bluuebunny/crossref_metadata_2025 huggingface dataset:
ANALYSING THE SUSPICIOUS BEHAVIOUR IN VIDEO SURVILLIENCE FOR CRIME DETECTION USING GAIT SPEED MONITORING
Authors:   Providence College for Women, India, H Sivalingan , N Anandakrishnan ,   Providence College for Women, India
Published: October 2024

One of the most emergent research is suspicious behaviour monitoring in video surveillance. In recent past, crime detection is powerful topic to identify the abnormal events or crime events. This work focused on the suspicious behaviour analysis which helps to detect the crime events in terms of gait parameter. This work describes the following tasks. First, tracking the pedestrians from video data using MM track algorithm i.e. (calibration process). Second, extracting the gait parameters based on proposed modules: 1) spatial coordinate module contains the speed profiles which helps to measure the suspicious behaviour of pedestrian. 2) Fixed coordinate system module, it also measures the suspicious behaviour in different way based on the list of components and axis of the pedestrians. This step performs the major role in measure the suspicious behaviour among the pedestrians’ movement for crime detection. Third, measure the suspicious behaviour in terms of walk ratio, Acceleration Auto Correlation (AAC) and gravity, dynamic, horizontal, vertical components of pedestrians as well this step ? value performs the validation role which is based on the reference range to validate the Walk Ratio value. The video helps to monitor the pedestrian’s movement. This work is compared to the different pedestrian’s detection technique such as DPM (Deformable Part Model) and Real Boost method foe efficiency in terms of true positive rate and pedestrian gait speed detection time parameters. Proposed work attains best result in both parameters.
ANALYSIS OF IMAGE PREPROCESSING TECHNIQUES TO IMPROVE OCR OF GARHWALI TEXT OBTAINED USING THE HINDI TESSERACT MODEL
Authors:   Doon University, India, Rawat Sukhbindra Singh , Sharma Ashutosh ,   Doon University, India, Gusain Rachana ,   Doon University, India
Published: October 2024

A huge amount of information exists in the form of textbooks, paper documents, newspapers, and other physical forms, that is required to be digitized for its effective access and long-time availability. Optical Character Recognition (OCR) is an effective way to digitize the text. In this study, we have used Google’s Tesseract as the OCR tool. The focus of our study is to improve Tesseract’s accuracy on machine-printed Garhwali documents by using image pre-processing techniques including Super-Resolution (SR), different binarization methods (Otsu and adaptive thresholding), skew correction, morphological operations, and ImageMagick methods. To improve the Tesseract results, we used the three proposed approaches – two approaches differed by the binarization method (Otsu and adaptive thresholding), and the third approach used ImageMagick methods for pre-processing. For evaluation purposes, we created a dataset by capturing images from a sample of five Garhwali textbooks using two mobile cameras with different resolutions; two books were captured by a high-resolution camera and the other three were captured through a low-resolution camera. Our experiments showed good results in specific cases, for high-resolution images, 88.13% accuracy was achieved for Otsu thresholding without applying the Super-Resolution and for low-resolution images, 87.44% accuracy was achieved for ImageMagick with Super-Resolution.
COMBINING DEEP RESIDUAL NEURAL NETWORK FEATURES WITH SUPERVISED MACHINE LEARNING ALGORITHMS FOR REAL-TIME FACE RECOGNITION-BASED INTELLIGENT SYSTEMS
Authors:   Vinayaka Mission’s Kirupananda Variyar Engineering College, India, Jyothi M Lakshaga , R S Shanmugasundaram ,   Vinayaka Mission’s Kirupananda Variyar Engineering College, India
Published: October 2024

Face Recognition is the domain of technology in Computer Vision that deals with the process of identifying faces of known and unknown per- sons-based on facial patterns. Despite all the recent researches these years on Face Recognition technology, the development of real-time face recognition has always been a challenging task. This kind of technology has its applications widespread like security, medical diagnosis, educational sectors, etc. The advancements in High-end processors and High-Definition cameras led to the design of Face recognition systems that use offline or real-time input datasets. In this paper, our main aim is to focus on real-time video feeds taken from a framed classroom environment to identify the students or the faculty members and tag their names, com- paring them with the already stored face databases. Attendance marking is a daily routine that follows calling the name or passing the attendance books, which is very time-consuming, and they tend to begin proxy at-tendances. This study proposes a attendance marking system using face recognition and Deep Learning techniques on a Raspberry Pi board. The proposed system delivers an approach to make real-time face recognition-based attendance systems by extracting deep facial features using deep residual network (ResNet)-based CNNs. Then that deep facial feature dataset is combined with Machine Learning Algorithm such as SVM to perform face detection and recognize the faces. The maximum recognition accuracy of 80% is obtained using the planned system on the real- time face images provided and will be further improved.
LEAF DISEASE RECOGNITION USING SEGMENTATION WITH VISUAL FEATURE DESCRIPTOR
Authors:   Bharathidasan University, India, D Angayarkanni , L Jayasimman ,   Bharathidasan University, India
Published: October 2024

Agriculture has become the main sources of the income for many developed countries. The productivity in agriculture can be affected by various diseases present in the plant due to climatic conditions. The key step to improve the productivity of crops are to detect the disease at the preliminary stage. Automation becomes the best solution for this because it is more difficult to observe the disorders in plants parts. For that an image of affected plant leaf is acquired and segments the affected portion and to recognize the disease by using image processing and computer vision and machine learning techniques. The extracted features from the segmented portion are descripted using Global and Local Visual descriptors. Finally, we use the classifier to recognize the disease. Extracting a meaningful feature from an image is a central problem for a variety of computer vision problems like recognition, image retrieval, and classification. In this research, visual feature descriptor that best describe an image with respect to its visual property is explored. It is specifically focusing on recognizing tasks. The experimental results have proved that the combination of visual descriptors with various classifiers such as SVM and Ensemble Classifier produces high quality outcomes when compared to individual descriptors.
A COMPARATIVE FRAMEWORK FOR BLOCKING ARTIFACTS REMOVAL OF DIGITAL IMAGES USING HYBRID MECHANISM
Authors:   IK Gujral Punjab Technical University, India, Prakram Manu , Singh Amanpreet ,   IK Gujral Punjab Technical University, India, Singh Jagroop ,   DAV Institute of Engineering and Technology, India
Published: October 2024

The restoration of an image with blocking artifacts due to compression at low bit rates is a challenging task and blocking artifact measurement algorithms have an important role to play in the computer vision field. An artifacts removal technique is an important step towards the reliability and security of image processing area that delivers a better understanding in many applications like pattern recognition, object classification, surveillance system and many more. We know that the removal of art objects is a scientific method used to provide better image analysis and for this purpose many methods of removal of art objects were already made by researchers during the processing of images such as line, motion, pattern, and hair. But in availability of group of artifacts in an image, they do not achieve an acceptable result. In this research, we proposed a comparative framework for blocking artifacts removal of digital images using hybrid mechanism. The main contribution of this research is developing a new neuro-fuzzy system-based hybrid artifacts removal mechanism to achieve better blocking artifacts efficiency. To remove artifact from an image the proposed framework has its own impact in quality parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of proposed framework is compare for all five techniques such as line, motion, pattern, hair and combination of all with each other and we observed that the achieved results justify the proposed hybrid artifact removal method in the field of image processing.
SEGMENTATION OF CAROTID ARTERY FROM INTRAVASCULAR ULTRASOUND (IVUS) IMAGES USING DEEP LEARNING TECHNIQUES FOR PLAQUE IDENTIFICATION
Authors:   Avinashilingam Institute for Home Science and Higher Education for Women, India, K V Archana , R Vanithamani ,   Avinashilingam Institute for Home Science and Higher Education for Women, India
Published: October 2024

The carotid artery is the major artery that supplies blood to the brain, neck region, and face. The plaque deposition in these arteries is caused mainly due to the deposition of cholesterol, calcium, and other cellular debris carried along with the bloodstream. Hence identification of plaque is essential to avoid stroke and other diseases related to the heart. This paper proposes a deep learning-based segmentation algorithm for the identification of plaque in carotid artery using Intravascular Ultrasound (IVUS) images. To compare the performance of the proposed algorithm with the existing algorithms, evaluation metrics such as Jaccard Index (JI), Dice Similarity Coefficient (DC), and Hausdorff Distance (HD) are computed. From the results, it is observed that the proposed algorithm exhibited a high value with JI of 0.9562, DC of 0.9587, and HD of 4.8080.
EVALUATING POPULAR SMARTPHONE BRANDS BASED ON TWITTER SENTIMENT USING TEXTBLOB
Authors:   Periyar University, India, K Gurumoorthy , P Suresh ,   Salem Sowdeswari College, India
Published: October 2024

In today’s world, social media plays a critical part in the advancement of industries, organizations, and businesses. It has been seen as a fundamental aspect that should be known to both businesses and individuals. In one way or another, everyone is associated with social media. People have been able to interact and exchange knowledge because of the mix of technology and social relationships. In the last 10 years or so, social media has become a governing medium for knowledge exchange. Sentiment Analysis (SA) allows users to express their emotions, perspectives, and opinions to the rest of the universe. Twitter is a big and quickly expanding microblogging social networking website wherein users may express themselves concisely and easily. A large number of consumer reviews for various items are emerging on Twitter. Mobile phones are a popular sector where a large number of consumer evaluations can be found. This makes it tough for a prospective consumer to read them and decide whether or not to purchase the goods. Only the precise aspects of the phones about which users have comments, as well as whether those opinions are good or negative are of importance to us. This paper proposes a solution to this problem by analyzing consumer sentiment from Twitter data to determine brand reputation based on customer happiness. In this work, Python programming is employed to perform tests on various tweets utilizing the Twitter API and for tweet pre-processing, the Natural Language Tool Kit (NLTK) package is used. The tweets dataset is then analyzed using Textblob and the intriguing results in negative, positive, and neutral emotions are displayed using various visualizations.
AN ENHANCED ENSEMBLE METHOD ON OPTIMIZATION FOR RESOURCE ALLOCATION IN SOFTWARE-DEFINED NETWORKING ENVIRONMENTS
Authors:   Unnamalai Institute of Technology, India, Swamidoss Mathumohan
Published: October 2024

Software-Defined Networking (SDN) offers flexibility and programmability in network management, but efficient resource allocation remains a challenge due to dynamic traffic patterns and diverse service requirements. This paper proposes an Enhanced Ensemble Method (EEM) for optimizing resource allocation in SDN environments. EEM integrates multiple ensemble learning techniques, leveraging their complementary strengths to enhance prediction accuracy and robustness. The key contribution lies in the novel integration of ensemble methods tailored for SDN resource allocation, offering improved adaptability to changing network conditions and service demands. Evaluation on real-world SDN datasets demonstrates that EEM outperforms existing methods in terms of both resource utilization efficiency and service quality. Notably, EEM achieves significant improvements in network throughput, latency reduction, and resource utilization balance.
ANT COLONY OPTIMIZATION ALGORITHM FOR FEATURE SELECTION IN SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA
Authors:   R.M.K. Engineering College, India, P Kavitha , S.D. Lalitha ,   R.M.K. Engineering College, India
Published: October 2024

Sentiment analysis of social media data involves extracting valuable insights from vast amounts of unstructured text. Feature selection plays a crucial role in enhancing the accuracy and efficiency of sentiment analysis algorithms. This study proposes the application of the Ant Colony Optimization (ACO) algorithm for feature selection in sentiment analysis. ACO is inspired by the foraging behavior of ants and has been successfully applied to various optimization problems. In this context, ACO is utilized to select the most informative features from the dataset, thereby improving the performance of sentiment analysis models. The contribution of this research lies in the adaptation of ACO for feature selection in sentiment analysis of social media data. By leveraging the inherent strengths of ACO, such as its ability to explore large solution spaces and adapt to dynamic environments, more accurate sentiment analysis models can be developed. Experimental results demonstrate that the proposed ACO-based feature selection approach outperforms traditional methods in terms of classification accuracy and computational efficiency. The selected features exhibit strong predictive power, leading to improved sentiment analysis performance on social media data.
A STACKED GENERALIZATION BASED META-CLASSIFIER FOR PREDICTION OF CLOUD WORKLOAD
Authors:   University of Allahabad, India, T. Singh Sanjay , Tiwari Mahendra ,   University of Allahabad, India
Published: October 2024

Cloud computing has revolutionized the way software, platforms, and infrastructure can be acquired by making them available as on-demand services that can be accessed from anywhere via a web browser. Due to its ubiquitous nature Cloud data centers continuously experience fluctuating workloads which demands for dynamic resource provisioning. These workloads are either placed on Virtual Machines (VMs) or containers which abstract the underlying physical resources deployed at the data center. A proactive or reactive method can be used to allot required resources to the workload. Reactive approaches tend to be inefficient as it takes a significant amount of time to configure the resources to meet the change in demands. A proactive approach for resource management is better in meeting workload demands as it makes an appropriate number of resources available in advance to cater to the fluctuations in workload. The success of such an approach relies on the ability of the resource management module of a data center to accurately predict future workloads. Machine Learning (ML) has already proven itself to be very effective in performing prediction in various domains. In this work, we propose an ML meta-classifier based on stacked generalization for predicting future workloads utilising the past workload trends which are recorded as event logs at Cloud data centers. The proposed model showed a prediction accuracy of 98.5% indicating its applicability for the Cloud environment where SLA requirements must be closely adhered to.
EMOTION RECOGNITION THROUGH DEEP LEARNING IN VARIOUS MODES
Authors:   Ministry of Electronics and Information Technology, Government of India, India, Gupta Bharat , Gupta Manas ,   Indian Institute of Technology Banaras Hindu University, Varanasi, India
Published: October 2024

The man-machine interface encompasses a crucial area—emotion recognition through facial expressions. Despite its significance, emotion recognition faces challenges such as facial accessories, non- uniform illuminations, pose variations, audio speeches, text conversations, and hand and facial gestures. Understanding emotions like happiness, anger, anxiety, joy, and shock, along with their varying degrees and overlaps, is essential for accurate recognition. These nuances, inherent to humans, pose difficulties and costs in achieving standard results through facial recognition. Recognizing someone’s mood through facial expression, conversation, voice modulation, and gestures is a skill humans excel at. However, replicating this ability through facial recognition has proven challenging and costly. This paper addresses these challenges by proposing diverse approaches to emotion detection. By exploring various modes, including facial expressions, conversation analysis, voice modulation, and gestures, the paper tackles current research problems and holds practical applications in public experiments and exhaustive sentiment analysis. The paper presents a good combo of various modes of emotion recognition on multiple datasets (tried and tested widely before amalgamating all to produce an excellent optimal result as an output of the model).
EMPOWERING REMOTE SENSING: ANT COLONY OPTIMIZATION AND RCNN FOR PRECISION ENVIRONMENTAL MONITORING
Authors:   Ramco Institute of Technology, India, Kumar A. Arun
Published: October 2024

Remote sensing has become a critical tool in environmental monitoring, offering precise data collection over large areas. However, traditional methods face challenges such as high computational costs and lower accuracy in complex environments. The primary challenge is optimizing data processing to improve accuracy and efficiency in extracting valuable environmental information from remote sensing data. This study proposes a novel approach combining Ant Colony Optimization (ACO) and Region-based Convolutional Neural Networks (RCNN) for enhanced precision in environmental monitoring. ACO, inspired by the foraging behavior of ants, is used to optimize the parameters and feature selection process. RCNN, a deep learning model, is employed to detect and classify environmental features from remote sensing imagery. The integration of ACO with RCNN aims to enhance the model’s performance by selecting the most relevant features and optimal parameters, thereby reducing computational costs and improving accuracy. The proposed method was tested on a dataset of satellite images for land cover classification. The hybrid ACO-RCNN approach achieved a classification accuracy of 93.2%, outperforming traditional methods by 8.7%, and reduced computational time by 25%. These results demonstrate the efficacy of the proposed method in precision environmental monitoring.
OPTIMIZATION TECHNIQUE AND INTELLIGENT AGENTS FOR SUSTAINABLE FARMING SOLUTIONS
Authors:   SGT University, India, Kumar Sunil , K Hussain ,   Sharad Institute of Technology College of Engineering, India, Virmani Deepali ,   Guru Tegh Bahadur Institute of Technology, India, Kumar Subodh ,   Katihar Engineering College, India
Published: October 2024

In the quest for sustainable farming solutions, the integration of advanced computational techniques offers significant potential. This study addresses the problem of optimizing resource allocation in farming systems to maximize yield while minimizing environmental impact. We propose a novel method combining the Penguin Optimization Algorithm (POA) with Deep Reinforcement Learning (DRL). The POA, inspired by the hunting strategies of penguins, is employed to optimize farming parameters. Simultaneously, intelligent agents using DRL are trained to adapt and make real-time decisions for resource management. Results demonstrate a 25% increase in crop yield and a 15% reduction in water usage compared to traditional methods. Additionally, soil nutrient levels were maintained at optimal levels 90% of the time, ensuring long-term soil health. This hybrid approach presents a promising pathway toward achieving sustainable and efficient farming practices.
INTELLIGENT AGENTS AND DEEP LEARNING ALGORITHM BASED SEMANTIC SEGMENTATION FOR PRECISION AGRICULTURE ON SMART FARMING SOLUTIONS
Authors:   Sri Krishna Arts and Science College, India, N Karthik , B Dhevahi ,   Paavai Engineering College, India, P Thangavel ,   Government College of Engineering, Erode, India, S Mohanasundaram ,   Government College of Engineering, Erode, India
Published: October 2024

Background: The precision agriculture sector benefits greatly from advanced semantic segmentation techniques for land cover mapping and crop monitoring. Traditional methods often struggle with accuracy and efficiency due to the complexity of agricultural environments. Problem: Existing segmentation methods lack the ability to handle the diversity and scale of agricultural images effectively, leading to suboptimal classification and segmentation results. Method: This study introduces a three-stage semantic segmentation process leveraging deep learning and intelligent agents. The process begins with feature extraction using Chaotic Evolutionary Agents and parallel coding, followed by feature fusion and enhancement to create comprehensive feature maps. In the segmentation stage, a dual approach is adopted: region-based classification with U-Net for region candidates and pixel-based classification for fine-grained results. The final stage involves post-processing with boundary optimization to refine segmentation outputs. Results: The proposed method shows a significant improvement in segmentation accuracy and computational efficiency compared to existing methods. The method achieves an average accuracy of 92.5% and a reduction in processing time by 30% compared to traditional algorithms.
ENHANCING MEDICAL IMAGING FOR DIAGNOSIS AND TREATMENT USING NEURO FUZZY SYSTEMS
Authors:   Sri Eshwar College of Engineering, India, Parveen H. Summia , S Karthik ,   SNS College of Technology, India, M.S. Kavitha ,   SNS College of Technology, India, R Sabitha ,   SNS College of Technology, India
Published: October 2024

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Accurate and early diagnosis is critical for effective treatment. Traditional methods of medical imaging analysis often lack precision and efficiency. The challenge lies in enhancing the accuracy and efficiency of medical imaging analysis for CVD diagnosis using advanced computational methods. This study proposes a novel approach that integrates extreme learning machines (ELM) for feature extraction with neuro-fuzzy systems for classification. The ELMs efficiently extract relevant features from medical images, while the neuro-fuzzy systems classify these features with high accuracy. Experimental results demonstrate a significant improvement in diagnosis accuracy. The proposed method achieved a classification accuracy of 95.7%, sensitivity of 94.3%, and specificity of 96.2%. These results outperform several existing methods in terms of both accuracy and computational efficiency.
Cyclic Exclusive Enteral Nutrition as an Alternative to Medical Maintenance in Crohn Disease—A Case Report
Authors: Blackwell Max G , Brown Stephanie C , Day Andrew S
Published: October 2024

We present a case report of an 11-year-old girl diagnosed with Crohn’s Disease and managed with maintenance cyclic exclusive enteral nutrition with satisfactory long-term biochemical and symptomatic control.
From Gross Disproportionality to Human Dignity: Redefining Section 12 in the Context of Mandatory Minimum Sentences
Authors: Manikis Marie , Lanctot Marianne
Published: October 2024

In this short paper, we contend that human dignity must remain at the heart of the section 12 analysis, and should be strengthened in future cases that will be tackling the remaining mandatory minimums. In so doing, we critically examine the Supreme Court’s recent development of the section 12 framework, particularly in light of the 2023 mandatory minimum jurisprudence, with a specific emphasis on the concept of human dignity. The analysis centers on the role of reasonable hypotheticals in advancing the primary objective of section 12 — safeguarding human dignity — by scrutinizing the three pivotal components of the gross disproportionality analysis. We suggest that the section 12 analysis focuses on proportionality and human dignity rather than the current “gross disproportionality” standard in assessing what constitutes cruel and unusual punishment. These suggestions are underpinned by an approach that considers proportionality a principle rooted in human dignity. Nevertheless, proportionality has its limits and therefore section 12 should be complemented by an approach that takes into account prison conditions and the effects of imprisonment on marginalized groups, which are rooted in inequalities. Finally, the analysis questions the third component of the gross disproportionality analysis, which calls for deference to the legislature in order to prioritize objectives that instrumentalize the individual and are contrary to human dignity. These objectives are problematic, we suggest, and best addressed within section 1 of the Charter.
Pemeriksaan Magnetic Resonance Urography (MRU) dengan Bahan Kontras Oral Seduhan Daun Kopi Arabica Kerinci
Authors: Sudiyono Sudiyono , Rasyid Rasyid , Yosa Yori Andra
Published: October 2024

Latar Belakang: Bahan aktif Gadolinium (Gd) biasa digunakan untuk mendapatkan hasil citra Magnetic Resonance Urography (MRU) yang optimal, namun pada penggunaan media kontras ini terdapat kontra indikasi terhadap pasien yang mengalami permasalahan pada ginjal. Unsur mineral logam Mangan (Mn) dan besi (Fe) dapat dijadikan sebagai alternatif bahan kontras Magnetic Resonance Imaging (MRI). Telah dilakukan uji laboratorium, Mangan (Mn) dan Besi (Fe) dapat diperoleh secara alami pada teh daun kopi Arabika Kerinci.Tujuan: Penelitian ini bertujuan untuk mengetahui potensi seduhan teh daun kopi Arabika Kerinci sebagai bahan kontras oral pemeriksaan MRU.Metode: Penelitian eksperimental (kuantitatif-deskriptif) dengan jenis penelitian R&D dan pendekatan one group pretest-posttest only. Model penelitian menggunakan pengembangan model ADDIE (Analyze, Design, Development, Implementation, Evaluation). Sampel yang dilibatkan merupakan volunteer dengan rentang usia 22-40 tahun sebanyak 10 orang.Hasil: Analisis pada data menunjukan adanya hasil yang signifikan pada SNR pelvis ginjal dengan nilai p=0,037 dan CNR antara pelvis ginjal dengan medula ginjal dengan nilai p=0,007. Sedangkan pada variabel lain tidak ditemukan perbedaan signifikan dengan nilai p>α (>0,05).Kesimpulan: Sediaan daun kopi Arabika Kerinci dalam bentuk seduhan teh memiliki potensi untuk dijadikan sebagai media kontras oral pada pemeriksaan MRU.
Research on the Practice of Ideological and Political Education in Mathematics Courses in Vocational Universities Based on Classroom-Training-Competition Mode
Authors: Wang Shi , Li Xiaoyan
Published: October 2024

The purpose of this study is to explore how to effectively integrate ideological and political education into the mathematics curriculum by proposing a "combination of classroom teaching, practical training projects, and competition activities". By organically combining classroom teaching, practical training projects, and competition activities, it promotes students' learning of mathematical knowledge while enhancing their sense of social responsibility and professional ethics. Research results show that this model has significantly improved students' mathematics performance, especially in terms of basic knowledge and modelling ability. At the same time, by participating in mathematical modelling competitions and practical training, students have demonstrated strong teamwork spirit and innovation ability, and their professional ethics have been significantly improved. Through questionnaires and interviews, the feedback results show that students attach great importance to the practical value of the curriculum and ideological and political education. Finally, the research suggests that the integration of ideological and political elements into more mathematics courses should be explored in the future, in order to improve the quality of education and cultivate high-quality talents with a sense of social responsibility and professional ethics.
Exploring the Effects of Flight Attendants' Trust in Supervisor in the Pre-merger Context at Acquired Airlines
Authors:   Korean Academy of Leadership, Song Hyunjoo , Lee Jeong Won
Published: October 2024

Mergers and acquisitions (M&A) are becoming more active around the world, and large domestic airlines are also undergoing major M&A. However, existing studies focused on the post-merger integration process, and there is little research on the management of members at acquired organization particularly in the pre-merger situation. This study focuses on flight attendants, and explores the effects of trust in supervisor among members of the acquired airline before M&A on their work engagement and willingness to support for change after M&A. In addition, the mediating effect of members' self-leadership in the relationship between trust in supevisors on work engagement and intention to support change was tested. We surveyed acquired crew members in the pre-merger situation and analyzed the responses of 280 members. As a result of the analyses, supervisory trust showed a positive influence on work engagement and change support intention after M&A, and self-leadership mediated these relationships. Based on these results, theoretical and practical implications for management of members of the merged airline were suggested.
The Impact of Transformational Leadership on Work Engagement: The Dual Mediating Effects of Knowledge Sharing and Psychological Empowerment
Authors:   Korean Academy of Leadership, Chae Yeon Joo , Zhu Xue
Published: October 2024

While studies have explored the relationship between transformational leadership and work engagement and its mechanisms, they tend to focus on internal aspects of subordinates (e.g., motive, the meaning of work) or external aspects (e.g., organizational culture). This study recognizes the need to consider both attitudinal and behavioral aspects of subordinates in the process by which transformational leadership influences work engagement. In today's organizational environment, where team-based work is prevalent, knowledge sharing and psychological empowerment are important factors that foster an individual's enthusiasm for their work. This study posits knowledge sharing in the behavioral dimension and psychological empowerment in the attitudinal dimension to examine their mediating effects and differences in the relationship between transformational leadership and work engagement. Analyzing data collected from 584 individuals in China working in the service and education industries, the study found that both knowledge sharing and psychological empowerment significantly mediate the relationship between transformational leadership and work engagement. Psychological empowerment had a greater mediating effect compared to knowledge sharing. The implication of the study lies in examining the effects and mechanisms of transformational leadership on subordinates' attitudes and behaviors in the context of today's organizational management, where enthusiasm, beyond mere satisfaction, is crucial.
Akademisyenlerde Serbest Zamanda Sıkılma Algısı ile Sosyal Dahil Olma İlişkisinin İncelenmesi
Authors: Sönmez Ali , Başoğlu Umut Davut , Şengül Osman
Published: October 2024

Bu araştırma, akademisyenlerin serbest zamanlarında sıkılma algıları ile sosyal dahil olma düzeyleri arasındaki ilişkiyi incelemek amacıyla yapılmıştır. Ayrıca, çeşitli değişkenlere göre akademisyenlerin serbest zaman sıkılma algısı düzeyleri ve sosyal dahil olma düzeylerini karşılaştırılması da hedeflenmektedir. Çalışmada ilişkisel tarama modeli ve kolayda örnekleme yöntemi kullanılmış olup çalışma grubu, çeşitli üniversitelerde görev yapan ve bilgileri Yüksek Öğretim Kurumu bilgi panelinde yer alan 403 (Ortyaş = 41.15 ± 11.27) akademisyenden oluşturulmuştur. Katılımcılara "Serbest Zaman Sıkılma Algısı Ölçeği (SZSAÖ)" ve "Sosyal Dahil Olma Ölçeği (SDOÖ)" uygulanmıştır. Elde edilen verilerin analizi için tanımlayıcı istatistikler, çarpıklık ve basıklık testleri, MANOVA, ANOVA ve Pearson korelasyon analizi yöntemleri kullanılmıştır. MANOVA sonuçlarına göre, cinsiyet değişkeninin "SZSAÖ"nin "doyumsuzluk" ve "sıkılma" alt boyutlarında katılımcıların ortalama puanları arasında istatistiksel olarak anlamlı farklılık bulunmuştur (p< .05). Görev yapılan üniversite türü değişkenine göre yapılan analiz sonuçları da katılımcıların "SZSAÖ"nin "doyumsuzluk" alt boyutu ortalama puanlarında istatistiksel olarak anlamlı farklılık olduğunu göstermiştir (p< .05). Katılımcıların "SZSAÖ" ve "SDOÖ" ortalama puanları akademik unvan değişkenine göre incelendiğinde istatistiksel olarak anlamlı bir farklılık bulunmamıştır (p> .05). Ayrıca, serbest zaman aktivitelerine katılım durumu değişkenine göre yapılan analiz sonuçları, "SZSAÖ"nin "sıkılma" ve "SDOÖ"nün tüm alt boyutlarında katılımcıların ortalama puanlarında istatistiksel olarak anlamlı farklılıklar olduğunu göstermiştir (p< .01). Sonuç olarak, akademisyenlerde serbest zaman sıkılma algısı ile sosyal dahil olma düzeyi arasında istatistiksel olarak negatif yönde anlamlı bir ilişki olduğu belirlenmiştir. Bu durum, serbest zaman sıkılma algısının azaldıkça sosyal dahil olmanın arttığını göstermektedir.
PATERNALISMO DO CUIDADOR E AUTONOMIA DO IDOSO NO SERVIÇO DE ATENÇÃO DOMICILIAR
Authors: Rocha Tatiana Silva da , Sena Juliana Pinto de , Silva Alandelon Dourado da , Nascimento Isabel Regiane Cardoso do
Published: October 2024

Due to the drop in birth and death rates, global population aging has been increasing on a large scale. In Brazil, it is estimated that, in 2050, the elderly population will total 21.87%. Aging changes epidemiological characteristics and intensifies health services, making it necessary to build care alternatives in the face of these challenges. The care provided to the elderly depends on their physical and psychological conditions and the caregiver's view of aging and their care skills. From this view, paternalistic or elderly-centered care practices may emerge. This study investigated how caregiver paternalism occurs and its repercussions on the autonomy of elderly people monitored by the Home Care Service (SAD) in Fortaleza-CE. In field research with a qualitative and exploratory approach, semi-structured interviews were carried out with 6 elderly people and their caregivers. Data were collected at the patients' homes, between November and December 2023. According to the content analysis coding process by Bardin (2016), 03 thematic categories were obtained: 1) Caregiver's perception of the care provided to the elderly; 2) Paternalistic practices of the caregiver in caring for the elderly; 3) Repercussions of the caregiver's paternalism on the elderly's autonomy. The results confirm a paternalistic care practice characterized by overprotective attitudes, compromising the autonomy of the elderly. This makes us reflect on the importance of care that stimulates the autonomy of the elderly, understanding their subjectivity, preserving their dignity and guaranteeing their quality of life.
DILEMAS ÉTICOS E LEGAIS NA ADMINISTRAÇÃO DO PATRIMÔNIO DE CRIANÇAS E ADOLESCENTES
Authors: Magalhães Júlia Gonçalves Queiroz , Júnior Júlio Alves Caixêta
Published: October 2024

The article addresses the limits of asset management and the lives of minor children, especially when they have considerable assets or have been public figures since childhood, as in the case of Larissa Manoela. The topic involves ethical, legal and social dilemmas regarding the responsibility of parents in the administration of assets and careers of these minors. The objective of the research is to analyze the current legislation and identify the limits and challenges of parental authority, seeking solutions to guarantee the best interests of children, protecting their privacy and autonomy. The methodology used is inductive, with a qualitative focus, based on the analysis of specific cases and Brazilian legislation, complemented by a comparison with international practices. The study concludes that, although the Civil Code and the Statute of the Child and Adolescent provide a basic legal framework, there is a need for clearer regulations, more rigorous monitoring and supervision mechanisms to prevent mismanagement and ensure asset protection and the integral development of minors.
Simulation of stainless ferritic-martensitic and austenitic steel hardening after irradiation in ion accelerator. Part 1. Development of a methodology for determining	the ion mode irradiation of ferritic-martensitic steels
Authors: Margolin B. Z. , Sorokin A. A. , Belyaeva L. A.
Published: October 2024

A methodology for determining the irradiation mode for ferritic-martensitic steels at ion accelerator has been developed and experimentally substantiated, providing radiation hardening of these steels, identical to that realized under neutron irradiation. The change in Vickers microhardness is used as a measure of radiation hardening. A study was carried out of radiation-induced changes in the microhardness of ferritic-martensitic steels 07Kh12NMFB and EP-823 after neutron and ion irradiation to damaging doses of 10–30 dpa in the temperature range 350–600°C. These materials were irradiated with neutrons in the reactors BOR-60, BN-600 and in the ion accelerator of the State Scientific Center of the Russian Federation – Institute for Physics and Power Engineering named after A.I. Leypunsky (IPPE) with Fe3+, Fe4+ ions and He+ ions to concentrations of 0.2 and 4 appm/dpa. A transition function has been established that connects the irradiation temperatures for neutron and ion irradiation at a given damaging dose, ensuring the same radiation hardening.
Influence of heat treatment on phase and structure formation and magnetic properties of soft magnetic alloy 80NKhS manufactured by additive technology
Authors: Knyazyuk T. V. , Mukhamedzyanova L. V. , Yakovleva N. V. , Manninen S. A. , Zhukov A. S. , Bobyr V. V. , Kuznetsov P. A.
Published: October 2024

The evolution of the structure and phase composition of the soft magnetic alloy 80NKhS, produced by selective laser melting and annealed at different temperatures, was studied by light and electron microscopy, X-ray spectral and X-ray structural analyses. It has been established that a weakening of structural anisotropy and an increase in the average grain size occurs only at temperatures of 1250°C, associated with the oxides of Al, Ti, Si, Mn, Cr and nickel silicide previously formed during additive alloying. These phases have high thermal stability and inhibit grain growth, limiting the magnetic permeability of the alloy. To achieve the required level of magnetic properties, the soft magnetic alloy 80NKhS, manufactured by the additive method, must be annealed at higher temperatures than specified in GOST 10160–75.
On the physical-mechanical and tribotechnical properties of antifriction carbon fiber based on a modified thermosetting matrix
Authors: Dvoryantsev D. D. , Lishevich I. V. , Sargsyan A. S. , Savelov A. S. , Sharko E. A.
Published: October 2024

The physical-mechanical and tribological properties of anti-friction carbon fiber plastic UGET based on low-modulus carbon fabric “Ural T-15R” have been studied in order to increase the magnitude of the ultimate deformation and reduce the elastic modulus through the use of a modified thermosetting matrix ET-4 instead of the traditionally used ET-2.

Modes of polymerization and of heat treatment of epoxy binders were selected considering the results of the experiments. Prototypes of prepregs were obtained by solution impregnation on the UPST-1000M line and then processed into PCM by hot pressing. Laboratory samples were made and physical and mechanical tests were carried out to determine the ultimate strength under compression, shear and bending stress under destruction, as well as Charpy impact strength. Steel 20Kh13 and oxidized titanium alloy PT-3V were used as counter body rollers to determine carbon fiber tribosets.

It was shown that carbon fiber samples based on chemically modified binder ET-4 with two-stage polymerization and heat treatment mode in the range of 90°C to 180°C both have better physical-mechanical and tribotechnical properties, in contrast with the analogue with a three-stage mode and carbon fiber carbon heat at friction over 20Kh13 steel and oxidized titanium alloy PT-3V.
La palabra monarquía y el alcance de su significación en el De Republica ciceroniano = The word monarchy and the scope of its meaning in Ciceronian De Republica
Authors: Facciuto Kaed Anabella
Published: October 2024

El objetivo de la presente comunicación es reconstruir el significado del término monarquía, a la luz del De Republica, de Marco Tulio Cicerón. Teniendo en cuenta que dicha obra fue escrita en el período de crisis tardo-republicana, se describen, primeramente, las características de ese contexto histórico como clave para la comprensión del fin que se proponía alcanzar el Arpinate al escribir su tratado. En segundo lugar, se examinan los libros I y II de la obra en los que el autor desarrolla sus ideas en torno a la monarquía como forma pura de gobierno. En tercer lugar, se pone especial atención a la cronología de los reyes de Roma como un recurso histórico-metodológico al que Cicerón apela como parte de su argumentación en favor de la monarquía. El análisis efectuado en este trabajo permite demostrar que en su obra Cicerón conjuga una preferencia puramente teórica por la monarquía con la tradición radicalmente antimonárquica del pueblo al que pertenece y de la que él sinceramente participa. Frente a los peligros de la tiranía, cuyo máximo exponente es Tarquino el Soberbio, recomienda una forma mixta de gobierno, donde se armonicen los principios de la monarquía, la aristocracia y la democracia. Esa forma mixta de gobierno -del cual él ha formado parte- se encuentra en crisis y por eso intenta revalorizarla, con la esperanza de que la restauración de la república aún resulta posible.
ABSTRACT: The objective of this paper is to reconstruct the meaning of the term monarchy in the light of Marcus Tullius Cicero's De Republica. Bearing in mind that this work was written in the period of the late Republican crisis, we first describe the characteristics of this historical context as a key to understanding the purpose that the Arpinate intended to achieve in writing his treatise. Secondly, books I and II of the work are examined, in which the author develops his ideas on monarchy as a pure form of government. Thirdly, special attention is paid to the chronology of the kings of Rome as a historical-methodologicalresource to which Cicero appeals as part of his argument in favor of monarchy. The analysis carried out throughout in this work allows us to demonstrate that in his work Cicero combines a purely theoretical preference for the monarchy with the radically anti-monarchical tradition of the people to which he belongs and in which he sincerely participates. Faced with the dangers of tyranny, whose greatest exponent is Tarquin the Proud, he recommends a mixed form of government, where the principles of the monarchy, aristocracy and democracy are harmonized. This mixed form of government - of which he has been a part - is in crisis and for this reason he tries to revalue it, with the hope that the restoration of the republic is still possible.
Wspieranie ubezpieczeń rolnych przez państwo – aspekty prawne, ekonomiczne i społeczne
Authors: Lipińska Izabela
Published: October 2024

Działalność rolnicza wiąże się z koniecznością podejmowania przez rolników decyzji, których efekty mogą być odmienne od spodziewanych. Aby w pewnym stopniu zniwelować niepożądane utraty dochodów, konieczne jest zastosowanie pewnych instrumentów zarządzania ryzykiem. Wśród nich doniosłą rolę odgrywają zawierane umowy ubezpieczenia. Celem artykułu jest zatem wskazanie pozycji państwa w zakresie stosowania ubezpieczeń rolnych. W UE cały czas toczą się prace nad nowymi sposobami zarządzania ryzykiem i stabilizacją dochodów rolniczych. Jednocześnie poszukuje się środków, które by motywowały rolników do zawierania odpowiednich umów, a państwu dawały możliwość większego ich wsparcia.
Kanały dystrybucji żywca wieprzowego i sposoby rozliczeń producentów z zakładami mięsnymi
Authors: Szymańska Elżbieta
Published: October 2024

Celem badań było rozpoznanie kanałów dystrybucji tuczników z gospodarstw wyspecjalizowanych w produkcji żywca wieprzowego oraz systemów rozliczeń między producentami i zakładami mięsnymi. W analizie wykorzystano informacje z literatury przedmiotu i badań własnych, które zrealizowano w 100 gospodarstw trzodowych o dużej skali produkcji. Z badań wynika, że system dystrybucji żywca wieprzowego w Polsce jest bardzo uproszczony. Producenci tuczników najczęściej sprzedają surowiec bezpośrednio do zakładów mięsnych albo pośrednikom. Ze względu na zmienną koniunkturę większość transakcji na krajowym rynku odbywa się bez umów kontraktacyjnych. Ponadto, procesy koncentracji produkcji trzody chlewnej i wymagania konsumentów w zakresie jakości żywca wieprzowego wymuszają stosowanie rozliczeń uwzględniających klasyfikację tusz.
Preferencje podatkowe w rolnictwie.
Authors: Zabłocki Adrian
Published: October 2024

Ulgi i zwolnienia w podatkach pośrednich i bezpośrednich dla rolnictwa stanowią pomoc państwa dla jego obywateli. Pomoc ta ma na celu przyczynienie się m.in. do rozwoju, ożywienia oraz utrzymania niskich cen żywności. Każde przyznane zwolnienie oraz udzielona ulga powoduje zmniejszenie dochodów budżetu państwa oraz jednostek samorządu terytorialnego. Tego rodzaju straty budżetowe stanowią pomoc publiczną dla obywateli, którzy w wyniku jej otrzymania mogą zapewnić sobie dalsze swobodne finansowanie, rozwój przedsiębiorstwa, a także tańszy dostęp do wybranych dóbr i usług dla konsumentów.
THE EVOLUTION OF THE SUPPORT FOR THE SMALL AND MEDIUM SIZE ENTERPRISES SINCE THE POLAND ENTRANCE TO THE EUROPEAN UNION
Authors: Biernat-Jarka Agnieszka , Planutis Ewa
Published: October 2024

This article presents the types, scale and importance of supporting small and medium size enterprises from the structural funds in Poland since the accession to the European Union to the present time. The article presents the definition of small and medium size enterprises and the types of non-refundable and refundable financial support from the EU. The study also shows the amount of support received by Poland in the framework of individual operating programs in the financial perspective of 2004-2006 and 2007-2013 and the possibilities of supporting the sector in the current perspective of 2014-2020. Based on the analysis of data obtained from the Polish Agency for Enterprise Development, the evolution of existing forms of support for the SME sector in the context of solutions offered by the current financial perspective was presented.