bluuebunny/crossref_metadata_2025 huggingface dataset:
Telemedicine for ENT: Quality of Care During the COVID-19 Pandemic at the Jose R. Reyes Memorial Medical Center in 2022
Authors: Castro Janella Mara , Chua Ryan
Published: November 2024

Objective: To assess the quality of care of telemedicine consultation during the COVID-19 pandemic at the Jose R. Reyes Memorial Medical Center in 2022Methods:Design: Cross-Sectional StudySetting: Tertiary Government Training HospitalParticipants: 210 patients
Results: Majority of the respondents were female, less than 40 years of age, unemployed, consulted for the first time, lived < 25km distance from the hospital, consulted because of an ear problem, consulted without symptoms suggestive of COVID-19 and had higher outcomes of face-to-face consultation. The overall satisfaction with teleconsultation was good with majority of patients satisfied and very satisfied with teleconsultation (4.29±1.083). Occupational status, technical problems encountered in the teleconsultation, and those who were bothered by the absence of clinical examination were associated with the perceived quality of care of the ENT teleconsultation (p < .05). Among the population who were satisfied to ENT teleconsultation, 82 (43%) were from the working population (OR 2.2 [1.1-4.7]), 158 (95%) were satisfied with quality of response (OR 23.7 [9.4-59.8]) and 154 (93%) were satisfied with image quality (OR 16.9 [7.3-39.0]).
Conclusion: Teleconsultation for ENT has proven useful in terms of satisfying the urgent concerns of patients in a pandemic setting.
A COMPARATIVE ANALYSIS OF DEMOCRATIC SYSTEMS: INDONESIA AND ENGLAND.
Authors: Ramadhani Salmaa Al Zahra , Taufiqi Muhammad Muammar
Published: November 2024

The democratic system basically realises balance and justice for the entire community or people without any discrimination. One of its implementations is the process in general elections conducted every five years to elect executives and legislatures. There is something wrong with the implementation of this democratic system from the process and even the rules used, this research wants to discuss the constitutional system in the democratic process that is wrong or just the way the process is taken. This research is expected to be an evaluation of the Indonesian State towards the electoral system used. Normative juridical research by analysing a regulation that is directly related to legal issues. The result of this research is that Indonesia can adopt the party system and election system from the United Kingdom because it uses a two-party party system that only has two parties, namely the winner and the opposition and a district election system that only nominates one candidate from one party for one region.
Pregão presencial e eletrônico: avaliação da contratação de bens e serviços do município de Palmácia/CE
Authors: Do Nascimento Cavalcante José Rodrigo , Oliveira Lima Alexandre , Dos Santos Ruan Carlos , Lopes Pinheiro Carlos Henrique
Published: November 2024

Este trabalho estuda as licitações da modalidade pregão no município de Palmácia/CE, município do interior do estado do Ceará que faz parte do Maciço de Baturité. A partir das especificidades de um município do interior, frente à barreira decorrente do período da pandemia de covid-19, a pesquisa direciona a observação para a modalidade de licitação pregão em seus modos presencial e eletrônico. A presente pesquisa tem como objetivo evidenciar como se comportaram as relações das variações da modalidade de licitação pregão (presencial e eletrônico) nas contratações de bens e serviços no município de Palmácia/CE entre os anos de 2019 a 2022. Para o atingimento desse objetivo, a pesquisa foi construída a partir de uma abordagem qualitativa e quantitativa, simultaneamente, com objetivos voltados para pesquisa exploratória, com procedimentos focados na pesquisa documental e de campo, a coleta de dados foi realizada por meio do site do Tribunal de Contas do Estado do Ceará (TCE), assim, obtendo documentações referentes aos certames realizados no período pesquisado. O período é marcado pela mudança na metodologia de contratações da modalidade pregão, onde o município parte em 2019 da utilização do pregão presencial como método de contratação, e durante o período desenvolve a utilização do pregão eletrônico, resultando, por fim, no majoritário uso do meio eletrônico na modalidade. Ademais, a pesquisa conclui que as demandas provenientes das barreiras impostas pela pandemia resultaram no desenvolvimento da modalidade pregão pelo município, desenvolvendo o pregão eletrônico como ferramenta que impulsiona a capacidade de contratação do município.
Transformation of Healthcare Analytics : Cloud-Powered Solutions with Data Science, ML, and LLMs
Authors: Santhosh Kumar Pendyala
Published: November 2024

Cloud-powered solutions have emerged as transformative forces in healthcare data analytics, revolutionizing how healthcare organizations process, analyze, and utilize patient data. These solutions leverage data science, Large Language Models (LLMs), and machine learning technologies to revolutionize patient care and enhance healthcare system efficiency. This comprehensive study analyzes the Cloud infrastructure integration in healthcare facilities demonstrating a remarkable 72.3% improvement in clinical documentation efficiency and average annual savings of $4.7 million per facility. Also, these technologies enhance healthcare accessibility, improve public health monitoring, economic outcomes, and strengthen data security on healthcare delivery across urban and rural settings. This article explores the impact of cloud computing across five critical dimensions: infrastructure capabilities, data science, machine learning integration, LLM applications, and societal impact. This paper provides quantitative evidence of cloud computing's role in advancing global healthcare innovation on precision medicine, real-time insights, unparalleled operational efficiencies while ensuring robust security and regulatory compliance.
A Comparative Analysis of GPT-3.5, GPT-4 and GPT-4.o in Heart Failure
Authors: Günay-polatkan Şeyda , Sığırlı Deniz
Published: November 2024

Digitalization have increasingly penetrated in healthcare. Generative artificial intelligence (AI) is a type of AI technology that can generate new content. Patients can use AI-powered chatbots to get medical information. Heart failure is a syndrome with high morbidity and mortality. Patients search about heart failure in many web sites commonly. This study aimed to assess Large Language Models (LLMs) -ChatGPT 3.5, GPT-4 and GPT-4.o- in terms of their accuracy in answering the questions about heart failure (HF). Thirteen questions regarding to the definition, causes, signs and symptoms, complications, treatment and lifestyle recommendations of the HF were evaluated. These questions to assess the knowledge and awareness of medical students about heart failure were taken from a previous study in literature. Of the students who participated in this study, 158 (58.7%) were first-year students, while 111 (41.3%) were sixth-year students and were taking their cardiology internship in their fourth year. The questions were entered in Turkish language and 2 cardiologists with over ten years of experience evaluated the responses generated by different models including GPT-3.5, GPT-4 and GPT-4.o. ChatGPT-3.5 yielded “correct” responses to 8/13 (61.5%) of the questions whereas, GPT-4 yielded “correct” responses to 11/13 (84.6%) of the questions. All of the responses of GPT-4.o were accurate and complete. Performance of medical students did not include 100% correct answers for any question. This study revealed that performance of GPT-4.o was superior to GPT-3.5, but similar with GPT-4
The Role of Proton Magnetic Resonance Spectroscopy in the Diagnosis of Galactosemia in Neonates
Authors: Çakır Salih Çağrı , Orcan Cengiz Gökhan , Dorum Bayram Ali , Özkan Hilal , Köksal Nilgün , Yıldız Sevil , Erdöl Şahin
Published: November 2024

Classic galactosemia, the most common form of galactosemia, is a disorder of galactose metabolism caused by the hereditary deficiency of the galactose-1-phosphate uridyl transferase (GALT) enzyme. Life-threatening toxic symptoms and brain edema occur during the neonatal period due to the accumulation of galactose and its metabolites in the tissues of patients without galactose restriction. Galactitol, a toxic substance that accumulates in the brain, is identified as abnormal peaks in Magnetic Resonance Spectroscopy (MRS). We have reported a 22-day-old galactosemic neonate diagnosed with a galactitol peak in brain MRS. Brain H-MRS is a valuable method for early diagnosis of galactosemia patients.
Biosecurity of service dog environments
Authors: Farionik T. , Kolechko A. , Krempa N.
Published: November 2024

The relevance of this study is due to the lack of information space regarding potential biological threats when keeping service dogs and their management methods. The purpose of the work is to analyze and clarify the meaning of the terms “bioprotection and biosecurity”, outline the methods of ensuring biological protection and safety when working with service dogs, expand and generalize the understanding of this issue both in practical and theoretical-legal fields of implementation and implementation in practice In the course of research and analysis of scientific literature, certain general conclusions were formed, which allow to better understand and distinguish specific practical and theoretical-legal components of ensuring biological protection and safety of service dog enclosures. Therefore, the biological management system should provide for and implement principles, rules, and recommendations, the implementation of which is intended to prevent penetration and the spread and release of potential carriers of biological hazards. It was established that to ensure the sanitary and hygienic well-being of the center for keeping service dogs, the “wind rose” principle should be taken into account. You should not neglect the generally accepted requirements for keeping primary documentation of animal records and preventive and curative features of their care, and stringent compliance with the prescriptions for deworming and vaccination of animals. Particular attention should be paid to preventive veterinary measures such as disinfection, namely disinfection of dishes for feeding and watering animals, premises for their taming and operation, disinsection, and derivatization, which, to a large extent, ensure a high level of internal biosafety and protection. Based on the research I conducted, I would like to draw attention to the need to clarify the regulatory and legal mechanisms for ensuring compliance and implementing the provisions of the current legislation of Ukraine and legal doctrine in the field of guaranteeing biological protection and safety.
Innovative potential of agribusiness development in Ukraine in the conditions of war and post-war recovery
Authors: Hrymak O. Ja. , Stybel R. V. , Ostafiichuk A. H.
Published: November 2024

The article analyses the state and dynamics of development of the agricultural sector of Ukraine in the pre-war period and during the period of hostilities. In general, it is noted that the agricultural sector suffers significant losses not only due to direct military actions, seizure or mining of territories, but also due to the economic consequences of the military aggression of the russian federation, namely the blockade of ports and land borders, respectively, a reduction in exports, broken supply chains, and rapidly rising prices for fuel and fertilizers. The current situation requires an urgent search for innovative ways to develop domestic agribusiness and, accordingly, ensure sustainable food security. Based on the European Innovation Scoreboard (EIS) in 2024, the authors have built a profile of Ukraine's innovative development and identified its relative weaknesses and strengths. It is proved that, despite the difficulties caused by the hostilities and the difficult economic situation, Ukraine's innovation system as a whole, including innovations in agribusiness, has a significant potential for development. It is noted that in order to restore the agricultural sector, it is necessary to introduce fundamental changes in agricultural practices, which include the introduction of modern organic farming technologies and various crop cultivation systems; production and processing of high-quality products that meet international standards; digitalisation of basic technological processes; flexible management decisions in the logistics system, etc. The authors also developed a scheme of innovative ways of doing agribusiness during the war and in the period of post-war recovery, which reflects the main ways of innovative development of agribusiness that will help Ukraine not only maintain but also increase its leadership position in the agricultural sector. As a result, the article identifies prospects for further research, which should focus on the introduction of innovative technologies aimed at increasing the efficiency, sustainability and flexibility of the Ukrainian agricultural sector.
Analysis of securities of the food and agricultural sectors using IT
Authors: Stepanyuk O. , Boyko O. , Senyk Y. , Petrivska N. , Senyk A. , Novosad V.
Published: November 2024

The relevance of the study of the agrarian market of Europe, and in particular of Ukraine, is connected with the growing competition of states on world food markets. Trade regulation does not produce the expected results, does not reduce the level of monopolization by transnational corporations of many industry and regional food markets. The problems of population dissatisfaction and ensuring the independence of states are intensifying, which, in turn, increases the role of rational protectionism of the domestic food market. The results of research on this problem will expand the possibilities of scientific approaches to create large-scale research and educational structures capable of participating in various areas of activity to ensure food security. Since the agricultural sector is still one of the most important sectors of the economy in many countries, providing employment and being the main source of income for significant sections of their population, it is not surprising that most of them are interested in the introduction of new technologies and the adaptation of agriculture to development. Changes in the scale of the world economy in recent years have significantly increased interest in investment tasks, which is evidenced by the intensification of trading in the shares of large and medium-sized international companies and, accordingly, causes a rapid increase in their values. As a special case in investment theory, the problem of decision-making regarding the formation and optimization of an investment portfolio is considered. This task is in the field of attention of both large investment companies and private investors, since, choosing among possible alternatives for the distribution of capital investments within the market of financial assets, the investor will get different results. As a result, it is necessary to understand the amount of income received during the period of ownership of the investment portfolio. In the presented work, using the C# programming language, a model is proposed that can be applied in the formation of a portfolio of securities, which allows potential investors to independently assess the effectiveness of the investment set by comparing the growth dynamics of shares available on the financial market. It is known that most of the information an investor encounters is tabular in nature, and according to the methodology of scientific knowledge, a person better perceives visualized ways of presenting information. The model proposed in the work uses visualization tools built into the software product, which presents available tabulated information in a structured graphic form.
State and main trends of milk market development in the European Union and Ukraine
Authors: Chemerys V. , Maksym V. , Dushka V. , Kulii V. , Ostapiv O.-I. , Brezden M.
Published: November 2024

Milk and milk products are essential in ensuring food security and are included in the consumer basket of most of the EU countries and Ukraine's population. The milk and milk products market is one of the leading food markets, which is characterized by certain features regarding the formation of the balance of supply and demand in the long term, taking into account several technological and economic factors that affect the production of products. The article examines the main trends in the milk market in the EU and Ukraine for 2015-2023. A comparative analysis of the leading indicators of the milk market development in Ukraine and the EU is carried out. The dynamics of changes in the number of cows and the level of productivity of milk production were studied. Estimated milk production volumes per 1 person in Ukraine and the EU countries were determined with justification of the main influencing factors. An analysis of the ratio of graphs of the average price dynamics and the rate of change of the number of cows in different EU countries and Ukraine was carried out. The level of milk supply in European countries was studied. According to the results of the study, it was established that the milk market is characterized by dynamic development during the studied period in most EU countries, at a time when in Ukraine there is a decline in production due to a rapid reduction in the number of cows, especially in households, the pace of which has accelerated in the conditions of full-scale war However, in the balance of demand and supply of milk and milk products on the market of Ukraine, due to the relatively higher rates of reduction in the number of consumers compared to the rates of reduction in production volumes, there is a slight surplus of products, which will contribute to the activation of exports and the reduction of import dependence in the short and medium term. However, in the long term, if the rate of reduction of the cow population in Ukraine remains high and cannot be slowed down, the domestic supply will not be able to meet the total demand for milk and milk products, which will stimulate the inflow of imports. As of the end of 2023, the largest concentration of milk production is observed in Germany, France, Great Britain, the Netherlands, and Poland. A high average milk yield per cow per year is typical for Denmark, Estonia, Spain, and Finland farms - more than 9,000 kg. According to the results of 2023, the average level of availability of milk and milk products in the EU states was more than 130%, which is approximately 20-25% higher than in Ukraine. One of the factors affecting the reduction of the domestic supply of milk production in Ukraine is the inefficient distribution of profits in the dairy sub-complex, the leading share of which is formed in enterprises engaged in processing and trade, taking into account the significant difference between high retail and one of the lowest wholesale purchase prices in Europe milk.
ВИКОРИСТАННЯ ТРАКТОРНИХ ПРИЧЕПІВ У ЛОГІСТИЦІ СІЛЬСЬКОГОСПОДАРСЬКИХ ПІДПРИЄМСТВ
Authors: Ярошенко Павло Миколайович , Арендаренко Володимир Миколайович
Published: November 2024

У статті розглянуті питання використання тракторних транспортних засобів в логістиці аграрних підприємств. Визначені граничні відстані використання тракторів з причепами та напівпричепами в порівнянні з автомобілями, що донедавна масово використовувалися в сільському господарстві. Знайдено аналітичний вираз по визначенню тягового опору тракторного потягу. З’ясовано можливі витрати палива деякими тракторними агрегатами. Визначені правила та принципи формування тракторних транспортних агрегатів при перевезенні вантажів різних класів. Обґрунтовані рекомендації по використанню спеціалізованих причепів при транспортуванні різних типів вантажів. Сучасний тракторний транспортний агрегат являє собою високоефективний транспортний засіб, який може транспортувати вантажі не тільки в середині господарства на невеликі відстані до 3…5 км. Трактор з причепом чи напівпричепом в сучасних умовах може ефективно перевозити вантажі на відстані вдвічі більші від заявлених вище. Це твердження ґрунтується на результатах досліджень українських науковців. Сучасний тракторний причіп чи напівпричіп, виготовлений із високоякісної сталі, має гальмівну, гідравлічну і електричну системи. Може обладнуватися системами як автономного завантаження так і розвантаження. Зазвичай має прямокутну форму кузова з відкритим, закритим або напіввідкритим верхом. Може перевозити як сипкі, так і монолітні вантажі, транспортувати як деревину, так і живих тварин. На ходу підбирати прямокутні або круглі тюки соломи чи сіна, вантажити їх на свою платформу і доставляти до місця зберігання. При цьому тракторні причепи і напівпричепи зберегли в своїй основі просту і надійну конструкцію. Можливість завантаження з різних боків або зверху та можливість розвантаження на три сторони не виходячи з кабіни трактора. В логістиці сільськогосподарських підприємств тракторні транспортні агрегати мали обмежене використання. Однак із збільшенням вантажопідйомності причіпної техніки, вдосконаленням способів з’єднання з трактором вони стали завойовувати все більшу популярність серед аграріїв. Все частіше тракторні потяги використовують при масових перевезеннях різних вантажів. Все більше на них розраховують при збиранні зернових і технічних культур. Все частіше такі транспортні агрегати включаються в логістику перевезень вантажів з невеликою питомою щільністю.
ПРОБЛЕМИ І ПЕРСПЕКТИВИ РОЗРОБКИ ТЕХНОЛОГІЇ МОДИФІКАЦІЇ ПОВЕРХОНЬ ДЕТАЛЕЙ НАСОСІВ АТОМНИХ ЕЛЕКТРОСТАНЦІЙ
Authors: Тарельник Наталія В’ячеславівна
Published: November 2024

В статті проведений аналіз конструктивних і технологічних особливостей насосного обладнання, що використовується на атомних електростанціях (АЕС). Аналіз літературних джерел показав, що насосні агрегати АЕС працюють у важких умовах експлуатації (високих тисках, швидкостях, криогенних і підвищених температурах, впливу радіаційного опромінювання) та піддаються негативному впливу навколишнього середовища. При цьому їх деталі підлягають різним видам зносу: окислювальному, абразивному, кавітації, утомі, фреттинг-корозії, електроерозії тощо. Дослідженнями і аналізом методів підвищення параметрів якості поверхонь деталей встановлено, що до найбільш ефективних способів управління параметрами якості поверхневих шарів слід віднести технології, що вживають концентровані потоки енергії (КПЕ), при використанні яких протікають нерівноважні умови нагрівання та охолодження, що дозволяє формувати принципово інші, ніж за традиційними методами обробки, структури поверхневого шару. Літературними та патентними дослідженнями доведено, що однією з найперспективніших сучасних технологій, здатної керувати параметрами якості поверхонь деталей і яка здійснюється шляхом застосування КПЕ, є електроіскрове легування, завдяки якому в поверхневих шарах формуються поверхневі структури, що мають унікальні фізико-механічні та трибологічні властивості на нанорівні. В роботі проаналізовані особливості формування поверхневих шарів технологіями електроіскрового легування (ЕІЛ) і виявлені резерви до їх удосконалення за рахунок дослідження впливу продуктивності обробки (Q), тобто кількості обробленої площини в одиницю часу (см2/хв), на параметри якості покриттів. Також визначені шляхи удосконалення технологій ЕІЛ для підвищення надійності деталей насосного обладнання АЕС шляхом: підвищення їх жаростійкості, формування самозмазувальних покриттів, розробки технологій наноструктурування стальних поверхонь і формування захисних покриттів тощо.
Таксономія правових заходів : монографія
Authors:   Національний університет «Одеська юридична академія» Миколаївський навчально-науковий інститут права Національного університету «Одеська юридична академія», Ківалов Сергій , Біла-Тіунова Любов , Білоус-Осінь Тетяна , Козачук Діана , Негара Родіон , Даниленко-Негара Юлія , Стрельников Андрій , Фролова Євгенія , Сарибаєва Ганна , Козаченко Олександр , Орловська Наталя , Коломієць Юлія , Мусиченко Ольга , Василяка Олеся , Харитонов Євген , Харитонова Олена , Данильченко Олена
Published: November 2024

До колективної монографії увійшли розділи, що підготовлені до оприлюднення для ознайомлення та подальшої дискусії як відомими представниками юридичної науки України, так і молодими дослідниками, які поєднали свої наукові зусилля з метою наукового аналізу можливості та доцільності використання таксономії в процесі систематизації та класифікації зовнішніх форм впливу права на суспільні відносини, що виникають у процесі вчинення делікту та пов’язаної з ним поведінки (правових заходів). Монографія адресується науковцям, аспірантам, практикуючим юристам, студентам вищих навчальних закладів юридичного профілю та всім, хто цікавиться проблемами теорії і практики правового впливу як реакції на факт вчинення протиправного діяння.
Guía de insectos acuáticos en las costas de Jalisco y Colima
Authors:   Universidad de Guadalajara, Rodríguez Contreras Francia Elizabeth , Martínez Rivera Luis Manuel
Published: November 2024

Esta guía contiene información que está dirigida principalmente a grupos ciudadanos y personal técnico de los municipios de la región para que funcione como una herramienta didáctica en el monitoreo ciudadano, pero basada en información científica que le da soporte al monitoreo. Cabe aclarar que no pretende sustituir a las guías taxonómicas existentes a nivel nacional o internacional que son excelentes, sin embargo, estas guías más especializadas se describen por orden taxonómico que incluso llegan a nivel de género, por lo tanto, llegan a ser más detalladas y con un lenguaje demasiado técnico que no se ajustan al objetivo que pretende cubrir la presente guía. En este libro se pone a disposición del lector una clave acotada con los insectos acuáticos que se han encontrado y potencialmente se van a encontrar en las costas de Jalisco y Colima.
Optimized Optical-Wireless Networks Convergence: Enabling Low-Latency xRANs via Hybrid Split and Reconfigurability
Authors: Nooruzzaman Md , Fernando Xavier
Published: November 2024

With the imminent arrival of the 5G and future 6G wireless network epochs, which require ultra-low latency services for applications like autonomous vehicles and augmented reality, the advancement of xRANs (cloud/virtualized/open radio access networks) introduces novel challenges to the fronthaul and backhaul networks. To effectively address this, the development of novel programmable converged optical-wireless networks with exceptional flexibility becomes crucial for the future of xRANs.

This study focuses on optimizing converged optical-wireless networks, empowered by open reconfigurable optical add/drop multiplexer (ROADM) technology, to facilitate high-capacity, low-latency, and energy-efficient 5G/6G end-to-end services. By appropriately deploying fast reconfigurable ROADMs through centralized control systems like software-defined networks (SDNs) and dynamic switching of radio equipment, a balance can be struck between conserving network resources and minimizing power consumption plus latency.

In this work, we investigate the feasibility of integrating high-layer and low-layer splits within the same network to achieve this objective. High-layer splits are needed for low latency in time-sensitive tasks, while low-layer splits suit non-time-critical broadband services. This work explores multiple aspects of converged optical-wireless networks, including the integration of mixed splits and the potential of open ROADMs for designing low-latency systems.

The paper concludes with a latency analysis and a cost evaluation of the networks in relation to their latency values. This article aims to assist network planners in making well-informed decisions that balance performance, cost, and latency requirements in upcoming network deployments.
Cost dynamics of converged optical-wireless networks: enabling low-latency xRANs through a reconfigurable hybrid split
Authors: Nooruzzaman Md , Fernando Xavier
Published: November 2024

Future 6G and beyond wireless networks are anticipated to be highly versatile, accommodating a wide range of services, from ultra-low-latency applications like autonomous vehicles and extended reality to enhanced mobile broadband and massive connectivity for the Internet of Things. In tackling this, xRANs (cloud/virtualized/open radio access networks) encounter significant challenges, including automation, interoperability, scalability, reconfigurability, and standardization, within crosshaul (comprising fronthaul, midhaul, and backhaul) networks. Therefore, the development of programmable converged optical-wireless networks with exceptional flexibility is crucial. This study concentrates on the design of integrated optical and wireless networks to achieve the reconfigurability necessary for automation and to fulfill diverse latency requirements. Initially, we analyze the latency contributions from different network segments and traffic factors in the xRAN, followed by a comprehensive examination of the associated cost dynamics. Subsequently, we investigate the feasibility of integrating high-layer and low-layer splits within the same network to achieve different latency levels. Finally, our study delves into the relationship between latency and cost for converged optical-wireless networks with varying mixed split scenarios and throughput levels. Overall, this article aims to assist network planners in making wellinformed decisions that balance throughput performance, cost, and latency requirements in upcoming network deployments. 
Intelligent Clustering for Energy Harvesting Cognitive Wireless Sensor Networks
Authors: Obaid Arif , Fernando Xavier
Published: November 2024

 Lifetime of Cognitive Wireless Sensor Networks (CWSN) can be significantly improved using RF Wireless Energy Harvesting (RFWEH). However, since time available for energy harvesting is severely limited in a CWSN, we propose a novel cluster-based cognitive MAC protocol which intelligently allocates energy harvesting slots to cluster heads without compromising network performance in terms of latency and throughput. The protocol utilizes Machine Learning (ML) techniques to select the most suitable cluster head based on several factors, including residual energy, remaining data packets, and node location on the grid. The proposed RMACC protocol is compared with three other protocols, including the previously developed distributed RMAC protocol, LEACHC, and KoNMAC. Simulation results indicate that RMAC-C outperforms the other protocols in terms of network lifetime by up to 40%, achieving self-sustainability, while maintaining acceptable levels of network latency and throughput. This improvement is achieved by harvesting enough energy while intelligently allocating energy harvesting slots to cluster heads, ensuring that the network remains operational. This protocol’s success demonstrates the potential of incorporating intelligent clustering techniques and energy harvesting into wireless sensor networks to improve network lifetime and sustainability. 
Co-operative Edge Intelligence for C-V2X Communication using Federated Reinforcement Learning
Authors: Gupta Abhishek , Fernando Xavier
Published: November 2024

 This paper examines the application of federated reinforcement learning (FRL) to enable resource-constrained vehicular edge nodes to learn their communication parameters from a central parameter server (PS). In cellular vehicleto-everything communication (C-V2X), non independently-andidentically-distributed (non-i.i.d.) data samples impose additional communication requirements and increase the training time for model convergence. By exploring correlations between local model updates and the global model aggregation distributions, we accelerate this convergence using FRL. In the proposed method, Q-values undergo weight adaptation at each training round to update the global model. Local gradient vectors at vehicles and global gradient vectors at the PS measure the contribution of vehicle local models. Furthermore, the Q-values are quantified via nonlinear mapping that reinforces positive rewards, leading to dynamic measurements of local model contributions. Using FRL, policy-based and value-based learning methods reduce the number of communication rounds by upto 40%. 
Automatic Modulation Classification for Cognitive Radio Systems using CNN with Probabilistic Attention Mechanism
Authors: Gupta Abhishek , Fernando Xavier
Published: November 2024

This paper studies automatic modulation classification (AMC) for cognitive radio systems. We propose a deep learning neural network approach enhanced with an intelligent attention mechanism to correctly classify, detect, and segment spatially distributed modulation data. AMC is achieved by training the neural network to focus only on specific significant regions learnt using the attention mechanism. The proposed approach is tested for detection efficiency and accuracy to distinguish different modulation data using the publicly available RML2016.10a dataset. The outcome shows the accuracy of the proposed scheme is comparable to other state-of-the-art deep learning algorithms with a reduced complexity. The real-time assessment of the temporal states is achieved based on the spectral characteristics of modulation constellation images at various signal-to-noise ratio (SNR) values. The model performance is evaluated using mean average precision (mAP), F1 score, and speed-accuracy trade-off. 
Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism
Authors: Gupta Abhishek , Illanko Kandasamy , Fernando Xavier
Published: November 2024

This paper addresses object detection and scene perception for connected and autonomous vehicles. Road object detection at high accuracy and fast inference speed is a challenging task for safe autonomous driving as false positives arising from false localization can lead to fatal outcomes. The paper proposes a convolutional neural network (CNN) to recognize images to enhance intelligent adaptive behavior in autonomous vehicles by correctly classifying, detecting, and segmenting spatially distributed objects in the driving environment. By focusing on specific regions of an image, the most significant region of the image is learned by appending a CNN with probabilistic attention mechanism aided with transformers. The proposed approach is analyzed for detection efficiency and accuracy to distinguish different objects to make appropriate driving decisions. The proposed method is validated on the publicly available Berkeley deep drive (BDD) dataset and shows an accuracy comparable to other state-of-the-art deep learning algorithms to make driving decisions based on real-time assessment of the temporal states encountered while navigating the driving environment. The proposed model performance is evaluated using mean average precision (mAP) and speed-accuracy trade-off. 
Deep Learning Based Traffic Flow Prediction for Autonomous Vehicular Mobile Networks
Authors: Shah Syed Ammad Ali , Illanko Kandasamy , Fernando Xavier
Published: November 2024

Accurate traffic flow prediction plays a crucial role in designing Ad hoc vehicular mobile networks in modern Internet of things (IoT) based intelligent systems. Several deep learning techniques have been deployed to predict traffic conditions to make vehicular communication more reliable. However, not all these approaches deal with complex road networks and spatial temporal dependencies of traffic data. In this paper, we analyze this problem using long short-term memory (LSTM), gated recurrent unit (GRU) and hybrid CNN-LSTM models. We trained our models using actual traffic flow data provided by the California Department of Transportation (Caltrans) over a 6 month duration and showed that our deep learning models outperform the traditional linear regression method. Moreover, an architectural study of deep learning models is carried out for the traffic flow prediction problem. The performance of these models is evaluated using MSE and MAE metrics. It is observed that the GRU model is the best to handle the complex vehicular traffic mechanisms. Also, that a complex hybrid model like CNN-LSTM does not always outperform the much simpler architectures such as LSTM and GRU. 
A Neural Network Based Recursive Least Square Multilateration Technique for Indoor Positioning
Authors: Adhikari Bhagawat , Fernando Xavier
Published: November 2024

 Fast emerging wireless indoor positioning enables many location based services. However, fluctuation receive signal strength due to multipath propagation is a major challenge. Different Machine Learning (ML) approaches are attempted to improve the accuracy in indoor positioning. In this paper, a hybrid technique combining Recursive Least Square (RLS) with Artificial Neural Network (ANN) has been presented to solve a multilateration problem with different sets of anchor nodes. Instead of using directly measured distances, estimated distances using ANN are used in RLS implementation. Results from the hybrid RLS-ANN are compared with pure RLS, pure least square (LS) and LS-ANN approaches. Hybrid RLS-ANN provides the least Root Mean Square Error (RMSE) among all the techniques and improves the accuracy up to 80% compared to the pure LS multilateration technique. Complexity of the proposed technique is relatively low with good accuracy 
An Energy Harvesting MAC Protocol for Cognitive Wireless Sensor Networks
Authors: Obaid Arif , Jaseemuddin Muhammad , Fernando Xavier
Published: November 2024

 Cognitive Wireless Sensor Networks (CWSN) are known to be energy constrained because they are required to perform cognitive functions in addition to sensing, which reduces the lifetime of CWSN. RF Wireless Energy Harvesting (RFWEH) is known to improve network lifetime by compensating energy losses through harvesting gain without any additional hardware. However, the allocation of time slots for energy harvesting results in degradation of network parameters such as latency and throughput. In this paper, we propose an energy harvesting MAC protocol (R-MAC) for CWSN that balances network energy losses with harvested energy gains, thereby resulting in a self sustainable CWSN. This is done such that both latency and throughput remain close to predefined target values. R-MAC employs TDMA and relies on clustering for creation and dissemination of the schedule. We also propose an algorithm to add harvesting slots based on a recursive regression technique to achieve the energy balance. Using simulation we compared its performance with the well known cognitive KoNMAC protocol, after incorporating energy harvesting in it. The simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, resulting in an energy self sufficient network while maintaining throughput and latency within acceptable limits. 
Hyperscale Data Center Networks with Interconnected Transparent Island Architecture
Authors: Nooruzzaman Md , Fernando Xavier
Published: November 2024

A large number of daily Internet users generates enormous quantities of new data, driving the sensational escalation of Internet giants, such as Google, Facebook, Amazon, IBM, Dropbox, Alibaba, Microsoft, and so on. New data from Synergy Research Group shows that the total number of hyperscale data centres increased to 504 at the end of 2019 [1]. This number was 338 in 2016 and will reach to 628 by the end of 2021 [2]. In order to cope with this massive demand, the size of the data centres is expanding, number of data centres is growing and a technological change in the data centres has been accelerated remarkably over the past decade. Since we are approaching towards the era where many services must meet strict latency requirements. Therefore, the distance (number of hops) between users and servers as well as between server and server must be reduced. In this paper, we propose a new hyperscale DCN called interconnected transparent island (ITI) to achieve reduced number of hops. The proposed ITI architecture is scalable horizontally while the number of hops remains constant as the data centre grows. 
Machine Learning Models for Predicting Thyroid Cancer Recurrence: A Comparative Analysis
Authors: Shah Alam  , Mohammad Abir Hider  , Abdullah Al Mukaddim  , Farhana Rahman Anonna  , Md Sazzad Hossain  , Md khalilor Rahman  , Md Nasiruddin
Published: November 2024

Thyroid cancer is considered the most common malignancy of the endocrine system and encompasses a broad category of diseases that involve abnormal growth in thyroid cells. Thyroid carcinoma accounts for about 3% of the total cases of cancer diagnosis in the USA. The chief objective of the research project is to design and compare the performance of machine learning models in the prediction of thyroid cancer recurrence to overcome the limitations observed in the current predictive tools. This study aims to develop and compare Machine Learning models. In particular, this study considered different machine-learning algorithms to identify which model can effectively forecast the recurrence of thyroid cancer.  The dataset used for the analysis was from Kaggle, the ‘Thyroid Gland Dataset.’ This source had a very elaborate dataset, containing records of patients who were diagnosed with thyroid issues, including demographic data on variables that would be needed to see the recurrence of any disease. Besides, it contained demographic information about the patients, which would serve to comprehend population trends in the patients; examples are age, gender, and ethnicity. The clinical history data included size, histological subtype, lymph node involvement, and staging at diagnosis. This comparative analysis mounted a variety of machine learning algorithms, each of which was chosen based on its capabilities to face structured medical datasets for robust predictions. Each model was chosen based on their different strengths that correspond to characteristics in the dataset and the general goals of the prediction problem. Performance metrics used for the models included overall accuracy, precision, recall, and the F1 score. Logistic Regression performed slightly better than the random forest and the support vector machines. However, this difference in accuracy was minimal and all three can make quite accurate predictions on this data.  Logistic Regression provides transparency and interpretability, Random Forest provides high versatility and robustness, while SVM offers precision for complex relationship modeling. The integration of machine learning predictive models into clinical practice has great potential to transform decision-making, particularly in the management of thyroid cancer and the risk of recurrence. These models will greatly assist clinicians by consequently advising them on which patients have a high chance of recurrence, so early intervention might be considered and follow-up care given as need sets in.
Introduction: New Readings of the Early Robin Hood Tradition
Authors: Harrison Perry Neil , Delony Mikee Chisholm
Published: November 2024

In the introduction of their 2013 work, Early Rymes of Robyn Hood: An Edition of the Texts, ca. 1425-1600, Thomas H. Ohlgren and Lister M. Matheson give a call to action for scholars in the field of Robin Hood Studies to examine these early texts in new ways. This introduction to the special issue, “New Readings of the Early Robin Hood Tradition,” puts forth the ways that the contributors to the matter push the field of Robin Hood Studies in new directions.
Integrating Machine Learning with Web Intelligence for Predictive Search and Recommendations
Authors: Santiago Maria , Febiansyah Hidayat , Dinarwati Dini
Published: November 2024

This study examines the integration of Machine Learning (ML) with Web Intelligence (WI) as a transformative approach for enhancing web-based search and recommendation systems. The objective is to utilize the combined strengths of ML and WI to significantly increase the accuracy, precision, and relevance of predictions, providing personalized and context-aware results that adapt in real-time. Employing a hybrid model that leverages both the predictive capabilities of ML and the dynamic adaptability of WI, this research methodologically assesses the performance against traditional models through rigorous testing. Results indicate that the integrated system substantially outperforms conventional models, demonstrating enhanced performance metrics across accuracy, precision, and recall. Theoretically, this integration contributes to the advancement of WI frameworks, while practically, it offers significant improvements for real-world applications, especially in optimizing user interactions and satisfaction. However, the study also recognizes limitations related to the scalability of the data and models used. Future research should focus on refining model complexity and enhancing real-time data processing capabilities. Additionally, the integration of these technologies supports several Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industrialization through advanced technologies, Goal 8 (Decent Work and Economic Growth) by fostering economic growth and employment in the tech sector, and Goal 12 (Responsible Consumption and Production) by enabling more informed consumer choices through better recommendations. These connections underline the role of innovative technologies in achieving sustainable development and enhancing global economic and social frameworks.
Acute myeloid leukemia drug tolerant persister cells survive chemotherapy by transiently increasing plasma membrane rigidity, that also increases their sensitivity to immune cell killing
Authors: Morgenstern Yael , Lee JongBok , Na Yoosu , Lieng Brandon Y. , Ly Nicholas S. , Gwynne William D. , Hurren Rose , Ma Li , Ling Dakai , Gronda Marcela , Arruda Andrea , Frisch Avraham , Zuckerman Tsila , Ofran Yishai , Minden Mark D. , Zhang Li , O’Brien Catherine , Quaile Andrew T. , Montenegro-Burke J. Rafael , Schimmer Aaron D.
Published: November 2024

Resistance to chemotherapy remains a major hurdle to the cure of Acute Myeloid Leukemia (AML) patients. Recent studies indicate a minority of malignant cells, termed drug-tolerant persisters (DTPs), stochastically upregulate stress pathways to evade cell death upon acute exposure to chemotherapy without acquiring new genetic mutations. This chemoresistant state is transient and the cells return to baseline after removal of chemotherapy. Yet, the mechanisms employed by DTPs to resist chemotherapy are not well understood and it is largely unknown whether these mechanisms are also seen in patients receiving chemotherapy. Here, we used leukemia cell lines, primary AML patient samples and samples from patients with AML receiving systemic chemotherapy to study the DTP state. We demonstrated that a subset of AML cells transiently increases membrane rigidity to resist killing due to acute exposure to Daunorubicin and Ara-C. Upon removal of the chemotherapy, membrane rigidity returned to baseline and the cells regained chemosensitivity. Although resistant to chemotherapy, the increased membrane rigidity, rendered AML cells more susceptible to T-cell mediated killing. Thus, we identified a novel mechanism by which DTP leukemic cells evade chemotherapy and a strategy to eradicate these persistent cells.