bluuebunny/crossref_metadata_2025 huggingface dataset:
Stock Price Prediction Model: Assessing the Performance of a Hybrid Deep Learning Model Employing Multi-Stream Data
Authors: Khan Usama Waheed , Saeed Muhammad Bilal , Nadeem Aleena
Published: September 2024

Purpose - The study investigates the effectiveness of the ConvLSTM model in the next-day closing price prediction for stocks using a novel combination of input features. These features include past prices, prices of related stocks, technical indicators of the target stock, mutation point impact on closing price, stock market sentiment, stock market index, interest rate, and dollar exchange rate
Study Design/Methodology/Approach - Sentiment analysis of financial news related to the Pakistan Stock Exchange (PSX) was performed using the pre-trained FinBERT model. Relevant stocks were identified through historical prices using Random Forests and XGBoost algorithms. A comparative analysis was conducted involving ConvLSTM1D and ConvLSTM2D models, as well as LSTM, CNN, and CNN+LSTM models, all having the same network structure.
Findings - The study found that there is not a universal best model for every stock. However, the ConvLSTM1D model exhibited the best average performance for Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) across the selected stocks.
Practical Implications- The findings suggest that while no single model is optimal for all stocks, the ConvLSTM1D model may be particularly effective for improving prediction accuracy in stock closing prices. This can aid investors and analysts in making more informed decisions based on the model's performance metrics.
Originality/Novelty- This study provides a novel approach by integrating a diverse set of input features and comparing multiple models in a comprehensive manner. The use of sentiment analysis and advanced machine learning techniques such as ConvLSTM adds significant value to the field of stock price prediction.
In-community Stakeholders Participation in the Developmental Projects Implementation: The Case of Participatory Development in Pakistan
Authors: Habib Shahid
Published: September 2024

Purpose - This research aims to examine the level of participation of in-community stakeholders in the implementation of developmental projects in Pakistan, with a focus on participatory development.
Study Design/Methodology/Approach -The study has employed a qualitative methodology, using in-depth interviews and focus group discussions with stakeholders from selected communities in Pakistan. The data collected were analyzed using thematic analysis to identify patterns and themes in the stakeholders' experiences and perceptions of participation in developmental projects.
Findings - The findings reported that limited capacity and resources, as well as a lack of political will and support, can hinder the effectiveness of community participation in developmental projects. Involving local community stakeholders in the planning and implementation of developmental projects increases the chances of project success and sustainability. Engaging with community stakeholders can also improve transparency and accountability in project implementation, reducing the risk of corruption and mismanagement.
Originality/Value - The findings of this research have contributed to understanding the factors that influence in-community stakeholders' participation in developmental projects and provide insights into the effectiveness of participatory development approaches in Pakistan. The results of this research were useful for policymakers and development practitioners in designing and implementing more inclusive and effective developmental projects in Pakistan.
Research Limitations/Implications - The findings of research in one specific area of Islamabad, Pakistan may not be applicable or transferable to other regions or countries.
TÜRK MÛSİKÎSİNDE MÜŞTEREK TAKSİM GELENEĞİ: EROL DERAN VE AKA GÜNDÜZ KUTBAY’IN SEGÂH TAKSİM ÖRNEĞİ
Authors: Bayrakçı Ömer Faruk
Published: September 2024

ÖZ

Bu çalışmada, gerek dini gerekse dindışı mûsikî formlarının içerisinde önemli bir yere sahip olan taksim formunun bir türü olan müşterek taksim hakkında bilgi vermek ve taksim formundaki icraları pek çok müzisyen için örnek olan Kanuni Erol Deran ve Neyzen Aka Gündüz Kutbay’ın Segâh makamındaki müşterek taksimlerinin incelenmesi amaçlanmıştır. Müşterek taksim formunun farklı örneklerine de yer verilen çalışma için Erol Deran ve Aka Gündüz Kutbay’ın segâh müşterek taksimi notaya alınarak incelenmiştir. Taksimdeki form ve makam anlayışı üzerine yapılan analizlerle müşterek taksimin icrası hakkında genel bir fikir elde edilmesi amaçlanmıştır.
Gözlem, doküman analizi gibi veri toplama yöntemlerinin kullanıldığı nitel bir araştırma olan çalışmada genel tarama modeli kullanılmıştır. Türk mûsikîsi formları içerisinde taksimin yeri ve türleri hakkında bilgi verildikten sonra müşterek taksim konusu detaylandırılmış ve bu taksim türüne ait bazı örnek icralar paylaşılmıştır. Erol Deran ve Aka Gündüz Kutbay hakkında kısa bir biyografi verilmiş ve segâh müşterek taksimin icra kaydına ait link verilmiştir. Bu icra notaya alınmış ve melodik cümleler halinde form, makam, süre kullanımı gibi konular üzerinden değerlendirmeler yapılmıştır. İlgili literatür incelendiğinde taksim ya da eser analizi gibi çalışmaların yapıldığı ancak müşterek taksim özelinde bir çalışma yapılmadığı görülmüştür. Bu yönüyle çalışmanın bundan sonraki çalışmalara ışık tutacağı ve literatüre önemli bir katkı sunacağı düşünülmektedir.
NAVIGATING THE LANDSCAPE OF AI INTEGRATION IN EDUCATION: OPPORTUNITIES, CHALLENGES, AND ETHICAL CONSIDERATIONS FOR HARNESSING THE POTENTIAL OF ARTIFICIAL INTELLIGENCE (AI) FOR TEACHING AND LEARNING
Authors:   Assistant Professor, Yadav Dr Seema ,   The Bhopal School of Social Sciences College, Bhopal
Published: September 2024

The introduction of Artificial Intelligence (AI) into education marks a significant transformation in the approaches to teaching and learning. This paper explores the opportunities and challenges of using AI in educational settings, highlighting its potential to personalize instruction, facilitate learning activities, and enhance assessment processes. It also explores how teachers are increasingly serving as facilitators, mentors, and ethical guides, utilizing AI to enhance student-centred learning and develop critical thinking abilities. Emphasizing equity, inclusivity, and ethical considerations, the abstract underscores the importance of responsibly harnessing AI to empower learners and prepare them for success in a rapidly evolving world. The incorporation of Artificial Intelligence (AI) into education has attracted considerable interest due to its potential to transform teaching and learning experiences. This paper explores the multifaceted landscape of AI in education, examining the opportunities, challenges, and ethical considerations inherent in its implementation. Opportunities abound, with AI facilitating personalized learning experiences through adaptive systems, intelligent tutoring, and automated grading. However, challenges arise in ensuring equitable access to AI technologies, safeguarding privacy and data security, and mitigating algorithmic bias. Furthermore, concerns emerge regarding the preservation of human connection in the educational process amidst the proliferation of AI-driven tools. Ethical and legal considerations, including accountability, transparency, and fairness, underscore the need for responsible AI deployment in educational settings. Navigating these complexities through thoughtful consideration and collaboration among educators, policymakers, and stakeholders can unlock the transformative potential of AI in education, promoting equitable, inclusive, and effective teaching and learning practices.
SYNERGIZING REMOTE SENSING, MACHINE LEARNING, AND IOT: CLOUD-BASED INNOVATIONS IN DROUGHT PREDICTION
Authors:   Research Scholar,  Associate Professor, Kumar Chourishi Dhruvendra , Rajput Anil ,   Department of Computer Science Govt. Polytechnic College, Agar Malwa, M.P. OSD, Higher Education Department Satpura Bhavan, Bhopal M.P.
Published: September 2024

This paper explores cutting-edge advancements in leveraging remote sensing, machine learning (ML), and Internet of Things (IoT) technologies within cloud-based frameworks for enhanced drought prediction. By integrating data from satellites and IoT sensors, ML algorithms generate predictive models to identify drought patterns. Cloud computing facilitates scalable infrastructure for data processing and model deployment, foster- ing collaboration and resource optimization. Challenges including data integration and user engagement are addressed through interdisciplinary collaboration. It illustrates the efficacy of this integrated approach in informing decision-making and bolstering resilience in water resource management. Synergizing remote sensing, ML, and IoT within cloud-based frameworks offers a promising avenue f ,r mitigating the complexities of drought prediction and adaptation in the face of a changing climate.
What We Should Teach the Children
Authors: Randall David
Published: September 2024

Timothy Goeglein makes a strong moral and cultural case for teaching the “Great American Story.”
Redeeming Fraternities
Authors: Carle Robert
Published: September 2024

Anthony B. Bradley believes college fraternities can help alleviate the crisis our young men are experiencing.
Wynton Marsalis: What We Need Now
Authors: Asia Daniel
Published: September 2024

Composer and Professor of Composition Daniel Asia traces the rise of Jazz trumpeter Wynton Marsalis
The climate and vegetation of Europe, northern Africa, and the Middle East during the Last Glacial Maximum (21 000 yr BP) based on pollen data
Authors: Davis Basil A. S. , Fasel Marc , Kaplan Jed O. , Russo Emmanuele , Burke Ariane
Published: September 2024

Abstract. Pollen data represent one of the most widely available and spatially resolved sources of information about the past land cover and climate of the Last Glacial Maximum (LGM; 21 000 yr BP). Previous pollen data compilations for Europe, the Mediterranean, and the Middle East, however, have been limited by small numbers of sites and poor dating control. Here we present a new compilation of pollen data from the region that improves on both the number of sites (63) and the quality of the chronological control. Data were sourced from both public data archives and published (digitized) diagrams. The analysis is presented based on a standardized pollen taxonomy and sum, with maps shown for the major pollen taxa and biomes and the total arboreal pollen (AP), and on quantitative reconstructions of forest cover and of winter, summer, and annual temperatures and precipitation. The reconstructions are based on the modern analogue technique (MAT) adapted using plant functional type (PFT) scores and with a modern pollen dataset taken from the latest Eurasian Modern Pollen Database (EMPD) (∼8000 samples). A site-by-site comparison of the MAT and the inverse modelling method shows little or no significant difference between the methods for the LGM, indicating that the presence of low-CO2 conditions and no modern analogue during the LGM does not appear to have had a major effect on MAT transfer function performance. Previous pollen-based climate reconstructions using modern pollen datasets show a much colder and drier climate for the LGM than both inverse modelling and climate model simulations do, but our new results suggest much greater agreement. Differences between our latest MAT reconstruction and those in earlier studies can largely be attributed to bias in the small modern dataset previously used and to differences in the method itself (Brewer et al., 2008; Salonen et al., 2019). We also find that quantitative forest cover reconstructions show more forest than previously suggested by biome reconstructions but less forest than suggested by simply the percentage of arboreal pollen, although uncertainties remain large. Overall, we find that LGM climatic cooling and drying were significantly greater in winter than in summer but with large site-to-site variance that emphasizes the importance of topography and other local factors in controlling the climate and vegetation of the LGM.
The impact of particle precipitation on the ion-neutral collision frequency analyzed with EISCAT measurements
Authors: Günzkofer Florian , Stober Gunter , Kero Johan , Themens David R. , Gulbrandsen Njål , Tsutsumi Masaki , Borries Claudia
Published: September 2024

Abstract. The ion-neutral collision frequency is a key parameter for the coupling of the neutral atmosphere and the ionosphere. Especially in the mesosphere lower-thermosphere (MLT), the collision frequency is crucial for multiple processes e.g. Joule heating, neutral dynamo effects, and momentum transport due to ion drag. Very few approaches exist to directly infer ion-neutral collision frequency measurements in that altitude range. We apply the recently demonstrated difference spectrum fitting method to obtain the ion-neutral collision frequency from dual-frequency measurements with the EISCAT incoherent scatter radars in Tromsø. A 60-hour-long EISCAT campaign was conducted in December 2022. Strong variations of nighttime ionization rates were observed with electron densities at 95 km altitude varying from Ne,95 ∼ 109 − 1011 m−3 which indicates varying levels of particle precipitation. A second EISCAT campaign was conducted on 16 May 2024 capturing a Solar Energetic Particle (SEP) event, exhibiting constantly increased ionization due to particle precipitation in the lower E region Ne,95 ≳ 5 · 1010 m−3. We demonstrate that the particle precipitation significantly impacts the ion-neutral collision frequency profile. Assuming a rigid-sphere particle model, we derive neutral density profiles and show that the particle precipitation heating causes a significant uplift of neutral gas between about 90–110 km altitude. We additionally test the sensitivity of the difference spectrum method to different a priori collision frequency profiles.
Key Learnings and Considerations for Utilizing Social Media Recruitment in Parasport Research
Authors: Erica Lo  , Misener Laura
Published: September 2024

Despite the rise of digital methodologies in qualitative health and sports research (Goodyear & Bundon, 2020), there remains a gap in the usage of online methods in parasport populations. People with disabilities are often underrepresented in research as they are a traditionally hard-to-reach population due to accessibility limitations, stigmatization, and mistrust of researchers (Banas et al., 2019). The existing role of social media as a space for advocacy and social support in the parasport community (Bundon & Clarke, 2014) makes social platforms a potential tool for qualitative parasport research. Project Echo looks to leverage social media as a participant recruitment tool in parasport populations by informing recruitment strategies with the social model of disability and by placing an emphasis on collaboration to avoid the historical medicalization and marginalization of participants with disabilities in research. This article draws on the experiences of social media recruitment from Project Echo and aims to inform researchers looking to utilize social media as a research tool in parasport populations with key learnings and considerations.
Banas, J. R., Magasi, S., The, K., & Victorson, D. E. (2019). Recruiting and Retaining People With Disabilities for Qualitative Health Research: Challenges and Solutions. Qualitative Health Research, 29(7), 1056–1064. https://doi.org/10.1177/1049732319833361
Bundon, A., & Hurd Clarke, L. (2014). Unless you go online you are on your own: Blogging as a bridge in para-sport. Disability & Society, 30(2), 185–198. https://doi.org/10.1080/09687599.2014.973477
Goodyear, V., & Bundon, A. (2020). Contemporary digital qualitative research in sport, exercise and health: Introduction. Qualitative Research in Sport, Exercise and Health, 13, 1–10. https://doi.org/10.1080/2159676X.2020.1854836
“Teatro militante”: revisión crítica de un concepto situado
Authors: Verzero Lorena
Published: September 2024

En este artículo proponemos una revisión crítica del concepto de “teatro militante”, originalmente planteado en Verzero (2013). A diez años de la publicación de aquella investigación de fondo sobre la militancia artística y específicamente escénica, revisitamos la noción de teatro militante comenzando por reponer sus ejes vertebradores. Para ello, primeramente recuperamos la actualidad de los debates respecto de las definiciones de “teatro” y de “militancia”, y dejamos planteadas tensiones entre militancia y activismo artístico. Examinamos luego la metodología y las perspectivas epistemológicas que guían la investigación, para finalmente hacer lo mismo con algunas problemáticas y enfoques que atañen a la cuestión. El artículo intenta, en definitiva, iluminar el fenómeno desde el frágil presente que nos atraviesa.
Prevalence of Significant Bacteriuria in Asymptomatic Pregnant Women Visiting the Antenatal Care Out Patient Department of a Tertiary Care Hospital
Authors: Senapati Janumoni , Barua Purnima
Published: September 2024

Uriny tract infection (UTI) is the extension of microbial invasion anywhere between renal cortex of the kidney to the urethral meatus. Anatomically UTI can be classified into lower urinary tract infarection involving the bladder and urethra and upper urinary tract infection involving the kidney, pelvis, and ureter. The high prevalence of asymptomatic bacteriuria during pregnancy, the significant consequences for women and for pregnancy along with the ability to avoid sequelae with treatment justifies screening of pregnant women for bacteriuria
The Study was undertaken with the following objectives:
1. To determine the trimester wise prevalence of asymptomatic bacteriuria in pregnant women attending the Antenatal Care Out Patient Department of Jorhat Medical College And Hospital.
2. To determine the antibiotic sensitivity of the isolated organism.
A total of 113 participants were included in the study population belonging to all the three trimesters of pregnancy, attending the Antenatal Care OPD of Jorhat Medical College Hospital. Information on socio-demographic profile and personal habits were obtained using pre-tested and pre-formed questionnaires. Urine culture and antimicrobial susceptibility testing was done for all samples.
The overall prevalence of asymptomatic bacteriuria was found to be17.69%. Also, it was found that prevalence was higher in primigravidas (70%) and during second trimester (50%). The antibiotic susceptibility testing revealed that 100% of isolates were sensitive to Ciprofloxacin and Ceftriaxone followed by Cefotaxime (90%) and Gentamicin (80%).

Keywords: Significant bacteriuria, Pregnant Women, UTI
Comparative Analysis of Laparoscopic and Laparotomic Access in the Radical Treatment of Gastric Malignancies
Authors: Meilakh B. L. , Ustyantseva L. S. , Kiselev E. A. , Golodnov V. A.
Published: September 2024

Introduction. Stomach cancer continues to be one of the leading causes of mortality among malignant diseases. The study of various methods of surgical treatment is one of the directions of scientific search.

The purpose of the study — comparison of the frequency of postoperative complications with laparoscopic and laparotomic access in patients who underwent radical treatment of gastric malignancies.

Materials and methods. Retrospective evaluation of 189 patients diagnosed with malignant neoplasms of the stomach, operating under stationary conditions of the department of abdominal oncology of the Sverdlovsk Regional Oncology Center in 2023.

Results and discussion. Length of hospitalization of patients operated by laparoscopic method, was reliably lower than the same number of patients, operated with laparotomy, which allows you to start adjuvant chemotherapy at an earlier date and thereby improve the distant results. In general, the traumatic indicators of surgery were less important in patients, operated laparoscopic method.

Conclusion. Based on the data we’ve gathered, that although there is no statistically significant difference in the incidence of intraoperative and postoperative problems when performing laparoscopic and open radical operations in patients with malignant neoplasms of the stomach, the more benefits have laparoscopic access.
Experience in Treating a Patient with a Giant Cell Tumor of Complex Localization
Authors: Gusev D. A. , Lipin G. I.
Published: September 2024

Giant cell tumor (GCT) of bone is a relatively rare, benign neoplasm characterized by local destructive growth. Also in a small percentage of cases (2–3 %) there is a primary malignant GCT or malignancy during treatment. The tumor has a high risk of local recurrence after primary treatment. Considering most often the epiphyseal and metaepiphyseal lesions, the tumor is characterized by a high risk of pathological fracture, and leads to damage to the joint and disruption of its function. Difficulty in treatment arises with GCT of complex localization of the pelvic bones, spine, and skull bones. Considering the high risks of surgical treatment, the option of conservative long-term targeted therapy with denosumab is proposed in order to stabilize the process. This article highlights a clinical case of treatment and observation of a patient with pelvic localization of the primary process. During treatment, the patient completely regained weight bearing ability and quality of life. Denosumab is becoming a good option in the treatment of giant cell bone tumors of complex localization. Drug treatment with proper patient support is an alternative method of treating patients if it is possible to avoid unnecessarily aggressive surgical tactics in order to preserve the patient’s quality of life.
RESEARCH OF PROBLEM FACTORS AFFECTING INNOVATIVE DEVELOPMENT OF SERVICE INFRASTRUCTURE ENTERPRISES IN THE CONDITIONS OF DIGITALIZATION OF THE ECONOMY
Authors:   Russian State University named after A.N. Kosygina, Moscow, Russian Federation, Zharov V.G. , Ivanov A.D. ,   Joint Stock Company «Institute of Regional Economic Research», Moscow, Russian Federation
Published: September 2024

The results of the study of the current state of the service infrastructure presented in the article showed that in the conditions of the growing use of tools and means based on digital technologies, contradictions arise that impede the growth of the quality of life of the population and slow down the development of innovation. The conclusion is substantiated that innovations, thanks to which revolutionary changes occur, are possible only if the appropriate infrastructure is used, in this case we are talking about its service component. In turn, innovations, penetrating into the service infrastructure, create the prerequisites for its development, which can generate new forms and types of interaction between economic entities, for example, ecosystems. Problematic factors are identified that hinder the innovative development of service infrastructure in the context of the development of digitalization of the economy, including technical and technological, economic, economic security, internal, social and institutional.
Constitutional and legal aspects of state and public control (in line with the views of Professor E.I. Kozlova)
Authors:   Department of Regulatory Policy of the Government of the  Russian Federation, Moscow, Russian Federation, Spiridonov A.A.
Published: September 2024

In the article the author, starting from the constitutional and legal views of Professor E.I. Kozlova, examines modern significant constitutional and legal foundations for the regulation and development of state, municipal and public control. A number of proposals have been formulated, including regarding: strengthening the focus in all control and supervisory activities on individual rights and freedoms; creating a unified Internet portal for public control in the Russian Federation and regulating the participation of the Public Chamber of the Russian Federation in ensuring its effective functioning; developing approaches to enshrining in the Constitution of the Russian Federation key basic provisions on state control (supervision), municipal control and public control. It is noted that the stated proposals lie in the general vein of those value-semantic attitudes that Professor E.I. Kozlovа, expressed in her works and conversations with studentsand and their implementation in the future will serve the further development of the Russian Federation as a constitutional state.
Recommendations for improving the existing support system for Russian exporters
Authors:   Institute of Economics and Finance, Moscow, Russian Federation, Andrianov K.N.
Published: September 2024

In this article, the author, on the basis of an earlier analysis of the effectiveness and efficiency of the current system of state support for domestic exporters in Russia, as well as on the basis of the analysis of the relevant international best practices, formulated recommendations and proposals for improving this system. In addition, the author gives specific recommendations and proposals for eliminating barriers and solving problems of domestic export-oriented enterprises in the context of the stages of the life cycle of an export project. At the same time, in this article, the author, on the basis of a survey conducted among representatives of Russian exporters (small and medium-sized enterprises), formulated additional proposals for state support measures to solve their problems arising at all stages of the life cycle of their export projects.In addition, the author formulated recommendations for improving the Federal Law "On the Foundations of State Regulation of Foreign Trade Activity".
The features of the Free Port of Vladivostok as a preferential regime-territory
Authors:   Institution of Science Institute of Economics of the Russian Academy of Sciences, Moscow, Russian Federation, Vilenskiy A.V.
Published: September 2024

The article presents an analysis of the national economic significance of cross-border trade for the constituent entities of the Russian Federation and the country's economy as a whole in the context of the application of collective Western sanctions against Russia. In Russia, it is not unreasonable to treat border areas as barriers to protecting the country’s territory from geopolitical threats. However, cross-border trade brings a full-fledged positive socio-economic effect with the maximum possible openness of the country’s economy, while minimizing administrative obstacles to its development. The desire for openness of Russian border trade must be maintained even in the face of tough Western sanctions. The article discusses mechanisms for maintaining a balance between border security and high economic activity.
Comparative analysis of investment conditions and opportunities for Russian business in Asian countries (using the example of the Republic of Uzbekistan and UAE)
Authors:   Chamber of Commerce and Industry of the Russian Federation, Moscow, Russian Federation, Ignatiev V.S. , Burak P.P. ,   Joint Stock Company «Institute  of Regional Economic Research», Moscow, Russian Federation
Published: September 2024

The article examines, in comparative terms, the main parameters of investment attractiveness for Russian business of the economies of the Republic of Uzbekistan and UAE. These countries are typical representatives of two types of economies - industrially developing post-Soviet economies and oil and gas financial centers of the Persian Gulf. Such parameters of national investment attractiveness as stability of exchange rates, labor costs, and the cost of energy resources are considered.
Optimization of the method of comprehensive assessment and analysis of the socio-economic efficiency of housing and civil construction in the conditions of integrated and sustainable development of territories
Authors:   Joint Stock Company «Institute  of Regional Economic Research», Moscow, Russian Federation, Rostanets V.G. , Klunduk M.A. ,   Joint Stock Company «Institute  of Regional Economic Research», Moscow, Russian Federation
Published: September 2024

Many cities of the Russian Federation are implementing a policy of integrated and sustainable development of territories. Investment projects implemented within the framework of this policy have features in terms of commercial return, forms of management, duration, social and environmental results. The article discusses methodological issues of assessing the effectiveness of such projects in order to maximize the accuracy of calculations, take into account all the main and accompanying factors, the dynamics of the market situation, and the pronounced stages of implementation of investment projects for the integrated and sustainable development of territories.
Evaluation of Head and Neck Squamous Cell Cancer Treatment and Outcomes: Low-Dose Nivolumab in Palliative Treatment, A Literature Review
Authors: Pastorello Julia , Lando Emanuela
Published: September 2024

The chemotherapy treatment based on Taxol, paclitaxel is widely used in the treatment of neoplastic conditions in the head and neck, however in the last decades immunotherapy has made a revolution in the context of oncological treatments. The Anti-PDL1 agent, Nivolumab, has been proving to be of great value in the treatment of solid tumors. Through the review method this study aims to synthesize and analyze treatments for advanced head and neck cancer, in addition to critically analyzing existing studies on the use of reduced doses of nivolumab in patients with head and neck cancer. Thus, we aim to elucidate the action of oncological agents used in the treatment of SCC, as well as confirm the superiority of immunotherapeutic treatment in cases of advanced stage SCC.
Tasa de egresos hospitalarios por fístula del conducto biliar en el periodo 2019 - 2022 en Chile
Authors: Garcia Delgado Loreto , Espinoza Muñoz Alonso , Meneses Barrera Natalia , Mocarquer Olivares Lourdes , De la Fuente Galvez Sebastián , Correa Santibañez Pablo
Published: September 2024

Las fístulas del conducto biliar son una patología rara que se desarrolla a partir de un proceso inflamatorio en la vía biliar. Este estudio describe la Tasa de Egresos Hospitalarios por fístula del conducto biliar en Chile entre 2019 y 2022. Metodología: Estudio observacional, descriptivo y transversal, utilizando datos del DEIS del Instituto Nacional de Estadística. Se analizaron los egresos hospitalarios por fístula del conducto biliar en Chile entre 2019 y 2022, según sexo y grupo etario (n=261). Se calculó la Tasa de Egresos Hospitalarios. Resultados: La Tasa de Egresos Hospitalarios del período fue de 0,37 casos por cada 100.000 habitantes. El año con mayor Tasa de Egresos Hospitalarios fue 2019 con 0,47, mientras que los menores fueron el 2020 con 0,26 y 2021 con 0,35. El sexo femenino tuvo la mayor tasa con 0,43. El grupo etario de 65-79 años presentó la mayor tasa con 1,24. Discusión: El estudio mostró una disminución de la Tasa de Egresos Hospitalarios por fístula del conducto biliar en 2020 y 2021, posiblemente debido a la pandemia de COVID-19, que redujo las cirugías electivas. El sexo femenino tuvo la mayor tasa, probablemente debido a factores de riesgo como obesidad y anticonceptivos orales. El grupo etario de 65-79 años tuvo la mayor tasa, lo que podría deberse al aumento de comorbilidades en pacientes mayores. Conclusión: Los estudios epidemiológicos son fundamentales para entender los factores de riesgo y orientar medidas de intervención en salud primaria y secundaria para pacientes con fístulas del conducto biliar.
Establishing The Relationship Between Profitability and Growth of Unicorns in India
Authors: Dwaraka Nath Mohapatra
Published: September 2024

This paper attempts to analyze if there is any relationship between profitability and the growth of Indian unicorns. Indian unicorns have attracted worldwide attention by getting huge investments and a growing club of over 100 unicorns. Indian unicorns have raised more than $100 Bn in funding as reported by INC42.com, a unicorn tracker website. The fact is that only one-third of Indian unicorns are profitable. As the Indian unicorns show contrasting characteristics of attracting huge funds with negative profitability, researchers are curious to find the peculiar phenomenon. For this study, the financial data of the ten listed unicorns were taken and analysis was made with the help of the Pearson correlation matrix. The result shows that negative EBIT does not significantly influence the growth of numbers and attracting investments. Our findings show that the impact of the interest and tax burden is minimal on the growth of Unicorns in India.
On Graph Polynomials of a Chain of Quadrilateral Graph
Authors: Mamoon F. Khalf
Published: September 2024

In this paper, we present the concept of Schultz, modified Schultz and Hosoya Polynomials to connect a chain of vertices of rings of a quadrilateral graph, half horizontal and half vertical, with a path of length one and two. In addition to some special results.
Stability Of Equilibrium States For A Stochastically Perturbed Pielou’s Equation [
Authors: A. Lallouche
Published: September 2024

It is widely recognized that the theory of stochastic difference equations is a mathematical area of great interest. Concrete systems and processes is one of its subareas, which is of some interest nowadays. Specifically, in fields such as systems biology, ecology, biochemistry, genetics, and physiology dynamics, numerous population models are described by studying linear and nonlinear difference equations and systems. These studies have been very productive and helpful to develop the basic theory of the qualitative behaviour of nonlinear rational and exponential difference equations and systems. In this paper, we investigate a type of Pielou’s difference equation that is subject to additive stochastic perturbations modelled as a sequence of independent random variables (ξ_n ) with zero mean and unit variance. These perturbations are proportional to the deviation of the system state \(X_n \) from its equilibrium points, resulting in the following stochastic difference equation. X_(n+1)=(αX_n)/(1+X_(n-1) )+σ(X_n-X^* ) ξ_(n+1)      where parameters  a and σ have arbitrary values. Utilizing the general method of constructing Lyapunov functional for stochastic difference equations in discrete time, we establish necessary and sufficient conditions for the asymptotic mean square stability of the two equilibrium points (zero and positive). These conditions also serve as sufficient conditions for the stability in probability of the equilibrium points of the original nonlinear equation. To validate the derived results, numerical examples and simulations of equation solutions are presented, accompanied by numerous graphical illustrations of stability trajectories of solutions.
Handling Missing Data through Artificial Neural Network
Authors: Geeta Chhabra
Published: September 2024

Missing data is a difficult problem to solve when working with real-world datasets. It is vital to improve data quality by imputing missing values in order to obtain effective data learning. Deep learning has recently risen to prominence as the most effective sort of machine learning technique for uncovering hidden knowledge in massive datasets and making accurate predictions. We have used “back-propagation artificial neural network” to impute categorical missing data. The main goal is to see how well “neural network” compares to statistics and machine learning for resolving categorical missing data. The results of “back-propagation” are compared with multiple imputation and random forest. It consistently outperforms alternative methods both in  “training” and “test” data sets, showing that “neural network” is a suitable for reconstructing “missing values” in “multivariate analysis”.
IoT-Enabled Real-Time Monitoring and Machine Learning-Based Prediction System for Optimizing Tea Withering Process
Authors: B. Rajkumar
Published: September 2024

The withering process in tea production is critical for ensuring high-quality tea but traditionally relies on manual monitoring, leading to inconsistencies. This paper presents an IoT-based real-time monitoring system integrated with machine learning to optimize the withering process. Using ThingSpeak as the IoT dashboard, the system gathers real-time environmental data such as temperature, humidity, and moisture content. Machine learning models, XGBoost and LightGBM, predict moisture content based on this data, enabling proactive adjustments. XGBoost achieved a Mean Absolute Error (MAE) of 0.99 and an R-squared (R²) value of 0.92, demonstrating high predictive accuracy. The system’s real-time insights help tea producers optimize the withering process, improving quality and reducing waste. This research offers a scalable solution for both small and large tea producers, showcasing the potential of IoT and machine learning in modernizing agriculture.Introduction: Tea production relies heavily on the withering process, traditionally monitored manually, which can be inconsistent. By integrating IoT and machine learning, real-time data is collected to predict moisture levels, offering more control. This research uses XGBoost and LightGBM models, with ThingSpeak for data visualization, to reduce errors and improve tea quality.Objectives: This research aims to create a real-time IoT-based system that monitors and controls environmental factors during tea withering. By predicting moisture levels using XGBoost and LightGBM, the system improves tea quality and operational efficiency. It also seeks to ensure scalability and reduce variability through automation and data-driven insights.Methods: DHT22 and moisture sensors connected to an ESP32 microcontroller collected environmental data, sent to ThingSpeak for analysis. XGBoost and LightGBM models, optimized with GridSearchCV, predicted moisture content. The system provided real-time visualizations and alerts for better decision-making during tea withering.Results: The system improved moisture control, with XGBoost achieving an MAE of 0.99 and LightGBM 1.01. Real-time monitoring via ThingSpeak enabled better decisions, adapting well to environmental changes. The system proved scalable, benefiting both small and industrial tea producers.Conclusions: IoT and machine learning enhance tea withering by providing real-time insights and predictive analytics. The ThingSpeak dashboard effectively visualizes data, helping optimize tea production. Future improvements will focus on refining models and enhancing the user interface for better scalability and usability.
Novel Picture of Topological Spaces in the Frame of Fuzzy Soft Sequential Sets
Authors: K. Japhia Tino Mercy
Published: September 2024

The goal of this paper is to introduce fuzzy soft sequential topological spaces over the universal set via fuzzy soft sequential set by merging the concept of fuzzy soft set and sequential set. We provide a comprehensive study of their  properties and explore a relationship between fuzzy soft topology and fuzzy soft sequential topology. We offers a new outlook on the theory of fuzzy soft sequential neighborhood of a fuzzy soft sequential set and we investigate the quasi-coincident relation.
Enhanced SVM Classification for Diabetes Prediction: A Comparative Analysis Using the Kaggles Diabetes Dataset
Authors: Vaman M. Haji
Published: September 2024

Diabetes mellitus is a significant global health concern that impacts a large number of individuals globally and imposes a substantial financial burden on healthcare systems. The aim of this study is to use machine learning methods, namely Support Vector Machines (SVM), to develop a prediction model for assessing the risk of diabetes using the Kaggle diabetes dataset. We used a comprehensive dataset sourced from Kaggle, which encompasses several health metrics such as age, body mass index (BMI), glucose levels, and other relevant factors. In order to identify patterns that indicate a potential risk of diabetes, our approach included doing data pre-processing, selecting relevant features, and implementing a Support Vector Machine (SVM) classifier. In order to assure the strength and reliability of the SVM model, it was trained and validated using standard cross-validation techniques. We evaluated its performance by using F1-score, accuracy, precision, and recall criteria. Based on our findings, the SVM approach shows promise in predicting the risk of diabetes, with an accuracy of 83.12% on the test set. Despite the encouraging findings, we acknowledge the need for future improvement of the model and the limits of our work. Subsequent investigations might use deep learning techniques or ensemble approaches to enhance the accuracy of predictions. This work contributes to the growing body of research on the use of machine learning in healthcare and has the potential to influence strategies for early identification and prevention of diabetes. Prior to considering actual implementation, more clinical validation is necessary. This introduction, written in APA format, specifically examines studies that used the Kaggle diabetes dataset or similar datasets for the purpose of predicting diabetes. It includes a minimum of 12 references.
Some Results on Non-Isolated Resolving Number
Authors: P. Jeya Bala Chitra
Published: September 2024

Connected graphs G. W = {w1, w2,..., wk} is a subset of V with a predetermined order. The line vector r(v|W) = (d(v, w1), d(v, w2),..., d(v, wk)) is the measurement depicting v with regards to W for each v ∈ V. If V's vertex utilize various metrics, W resolves G. Their fundamental magnitude, dim(G), is their lowest cardinality. A resolved set W is non-isolated if its influenced subsection ⟨W ⟩ has no single vertex. The simplest connection of a non-isolated resolved set of G is nr. An nr-set for G is a non-isolated resolution set of cardinality nr(G). In this study, we prove that the chart G has a unique nr-set. We also build a 2n-vertex graph G using nr-set. W which means nr(G) = n and r(vi|W) = (1,...2, 1), where 2 is in the ith location, represents every vertex not in W. Further we established the nr-value for the highly irregular graph Hn,n and for the Wheel Wn. Also we determined the nr-value for corona product of some graphs.
Empowering Customer Feedback Analysis through LSTM-Enhanced Natural Language Processing on Audio Recordings
Authors: Sapna Sharma
Published: September 2024

Social media platforms such as Twitter, Facebook, blogs and others act as huge repositories of global information, contributing to the increase in the amount of data including images/video, sound and text due to the consumption of social media and e-commerce websites for large application Despite the proposed techniques, challenges remain, especially in managing large volumes of audio recordings. The on-going challenge of accurate polarity detection in consumer surveys is complicated by extracting accurate meaning from audio data including surveys, voice messages, comments, tweets, and posts, which has to do with sentiment analysis matching audio data The method includes data collection, pre-processing, feature coding , and segmentation steps. The importance of appropriate data collection, pre-processing, and classification analysis is emphasized when interpreting such data. Audio datasets were used to evaluate the effectiveness of the proposed models. The proposed sensitivity prediction method exhibits superior or comparable results with reduced computational complexity. The results not only highlight the critical importance of sentiment analysis in deriving meaningful insights from audio data in consumer research and social media but also explore the integration of LSTM algorithms with captured audio into a rope, thus initiating a new phase of sensory analysis research. Furthermore, accuracy rates were examined for sensitivity analysis in diversity data sets. The Amazon Fine Food dataset achieved 98.5% accuracy, while the Phones & Accessories dataset achieved 98% accuracy. Additionally, the Amazon product dataset achieved 96% accuracy. These high accuracy rates highlight the effectiveness of the sensitivity analysis methodology used, especially in reviewing various items.
A Deep Learning-Based Model for the Detection and Tracking of Animals for Safety and Management of Financial Loss
Authors: Nidhi Jain
Published: September 2024

Introduction: The paper presents a deep learning-based model for detecting and tracking animals to protect agricultural fields. It addresses the challenge of crop destruction by stray animals, a significant issue for farmers, by proposing an intelligent camera-based solution. This solution utilizes IoT for real-time information transfer and employs advanced image and video processing methods, including YoloV5, for efficient animal detection and classification. The study evaluates various existing solutions and highlights the advantages of the proposed model in terms of accuracy and cost-effectiveness, offering a promising approach to mitigate animal raids on crops.
MDVPB: Design of an Efficient Modified Densenet-161 Model for Advanced Vein Pattern Recognition in Biomedical Systems
Authors: Manisha A.Gawande
Published: September 2024

There is an ever-increasing need for an efficient, and accurate vein detection system that can work in clinical scenarios. Traditional methods, while effective, grapple with challenges like high error rates and susceptibility to environmental variations, making the need for advanced solutions more pressing. This study addresses these limitations by unveiling a groundbreaking Convolutional Neural Network (CNN) architecture, tailored specifically for the nuanced recognition of vein patterns, a critical component in biomedical systems. The existing frameworks in vein pattern recognition often falter in handling intricate variations, leading to diminished accuracy and reliability levels. The proposed model revolutionizes this landscape by integrating a modified Densenet-161 structure for analyzing blood flow velocity and recognizing dorsal hand vein locations. The ingenuity lies in the removal of the conventional classification layer, transforming it into a robust feature embedder. This strategic alteration enables the extraction of remarkably distinct vein features from hand images, a leap forward in capturing the unique identifiers crucial in biomedical systems. The methodology employed is twofold: a meticulous training and validation phase, followed by a testing phase under different verification scenarios. The training harnesses the power of the CNN feature embedder, extracting and classifying features with an unprecedented precision. The testing phase introduces a dual input system, comparing known and unknown vein patterns using Euclidean distance, a method that quantifies similarity with remarkable accuracy levels. This process significantly reduces intra-class variations while amplifying inter-class differences, culminating in a system that is not only highly accurate but also reliable for clinical scenarios. The model's superiority is further evidenced by its performance on various datasets, showcasing improvements in precision, accuracy, recall, AUC, and reduced delay, outperforming existing methods by notable margins. The model can thus be used for intravenous catheterization, blood check-up, and analysis of different veins identified related to different diseases.