bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Performance Analysis of the Dynamic Reduction of Intrinsic Interference Suppression and BER using QAM-based FBMC for MU-MIMO Communications
Authors: Kumari. C Sapna , Niranjankumar Niranjankumar , CR Revanna , Neelappa Neelappa , Yatnalli Veeramma
Published: September 2023

The present research work is focused on the study of co-channel interface with its minimization techniques without influencing its performance, in turn, which is desired to achieve the minimized complexity of Quadrature Amplitude Modulation (QAM)-based Filter Bank Multi-Carrier (FBMC) to minimize the interference and increase the spectral features with consideration of intrinsic features extractions for the ML (Maximum Likelihood) synthesis systems. The valid measures are given various concerns under consideration, to start with the consideration of the evaluation of the Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFD performance metrics along with the FBMC/QAM in signal transmission in a dedicated fading channel for the evaluation of the modulation order and BER as a required trade-off for quality assessments. From the results, it can be noted that the proposed FBMC QAM has performed better when compared with conventional FBMC systems. The present research also includes considering and calculating the efficiency of nonlinear channels with the Multi-User Multiple Input Multiple Output (MU-MIMO) and FBMC/QAM techniques. In continuation, the obtained results are dominating significantly to access the possible solution to meet the efficiency of the proposed system. In the next part of the research, it is considered with implementation of the sub-detector during the downlink of the system with the technique of threshold-driven strategy for better accuracy and minimization of the complexity in terms of ML detection in terms of order of its modulation. The calculations of the proposed technique with better BER are done on the recent MATLAB platform with its simulation demonstration for its detailed observation.
Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance
Authors: Kaseebhotla Rajasekhar , Rao K. Raghava , Rao Mallikarjuna
Published: September 2023

With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout the classification process increase the computing load. This article's goal is to use attribute selection to reduce the size of high-dimensional data, which will lessen the computational load. Considering selected attribute subsets that cover all attributes. As a result, there are two stages to the process: filtering out superfluous information and settling on a single attribute to stand in for a group of similar but otherwise meaningless characteristics. Numerous studies on attribute selection, including backward and forward selection, have been undertaken. This experiment and the accuracy of the categorization result recommend a k-means based PSO clustering-based attribute selection. It is likely that related attributes are present in the same cluster while irrelevant attributes are not identified in any clusters. Datasets for Credit Approval, Ionosphere, Annealing, Madelon, Isolet, and Multiple Attributes are employed alongside two other high-dimensional datasets. Both databases include the class label for each data point. Our test demonstrates that attribute selection using k-means clustering may be done to offer a subset of characteristics and that doing so produces classification outcomes that are more accurate than 80%.
A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images
Authors: Thamaraikannan N. , Manju S.
Published: September 2023

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas.  Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.
Enhancement of Performance Metrics of Heterogeneous Wireless Camera Sensor Network with Functionalization of Extensive Zonal Stable Election Protocol using Threshold Amplification and Residual Energy
Authors: Tripathi Nishant , Singh Charanjeet , Sharma Kamal Kumar
Published: September 2023

We report a new improved energy competent and optimized data packet flow protocol with Hierarchical Clustering utilized in Wireless camera Sensor Network.The existing Extensive Zonal Stable Election Protocol has been modified along with the threshold parameters amplification and residual energy. It incorporates dynamic hybrid method with finite number of Member Sensor nodes (MSN) in proximity with the base station share their data directly , while the rest of the farther nodes form a cluster for data transference using Cluster Head. The performance metrics accompanied by heterogeneity, longer network survival and better throughput have been improved. The network field was divided into 4 zones with a gateway for defined region 2, 3, and 4. The criterion for zone division remained on the energy status (residual) of the MSNs and distance from the BS and the formulated field characteristics in the simulation were kept unknown. The obtained results demonstrate that our proposed modified version of EZSE protocol considerably performs better than existing EZ-SEP, Z-SEP, SEP, LEACH, Mod-Leach protocols during entire stability timeframe. The notable achievement is also reported in throughput as the same is enhanced more than  by ~ 39%, 43%, 49% ,56%, 53% while total packets communicated with base station has been increased more than by ~ 127%, 131%, 147%, 151%, 148% stability of the network is also improved more than by ~ 37%, 42%, 45%, 49%, 51% with the corresponding increase in the heterogeneity of networks.
Uncovering Semantic Inconsistencies and Deceptive Language in False News Using Deep Learning and NLP Techniques for Effective Management
Authors: Chopra Yash , Kaushik Priyanka , Rathore Saurabh Pratap Singh , Kaur Pramneet
Published: September 2023

In today's information age, false news and deceptive language have become pervasive, leading to significant challenges for individuals, organizations, and society as a whole. This study focuses on the application of deep learning and natural language processing (NLP) techniques to uncover semantic inconsistencies and deceptive language in false news, with the aim of facilitating effective management strategies.

The research employs advanced deep learning models and NLP algorithms to analyze large volumes of textual data and identify patterns indicative of deceptive language and semantic inconsistencies. By leveraging the power of machine learning, the study aims to enhance the detection and classification of false news articles, enabling proactive management measures. The proposed approach not only examines the superficial aspects of false news but also delves deeper into the linguistic nuances and contextual inconsistencies that are characteristic of deceptive language. By employing advanced NLP techniques, such as sentiment analysis, topic modeling, and named entity recognition, the study strives to identify the underlying manipulative strategies employed by false news purveyors.

The findings from this research have far-reaching implications for effective management. By accurately detecting semantic inconsistencies and deceptive language in false news, organizations can develop targeted strategies to mitigate the spread and impact of misinformation. Additionally, individuals can make informed decisions, enhancing their ability to critically evaluate news sources and protect themselves from falling victim to deceptive practices.

In this research study, we suggest a hybrid system for detecting fake news that incorporates source analysis and machine learning techniques. Our system analyzes the language used in news articles to identify indicators of fake news and evaluates the credibility of the sources cited in the articles. We trained our system using a large dataset of news articles manually annotated as real or fake and evaluated its performance measured by common metrics like F1-score, recall, and precision. In comparison to other advanced fake news detection systems, our results show that our hybrid method has a high level of accuracy in detecting false news.
Multi-Network Feature Fusion Facial Emotion Recognition using Nonparametric Method with Augmentation
Authors: Devi Vandana , Sharma Avinash
Published: September 2023

Facial expression emotion identification and prediction is one of the most difficult problems in computer science. Pre-processing and feature extraction are crucial components of the more conventional methods. For the purpose of emotion identification and prediction using 2D facial expressions, this study targets the Face Expression Recognition dataset and shows the real implementation or assessment of learning algorithms such as various CNNs. Due to its vast potential in areas like artificial intelligence, emotion detection from facial expressions has become an essential requirement. Many efforts have been done on the subject since it is both a challenging and fascinating challenge in computer vision. The focus of this study is on using a convolutional neural network supplemented with data to build a facial emotion recognition system. This method may use face images to identify seven fundamental emotions, including anger, contempt, fear, happiness, neutrality, sadness, and surprise. As well as improving upon the validation accuracy of current models, a convolutional neural network that takes use of data augmentation, feature fusion, and the NCA feature selection approach may assist solve some of their drawbacks. Researchers in this area are focused on improving computer predictions by creating methods to read and codify facial expressions. With deep learning's striking success, many architectures within the framework are being used to further the method's efficacy. We highlight the contributions dealt with, the architecture and databases used, and demonstrate the development by contrasting the offered approaches and the outcomes produced. The purpose of this study is to aid and direct future researchers in the subject by reviewing relevant recent studies and offering suggestions on how to further the field. An innovative feature-based transfer learning technique is created using the pre-trained networks MobileNetV2 and DenseNet-201. The suggested system's recognition rate is 75.31%, which is a significant improvement over the results of the prior feature fusion study.
A Hybrid Machine Learning Model to Recognize and Detect Plant Diseases in Early Stages
Authors: Govathoti Sudeepthi , Kamidi Deepthi , Reddy Y. Madhavi , Kotha Mahesh , Yedukondalu Gangolu , Bhargavi Y. Krishna , Prashanthi Bh.
Published: September 2023

This paper presents an improved Inception module to recognise and detect plant illnesses substituting the original convolutions with architecture based on modified-Xception (m-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach is capable of achieving the specified level of performance, with an average recognition fineness of 99.73 on the public dataset and 98.05 on the domestic dataset, respectively.
Design of Multiplexers using Reversible Logic Technique
Authors: Sooriamala A. P. , Solomi V. Sherlin , Korah Reeba , Thomas Aby K.
Published: September 2023

Reversible logic is promising as it can be applied to   different applications in low power like nanocomputing especially in quantum computing. Reversible logic is a technioue for power reduction. Reversible circuits are similar to digital circuits but they work using reversible logic gates. This study focuses on reducing the garbage output and ancilla inputs in reversible multiplexers, thereby reducing the power consumption. In this study two designs of Multiplexers are given. Design1 is using TwinSJ gate and AJ gate. 2:1, 4:1 and 8:1 multiplexers are built. In Design 2, a new gate (SJ gate) is built which functions as 2:1 multiplexer. It has 4*4 configuration.  The inputs are suitably configured so that it performs various logic functions. Using this SJ gate and other basic reversible logic gates, 2:1, 4:1 and 8:1 multiplexers are built.  In 2:1 multiplexer, Ancilla inputs are improved to '0' from 5 and garbage output has been reduced to 2 against 7 in existing design.  4:1 multiplexers are built with ‘0’ ancilla inputs against 2 and 11 in existing designs. Garbage output of the proposed 4:1 multiplexer is 5 against 6 and 16 in existing designs.  8:1 multiplexer is built with 1 ancilla input and 11 garbage output against 2 and 12 respectively in existing design. This is designed using VHDL code - xilinx 14.7 for verification purpose and simulated on ISIM.
Wireless Charging: Its types, Standards and Applications
Authors: Patkar U. C. , Kartik Kaveti Nani , Patil Uday S. , Bharadwaj Shika , Sinkar Yogita Deepak , Antad Sonali Mallinath
Published: September 2023

An electrical gadget can be powered without cords by providing electrical via an air pocket to the device in order to re-charge its capacity. The performance and practicality of cordless charging tech have noticeably enhanced lately. The introduction to cordless charging in this paper covers its basics. The evaluation of standards, which includes Qi and the A4WP, is then given, as well as a focus on their communications channels. Next, we put out a cutting-edge idea for cordless charger networking, which enables chargers to be linked for easier data gathering and management. We explain how the wireless charger network can be used to assign users to chargers, which demonstrates the usefulness in terms of a reduction costs for users to find the best chargers to recharge their mobile devices.
Exploratory Data Analysis for Textile Defect Detection
Authors: Thivakaran T. K. , Priyanka N. , Antony J. Cruz , Surendran S. , Mohan E. , Innisai Rani P. Jona
Published: September 2023

The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19.  The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE.  The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study.
Evaluation of Best Satellite-Receiver Geometry for Improved IRNSS/GPS Position Accuracy
Authors: Ramavath Anil Kumar , Kumar P. Naveen
Published: September 2023

A GPS-IRNSS combined navigation system is emerging in India. It is possible to increase the accuracy of the receiver or user's position at any time and under any weather conditions with a combined system. It is possible to use these navigation systems for finding the precise location of receivers in civil aviation, precision applications such as surveying, disaster management, and other applications. Usually, satellite navigation systems are based on a geometric relationship between satellite and receiver, with errors caused by atmospheric conditions, multipath, and other factors affecting accuracy. Geometry of satellite receivers is measured by the Dilution of Precision (DOP), a unit less quantity. This paper investigates the possibility possible to obtain better accuracy by combining GPS and IRNSS constellations. This improved accuracy is typically useful for precision applications such as surveying and disaster management.
WeedFocusNet: A Revolutionary Approach using the Attention-Driven ResNet152V2 Transfer Learning
Authors: Devi B. Sunitha , Sandhya N. , Chatrapati K. Shahu
Published: September 2023

The advancement of modern agriculture is heavily dependent on accurate weed detection, which contributes to efficient resource utilization and increased crop yield. Traditional methods, however, often need more accuracy and efficiency. This paper presents WeedFocusNet, an innovative approach that leverages attention-driven ResNet152V2 transfer learning addresses these challenges. This approach enhances model generalization and focuses on critical features for weed identification, thereby overcoming the limitations of existing methods. The objective is to develop a model that enhances weed detection accuracy and optimizes computational efficiency. WeedFocusNet, a novel deep-learning model, performs weed detection better by employing attention-driven transfer learning based on the ResNet152V2 architecture. The model integrates an attention module, concentrating its predictions on the most significant image features. Evaluated on a dataset of weed and crop images, WeedFocusNet achieved an accuracy of 99.28%, significantly outperforming previous methods and models, such as MobileNetV2, ResNet50, and custom CNN models, in terms of accuracy, time complexity, and memory usage, despite its larger memory footprint. These results emphasize the transformative potential of WeedFocusNet as a powerful approach for automating weed detection in agricultural fields.
Recommendations in Social Media Applications to Ensure Personification and Safety using Machine Learning
Authors: Gawade Prema , Joshi Sarang
Published: September 2023

Myriads of social media utilization lead to various issues like personalization hacks, data security problems, and safety. A recommendation is of paramount importance to alleviate this problem when there is a huge amount of data and the number of participants on the platform is increasing exponentially. Unfortunately, modern social media research has enhanced the performance and personalization of recommendations in many fields, yet largely underutilizes the power of artificial intelligence to enable personalized recommendations system for social media platforms like WhatsApp, Facebook, Twitter, etc. With advancement inside the global of technology every hour and every day new features are delivered to the list.  In a manner, social platforms are merging into our actual existence, and to achieve personification and related safety, users can get any one safety factor from all 6 classes with this approach. This factor provides the basis for personification and the implementation of safety precautions.

This research proposes recommendations for personification in social media applications. The proposed Modified Inception Resnet V4 Convolutional Neural Network (MInReCNN) outperforms embedded media persona analysis and classification through text, image, and video data. Using these prediction classes better decisions can be made in given social media domain.
AIWFF: A Machine Learning based Framework for Automatic Weather Forecasting
Authors: Bharathi K Sowjanya , Babu Boddu Sekhar
Published: September 2023

In the contemporary era witnessing global warming effects, weather is a dynamic phenomenon which is highly uncertain. The conventional approaches that rely on certain physical processes governing atmosphere are capable of serving a great deal in weather forecasting. However, robustness to perturbations is still desired. In this content Artificial Intelligence (AI) innovations assume significance to bring about more reliable forecasting alternative which may complement conventional methods. In this paper, we proposed a framework known as AI-enabled Weather Forecasting Framework (AIWFF) which exploits machine learning (ML) models that are robust to time series data and underlying perturbations for improving forecasting performance. An algorithm known as Learning based Intelligent Weather Forecasting (LIWF) is proposed and implemented.  This algorithm has required pre-processing, feature section and a pipeline of ML models to learn from data and then forecast weather more accurately. Another algorithm known as Hybrid Method for Feature Selection (HMFS) is proposed to leverage training quality in LIWF algorithm. The framework results in three trained knowledge models saved to secondary storage. These models are known as Random Forest Regressor, Linear Regressor and Decision Tree Regressor. An application with Graphical User Interface (GUI) is developed to make use of these knowledge models and provide forecasting on user requests. The empirical results revealed that the proposed framework shows better performance.
Expanded Noise Margin 10T SRAM Cell using Finfet Device
Authors: Jain Prashant Udaychand , Tomar Vinaykumar
Published: September 2023

Static random access memory (SRAM) cells are being improved in order to increase resistance to device level changes and satisfy the requirements of low-power applications. A unique 10-transistor FinFET-based SRAM cell with single-ended read and differential write functionality is presented in this study. This cutting-edge architecture is more power-efficient than ST (Schmitt trigger) 10T or traditional 6T SRAM cells, using only 1.87 and 1.6 units of power respectively during read operations. The efficiency is attributable to a lower read activity factor, which saves electricity. The read static noise margin (RSNM) and write static noise margin (WSNM) of the proposed 10T SRAM cell show notable improvements over the 6T SRAM cell, increasing by 1.67 and 1.86, respectively. Additionally, compared to the 6T SRAM cell, the read access time has been significantly reduced by 1.96 seconds. Utilising the Cadence Virtuoso tool and an 18nm Advanced Node Process Design Kit (PDK) technology file, the design's efficacy has been confirmed. For low-power electronic systems and next-generation memory applications, this exciting 10T SRAM cell has a lot of potential.
Machine Learning-Based Classification of Hybrid BCI Signals using Mayfly-Optimized Multiclass Weighted Random Forest
Authors: Shelishiyah R. , Beeta Thiyam Deepa , Margaret M. Jehosheba
Published: September 2023

The Brain-Computer Interface (BCI) technologies have excellent clinical and non-clinical uses. Among the most popular imaging methods adopted in BCI technologies is electroencephalography (EEG). But EEG signals are typically quite complicated, so analyzing them necessitates a significant amount of effort. With the help of machine learning (ML), this research investigates the feasibility of a BCI platform based on the motor imagery (MI) concept. The steps of pre-processing, feature extraction and classification are the underpinning of any conventional ML model. To train such a model, however, a large amount of data is needed. To address this gap, this work introduces a new mayfly-optimized multiclass weighted random forest (MFO-MWRF) technique that uses retrieved features as input to mitigate the need for this supplementary data. In this study, we gather a dataset of hybrid EEG and fNIRS motor imagery that can be pre-processed using a Wiener filter (WF) to filter out noisier signals without affecting the high-quality images. The characteristics are extracted using the discrete wavelet transform (DWT). The research results indicate that the proposed approach achieves the best performance compared to existing approaches for classifying motor movement images.
CNN based Blockchain Information Protection Model for Emerging Cloud Applications
Authors: Kulkarni Chaitanya , Patil Kamlesh Vasantrao , Rane Mugdha Arvind , Kanade Ashwini Vitthal , Sawant Kavita Shantanu
Published: September 2023

In the age of mobile internet, the amount of data is growing, and ability to process service data is always getting better. So, protecting data privacy and making sure the service environment is trustworthy have become very important. This paper looks at a trusted privacy service computing model for common uses of convolutional neural networks. The goal is to find data and model calculation methods that support homomorphic encryption to protect data privacy. Build a service process certificate and a method for distributing calculation rights based on blockchain and new contract technology to make sure that service calculations are open, trustworthy, and easy to track. Explore how the new cloud environment resource data service model helps resource providers, model owners, and users work together to make the most of their resources and grow the sharing economy. Lastly, experiments are done to figure out how the model protects privacy.
Reprint: Theories of Reproduction and Resistance in the New Sociology of Education: A Critical Analysis
Authors: GIROUX HENRY A.
Published: September 2023

In the past ten years radical educators have developed several theories around the notions of reproduction and resistance. In this article, Henry Giroux critically analyzes the major positions of these theories, finding them inadequate as a foundation for a critical science of schooling. He concludes by outlining the directions for a new theory of resistance and schooling which contains an understanding of how power, resistance, and human agency can become central elements in the struggle for social justice in schools and in society.
Correlações e Análise de Trilha em Caracteres Agronômicos de Híbridos de Milho Segunda Safra Cultivados em Balsas-MA
Authors: Grespan Daniel Silva , Zuffo Alan Mario , Rego Marlon da Silva , Santos Joel Cabral dos , Silva Francisco Charles dos Santos , Barrozo Leandra Matos
Published: September 2023

O cultivo do milho na segunda safra está sujeito ao estresse hídrico o que pode ocasionar alteração na redistribuição de fotoassimilados e modificar os caracteres morfológicos envolvidos diretamente na produtividade dos grãos. Portanto, o objetivo do trabalho foi avaliar por meio da análise de trilha, a influência de caracteres agronômicos na produtividade de grãos em híbridos de milho segundo safra cultivado em Balsas-MA. O delineamento experimental utilizado foi o de blocos ao acaso, sendo 10 cultivares de milho, com quatro repetições. Quando a cultura atingiu a fase de maturidade fisiológica, foi avaliado a altura das plantas, a altura de inserção de espiga, o comprimento de espiga, o número de fileiras por espiga, o número de grãos por fileiras, o diâmetro da espiga, a massa de mil grãos e a produtividade dos grãos. Os caracteres número de grãos por fileira e diâmetro de espiga são os componentes que apresentam maiores efeito direto positivo e a massa de mil grãos apresentou efeito direto negativo na produtividade dos grãos de milho segundo safra cultivado em Balsas-MA.
Palavras-chave: Milho Safrinha. Seleção Direta. Seleção Indireta. Zea mays L.
AbstractCorn cultivation in the second crop is subject to water stress, which can cause changes in the redistribution of photoassimilates and modify the morphological characteristics directly involved in grain yield. Therefore, the objective of this work was to evaluate, through trail analysis, the influence of agronomic characteristics on grain yield in corn according to crops grown in Balsas-MA. The experimental design used was randomized blocks, with 10 maize cultivars and four replications. When the culture reached the physiological maturity stage, the plant height, ear insertion height, ear length, number of rows per ear, number of grains per row, ear diameter, mass thousand grains and grain yield were measured. The number of grains per row and ear diameter are the components that present the greatest positive direct effect, and the mass of one thousand grains presented a negative direct effect on corn grain yield according to harvest grown in Balsas-MA.
Keywords: Off-Season Corn. Indirect Selection. Direct Selection. Zea mays L.
Un estudio cualitativo sobre la medición científica colombiana desde la perspectiva de la investigación-creación
Authors: Ballesteros-Mejía Melissa , Rodríguez-Pomeda Jesús , Casani Fernando
Published: September 2023

The tensions generated in the scientific assessment expose flaws in the mechanisms used for this purpose, especially for the social sciences and humanities, which insist on being different from traditional research methodologies. Including the concept research-creation in the Colombian National System of Categorization of Researchers (SNCI by its Spanish acronym), is a milestone in recognizing these differences. However, its impact on the practice of humanities researchers is scarcely known. Through a qualitative study, this article seeks to improve understanding of the perceptions in the Colombian research landscape since the research-creation appeared in the measurement system. The descriptive analysis of the opinions of researchers in this area allows us to recognize that, although it made visible an important research production, it perpetuates practices that are harmful to research, and corroborates the need for a profound reorientation of how knowledge production is observed.
Changing trends in implant designs: A review
Authors: Mishra Arti , Khatri Manish , Bansal Mansi , Mohd. Rehhan  , Gaind Sakshi , Khan Shamiya
Published: September 2023

Implantology is an ever-evolving scientific field that undergoes continuous refinement and innovation. Dedicated research and development efforts are focused on consistently improving the success rates of implants through innovative redesign and advancements. The introduction of advanced technologies has revolutionized the evaluation of patients in three dimensions, enabling clinicians to utilize precise and predictable approaches for diagnosis, planning, and treatment. This multidisciplinary patient-centric framework has opened new avenues for providing tailored and effective healthcare solutions. Therefore, it is of utmost importance for clinicians to conduct a comprehensive analysis of each patient's condition, ensuring meticulous selection of the suitable implant design and material, and making informed decisions regarding the most appropriate technique to be employed.
Regenerative approach for root coverage with platelet-rich fibrin: A case report
Authors: Roy Tulika Rani , Kumari Nimisha , Perween Raziya , Mehrotra Roopansi , Singh Anamika
Published: September 2023

Gingival recession (GR) exposes the root surface and causes sensitivity, if left untreated it will lead to the root exposure with the bone loss around the teeth and eventually more complex periodontal defect. There are numerous surgical interventions are present in literature for treating gingival recession, the Coronally advanced flap (CAF) surgery is one of the most common procedures for the treatment. The Predictability of the treatment outcome can be increased by combining with other regenerative procedures. The platelet rich fibrin (PRF) is preferred to combine with CAF. PRF is prepared as the fibrin membrane that contains constituents of blood and favours healing at the surgical site. In this case report we are presenting treatment of Miller’s class II recession with CAF combining PRF.
Knowledge based quality medical education by utilizing the means of technology application
Authors: Raju V Rama
Published: September 2023

In the 21 century`s hi-tech world, imparting the scientific medical health educational instruction is changing fast, which is inclined through several indeed many factors which includes altering health care milieu, role-of Doctor of Medicine, transformed societal prospects, swiftly varying remedial-medicinal, medical and therapeutic-science, plus range of didactic (pedagogical) methods, techniques, and prototype models.

Changes and transformations in `societal-expectations` set `patient-safety` in front of, also raise `ethical-issues` of learning connections plus measures over live subjects, through venerable teaching is any more suitable. Aims of education through technology in medicine comprise, providing `knowledge-acquisition’, civilizing resulting, development of perceptual disparity, enhancing quality-skills harmonization, working for unusual or grave measures, knowledge side imparting guidance, plus humanizing psycho motor skills – perceptual abilities and motor controls. Thus, various technologies could address the above objectives.

Newer technologies like podcast s, videos through flipped class-work-learning’s cellular-mobiles yapps, videogames, computer-simulations model-prototypes, amateur trainers, incorporated model-simulators, virtual-reality, plus smart-wearable devices-Google glass, are few techniques accessible to tackle the varying educational-instructive milieu.

This study addresses the application of technology which yields the infrastructural network plus basis for understanding several confronts given that `medical education` pro (in support of the) prospect.
Social media addiction - A threat to human society
Authors: Ramanadin P Vadivukkarasi , Kanmani S , Sriram N , Mutalikadesai Sudheendra , Patel Nirukumari , Mulla Amar Alim
Published: September 2023

Utilizing the technology made our life very easier and brought the globe in our hand which has got both pros and cons. Young generation is more of techno oriented than the values that makes them to be depending on the social medias easily that affects the domains of health. A study was conducted to assess the Social media addiction among the paramedical students. Quantitative research approach with non experimental, descriptive research design was used. Non probability convenient sampling technique was used to select 140 para medical students who fulfills the inclusion criteria. Self administered structured questionnaire was used. Modified social media addiction likert scale was used with 20 items. Findings of the study shows that vast majority (103(74%)) of the students were addicted to the social media. To conclude, it is the high time for the policy-makers to restrict on this and make provision to improve the interaction skills.
Remote Work Efficacy: A Quantitative Analysis of Administrative Staff Performance in Ciudad Juárez's Industrial Sector Amidst the COVID-19 Pandemic
Authors: Márquez-Miramontes Blanca Lidia , Cobos-Floriano Sixta
Published: September 2023

In response to the COVID-19 pandemic, organizations globally have adopted novel strategies for survival, among which the abrupt transition to remote work stands out. This study aims to elucidate the benefits and implications of remote work on the performance of administrative employees within Ciudad Juárez's maquiladoras. A correlational, non-experimental, cross-sectional, and descriptive research design was employed from August 2021 to May 2022. A non-probabilistic sample of 273 administrative staff members, who had engaged in remote work at least once, was utilized and a questionnaire was administered. The subsequent statistical analysis involved frequency and percentage computations. Further, two distinct groups, each comprising 50 administrative staff members-one working remotely and the other onsite-were established. Cross-tabulation descriptive analysis was performed to discern potential associations. Preliminary results revealed a marked prevalence of remote work (81.3%) over onsite work (18.7%). A significant association (p<0.05) between remote work and job performance was evident in most items, thereby indicating that remote work positively contributes to organizational objectives and enhances employee performance.
MOVEMENT ACTIVITY OF ROUND GOBY (NEOGOBIUS MELANOSTOMUS PALLAS, 1814) UNDER THE INFLUENCE OF GEOMAGNETIC FIELDS
Authors: Karavansky Y. V. , Zamorov V. V. , Shvandt M. A.
Published: September 2023

Introduction. Behavioral traits of fish are an important manifestation of their response to external stimuli. Locomotor activity reflects the influence of specific factors on fish behavior and the role they play in their lives. Among the least studied factors are fluctuations in the geomagnetic field and their impact on the locomotor activity of the round goby Neogobius melanostomus Pallas, 1814.
Aim. The aim of this study was to investigate the influence of fluctuations in the geomagnetic field on the locomotor activity of the commercially important speciesof benthic ichthyofauna, the round goby Neogobius melanostomus Pallas, 1814, in the Black Sea.
Methods. The ichthyological material was collected in the coastal waters of the Odesa Bay, ranging from Cape Northern Odesa to Cape Velikyi Fontan, during a scientific research survey conducted by the I.I. Mechnikov Odesa National University (ONU). Laboratory investigations were conducted in the aquarium room of the Aquatic Biology and General Ecology Department of the I.I. Mechnikov Odesa National University. Hourly changes in fish locomotor activity were recorded and analyzed as a result of the study. During the specified observation period, periods of increased geomagnetic activity were identified and compared to periods when this activity was within normal range. An original methodology called “Computer Vision Method” was employed for processing the visual data and tracking of the laboratory animals.
Results. The maximum values of geomagnetic activity during the observation period, classified as a first intensity magnetic storm of class G1, were observed on February 3, 2022, from 6 to 12 hours, and on February 4 and 10, 2022, from 15 to 21 hours. When comparing the levels of fish locomotor activity during these time intervals with their activity during the time intervals on days with minimal geomagnetic activity (February 8, 9, and 15, 2022), a decrease in locomotor activity of individuals during magnetic storms of 27% to 42% can be observed.
Conclusion. The fluctuations in locomotor activity of the round goby during increased geomagnetic activity up to the level of magnetic storms indicate the ability of this species to perceive magnetic fields and respond to changes in its properties. Specifically, when the Kp-index values corresponded to a first intensity geomagnetic storm (G1), a decrease in fish locomotor activity was observed compared to the activity during the same time intervals on days with minimal geomagnetic activity.
PECULIARITIES OF BEHAVIORAL REACTIONS OF RATS IN THE BARNES' LABYRINTH AGAINST ALUMINUM CHLORIDE INTOXICATION
Authors: Kirilenko N. A. , Tyniana M. Y. , Hladkii T. V.
Published: September 2023

Relevance. Accumulation of aluminum in brain tissues has a negative effect on the nervous system, which can subsequently lead to memory impairment, emotional instability, decreased motor activity, and therefore to various neurodegenerativediseases.
The aim of the work was to evaluate the peculiarities of behavioral reactions in rats against the background of aluminum chloride intoxication and their correction by means of preventive measures.
Materials and methods. The experiment was conducted on 32 male white rats weighing from 239 to 268 g, which were divided into four groups: group 1 – intact animals (n = 8); group 2 – injection of 0.5 ml of 12% AlCl3 ×6H2O solution (80 mg Al/kg) (n = 8); group 3 – introduction of a preventive complex against the background of aluminum chloride intoxication: «Lequin» (NPA «Odeska Biotechnology», Ukraine) at a dose of 500 mg/kg, «Gotu Cola» («Nature’s Answer», USA) at a dose of 300 mg/kg and «Sklerosin» (TOV «ECOSVIT OIL», Ukraine) at a dose of 300 mg/kg (n = 8); group 4 – administration of Minerol (NVMP «GOBOR», Ukraine) at a dose of 1000 mg/kg of body weight (n = 8).
On the 60th day of aluminum intoxication, the peculiarities of the behavioral reactions of rats of all groups were studied in the Barnes maze using extraneous stimuli (bright light and loud sound).
Results and conclusions. A study of the nature of the behavior of rats in the Barnesmaze showed that when performing the task (searching for the «rescue» hole), the animals of each group made mistakes. The highest average number of errors (10) was observed in animals with aluminum intoxication on the 9th day of the experiment (р ≤0.05, Uemp=15.5) compared to control. The average number of errors in this period decreased to 5 and 7, respectively, compared to the previous group, in animals that received the preventive complex and «Minerol» against the background of intoxication.
The time to pass the maze (completed task) by rats of the control group compared to the others was the smallest during the entire experiment and from the 5th to the 12th day of the study it further decreased from 48 to 36 s, respectively, and in animals with aluminum intoxication it was the largest. The use of both the preventive complex and «Minerol» reduced the time of passing the maze until the end of the experiment, but these indicators did not reach the values of the control group.
Males of the control group chose a direct search strategy in 65-75% of cases. For animals with aluminum intoxication, refusal to perform the task is characteristic both at the beginning of the study and at the end.
LIFE IN SCIENCE ACCORDING TO THE CALL OF THE SOUL
Authors: Smyntyna Olena , Petrova Natalia
Published: September 2023

In 2021, the 70th anniversary of the Dean of the Faculty of History and Philosophy, Doctor of History Kushnir Vyacheslav Grigoryevich. A sincere man with a great soul, extremely friendly and always smiling, he is an example for all of us.
A EVASÃO DE CURSOS EM UMA INSTITUIÇÃO DE ENSINO SUPERIOR(IES) MULTICAMPUS EM IMPLANTAÇÃO
Authors: Tischer Wellington , Nierotka Rosileia Lucia
Published: September 2023

O artigo investiga a associação entre a evasão de curso e características institucionais tais como localização dos campi, infraestrutura e o grau acadêmico em uma universidade pública em implantação. Trata-se de uma pesquisa exploratória e descritiva a partir de dados secundários de 11.120 inscritos entre 2010 e 2014 na Universidade Federal da Fronteira Sul. Os resultados apontam para: a) Evasão elevada durante a implantação (60%); b) os campi em cidades menores não estão associados com o abandono de cursos; c) a mudança das instalações provisórias para os campi definitivos sugere leve mudança na trajetória de evasão, mas não aponta para aumento da conclusão dos cursos de graduação; d) Bacharelados têm percentuais de abandono ligeiramente menores em comparação com os Licenciaturas, e e) Sugere-se uma política institucional com enfoque local de combate à retenção e evasão.