Vaishnavi
Published: August 2024

One of the main and most difficult problems in computer vision research has been removing road networks from high-resolution remote sensing photos. Numerous applications, such as urban planning, traffic management, disaster response, and environmental monitoring, depend on the accurate identification of road networks. The field of artificial intelligence has witnessed tremendous advancements and advances in road extraction technology, especially in the area of deep learning. This research provides an extensive analysis of deep learning techniques for road extraction from remote sensing imagery, emphasizing how these technologies improve process accuracy and efficiency. Deep learning techniques are divided into fully supervised, semi-supervised, and unsupervised learning approaches, each having more focused subcategories, depending on the kind of annotated data that is used. The tenets, advantages, and disadvantages of these approaches are contrasted and examined. The review also provides an overview of the metrics applied to assess road extraction algorithms and the high-resolution remote sensing picture datasets used for these kinds of activities. In conclusion, we go over the main obstacles and potential applications of computational intelligence methods to enhance the precision, automation, and intelligence of road network extraction.
Applying Machine Learning Techniques to Detect Credit Card Fraud
Authors: Anjum Prof. Gulafsha , Pateriya Prof. Nidhi , Thakre Prof. Neha , Tiwari Aayush , Mahanoor Shivanjay
Published: August 2024

Credit card fraud presents a major challenge to financial institutions, resulting in significant economic losses each year. This paper investigates the use of machine learning techniques to detect fraudulent credit card transactions. By analyzing a publicly available dataset, we evaluate the performance of various algorithms, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. The objective is to identify the most effective model for accurately detecting fraudulent activities while minimizing false positives. Additionally, the paper addresses the challenges of dealing with imbalanced datasets and the strategies employed to overcome these issues.
A Review Paper on Data Mining Methods and their Applications
Authors: Jain Dr Prerna , Niveditha Prof. Kalukuri Princy , Thakre Prof. Neha , Sapre Ridhima , Jaiswal Rishita
Published: August 2024

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.
A Review Paper on Data Mining Methods and Their Applications
Authors: Jain Dr Prerna , Niveditha Prof. Kalukuri Princy , Thakre Prof. Neha , Sapre Ridhima , Jaiswal Rishita
Published: August 2024

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.
Edge AI and On-Device Machine Learning For Real Time Processing
Authors: Jain Dr. Prerna , Pateria Prof. Nidhi , Anjum Prof. Gulafsha , Tiwari Ashwini , Tiwari Ayush
Published: August 2024

Edge Artificial Intelligence (Edge AI) and on-device machine learning represent significant advancements in computing paradigms, enabling real-time data processing directly at the edge of the network. This approach minimizes the need for centralized cloud-based processing, thereby reducing latency, enhancing privacy, and improving operational efficiency. This paper provides a comprehensive review of Edge AI and on-device machine learning, focusing on their applications, challenges, and future directions. Key applications include smart home devices, healthcare monitoring, autonomous vehicles, and smart city infrastructure, where local data processing facilitates immediate responses and decision-making. However, deploying AI models on edge devices introduces challenges related to model compression, limited computational resources, and power constraints. Security and privacy concerns also arise, necessitating robust data protection and privacy-preserving techniques. Furthermore, the paper explores emerging technologies such as 5G integration and advances in AI hardware, which are expected to drive future developments in Edge AI. Research directions are highlighted, emphasizing the need for new algorithms tailored to edge environments and enhancements in system robustness and reliability. By addressing these challenges and leveraging emerging technologies, Edge AI is poised to transform a wide range of sectors, offering enhanced real-time processing capabilities and paving the way for innovative applications.
A Comprehensive Literature Review on AI and Its Impact on Business Value
Authors: Niveditha Kalukuri Princy , Jain Prerna , Thakre Neha , Dubey Aryan , Awasthi Chinmay
Published: August 2024

This comprehensive literature review explores the intersection of artificial intelligence (AI) and business value, aiming to provide a holistic understanding of how AI technologies influence organizational performance and economic outcomes. The review synthesizes research findings from diverse studies to examine the multifaceted impacts of AI on various aspects of business value, including operational efficiency, strategic decision-making, customer experience, and competitive advantage. The review begins by defining key AI technologies such as machine learning, natural language processing, and robotics, and their applications within different industry sectors. It then delves into how these technologies contribute to enhanced business processes, cost reduction, innovation, and revenue growth. Special attention is given to case studies and empirical research that demonstrate the tangible benefits and challenges associated with AI adoption in business contexts. Additionally, the paper addresses the role of AI in fostering datadriven decision-making and its implications for organizational agility and strategic alignment. The review also highlights gaps in the existing literature, such as the need for more longitudinal studies and sector-specific analyses to better understand the long-term effects of AI on business value. By providing a consolidated view of current research and identifying future research directions, this review offers valuable insights for academics, practitioners, and policymakers interested in leveraging AI to maximize business value and achieve sustainable competitive advantage.
Harnessing Machine Learning for Enhanced Air Quality Prediction
Authors: Choubey Prof. Jaya , Pandey Prof. Divya , Dwivedi Prof. Mallika , Kushwaha Sneha , Saraf Riya
Published: August 2024

Air pollution poses significant health risks and necessitates the need for accurate air quality monitoring systems. This research paper focuses on predicting the Air Quality Index (AQI) using both regression and classification models. Various machine learning algorithms such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, and K-Nearest Neighbors Classifier were employed to estimate AQI based on pollutant concentrations of SO₂, NO₂, RSPM, and SPM. The performance of these models was evaluated using metrics like RMSE, R² score, accuracy, and Cohen's Kappa score. The results demonstrate the efficacy of the models in predicting AQI, with Random Forest models exhibiting superior performance in both regression and classification tasks. This study underscores the potential of machine learning techniques in enhancing the accuracy and reliability of air quality predictions, thereby contributing to better environmental management and public health policies.
Comparative Study of Healthcare Systems using RBAC Alone versus Integrating RBAC with Digital Watermarking for Enhanced Data Security
Authors: Verma Prof. Saurabh , Sahu Prof. Ranu , Chourey Saloni , Chourasiya Radhika
Published: August 2024

This comparative study examines the effectiveness of Role-Based Access Control (RBAC) alone versus its integration with digital watermarking in enhancing data security within healthcare systems. RBAC is employed widely to regulate access based on user roles, while digital watermarking adds an additional layer of security by embedding imperceptible markers for data integrity verification. The study evaluates security incidents, operational efficiency, regulatory compliance, and user satisfaction across two groups: healthcare systems using RBAC alone (Group A) and those integrating RBAC with digital watermarking (Group B). Findings indicate that Group B exhibits reduced unauthorized access and enhanced detection of tampering attempts compared to Group A. Group B also demonstrates improved compliance with healthcare regulations and maintains operational efficiency despite minimal processing overhead from watermarking. User feedback suggests initial adaptation to digital watermarking but highlights perceived security benefits and trust in data integrity. This study underscores the value of integrating RBAC with digital watermarking to bolster data security in healthcare settings, offering insights into optimizing security strategies for sensitive healthcare.
Challenges and Limitations of using AI in Cybersecurity
Authors: Soni Prof. Kuldeep , Jain Dr. Prerna , Niveditha Prof. Kalukuri Princy , Dwivedi Harshit , Chouhan Nitin
Published: August 2024

In an era of widespread digital connectivity, a resilient and secure cybersecurity environment is more crucial than ever. This research paper explores how developing technological innovation can serve as the foundation for building resilience in cyberspace. The study covers important aspects of cybersecurity, including its conceptual foundations, a wide range of applications, risk-reduction strategies, and the crucial role that policy regulation plays in the digital environment. The paper concludes that technological innovation is the driving force behind transformative change in cyber security. Policymakers, business executives, cyber security professionals, and researchers are all affected by the implications, which call for a coordinated and flexible strategy to meet the challenges of the digital frontier.
Evaluating Transparency and Explainability in AI-Driven Planning and Scheduling: A Comprehensive Literature Review
Authors: Thakre Prof. Neha , Sahu Prof. Ranu , Soni Prof. Kuldeep , Bisen Deepshika , Sahu Anshika
Published: August 2024

As AI technologies rapidly integrate into our work environments, the need for systems that effectively collaborate with humans grows. A crucial aspect of this interaction is the explainability of AI behavior to human users. While AI has surpassed human decision-making abilities in many areas, its opaque algorithms often produce accurate yet incomprehensible results. This lack of transparency can hinder trust between humans and machines. The TUPLES project, a three-year Horizon Europe research initiative, seeks to address this by developing AI-based planning and scheduling tools with a human-centered focus. By utilizing both data-driven and symbolic AI methods, TUPLES aims to create scalable, transparent, robust, and secure systems. The project employs a use-case-oriented approach, drawing from industry expertise to ensure relevance in areas such as manufacturing, aviation, and waste management. The EU guidelines for Trustworthy AI emphasize key principles like human oversight, transparency, fairness, societal well-being, and accountability. The Assessment List for Trustworthy Artificial Intelligence (ALTAI) serves as a selfassessment tool for evaluating AI systems. Current planning and scheduling tools only partially meet these criteria, highlighting the need for innovative AI development. Our literature review explored the transparency and explainability of AI algorithms in planning and scheduling, identifying metrics and recommendations. The study underscores the role of Explainable AI (XAI) in addressing the black box problem, using techniques that make AI systems more understandable and accurate. XAI relies on human explanation concepts to ensure a human-centered approach, offering specific methods and guidance for selecting appropriate tools in planning and scheduling contexts.
Evolution of Database Management System in Hybrid and Multi Cloud Platform: Challenges, Solutions and Innovations
Authors: Pali Prof. Pankaj , Vishwakarma Prof. Abhishek , Choubey Prof. Jaya , Jaiswal Rishita , Soni Pranjil
Published: August 2024

The rapid advancement in cloud computing has catalyzed the evolution of database management systems (DBMS), particularly in the context of hybrid and multi-cloud platforms. This review paper examines the transformative journey of DBMS, focusing on the challenges, solutions, and innovations that have emerged in hybrid and multi-cloud environments. Key challenges include data consistency, security, latency, and cost management, which are compounded by the heterogeneity and distributed nature of these platforms. The paper discusses state-of-the-art solutions, such as advanced data replication techniques, automated policy-driven management, and robust security protocols, that address these challenges. Furthermore, it highlights groundbreaking innovations in database architecture, including cloud-native DBMS, serverless databases, and AI-driven database optimization, which are reshaping the landscape. By providing a comprehensive analysis of these developments, this review aims to offer valuable insights into the current trends and future directions of DBMS in hybrid and multi-cloud platforms, serving as a resource for researchers and practitioners in the field.
Innovative Framework for Context-based Recommendation using TF-IDF and Cosine Similarity
Authors: Tiwari Shivam , Sahu Ranu , Shripal Sourbh , Rajpoot Siddhant Singh
Published: August 2024

The study investigates a novel framework for context-based recommendation systems that makes use of cosine similarity and TF-IDF (Term Frequency-Inverse Document Frequency) methods. Through the recommendation of pertinent things based on context and user preferences, recommendation systems significantly improve user experience. Recommendations from traditional systems are frequently not as good because of their inability to properly include contextual information. In order to close this gap, this work suggests a unique method that represents item and user profiles using TF-IDF and calculates cosine similarity to capture semantic similarities. The framework's ability to adjust dynamically to shifting user situations improves the accuracy of recommendations in a variety of industries, including personalized services, streaming media, and e-commerce. From a methodological point of view, the study entails using and assessing the framework on a dataset customized for context-aware recommendation situations. Comparing the approach to baseline methods, experimental results show that it is successful in enhancing recommendation quality. By incorporating strong contextual understanding, the knowledge obtained from this work advances recommendation systems and opens the door to more customized and flexible user experiences in digital contexts. The concept, methods, and main conclusions of the study are briefly described in this abstract, which also emphasizes the novel use of TF-IDF and cosine similarity for context-based recommendations.
Advanced Detection of Document Tampering Using Structural Similarity Index and Image Analysis Techniques
Authors: Pandey Prof. Divya , Vishwakarma Prof. Zeba , Dwivedi Prof. Mallika , Pasi Jatin , Pandey Shambhavi
Published: August 2024

This research focuses on the application of the Structural Similarity Index (SSIM) technique for detecting tampering in various identity documents, such as PAN cards, Aadhar cards, and voter IDs. The SSIM method is employed to assess the structural similarity between the original and provided document images. Additionally, grayscale conversion and thresholding techniques are utilized to analyze shapes and contours, further aiding in the identification of tampered areas. Experimental results show that a low SSIM score indicates potential tampering in the provided image. Visualizations, including contour overlays, difference maps, and threshold-based comparisons, enhance the clarity of differences between original and tampered document images.
Coherence of socio-cultural processes in the Eurasian space
Authors: Shmakov Vladimir
Published: August 2024

The study of the evolution of the Eurasian socio-cultural space allows us to reflect
the positive and negative aspects and features of the transformation of socio-culture. The aim of the study is to analyze the influence of coherence on the evolution of the Eurasian socio-cultural space, to identify models of sociocultural variability in the context of globalization. The synthesis of a systemic and socio-cultural approach makes it possible to explore the trends of socio-cultural transformation more deeply. It is shown that in the process of evolution of the socio-cultural space of Eurasia, multidirectional lines of development are formed. The socio-cultural system is losing its stability, which leads to disintegration, the formation of new structures that initiate instability and conflict. The emerging models of socioculture reflect the multidirectional trends of integration and differentiation, fixing the problem of preserving local socio cultural originality and identity, maintaining traditional socio-cultural standards. Coherence acts as a system of communication and dialogue of sociocultures, actualizing the perception and recognition of values, interaction, and integration of subcultures.
Structures are being developed that are more resistant to innovations and more actively adapt to new conditions. In the process of interaction, a socio-cultural environment is crystallized with a pronounced dominance of the innovative layer in socioculture, rejection of traditions. On the other hand, the need to preserve traditional culture and sociality is determined in local communities. In conditions of modification of the socio-cultural environment, the principle of coherence contributes to the definition of culture and sociality as a local integrated experience, transformed from generation to generation. Coherence serves as a kind of basis for the concept of fixing, integrating historical, traditional, functional, information relations, communications, contributing to the adoption and institution of innovations in conjunction with traditional values.
The emerging models of the socio-cultural space reflect the processes of transformation of the life system and lifestyle of local communities, determining the transition from the paradigm of sustainable traditional development to the paradigm of innovative transformations, while preserving traditional values to the utmost.
Restaging magnetic resonance imaging of the rectum after neoadjuvant therapy: a practical guide
Authors: Horvat Natally , Miranda João , Kinochita Fernanda , Carvalho Tiago Lins de , Torri Giovanni Brondani , Lopes Thiago José Pinheiro , Nomura Cesar Higa
Published: August 2024

Abstract Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death. Rectal cancer accounts for approximately one-third of new colorectal cancer cases, with adenocarcinoma as the predominant subtype. Despite an overall decline in colorectal cancer incidence and mortality, due to advancements in screening, early diagnosis, and treatment options, there is a concerning increase in incidence rates among young patients. Recent significant advances in managing locally advanced rectal cancer, such as the establishment of different surgical approaches, neoadjuvant treatment using different protocols for high-risk cases, and the adoption of organ-preservation strategies, have increased the importance of the role played by radiologists in locoregional assessment on magnetic resonance imaging at baseline, at restaging, and during active surveillance of patients with rectal cancer. In this article, we review the role of restaging rectal magnetic resonance imaging after neoadjuvant therapy, providing radiologists with a practical, step-by-step guide for assessing treatment response.
Online course on vaccinating people with HIV/AIDS - effectiveness in the knowledge of nursing professionals
Authors: Gerin Larissa , Gir Elucir , Neves Lis Aparecida de Souza , Passos Luzia Márcia Romanholi , Kfouri Renato de Ávila , Reis Renata Karina
Published: August 2024

Abstract Objective: analyzing the effectiveness of an educational intervention on the knowledge of nursing professionals regarding the immunization of people with the human immunodeficiency virus. Method: a quasi-experimental study evaluated professionals’ knowledge through a knowledge test applied before and after the development of an online training course. The data was analyzed using frequency, median, mean, standard deviation, and association tests. Results: the sample consisted of 77 nursing professionals whose mean age was 43.2 years (SD+/-8.2). More than half of the individuals worked in basic health units (58.4%), 22.1% worked in specialized services that provide clinical monitoring for people with the human immunodeficiency virus, and 42 (54.5%) were nursing assistants or technicians. The professionals’ performance improved after the intervention, with an increase in the median number of correct answers from 23.0 to 27.0 (p<0.001). Conclusion: offering an online training course on the immunization of people with the human immunodeficiency virus, as a continuing education activity, proved to be effective in improving nursing professionals’ knowledge on this subject.
Curso online sobre vacinação de pessoas com HIV/aids - efetividade no conhecimento de profissionais de enfermagem
Authors: Gerin Larissa , Gir Elucir , Neves Lis Aparecida de Souza , Passos Luzia Márcia Romanholi , Kfouri Renato de Ávila , Reis Renata Karina
Published: August 2024

Resumo Objetivo: analisar a efetividade de uma intervenção educativa no conhecimento de profissionais de enfermagem em relação à imunização de pessoas com o vírus da imunodeficiência humana. Método: estudo quase-experimental que avaliou o conhecimento dos profissionais por meio de um teste de conhecimento aplicado antes e depois do desenvolvimento de um curso de capacitação online. Os dados foram analisados mediante frequência, mediana, média, desvio-padrão e testes de associação. Resultados: a amostra foi constituída por 77 profissionais de enfermagem, cuja média de idade foi de 43,2 anos (DP+/-8,2). Mais da metade dos indivíduos trabalhava em unidades básicas de saúde (58,4%), 22,1% trabalhavam nos serviços especializados que realizam o acompanhamento clínico de pessoas com o vírus da imunodeficiência humana, 42 (54,5%) exerciam a função de auxiliar ou técnico de enfermagem. O desempenho dos profissionais melhorou após a intervenção, com aumento na mediana de acertos de 23,0 para 27,0 (p<0,001). Conclusão: o oferecimento de um curso de capacitação online acerca da imunização de pessoas com o vírus da imunodeficiência humana, como uma atividade de educação permanente, se mostrou efetivo para melhorar o conhecimento dos profissionais de enfermagem nesta temática.
Nutritional balance in mango plants in the Brazilian semiarid using nutritional diagnostic methods
Authors: Rezende Jefrejan S. , Freire Fernando J. , Silva Suellen R. V. da , Musser Rosimar dos S. , Cavalcante Ítalo H. L. , Saldanha Eduardo C. M. , Santos Renato L. dos , Cunha Jailson C.
Published: August 2024

ABSTRACT Nutritional assessment of mango trees based on diagnostic methods considering nutritional balance is recommended. This study aimed to establish optimum nutritional ranges using diagnostic methods, compare them, identify the most efficient diagnostic method, and select the nutrients responding best to the application of the diagnostic method. The study was conducted in commercial mango orchards in the São Francisco Valley. Nutritional content was calculated using the diagnosis and recommendation integrated system (DRIS-Beaufils, DRIS-Jones), modified DRIS (M-DRIS-Beaufils; M-DRIS-Jones), compositional nutrient diagnosis (CND), and mathematical chance (ChM) methods and compared using the chi-square test. Principal component analysis was applied to select the most efficient diagnostic method and the nutrients responsible for the greatest variability. The DRIS-Beaufils, M-DRIS-Beaufils, DRIS-Jones, M-DRIS-Jones, CND, and ChM methods generated nutritional sufficiency ranges for the evaluated cultivars. The nutritional diagnoses of the DRIS-Beaufils and M-DRIS-Beaufils methods were similar and discordant with those of DRIS-Jones, M-DRIS-Jones, and CND. The DRIS-Beaufils method, updated by Maia, proved to be more consistent for the nutritional assessment of mango trees. The nutrients N, P, K, Mg, and S in the Tommy Atkins cultivar; N, P, Mg, S, B, Mn, Zn, Mo, and Cl in the Kent cultivar; and N, P, K, Ca, S, B, Cu, Fe, Zn, Mo, and Cl in the Keitt cultivar showed significant responses to the application of the DRIS-Beaufils method updated by Maia.
Anaerobic mono-and co-digestion of fruit and vegetable residues: Effects on biogas yield and biofertilizer
Authors: Hinterholz Bruna , Costa Mônica S. S. de M. , Lucas Junior Jorge de , Pereira Edilene da S. , Buligon Eduardo L. , Lima Jessica C. de , Marostica Ritieli
Published: August 2024

ABSTRACT The proper disposal of fruit and vegetable waste is essential to promote environmental sustainability, reduce the carbon footprint, improve soil quality, and contribute to the transition to a circular economy. This study examined the impact of incorporating dairy cattle wastewater (DCWW) into anaerobic co-digestion with fruit and vegetable residues and utilization of the resulting biofertilizer as a diluent in the anaerobic mono-digestion of fruit and vegetable residues (recycle). The specific biogas and methane production and the agronomic quality of the biofertilizer were evaluated in a semi-continuous trial. Two startup strategies were employed: initially, the reactors were entirely fed with inoculum (biofertilizer derived from DCWW), and fruit and vegetable residue feedstock was gradually introduced. Anaerobic co-digestion of fruit and vegetable residues with DCWW yielded the highest specific production of biogas and methane at 720 and 436 L kg-1 of volatile solids (VS), respectively. The addition of DCWW increases fruit and vegetable residue alkalinity by 249% and reduces the volatile acidity by 83.4%. Anaerobic mono-digestion of fruit and vegetable residues enhances macronutrient recovery in the biofertilizer through recycle. Logistically, directing fruit and vegetable residues to rural areas for anaerobic co-digestion with DCWW promotes agricultural biofertilizer use.
Estimating energy efficiency of the aeration process of stored grains through machine learning
Authors: Ferreira Junior Weder N. , Resende Osvaldo , Oliveira Daniela C. de , Oliveira Daniel E. C. de , Rosa Elivânio dos S.
Published: August 2024

ABSTRACT Aeration is carried out by blowing external air into the silo, with the aim to keep the temperature in the mass of stored grains at safe levels. In the present study, the energy efficiency of aeration of stored sunflower grains was estimated, and a model was proposed and tested to estimate the energy efficiency of aeration, using different algorithms in supervised and unsupervised machine learning. The objective of the work was to develop a Web application based on data mining and modeling with machine learning. The database was composed of information on the average temperature at the height of the sensors, average temperature of the silo, external ambient temperature, occurrence of aeration, if there was cooling, heating and direct heating during aeration, and the energy efficiency of the aeration process. The model for estimating the energy efficiency of the aeration process proved to be efficient, identifying that the energy efficiency was 97.78% during the aeration of stored sunflower grains. Among the classifier algorithms tested, Support Vector Machine (SVM-Poly) showed the best metrics and indicators, hence being recommended for implementation in system development networks capable of predicting the aeration status of stored grains.
Curricularização da extensão: realidade da Regional Sul I da ABEPSS
Authors: Silva Diego Tabosa da , Coelho Kathiuscia Aparecida Freitas Pereira , Damascena Monique Bronzoni , Fortuna Sandra Lourenço de Andrade
Published: August 2024

Resumo Objetiva-se refletir sobre as particularidades do processo de curricularização da extensão nas Unidades de Formação Acadêmica (UFAs) do Paraná, Santa Catarina e Rio Grande do Sul. Os dados ora analisados foram extraídos dos mapeamentos e das atividades organizadas pela Regional Sul I da ABEPSS, entre os anos de 2021 e 2023. Trata-se de um tema urgente diante dos inúmeros desafios e da necessidade de incorporar, de modo qualificado a extensão universitária nos currículos dos cursos de Serviço Social. Os resultados coletados sinalizam a sua importância no interior da formação e do trabalho profissionais; promover debates contínuos para que alcancemos práticas extensionistas que consolidem o tripé do ensino superior e respondam verdadeiramente às demandas sociais. Denotam também a necessidade de superar os processos burocráticos institucionais, no sentido de garantir uma concepção de extensão que seja vinculada às lutas e resistências instituídas pela classe trabalhadora.
A abordagem ciência-tecnologia-sociedade no ensino da física com enfoque na energia: uma revisão sistemática da literatura
Authors: Rodrigues José Jorge Vale , Oliveira Eniz Conceição , Guerra Cecília
Published: August 2024

Resumo Este artigo apresenta uma revisão bibliográfica de estudos que recorreram à abordagem CTS (Ciência, Tecnologia e Sociedade) no ensino de física. Especificamente, pretendeu-se avaliar como é que a abordagem CTS tem sido implementada no ensino do tema de energia. A pesquisa de artigos científicos foi realizada em duas bases de dados: a Scopus e o Portal de Periódicos CAPES. A pesquisa centrou-se em artigos publicados em revistas entre 2003 e 2022, em língua portuguesa, inglesa e espanhola. Os acrônimos utilizados na pesquisa foram: “Science Technology and Society” AND “Physics teaching”/“Ciência Tecnologia e Sociedade” AND “ensino de física”. Foram aplicados critérios de inclusão e exclusão, nomeadamente a leitura dos títulos e dos resumos dos artigos. Tal permitiu excluir, por exemplo, estudos que não relacionam a abordagem CTS com o ensino de física. A amostra final é composta por 20 artigos. Através da técnica de análise de conteúdo dos artigos selecionados, foi possível perceber que os alunos envolvidos no processo de ensino de física, seguindo princípios da abordagem CTS, revelaram um maior interesse e engajamento na aprendizagem do tema da energia. No entanto, apesar da abordagem CTS aparecer em muitos currículos escolares, existe uma escassez na produção científica a respeito do tema energia.
Italo Moriconi sobre Ana Cristina Cesar
Authors: Galvão Raquel Machado , Moriconi Italo
Published: August 2024

Trata-se de um depoimento concedido por Italo Moriconi Júnior em agosto de 2023. Compõe o arquivo online da pesquisa de pós-doutorado "Conexões críticas de Ana Cristina Cesar: novos olhares sobre a literatura brasileira do século XX" (FAPESP – FFLCH – USP) e aborda a relação de Cesar com as teorias de Mário de Andrade e de Antonio Candido, assim como descreve a atividade intelectual por ela mobilizada no jornal Opinião durante os anos 1970.
Microphytobenthos of Water Bodies of Some Islands of the Indian Ocean
Authors: Gerasimyuk V. P. , Ryzhko I. L. , Gerasimyuk N. V.
Published: August 2024

Microphytobenthos of water bodies of some Maldivian Islands (Kelaa and Filladho) and the Madagascar Island of the Indian Ocean was studied in autumn (September and November) 1981. As a result of the performed investigations, 43 species of microscopic algae belonging to 30 genera, 25 families, 21 orders, 5 classes, 3 divisions, 2 kingdoms, and 2 empires (domains) were found. Among them, diatoms (38 species) prevailed over Dinoflagellata (4) and Cyanobacteria (1).

The Sorensen - Chekanovsky coefficient of community similarity between the Maldivian and Madagascar floras was 0.56 and the Jaccard coefficient of community similarity was 0.36. Microphytobenthos of the investigated water
bodies was of marine type. It was represented by alkaliphilous species and cosmopolitan (27 species) and tropical (10) groups with boreal-tropical (4) and boreal-tropical-notal (2) elements.
New Discovery of the Rare Freshwater Brown Alga Pleurocladia lacustris A. Braun (Ectocarpales, Phaeophyceae) in Ukraine (the Khadzhybey Estuary)
Authors: Tkachenko F. P.
Published: August 2024

A new locality of the freshwater brown alga Pleurocladia lacustris A. Braun, rare for Ukraine, was found. This species is classified as endangered in many European countries and is included in various protection lists. On the whole, about 50 habitats of Pleurocladia lacustris are known in the world, including Europe, North America, Australia, and Africa. For many decades, this is the second record of this alga in Ukraine, which was previously considered extinct here. It was found in the Khadzhybey estuary of the Black Sea in autumn 2023 at a depth of 0.5 m, water temperature 5.3 °C, salinity 4.83 ‰, and pH 8.58. The alga occurred as an epiphyte on Phragmites australis (Cav.) Trin. ex Steud. and Ruppia maritima L. Its thallus was up to 5 mm long, slightly branched on one side, cells somewhat slimy, at the base impregnated with lime. The fouling formed a rather hard cover on the parts of flowering aqautic plants. In the fouling, Pleurocladia lacustris was accompanied by Cladophora glomerata (L.) Kütz. and Ulva flexuosa Wulfen.
Expression of Lactate Dehydrogenase in the Black Sea Shrimp Palaemon elegans
Authors: Subota V. V. , Petrov S. A. , Andrievsky O. M.
Published: August 2024

The activity of lactate dehydrogenase enzyme was studied in the Black Sea shrimp Palaemon elegans. To detect the activity of lactate dehydrogenase, the electrophoretic method and the histochemical reaction of the formation of its products were used. The highest expression was found when shrimp homogenate was prepared in a phosphate buffer with pH of 7.4. Totally two forms of the enzyme have been identified. The results of determining the activity of the studied enzyme are presented.
ENHANCING HEAT TRANSFER WITH NEW HYBRID NANOFLUIDS TYPE CORE@SHELL NANOPARTICLES
Authors: Iachachene Farida , Achab Louiza , Cheradi Hanane
Published: August 2024

This numerical study aims to enhance heat transfer in non-uniformly heated systems using a novel hybrid of core@shell nanoparticles with a ZnO@Ag structure. The primary objective is to evaluate the effectiveness of these nanoparticles in improving thermal performance. The analysis spans a range
of Rayleigh numbers while maintaining a constant nanoparticle density of 0.1%. The finite volume
method is employed to solve the governing equations, using the second-order upwind method for convection
contributions and the SIMPLE approach for coupling velocity and pressure fields. Pressure discretization is performed using the PRESTO method, and convergence is ensured with an under-relaxation scheme, achieving an absolute residual below 10-6. The results demonstrate a significant improvement in Nusselt numbers, with ZnO@Ag nanoparticles achieving a 20.15% enhancement. This improvement is attributed to the even coating of Ag, which possesses superior thermal properties onto ZnO, leading to increased overall thermal conductivity. These findings underscore the promising potential of core@shell nanoparticles for enhancing heat transfer in engineering applications. This study motivates further research and optimization efforts to harness the full potential of these advanced materials in thermal management systems.
Modifying Factors of the Incidence of Type 1 Diabetes Mellitus in Children under 18 Years of Age in Northern Spain in the Last 3 Decades
Authors: Sandra Maeso Mendez , Rut Gago Martin , Eider Jauregui Benito , Laura Costa Serra , Andrea Gainzarain Serna , Annabel Prigent Diaz , Carlos Sola Sarabia , Mikel Ogueta Lana , Ignacio Diez Lopez
Published: August 2024

Introduction: Type 1 Diabetes Mellitus is a chronic disease of autoimmune etiology that is highly prevalent in childhood. In the last decade, the possibility of the influence of the Bacillus Calmette-Guérin vaccine, different autoimmune diseases, modification of the immune response, and COVID19 itself of a simple population change due to immigration. Objectives: The objective of this work is to study the incidence of Type 1 Diabetes Mellitus in a cohort of children from Euskadi, north of Spain in the last 15 years. Methods: For the epidemiological study in Euskadi, a comparison of accumulated incidences between vaccinated and unvaccinated population groups was made. Results: The results of our study were not conclusive, but we believe that it is necessary to continue with long-term studies to eliminate bias.
ANTIBIOGRAM AND PREVALENCE OF ESBL GENES IN COMMENSAL E. COLI ISOLATED FROM THE RESIDENTS OF GHANAIAN ELDERLY NURSING CARE HOMES v1
Authors: Odartei Armah Emmanuel , Osae Nyarko Lawrencia , Kwame Ahiabu Mawutor , Isaac Agyapong , Bright Idun , Freda Boampong Kwarteng , Mercy Oppong , Naael Mohammed , C. N Kotey Fleischer , Mike Yaw Osei-Atweneboana , Nicholas Dayie
Published: August 2024

Weisolated E. coli from urine samples of elderly patients and determinedthe antibiogram of these pathogens. We further determined the Extended SpectrumBeta-lactamase (ESBL) genes they harbored and their phylogenetic grouping. Theexpected results were to reveal the prevalence and antibiogram of the isolated commensalE. coli, their carriage rate of ESBL genes, and thephylogenetic groupings.
Heron Data Suite: biomedical quantitative analysis applications for Skyline results files v1
Authors: Cunningham Mary , Cunningham Stephen , Renfrow Matthew
Published: August 2024

This protocol describes the use of the Heron Data Suite application (https://www.herondata.app/). The Heron Data Suite simplifies the analysis of biomedical proteomics results processed in Skyline. Post-data processing, Heron Data organizes results and goes the extra mile to account for technical replicates. Heron Data contains two apps currently: Replicate Absolute Quantification (Heron Quant) and Glycopeptide Area Under the Curve (Heron Glyco). The purpose of Heron Quant is to further analyze 'Peptide Ratio Quantification' .csv exports from the open source proteomics tool Skyline (https://skyline.ms/project/home/software/Skyline/begin.view). Heron Quant improves the rigor and reproducibility of Skyline for biomedical proteomics purposes by averaging replicates of a single sample or standard and plots the averaged calibration curves and outputting concentration averages. Heron Quant will calculate the R-squared value and linear equations of the calibration curves, as well as the coefficient of variance and the standards deviation of replicates. Heron Glyco is designed specifically for analysis of glycopeptide area under the curve CSV files from Skyline. Heron Glyco will organize your Skyline AUC data by glycopeptide into separate worksheets, calculate the percent relative abundance of glycopeptide for each replicate, and plot the percent relative abundance in pie charts of all grouped glycopeptides in each sample. Heron Data Suite will receive future updates and new application additions. Need support? Contact us using the form on our website: https://www.herondata.app/contact
USDA LTAR Common Experiment measurement: Cereal crop mycotoxin concentration v1
Authors: J. Wilke Brook , I. Chilvers Martin
Published: August 2024

Crop pests and diseases can reduce crop yields, but they can also worsen crop quality via reduced grain density or accumulation of toxins produced by fungal diseases. Several mycotoxins are known to harm crop quality, with deoxynivalenol (a.k.a. Vomitoxin or DON) being one of the most common, especially in corn and cereal grains. Subsequently, deoxynivalenol concentration can be correlated with disease severity. This protocol describes a process for measuring DON in cereal grains harvested from LTAR plots and fields.