bluuebunny/crossref_metadata_2025 huggingface dataset:
Tijolos de adobe de solo-cimento com adição de resíduo de serragem de madeira
Authors: Oliveira Barroso Kamila , Cardoso Alves Novato Fernanda Gabriela , Ferreira Raul
Published: September 2024

A degradação e os impactos ambientais causados pela indústria da construção civil (ICC) são observados em todas as fases de seu processo produtivo. Nesse contexto, tem-se buscado alternativas para reduzir o consumo acelerado de recursos naturais, os danos ambientais, o alto consumo de energia na fabricação de materiais e os custos dos produtos finais, como o desenvolvimento de técnicas construtivas e materiais sustentáveis. Dentro desse contexto, destaca-se o reaproveitamento dos resíduos gerados pela própria indústria da construção civil, de agronegócio e outros setores. O resgaste da terra crua para produção de blocos de solo estabilizado é uma das possibilidades de reutilização desses materiais e desenvolvimento sustentável da ICC. Este estudo teve como objetivo verificar a viabilidade da incorporação de serragem de madeira em matriz de solo-cimento para a fabricação de tijolos de adobe, com enfoque em apresentar materiais de construção alternativos e de baixo impacto ambiental. Para isso, foram realizadas análises de resistência à compressão simples, capacidade de absorção de água e retração dos blocos para o período de cura de 7 dias. Os blocos fabricados com a adição de serragem de madeira apresentaram adequado desempenho no que se refere à retração.
Absorção do Building Information Modeling (BIM) na gestão municipal do Estado de Mato Grosso, Brasil
Authors: Durante Luciane Cleonice , Da Silva Fiuza Pina Patrícia , DE ANDRADE CARVALHO ROSSETI KARYNA , Ferreira Senra Rodrigo , NAVES BLUMENSCHEIN RAQUEL , OLIVEIRA CHAVES FONTES THAIS
Published: September 2024

Na esfera pública brasileira, registram-se diversos problemas relacionados aos processos de contratação, fiscalização e execução de projetos de edifícios e obras, o que denota a necessidade de melhorias de gestão desses serviços. A elaboração de projetos de Arquitetura e Engenharia (A&E) em Building Information Modeling (BIM) pode contribuir com a precisão e compatibilização dos projetos, a rapidez na elaboração de documentações técnicas e orçamentos, a qualidade e gestão das obras, além da operação mais assertiva dos ativos. O objetivo deste artigo é diagnosticar o panorama de absorção de conhecimento em BIM pela gestão municipal, no estado de Mato Grosso, Brasil. Na metodologia foram empregadas em três etapas: definição de critérios para classificação da fase de absorção do BIM nas secretarias municipais, elaboração de questionário estruturado, coleta de dados e análise dos dados relacionados aos perfis de atuação de cada secretaria. Os resultados evidenciam que a absorção do BIM nas secretarias municipais em que se elaboram, contratam ou fiscalizam a execução de projetos e obras de construção civil e infraestrutura é incipiente, uma vez que 79% dos respondentes declararam não o utilizar. Por outro lado, as secretarias que utilizam o BIM, o fazem de maneira isolada ou em projetos pilotos, em sua maioria, aplicando-o nas etapas de planejamento, projeto e orçamentação. Os resultados fornecem um diagnóstico do uso do BIM em nível municipal e evidenciam a necessidade de planejar a incorporação do BIM nos processos de trabalho realizados, além de subsidiar políticas públicas que visem reduzir as barreiras de sua implementação.
Desenvolvimento de código aberto em Octave para ajustes de funções através de linearização e MMQ
Authors: Magalhães da Cruz Daniel , Melito Ignacio , Cruz Júnior Aleones José da , Sales Marcel Willian Reis , Popiolek Júnior Tales Luiz , Guilherme Carlos Eduardo Marcos
Published: September 2024

Todos os estudos de engenharia recorrem a bases matemáticas para sua realização, sejam elas da probabilidade, da estatística, do cálculo diferencial e integral, da álgebra vetorial, etc. Fato é que a abordagem matemática é parte crucial do desenvolvimento. Nos estudos que lidam com dados discretos, é evidente a necessidade de parametrizações que demonstrem o comportamento de tal conjunto de dados, tornando o ajuste de curva imprescindível. Nesse sentido, destaca-se o contexto amplo do trabalho, que pretende escrever um código em Octave para parametrização de dados discretos. A metodologia implementada é o Método dos Mínimos Quadrados (MMQ), bem como a sua avaliação quantitativa de qualidade feita através do coeficiente de correlação ( ). Além disso, recursos matemáticos de linearização de modelos são utilizados para posterior adequação no MMQ. Como resultado, obtém-se um código em Octave que realiza a parametrização para os modelos: linear, de potência, exponencial, recíproca e Michaelis-Menten. O código é verificado com diferentes resultados da literatura. Como conclusão, observa-se a facilidade do script em Octave, que não apenas permite a parametrização e ajuste de curva para conjuntos de dados, mas também fornece base para inserção de demais modelos dentro do script. Além disso, o código desenvolvido conduz a novas proposições, como programas que façam algo de forma similar para 3D, ou mesmo programas que realizem cálculos de forma automatizada.
Desempenho de argamassas para revestimento de estruturas de concreto armado situadas em ambientes marítimos
Authors: Miranda Thiago Rodrigues , Bandeira Jorge Luiz Saes , Tessaro Alessandra Buss , Moraes Ingrid Telles , Oliveira Julio Cesar , Viana João Pedro
Published: September 2024

Pode-se afirmar que o conhecimento sobre como os revestimentos de argamassa possam influir na penetração de íons cloreto, em estruturas de concreto armado, é ainda incipiente, especialmente naquelas situadas em ambientes marítimos com alta exposição a esses agentes agressivos. Nesse contexto, a avaliação do desempenho de argamassas para revestimento pode auxiliar no desenvolvimento de soluções mais eficazes para proteger as estruturas de concreto armado, trazendo vantagens para a indústria da construção civil e para o meio ambiente, visto que, aumentando a durabilidade das estruturas, os custos de manutenção e os danos ambientais diminuem. Sendo assim, este trabalho tem por objetivo verificar o desempenho quanto à penetração de cloretos de argamassa mista composta de cimento e cal hidráulica como aglomerantes, e outra argamassa mista com a substituição de 10% de cimento Portland por cinza proveniente da queima de carvão mineral além de cal hidráulica na mesma proporção da primeira e por fim comparar estes dois traços com estudos similares. Para isso, foram confeccionados corpos de prova e submetidos a ensaios de difusão de cloretos, com variações do grau de saturação (GS) em 100%, 85% e 65%, o que permitiu através de ensaios de titulação potenciométrica, obter os perfis de cloretos de cada amostra. A partir dos achados, observou-se que as amostras possuem adequada resistência mecânica e alta permeabilidade e índices de vazios.
Pharmacogenetics of acute coronary syndrome: the role of single nucleotide polymorphisms (literature review)
Authors: Ulitina A. S. , Sirotkina O. V. , Vershinina E. G. , Eskerova M. F. , Babenko A. Yu. , Vavilova T. V.
Published: September 2024

Cardiovascular diseases are the most common cause of death both in Russia and throughout the world. Acute coronary syndrome (ACS) develops during the coronary heart disease and represents a serious medical and social problem. The effectiveness and safety of pharmaco­therapy for ACS can be influenced by the individual genetic characteristics of the patient, primarily single nucleotide polymorphisms (SNPs) of the primary DNA structure. The literature review contains information about the epidemiology of ACS, the nomenclature of SNPs, and the molecular basis of the influence of SNPs on physiological and pathological processes in the human body. The main groups of drugs used for ACS and the main functional groups of protein­coding genes, SNPs of which can modulate an individual’s response to pharmacother­apy, are listed in the review. SNPs of non­coding RNA genes have been characterized as promising objects of study. The review shows concept of multilevel regulation of the interaction between drug and human organism and the role of SNPs in that concept. Detection of SNPs is an important component of studying the pharmacokinetics and pharmacodynamics of drugs, since information about the patient’s genetic status is the basis for a personalized approach to pharmacotherapy.
Modern methods of visualization of the tetralogy of Fallot
Authors: Beregovskii D. A. , Filin Ya. A. , Shapieva A. Sh. , Shatokhina M. G. , Trufanov G. E.
Published: September 2024

Tetrad of Fallot is one of the ten most common congenital heart defects that cause significant circulatory problems and lead to severe clinical manifestations. It is characterized by a combination of four anatomical defects: obstruction of the outflow tract of the right ventricle or pulmonary artery stenosis, ventricular septal defect, aortic dextraposition and right ventricular hypertrophy. This disease causes important social and economic consequences for the health care system, as it requires complex surgical intervention and a long period of rehabilitation. Timely diagnosis and an integrated approach to treatment, which includes surgical correction and postoperative rehabilitation, can improve the prognosis for patients, increase life expectancy and its quality. Modern complex methods of cardiological imaging, such as echocardiography, computed tomography and magnetic resonance imaging, help diagnose tetrad Fallot, accurately assess the degree of defect, hemodynamics and plan the most effective treatment.
Modern methods of radiation imaging of the pathology of the temporomandibular joint
Authors: Filin Ya. A. , Bakhtin M. A. , Beregovskii D. A. , Shapieva A. Sh. , Tyuleneva S. V.
Published: September 2024

Modern methods of radiation imaging play an important role in the diagnosis and treatment of temporomandibular joint (TMJ) pathologies. This article examines the main imaging tech­niques, including radiography, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (ultrasound) and cone beam computed tomography (CBCT). Radiography, including panoramic and transcranial images, remains the main method of primary assessment of TMJ bone structures, due to its accessibility and low cost. Computed tomography with subsequent reconstruction provides detailed three­dimensional images of bones, which is es­pecially useful when planning surgical interventions, but have a high radiation dose. Magnetic resonance imaging is considered the “gold standard” for imaging TMJ soft tissues, such as the articular disc and ligaments, without the use of ionizing radiation. Ultrasound, being an affordable and non­invasive method, allows you to evaluate soft tissues in real time, but its diagnostic value depends on the experience of the operator. Cone beam computed tomography (CBCT) Combines the advantages of CT and radiography, providing high­resolution images with low radiation dose, making it ideal for evaluating bone structures. Current research is also aimed at integrating hybrid imaging techniques such as PET­CT and PET­MRI, and using artificial intelligence to automatically interpret images. The correct choice of imaging method depends on the specific clinical situation and the objectives of the study. Modern radiation imaging technologies significantly improve the accuracy of diagnosis and the effectiveness of treatment of TMJ pathologies, providing better medical care to patients.
Laboratory diagnosis of cryptococcal meningitis in an HIV-infected patient
Authors: Gorbova I. V. , Zhilenkova Yu. I. , Filippova P. B. , Shulgina M. V.
Published: September 2024

Experiences in Cryptococcus neoformans meningitis laboratory diagnosis in HIV infected patient are presented.
Determination of the Factors Influencing Adaptation Strategies of Tank-irrigated Farmers: Empirical Evidence from Tamil Nadu
Authors: Mohanraj K. , Karthikeyan C. , Paramasivam R. , Tamilarasu Arivelarasan , Malaiarasan Umanath , Anbarasan P. , Rajasekaran R. , Kumar Thangavel Pradeesh
Published: September 2024

Background: Tank-irrigated farmers using tank irrigation systems are implementing adaptation strategies to enhance the sustainability of the agricultural ecosystem. In this particular context, The current research aims to thoroughly examine the various adaptation strategies employed by tank-irrigated cultivators, along with the crucial factors that impact the choice and execution of these strategies. Methods: The data was gathered from 300 tank-irrigated cultivators using a multistage random sampling procedure in ten tank intensive districts’ of Tamil Nadu. The Heckman two-stage procedure was employed to identify the factors affecting tank-irrigated cultivators adaptation decisions. Result: This research found that 83.00 per cent of tank-irrigated cultivators are aware of climate change and 72.33 per cent of them have implemented adaptation strategies. Soil and water management practices (SWP), changing sowing dates and crop insurance were the popular adaptation methods of the tank-irrigated cultivators in the study locale. The model results indicate that adaptation strategies of tank-irrigated farmers are positively influenced by various variables, including age, gender, level of education, access to climate information, availability of credit and ownership of livestock. Moreover, variables such as age, level of education, number of contacts with extension agents and credit access exhibit a substantial and positive impact on the farmers¢ level of awareness.
Mass Spectrometry in Crop Pathogens Diagnosis: A Comprehensive Review
Authors: Kalaignan Kalaichelvi , Michael Jeya Rani Maria , Selvaraj Vinothini , Subbaiah Shanmugapackiam , Salama Ehab Abdellah Abdelaziz , Perumal Murali Sankar
Published: September 2024

Crop pathogens pose a significant threat to global food security, causing substantial economic losses and food safety concerns. The identification and quantification of compounds produced by these pathogens are crucial for understanding their diagnosis by biochemistry, virulence mechanisms and predict for disease control. Initially, diagnosed by mass spectrometry such as GCMS and LCMS are powerful analytical techniques used for the identification and quantification of compounds in various fields, including environmental science, pharmaceuticals and food analysis. This study aims to explore the application of mass spectrometry for the diagnosis of crop pathogens, focusing on the identification and quantification of volatile organic compounds (VOCs) and secondary metabolites produced by fungal and bacterial pathogens. The study will also evaluate the advantages and disadvantages of GCMS and LCMS for crop pathogen analysis, including sensitivity, resolution, selectivity, range of applications, sample preparation requirements, cost and data interpretation challenges. The results will provide valuable insights into the potential of GCMS and LCMS for crop pathogen diagnosis and highlight areas for further research and development in this field.
On the Positive Effects of Overconfident Self-Perception in Teams
Authors: Wichardt Philipp C , Ludwig Sandra , Wickhorst Hanke
Published: September 2024

In this paper, we study the individual payoff effects of over confident self-perception in teams. In particular, we demonstrate that the welfare of an overconfident agent who works in a team with a rational agent or in a team with an overconfident agent can be higher than the welfare of the members of a team of two rational agents. This result holds irrespective of the assumption about the agents’ awareness of their colleague’s bias. Moreover, we show that an overconfident agent is always better off when he is unaware of a potential bias of his colleague. Thus, our results provide a potential rationale for the widespread dissemination of overconfidence.
Dysregulation of sphingolipid metabolism in liver fibrosis
Authors: Wu Nan , Song Meiyi , Zhang Fan , Aseem Sayed Obaidullah , Hylemon Phillip B. , Zhou Huiping
Published: September 2024

The dysregulation of sphingolipid metabolism emerges as a pivotal factor in the development and progression of liver fibrosis, a condition marked by the overproduction and buildup of extracellular matrix proteins that can lead to liver cirrhosis and failure. Sphingolipids, a diverse class of lipids essential for cellular structure and signaling, are integral to numerous biological functions such as cellular proliferation, morphological differentiation, and programmed cell death. In the context of liver fibrosis, changes in sphingolipid metabolism have been associated with the activation of hepatic stellate cells, the primary cells responsible for fibrogenesis in the liver. These metabolic disruptions lead to an imbalance between profibrotic and antifibrotic sphingolipids, notably sphingosine-1-phosphate and ceramide, contributing to the pathophysiological mechanisms that drive fibrosis. The intricate relationship between sphingolipid metabolism and fibrotic pathways underscores the potential of targeting sphingolipid metabolic enzymes and receptors as therapeutic strategies to mitigate liver fibrosis. The core of this review delves into how disruptions in sphingolipid metabolism contribute to liver fibrosis, exploring biomarkers and potential therapeutic targets. Challenges in research and future directions for comprehensively understanding sphingolipid roles in liver fibrosis are discussed, aiming to open new pathways for therapeutic intervention.
Transparent p-type copper iodide for next-generation electronics: fundamental physics and recent research trends
Authors: Kim Ga Hye , Lee Jiyong , Ahn Kyunghan , Kim Myung-Gil
Published: September 2024

Development of transparent and flexible p -type semiconductors has been a significant challenge for scientific curiosity and industrial interest. Unlike n -type metal oxide semiconductors, such as zinc oxide (ZnO), In2O3, and SnO2, transparent p -type oxide semiconductors have suffered from low optical transparency and poor electrical performance. To overcome the intrinsic limitation of p -type oxide semiconductors, copper iodide (CuI) is gaining attention as a multifunctional p -type semiconductor with excellent optical transparency, decent mechanical flexibility, high hole mobility, high electrical conductivity, and even promising thermoelectric performance. Here, we present the recent progress of CuI-based transparent p -type electronics from materials to applications. In this review, we summarize the physical and chemical properties of CuI by reviewing computational studies, focusing on the band structure, intrinsic defects, and promising dopants. Additionally, various applications of CuI, including its use as active layers, hole transport layers (HTLs), transparent electrodes, and energy harvesters, are examined, highlighting important studies and their findings. Strategies to enhance device performance, such as controlling carrier concentrations and refining fabrication methods, are discussed, offering insights for developing next-generation electronic devices. Finally, we discuss current challenges and perspective opportunities of CuI-based transparent p -type electronics.
Community Paramedicine Supporting Community Needs: A Scoping Review
Authors: Lunn Tyne , Bolster Jennifer , Batt Alan
Published: September 2024

Health and social needs exist along a dynamic continuum. Recognizing that health status is inextricably impacted by social determinants of health, community paramedicine has opportunities and a responsibility to reduce inequities. The objective of this scoping review was to investigate peer-reviewed and grey literature to explore how community paramedicine supports community needs along a health and social continuum. We conducted a scoping review of English language literature using the JBI Scoping Review methodology. We searched CINAHL, EMBASE, MEDLINE, Google Scholar, and organisational websites. 30 peer-reviewed and 13 grey literature articles met inclusion criteria. The findings describe the ways community paramedicine models evolved from minimising system pressures on emergency health services to addressing health and social needs. A key recommendation across the literature was the need to meaningfully engage communities early in program development to understand how best to implement and co-design integrated service models addressing specific community needs, though there was a lack of evidence to guide this approach. There is a notable lack of evidence pertaining to optimising technologies in program design and implementation. Results highlight opportunities to determine best practices for conducting holistic community needs assessments that include equitable stakeholder engagement and enhancing education to prepare paramedics for expanded roles. Community paramedicine provides opportunities to better meet the needs of structurally marginalised communities. There is a social responsibility and opportunity to engage communities to co-design service delivery, advance paramedic education, and enhance interprofessional collaboration to better support community needs and generate upstream solutions for individuals and communities
Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis
Authors: Hussain Adil , Aslam Ayesha , Tripura Sajib , Dhanawat VIneet , Shinde Varun
Published: September 2024

Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.
Perturbed FLRW Metric Explains the Difference in Measurements of the Hubble Constant
Authors: Belayev Wladimir
Published: September 2024

The expansion of the universe yields two consistent differing values of a Hubble constant depending on the methods of measurement. The aim of this work was to explain the Hubble tension by properties of space corresponding to a radially inhomogeneous metrics. A cosmological model described by this perturbed FLRW metric has zero pressure and exponentially expanding space. But the observed space appears to be compressed in the radial direction. We consider the dependence of the change rate of photon energy on the direction of its motion. It is found applying the principle of the photon's energy integral extremum and the relationship between it and the energy of a material particle obtained using Lagrange mechanics. The change rate of a photon's energy varies depending on whether it moves in a radial direction or has an angular component. The metric coefficients are determined by the difference in measurements of the Hubble constant using gravitational lensing and a distance ladder.
The Secret to Fixing Incorrect Canonical Quantizations
Authors: Klauder John , Fantoni Riccardo
Published: September 2024

Covariant scalar field quantization, nicknamed $(φ^r)_n$, where $r$ denotes the power of the interaction term and $n=s+1$ where $s$ is the spatial dimension and $1$ adds time. Models such that $r<2n/(n-2)$ can be treated by canonical quantization, while models such that $r>2n/(n-2)$ are trivial, or, if treated as a unit, emerge as `free theories'. Models such as $r=2n/(n-2)$, e.g., $r=n=4$, again using canonical quantization also become `free theories', which must be considered quantum failures. However, there exists a different approach called affine quantization that promotes a different set of classical variables to become the basic quantum operators and it offers different results. It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.
Solving NP-Complete Problems Efficiently
Authors: Vega Frank
Published: September 2024

The P versus NP problem is a fundamental question in computer science. It asks whether problems whose solutions can be quickly verified can also be quickly solved. Here, "quickly" refers to computational time that grows proportionally to the size of the input (polynomial time). While the problem's roots trace back to a 1955 letter from John Nash, its formalization is attributed to Stephen Cook and Leonid Levin. Despite extensive research, a definitive answer remains elusive. Closely tied to this is the concept of NP-completeness. If a single NP-complete problem could be solved efficiently, it would imply that all problems in NP can be solved efficiently, proving that P equals NP. This work posits that MONOTONE ONE-IN-THREE 3SAT, a notoriously difficult NP-complete problem, can be solved efficiently, thereby establishing the equivalence of P and NP.
Digital Transformation in University Architecture: Building Data Management on the Pontificia Universidad Javeriana Campus
Authors: Carrasco-Beltrán Daniela , Serrano-Sierra Alejandro , Cuervo Pulido Roberto , Valbuena-Bermúdez Carolina , Pavlich-Mariscal Jaime A. , Granados-León César
Published: September 2024

The integration of digital technologies in managing technical and design information is trans-forming the fields of architecture and construction. This article presents a website developed to compile detailed information on the construction processes of buildings at Pontificia Universidad Javeriana (PUJ) campus. The site offers comprehensive construction records and interviews with designers, engineers and other professionals who collaborated to align design approaches with technical construction requirements. A chronological record of the construction processes was maintained, from inception to completion of the buildings. Digital tools include interactive modules that facilitate access and data updates, improving coordination and efficiency for man-aging future projects. Gamification elements and social media dissemination boost user engage-ment. The study also assesses the impact of the website on the university community, analyzing usability and user (N=235), among students, teachers, and staff members. Findings indicate that digital technologies optimize construction processes and improve the management of technical and design information, and reveal opportunities for future projects and research, benefiting both internal and external stakeholders.
Technical Fouls Effect on Momentum Change: Does the Season (Regular vs. Playoffs) Matter?
Authors: Tenenbaum Gershon , K. Maymon Yaniv , Ben-Zion Tomer , Lev Assaf
Published: September 2024

In a follow-up to Tenenbaum et al.’s (2024) study we explored differences in momentum shift between regular season and playoff matchups, considering home vs. away games and score at the time of the technical foul call (leading or trailing). Analyzing 4,196 cases of technical fouls (TFs) against coaches from 2000-2021 (3,950 in the regular season, 246 in playoffs), logistic regression revealed a significant (p < .03) season by location interaction showing that in the regular season, home games TFs resulted in a 44%-win rate versus 28% in away games. In the playoffs, this changed to 50% at home and 23% away. The findings provide insights and practical considerations for the TF-momentum shift phenomenon.
Analysis of Error-Based Switched Fractional Order Adaptive Systems: An Error Model Approach
Authors: Aguila-Camacho Norelys , Gallegos Javier A. , Chen YangQuan , Travieso-Torres Juan C.
Published: September 2024

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.
The Emerging Role of Long Noncoding RNAs’ in Sorafenib Resistance within Hepatocellular Carcinoma
Authors: Vij Puneet , Hussain Mohammad Shabir , Satapathy Sanjaya Kumar , Cobos Everardo , Tripathi Manish Kumar
Published: September 2024

Hepatocellular carcinoma (HCC) is a liver cancer that originates from hepatocytes. It is a primary health issue and one of the most common malignant tumors. The US FDA approved Sorafenib as the first-line anti-cancer drug for advanced HCC patients. A few patients with advanced HCC may benefit from Sorafenib treatment, but most of the patients eventually develop resistance to Sorafenib, which leads to its poor prognosis. Long-noncoding RNAs (lncRNA) have been found to play a critical role in tumorigenesis and the development of HCC, as well as various cancers. It also plays a vital role in tumor drug resistance, while the mechanisms of LncRNAs in HCC sorafenib resistance have not been characterized yet. In this review article, we summarized the molecular mechanisms, such as the involvement of transporters, receptors, cell death regulation, and other factors impacting sorafenib resistance in HCC.
A Pandemic Risk Index to Monitor the Risk of Pará’s Coastal Municipalities during COVID-19: Addressing Vulnerabilities and Open Space Management
Authors: Dias Ana Beatriz Brito , Jimenez Jose Antonio , Pereira Luci Cajueiro Carneiro , Silva Brenda Ribeiro Padilha da , Rabelo Luan , Fernandes Farley Darlan dos Santos
Published: September 2024

Amidst the Covid-19 pandemic, open spaces provided solace for people during social
isolation but also heighten risks, particularly in areas lacking proper management. This
was especially critical in regions already facing challenges like low human
development, extreme poverty, limited healthcare access, as seen in Pará Coastal
Zone (NE Brazil). To address this, this study introduced the Pandemic Risk Index
(PRI), evaluating Covid-19 risk using demographic, social, economic, and
epidemiological indicators. Applied to municipalities in the area, PRI highlighted preexisting vulnerabilities shaping the region’s pandemic response and overall risk. PRI
analysis revealed temporal and spatial variations in risk levels, identifying areas where
the use of open spaces like beaches, presented a higher transmission risk. The results
emphasized the significance of effective governance, improved healthcare, and
containment measures, lowers risk levels. affect risk levels, and becomes one of the
most important challenges, especially in remote areas with limited services and
access.
DNA Gene's Basic Structure as a Nonperturbative Circuit Quantum Electrodynamics: Is RNA Polymerase II the Quantum Bus of Transcription?
Authors: Riera Aroche Raul , Ortiz García Yveth M. , Sánchez Moreno Esli C. , Enriquez Cervantes José S. , Machado Sulbaran Andrea C. , Riera Leal Annie
Published: September 2024

Previously, we described that Adenine, Thymine, Cytosine, and Guanine nucleobases were superconductors in a quantum superposition of phases on each side of the central Hydrogen bond acting as a Josephson Junction. Genomic DNA has two strands wrapped helically around one another, but during transcription, they are separated by the RNA polymerase II to form a molecular condensate called the transcription bubble. Successive steps involve the bubble translocation along the gene body. This work discusses how quantum information transfer and entanglement can be achieved by modeling DNA as a combination of ? nonperturbative circuits quantum electrodynamics with nine Radio-Frequency- Superconducting Quantum Interference Devices (SQUIDs) inside. A bus can be coupled capacitively to a single-mode microwave resonator. The cavity mode and the bus can mediate long-range, fast interaction between neighboring and distant DNA SQUID qubits. RNA polymerase II produces decoherence during transcription. This enzyme is a multifunctional biomolecular machine working like an artificially engineered device. Phosphorylation reactions catalyzed by protein kinases constitute the driving force. The coupling between n phosphorylation pulses and any particular SQUID qubit can be obtained selectively via frequency matching.
Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data
Authors: Maiora Josu , Rezola-Pardo Chloe , García Guillermo , Sanz Begoña , Graña Manuel
Published: September 2024

Falls are a major public health problem among older adults, therefore predicting the risk of having falls in the near future is of great importance for health and social systems worldwide. Nowadays, prospective fall risk assessment relies on clinical and functional mobility assessment tools, such as the Timed Up and Go (TUG) test. Recently, wearable inertial measurement unit (IMU) sensors measurements have been proposed for fall detection. We hypothesize that the IMU readings captured during TUG test realizations can provide prospective fall risk prediction. In this study, analysis by deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) were used for fall prediction based on features extracted from IMU data acquired during TUG test realizations. Data is obtained from a cohort of 106 older adults wear-ing a wireless inertial sensor with a sampling frequency of 100 Hz while performing the TUG test. Prospective fall incidence was obtained in a six-month follow-up period and used as the ground truth for the supervised training and validations of the deep learning and competing machine learning approaches. Further data collection could lead to potential fall risk biomarker. A repeated hold out cross-validation process using 75 subjects for training and 31 subjects for testing was carried out. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.
The Impact of IT Strategic Planning Process on SME Performance: A Systematic Review
Authors: Molete Olebogeng B. , Mokhele Selloane E. , Ntombela Somquba D. , Thango Bonginkosi A.
Published: September 2024

IT strategic planning is a critical driver of performance for small and medium-sized enterprises (SMEs), improving operational efficiency, decision-making, and innovation. This systematic review synthesizes findings from eighty studies, highlighting the varied impacts of IT strategic planning on SME performance. Quantitative analysis was used in 45% of studies, while thematic analysis was applied in 25%, showing the growing preference for data-driven insights. Cloud-based solutions, adopted in 11.25% of cases, and hybrid IT setups (17.5%) were identified as key enablers of flexibility and cost-efficiency. SMEs aligning IT strategies with business goals reported up to 20% reductions in operational costs and 15% improvements in customer satisfaction. However, 60% of studies identified resource constraints as a major challenge to IT adoption. Despite these barriers, 60% of studies demonstrated that effective IT planning enhances long-term sustainability and competitive advantage. This review provides actionable insights and strategic frameworks to guide SMEs in optimizing their IT investments for sustainable growth.
IoHT and Edge Computing Aided Pandemic-Compliant, Resilient and Perceptive Platform for Smart-City Human Habitat
Authors: --- Atlanta Choudhury , Sarma Kandarpa Kumar , Misra Debashis Dev , Guha Koushik , Iannacci Jacopo
Published: September 2024

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.
Innovations in Food Packaging: From Bio-Based Materials to Smart Packaging Systems
Authors: D'Almeida Alan Portal , de Albuquerque² Tiago Lima
Published: September 2024

This review highlights recent innovations in food packaging, emphasizing the shift from conventional petroleum-based materials to bio-based alternatives and smart packaging systems. Bio-based materials, such as starch, cellulose, and polyhydroxyalkanoates (PHA), offer sustainable solutions due to their biodegradability and reduced environmental impact. These materials are positioned as eco-friendly alternatives to traditional plastics, but face challenges related to production costs and scalability. Additionally, advancements in smart packaging technologies, including sensor and indicator systems, provide real-time food quality monitoring, enhancing food safety and reducing waste. Active packaging technologies, incorporating natural antioxidants and moisture control, extend product shelf life and improve food preservation. Also, these biopolymers usually present lower CO2 footprint, energetic cost, and water consumption during their production, in comparison to traditionally used synthetic plastics. The review identifies critical challenges, such as regulatory barriers and technological limitations, but also outlines significant opportunities for future research and innovation in the food packaging sector, aiming for more efficient, safer, and environmentally sustainable packaging solutions.
Co-extraction of DNA and RNA from Candida albicans Using a Chemical Method in Conjunction with Silicon Carbide with Few Cells
Authors: Freitas Elizabeth Cristina Vieira de , Santos Francisca Alves dos , Lopes Maria Raíssa Vieira , Sousa Júnior Dárcio Luiz de , Yafawi Tássia Thaís Al , Araújo Ana Carolina Ferreira , Freitas Priscilla Ramos , Menezes Irwin Rose Alencar de , Coutinho Henrique Douglas Melo , Leandro Maria Karollyna do Nascimento Silva
Published: September 2024

The study aimed to optimize protocols for the joint extraction of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from 0.025 x 106 CFU of Candida albicans, aiming to overcome challenges in the extraction of these genetic materials. From this, treated Silicon Carbide (SiC) granules were added to fungal samples from methods 1, 2 and 3 obtained from aliquots of BHI or Sabouraud medium to cause cell lysis and enable the isolation of these macromolecules by phenol and chloroform. The concentration and integrity of nucleic acids were obtained, respectively, by spectrophotometry using A260/A280 ratios and 1% agarose gel electrophoresis. Therefore, method 3 is the one that most comprises samples considered pure of both DNA and RNA, simultaneously. Furthermore, the presence of intact RNAs corresponding to the base pair size, such as 5.8 S rRNA and tRNA, was verified during electrophoresis, taking into account the particularities of RNA, which makes it very unstable and easily degraded. Therefore, it results in a faster and simpler method in addition to obtaining promising results using minimal amounts of biological sample, thus offering a valuable alternative for small laboratories working with molecular biology.
The Thermomechanical, Functional and Biocompatibility Properties of an Au-Pt-Ge Alloy for PFM Dental Restorations
Authors: Majerič Peter , Lazić Minja Miličić , Mitić Dijana , Lazić Marko , Lazić Ema Krdžović , Vastag Gyöngyi , Anžel Ivan , Lazić Vojkan , Rudolf Rebeka
Published: September 2024

Thermomechanical, biocompatibility, durability, workability and economic characteristics are the main criteria for development of dental alloys. In an attempt to provide a noble metal dental alloy without the known adverse metallic elements, a novel alloy was produced with the addition of Germanium (Ge), as a more cost-effective alternative to other precious alloying metals. The objective of this investigation was to analyse the thermomechanical and biocompatibility properties of a high noble Au-Pt-Ge porcelain-fused-to-metal (PFM) alloy. These properties were investigated with DSC analysis, hardness, density and coefficient of thermal expansion (CTE) measurements, as well as with metallographic and SEM/EDX microstructure investigations, with the production of a test PFM dental bridge, static immersion testing for metal ion release, and an in vitro primary biocompatibility testing on human gingival cells. The results of the thermomechanical testing showed alloy properties suitable for dental restorations and clinical use, with somewhat lower hardness, making the alloy not suitable for extensive multiunit fixed restorations. The microstructure investigations showed segregations of Ge in a homogeneous alloy matrix, which reduce the alloy’s mechanical properties. The produced PFM dental bridge shows excellent workability of the alloy in a dental laboratory setting, as well as a high standard of the final dental restoration. The ion release was negligible, well below any harmful quantities, while the cell viability examination showed significantly higher viability ratings on polished alloy samples as compared to as-cast samples. The results showed that a dental substructure in direct contact with oral tissue and fluids should be highly polished.
Enhancing Parameters Tuning of Overlay Models with Ridge Regression: Addressing Multicollinearity in High-Dimensional Data
Authors: Magklaras Aris , Gogos Christos , Alefragis Panayiotis , Birbas Alexios
Published: September 2024

Extreme Ultraviolet (EUV) photolithography process, is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary Least Squares (OLS) is the most widely used method for parameters tuning of Overlay models but in most cases it fails to compensate for such phenomena. In this paper we propose the usage of Ridge Regression, a widely known Machine Learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method has been applied in perturbed data from a 300 mm wafer fab and the results show reduced residuals when ridge regression is applied instead of OLS.
Sustainable Consensus Algorithms Applied to Blockchain: A Systematic Literature Review
Authors: Pineda Magda , Jabba Daladier , Nieto-Bernal Wilson , Pérez Alfredo
Published: September 2024

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the Blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the Blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. For example, Satoshi Nakamoto's (2008) original paper on Bitcoin introduced proof-of-work (PoW) as a method of achieving consensus on the network. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.