bluuebunny/crossref_metadata_2025 huggingface dataset:
Fairness in Higher Education Research and Innovation Funding in the UK
Authors: Liyanage Champika L. , Villalba-Romero Felix , Carmichael Andrew
Published: October 2024

This research analysed the distribution of publicly awarded funding, by United Kingdom Research and Innovation (UKRI) to defined University Groups and compared it against the aim and objectives of UKRI’s Equality, Diversity, and Inclusivity (EDI) strategy. Previous work has identified failures in the allocation of research awards, with some groups being under-represented in successful bids. UKRI have acknowledged the situation needs to improve yet concern exists over whether change has been enacted. Publicly accessible information provided freely by UKRI was used in conducting a comprehensive analysis of the current position. The results showed that the marginalisation of groups applies at a University Group level, with over-representation of the elite universities of the Russell Group in successfully funded project bids. The data shows how, both in total financial expenditure and in the number of projects financed, the awarding of research funding to the universities with the largest popular base, namely the group identified as Post-92 universities, is relatively very small. These universities, as identified by measurements such as social inclusion within university rankings, contribute to greater equality of opportunities for student populations and reduce the impact of economic discrimination. By greatly minoritizing the Post-92 universities through funding UKRI is effectively reducing access for their disadvantaged students.
Single Solution Based Metaheuristics With Graph Network Based Machine Learning
Authors: Cryzan Mia
Published: October 2024

Simulated Annealing (SA), Tabu Search (TS), and Variable Neighborhood Search (VNS) are well-established single-solution-based metaheuristics known for their efficiency. In this paper, we explore the application of these techniques to enhance the performance of graph network models, specifically Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). By leveraging these metaheuristics, we aim to optimize key aspects of graph-based learning, such as hyperparameter tuning, architecture design, feature selection, and graph structure learning. Our approach demonstrates how these metaheuristics can improve the generalization, efficiency, and robustness of GNNs/GCNs, facilitating their use in large-scale graph-based tasks. The proposed framework illustrates the potential of combining advanced optimization techniques with state-of-the-art graph machine learning models.
A New Understanding of Einstein-Rosen Bridges
Authors: Gaztañaga Enrique , Kumar K. Sravan , Marto João
Published: October 2024

In 1935, Einstein and Rosen (ER), in their seminal paper "Particle Problem in General Relativity" proposed that "a particle in the physical Universe has to be described by mathematical bridges connecting two sheets of spacetime" in order to achieve a consistent understanding of quantum fields in curved spacetime. We discuss similar conclusions independently arrived at by Schr\"{o}dinger in 1956 and 't Hooft in 2016 in the context of cosmological and black hole spacetimes. Quantum effects at gravitational horizons involve inverse harmonic oscillators. We demonstrate that an analogous ER proposal of a mathematical bridge is envisioned by Berry and Keating for the description of a quantum inverse harmonic oscillator that has horizons in its phase space. Recently proposed direct-sum quantum theory reconciles the ER's vision by expressing a single quantum state in the physical space as a direct-sum of two components in the parity conjugate regions with opposite arrows of time. We discuss the implications of this framework for offering a new understanding of the ER bridges, promising a unitarity description of quantum fields in curved spacetime along with observer complementarity. Furthermore, we present compelling evidence for our new understanding of ER bridges in the form of large-scale parity asymmetric features in the cosmic microwave background. We finally discuss the implications of this new understanding for our endeavors in combining gravity and quantum mechanics.
Precision Medicine for Acute Lymphoblastic Leukemia in Children: A Review
Authors: Ray Anish , Levitt Michael , Efunkoya Toluwalope , Trinkman Heidi
Published: October 2024

The clinical outcome for children diagnosed with acute lymphoblastic leukemia is a testimony to the success of modern medicine. Over the past several decades, survival has climbed from ∼ 10% to > 90% for certain subgroups. Yet, the outcome for those with relapsed disease is often poor and survivors struggle with a multitude of healthcare issues, some of which are lifelong. In recent years, the advent of widespread sequencing of tumors has made available previously unrecognized subtypes of leukemia patients who have the potential to benefit from the addition of targeted therapies. Indeed, the promise of precision medicine encompassing a person's environment, genetics and lifestyle is likely to have profound impact on further tailoring therapies that are likely to improve outcomes, diminish toxicity and ultimately pave the pathway for a healthier population.
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce
Authors: Brodzinski Carl
Published: October 2024

This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.
Study of Thermal Fields Inside Pipes in Solar Collectors Working with Ionanofluids by Means of the Heatt® Platform
Authors: Alarcón Mariano , Seco-Nicolás Manuel , Luna-Abad Juan-Pedro , Moulefera Imane , Víllora Gloria
Published: October 2024

Solar electric and solar thermal energies are often considered as part of the solution to the current energy emergency. In the present work, a study of the characteristic length of tubes in flat photovoltaic-thermal (PVT) hybrid solar devices has been carried out using three heat transfer fluids: water, Emim-Ac ionic liquid and ionanofluid of graphene nanoparticles suspended in the above ionic liquid. Experimental measurements and numerical simulations have been performed using the HEATT® platform. Slower fluid velocities are recommended if high fluid outlet temperatures are the goal of the application, but this penalizes the overall thermal energy production. The characteristic process length, or most efficient distance at which heat transfer occurs, is not typically achieved in parallel tube PVT collectors; coil-type panels can improve the thermal and temperature performance of INFs in PVT solar collectors depending on the application.
Narratives of Sustainable Transformation: The Power of Speculative Fiction in Innovationology
Authors: Moleka Pitshou
Published: October 2024

As humanity confronts the existential threats of climate change, biodiversity loss, and growing socioeconomic inequalities, the need for transformative, systemic change has never been more urgent. Conventional innovation approaches, often focused on technological solutionism and market imperatives, have consistently fallen short in delivering the comprehensive, equitable, and sustainable solutions required to navigate these interconnected crises. This pioneering article explores the profound intersection between speculative fiction, sustainable innovation, and the burgeoning field of innovationology. By harnessing the transformative power of imaginative narratives, the integrated praxis of innovationology and sustainability science can challenge existing mental models, spark collective imagination, and foster agency to co-create sustainable futures. Through a rigorous examination of cutting-edge research, empirical evidence, and case studies, this article positions speculative fiction as a critical lever for catalyzing the systemic changes required for a more just, regenerative, and resilient future.
The Efficacy of Food Bioactive Compounds in The Treatment of Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis
Authors: Ibirogba Damilola , Olukogbe Oluwagbemisola , Haile Ebsitu , Asori Moses , Engmann Stephen , Abdulraheem Abdullahi , Edeh Gabriel , Mergerssa Thomas , Effiong Fortune , Olowu Babatunde , Kusemwa Prettyossa , Okafor Ukamaka , Omulepu Isaac , Eneh Stanley , Aderinola Taiwo , Thami Renuka , Okwose Nduka
Published: October 2024

Background: Food Bioactive Substances (FBS), extra-nutritional constituents found in foods are promising strategies in the treatment of Non-Alcoholic Fatty Liver Disease (NAFLD). Existing clinical studies showed contradictory results regarding their therapeutic effect. This review aimed to evaluate the effectiveness of food-bioactive substances in patients with NAFLD and non-alcoholic steatohepatitis (NASH). Methods: Six controlled databases (PubMed, Embase, Scopus, ProQuest, CINAHL and Cochrane) were searched, yielding 10969 articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed, while screening, quality assessment and data extraction of search results were conducted using Covidence. Results: Thirty-two studies were included in the systematic review, while 8 RCTs with 540 patients with NAFLD were included in the meta-analysis. The meta-analysis underscores the effects of the three most studied FBS (silymarin, curcumin and resveratrol) on key cardiovascular and metabolic markers, including BMI, waist circumference, AST, ALT, LDL, HDL, cholesterol and triglycerides of these patients. Overall, FBS showed a statistically significant reduction in BMI (SMD = -0.28; 95% CI: -0.45 to -0.10, p < 0.01), a slight reduction in LDL (SMD = -0.22; p = 0.02), HOMA-IR (SMD = -0.26, 95% CI = -0.51 to -0.02; p = 0.036), total cholesterol level SMD = -0.15 (95% CI: -0.40 to -0.10; I2 = 13%; p = 0.33), especially with silymarin showing the greatest statistically significant reduction in HOMA-IR (SMD = -0.16); p = 0.01), Total Cholesterol (SMD = -0.45, p <0.01), AST (SMD = -0.23, p <0.01) Conclusion: Silymarin appeared to be the most promising intervention, particularly for lowering BMI, LDL, cholesterol and triglycerides, but the high variability between studies limits the generalizability of these results
Global Aging and the Importance of Research Collaboration in Sick Sinus Syndrome: A Comprehensive Network StudyNetwork Study
Authors: Ogasawara Naruaki
Published: October 2024

Aim: This study aims to investigate the global collaboration network among researchers studying Sick Sinus Syndrome (SSS) from 2000 to 2023. Given the syndrome's increasing prevalence in aging populations, understanding the structure of research collaboration is crucial to advancing the field. This analysis explores co-authorship networks to identify key contributors, institutions, and trends in SSS research. Method: This study utilized network analysis techniques to evaluate 1,693 publications related to SSS research indexed in the Web of Science (WoS) Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The analysis revealed an overall increase in collaboration over time, with rising network density and stronger local clusters, although the network remained fragmented. Key researchers, including Boriani, Giuseppe (Italy), and Glikson, Michael (Australia), emerged as central figures, demonstrating high degrees of centrality in different periods. The results also highlight persistent regional and institutional collaboration patterns, reflecting the growing importance of global partnerships in addressing SSS.Conclusion: This comprehensive network analysis underscores the importance of international collaboration in advancing SSS research. Despite growing collaborative efforts, the field remains somewhat fragmented, suggesting further opportunities for integration. This research has enabled us to identify influential researchers and research institutions, and we will be able to promote research collaboration to further understand and improve treatment methods for SSS as a response to the aging of the world's population.
Hydrogen Combustion in ICE, Simulation of Optical Engines, Blow-By and Thermal Losses
Authors: Flesch Evelyn , Glocker Erik , Prager Maximilian , Jaensch Malte
Published: October 2024

Hydrogen combustion in internal combustion engines is a strongly awaited technology on the way to carbon neutrality, but still with major challenges to takle. There are plenty of combustion anomalies like preignition, backfire, knock and lube oil ignition that come along with the technology, hence not yet controllable. To gain more knowledge about their origin, optical engines and CFD simulations are well established methods to investigate and develop knew combustion processes. Hydrogen’s wide flammability range and its short quenching distance bring new challenges. However, optical engines give the best insight into the real combustion behavior. Therefore, their understanding and proper modeling in the simulation is of great importance to calibrate the simulation models accurately. For optical engines there are diverging opinions on how to deal with their blow-by losses. Due to their lack of oil different sealing mechanisms exist and those seem to be crucial for the question on how or wether the blow-by has to be modelled. proposed a numerical multi-zone blow-by model whereas the opinion at is to neglect those losses.
A Two-Way Street: How Are Yeasts Impacted by Pesticides, and How Can They Help Solve Agrochemical Contamination Problems?
Authors: Pritsch Eduardo José Pedroso , Schutz Danielli , de Oliveira Camila Girardi , Camargo Aline Frumi , Cabrera Liziara Costa , Dos Santos Angela Alves , Mossi Altemir José , Treichel Helen , Alves Jr Sergio Luiz
Published: October 2024

Plant-associated yeasts can also be part of soils’ and pollinating-insects’ microbiomes, where they play significant ecological roles. Indeed, in previous studies, we have shown that yeasts may help pollinators find nectar, which is crucial for their nutrition and for the reproduction of many angiosperms, and, in the soil, can act as plant-growth promoters. Given the importance of yeasts for plant development, in this review, we first delve deep into the biochemical processes behind the ecological role of these microorganisms in soil, insects, and in direct association with plants. Based on this premise, we discuss the influence of this relationship on agricultural production and the possible effects of pesticide use on the yeast microbiota. Finally, we address the most relevant studies in the literature that support the potential of these microorganisms (either indigenous or genetically engineered) as bioremediation agents for soils and foods contaminated by pesticides. Our review indicates that yeasts can be satisfactorily employed in organic agriculture to increase plant growth or bioremediate contaminated soil or food.
Applications of Entropy in Data Analysis and Machine Learning: A Review
Authors: Sepúlveda-Fontaine Salomé A. , Amigó José M.
Published: October 2024

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann-Gibbs, von Neumann, Shannon, Kolmogorov-Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), other commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. Precisely, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review finely illustrate the power and versatility of entropy in data analysis and machine learning.
Exploring the Lived Experience of Living with Cardiac Arrhythmia Developed During the Pregnancy Period
Authors: Metersky Kateryna , Chandrasekaran Kaveenaa , El-hajj Yoland , Fredericks Suzanne , Sonar Priyanka Vijay
Published: October 2024

Approximately half of all women develop palpitations during pregnancy with a quarter experiencing arrhythmias. While most presentations are benign, some cases can result in sudden cardiac death or serious symptom development. Considering such clinical presentation, healthcare providers must acquire knowledge in this area to provide comprehensive prenatal, perinatal, and postnatal care. However, no study could be located that focused on women’s lived experiences of such complications during or in the post-pregnancy period. The study aims to share the results of a study that explored the lived experience of one woman who developed non-sustained ventricular tachycardia during her third pregnancy that lasted into the post-partum period. Using Narrative Inquiry self-study methodology, a woman’s experiences were explored to uncover the challenges she faced in coping with such complications during a period of transition for herself and her family. This methodology allowed for an in-depth understanding of how these complications could affect all aspects of her life. Four narrative threads were produced: 1) diagnostic challenges and delayed recognition; 2) impact on maternal identity and family dynamics; 3) navigating healthcare systems and treatment decisions; and 4) long- term adaptation and resilience. The intention was to add to this topic area to ensure future researchers, current and future healthcare providers, and patients have literature they can refer to when studying, providing care for, or experiencing similar health complications. Acquiring this knowledge can aid healthcare professionals to ensure appropriate care is provided, risks are minimized and their recovery is well supported.
Analysis of the Liquefaction Potential at the Base of the San Marcos Dam (Cayambe, Ecuador). A Validation in the Use of the HVSR
Authors: Alonso-Pandavenes Olegario , Torrijo Francisco Javier , Torres Gabriela
Published: October 2024

The analysis of the liquefaction potential of the ground is a fundamental characterization in areas with continuous seismic activity, such as Ecuador. Geotechnical liquefaction studies are usually approached from the application of dynamic penetration tests, which pose problems both in their execution and in their evaluation. The proposed research involves analyzing dynamic penetration tests and microtremor geophysical surveys (Horizontal to Vertical Spectral Ratio, HVSR technique) at the base of the San Marcos dam, a reservoir destined for the irrigation water located in Cayambe (Ecuador). Based on the investigations performed at the time of construction of the dam (drilling and geophysical refraction profiles) and the application of 20 microtremor observation stations performed by the HVSR technique, an analysis of the Safety Factor of liquefaction (SFliq) has been conducted, proposed by Youd and Idriss in 2001, and based on the values of the Standard Penetration Test (SPT) applied in granular materials (sands). In addition, the vulnerability index (Kg) proposed by Nakamura in 1989 was analyzed through the HVSR passive seismic records related to the Ground Shear Strain (GSS). The results obtained in the HVSR surveys indicate the presence of a zone of about 100 m length in the central part of the foot of the dam, whose GSS values are identified with a condition of susceptibility to liquefaction. In the same area, the analysis applied from SPT essays in the P-8A drill hole executed in said area also shows a potential susceptibility to liquefaction in earthquake conditions greater than a moment magnitude (Mw) of 4.5, which could occur in the environment, for example, with a new activity condition of the nearby Cayambe volcano or even from an earthquake from the vicinity fractured zone.
Exploring and Evaluating Large Language Model Survey Paper Categories
Authors: Gresham Dylan
Published: October 2024

In February of 2024, Dr. Jun Zhuang and Dr. Casey Kennington published a paper (Zhuang and Kennington, 2024) in which they classified large language model (LLM) survey papers into different taxonomies utilizing graph learning. In this paper, I evaluate the dataset they created and used and propose that in its current state, there is not enough samples to classify other survey papers into specific categories.
Harnessing Regression Models to Optimize Project Duration Estimation and Reduce Delays in South African Infrastructure Development
Authors: Ramabodu Molusiwa Stephan
Published: October 2024

Accurately estimating project duration is critical for the successful delivery of construction projects, particularly in the dynamic and resource-constrained environment of South Africa. This study aims to assess the key factors influencing construction project duration in the South African context through the development of a regression model. By analyzing data from a diverse range of construction projects, the research identifies significant variables such as project size, complexity, location, resource availability, and stakeholder involvement that contribute to delays or expedited completion. The regression model is designed to quantify the impact of these factors, offering a predictive tool that can enhance project planning and scheduling accuracy. The findings provide valuable insights for project managers, contractors, and policymakers, enabling them to make informed decisions that optimize project timelines and improve the overall efficiency of the construction industry in South Africa.
Population Structure, Morphology, and Health Assessment of Philippine Swamp Buffalo (Bubalus bubalis) in Calayan Island, Cagayan
Authors: Villamor Lilian Pagaduan , Cuanang Aivhie Jhoy Escuadro , Cailipan Therese Patricka Cinense , Paraguas Alexander Manahan , Flores Ester Battad , Villanueva Marvin Ardeza , Balbin Michelle Macugay , Belotindos Lawrence Pascual , Rellin Franklin Tavera
Published: October 2024

Understanding the state of the swamp buffalo population in Calayan Island is important to strengthen the conservation and management program in the country. This study aimed to provide insights into the morphology, population structure, and health profile of the swamp buffaloes on the island. In total, 35 fresh blood samples were analyzed using 27 polymorphic microsatellite markers to determine the population structure analysis. Data were gathered for the morphological features of Calayan swamp buffaloes and served as the baseline information for the descriptive traits. Furthermore, samples were tested for surra and brucellosis using PCR and serological tests respectively. Results showed that Calayan swamp buffaloes were morphologically bigger except for body length compared with other populations. Genotype analysis using microsatellite markers showed remarkable discriminatory power to distinguish distinct populations within the tested population and could discriminate subspecies of swamp and river types plus crossbreds. The study also reports the first incidence of surra and brucellosis on the island. Overall, the new insights provided on newly detected Philippine carabao lineage in Calayan Island would be highly recommended for ex-situ conservation and animal health control strategy. The conservation strategy would encompass collecting, cryopreserving, and storing viable germplasms from local swamp buffalo on Calayan Island.
Influenza A Viruses in Swine Population – a Scientific Review
Authors: Klivleyeva Nailya , Ongarbayeva Nuray , Glebova Tatyana , Lukmanova Galina , Saktaganov Nurbol , Webby Richard
Published: October 2024

Influenza viruses are important pathogens that affect human and animal health. The ecology and epidemiology of influenza viruses are very complex because they have a diverse host range. Influenza A viruses are responsible for serious epidemics. Influenza B viruses cause respiratory disease in humans, seals, and pigs. Influenza C viruses are infectious to humans and pigs. Influenza D virus infects cattle, although it was first isolated from a sick pig in 2011. Only influenza A viruses are of clinical importance in pigs. Although influenza A viruses infect a variety of animal species, pigs are one of the natural hosts of these viruses. Swine influenza is a very serious respiratory disease in pigs, and its main causative agents are the H1N1, H1N2, and H3N2 subtypes, which are antigenically related to human influenza viruses. Pigs are susceptible to infection by avian and human influenza viruses and are thought to play a significant role in the ecology of human influenza. This review examines scientific articles on the zoonotic spread of swine influenza A viruses to humans, their circulation in pig populations worldwide, reverse zoonosis from humans to pigs, and the role of pigs in interspecies transmission.
Leveraging Chatbots for Effective Educational Administration: A Systematic Review
Authors: Al-Jaf Kanaan , Öz Cemil , Mahmud Hoger , Rashid Tarik A.
Published: October 2024

Educational institutions are increasingly utilizing chatbots to enhance communication and streamline administrative tasks, including answering frequently asked questions (FAQs). Serving as the first review about employing chatbots in educational administration, this systematic review explores the potential of chatbots to improve educational administration. While research on chatbots in education has primarily focused on their role in learning, their application for administrative tasks remains underexplored. This gap necessitates a systematic investigation of how chatbots are utilized and implemented to streamline administrative processes. Following the PRISMA framework, this review analyzes 54 studies to address five key areas: functionalities performed, data employed, technologies used, evaluation methods, and future development challenges. The review reveals a critical need for a comprehensive framework for developing administrative chatbots that are specifically tailored to educational institutions. This review underscores the importance of such a framework in addressing identified gaps in the literature and in incorporating generative AI into educational administrative tasks management. The review also highlights a noticeable lack of research in this domain, including the potential of generative AI, like ChatGPT, to improve administrative efficiency. Furthermore, the study discusses the potential impact, practical applications of administrative chatbots in education and future research directions.
Antimicrobial Peptides in the Fight Against Drug-Resistant Superbug Infections
Authors: Baindara Piyush , Kumari Sumeeta , Dinata Roy , Mandal Santi M
Published: October 2024

The discovery of antibiotics was one of the greatest achievements in human history, however, antibiotic resistance evolved no later than the introduction of antibiotics. The rapid evolution of antibiotic-resistant pathogens soon became a nightmare and remained a global healthcare threat. There is an urgent need to have new alternatives or new strategies to combat the multi-drug resistant superbugs such as methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), carbapenem-resistant Pseudomonas aeruginosa (CR-PA), extended-spectrum β-lactamases (ESBL) bearing multidrug-resistant Acinetobacter baumannii (MDR-AB), Escherichia coli, and Klebsiella pneumonia. Antimicrobial peptides (AMPs) have been considered promising agents equipped with unique mechanisms of action along with several other benefits to fight the battle against drug-resistant superbugs. Overall, the current review summarizes the mechanisms of drug-resistant development, the mechanism of action adopted by AMPs to combat drug-resistant pathogens, and the immunomodulatory properties of AMPs. Additionally, we have also reviewed the synergistic potential of AMPs with conventional antibiotics and the associated challenges and limitations of AMPs in the way of pharmacological development for therapeutic applications in clinical settings.
An Overview of Family and Community Nurse Specialists’ Employment Situation in Spain: A Qualitative Study
Authors: Sánchez-Muñoz Francisca , Fernández-Medina Isabel María , Ventura-Miranda María Isabel , Ortega-Galán Angela María , Jiménez-Lasserrotte María del Mar , Ruiz-Fernández María Dolores
Published: October 2024

Background: Family and Community Nurse specialists are advocates of a holistic model of care in multidisciplinary primary care teams. The study aimed to describe and understand the experiences and perceptions of family and community nurse specialists with regard to their employment situation in primary care teams in Spain; Methods: A Qualitative descriptive study was conducted. Eighteen family and community specialist nurses from different autonomous communities in Spain participated. Individual interviews and a focus group were conducted; Results: The results showed two themes and four sub-themes: 1) Current work situation of the Family and Community Nursing specialist: a) The lack of social and work recognition, and b) The advantages of working with Family and Community Nursing specialists; and 2) Support network and system of rejection: a) Systematic ambivalence towards Family and Community Nursing and b) Need for institutional support. Conclusions: Their inclusion in primary care teams fosters the nurse-patient bond, increases and/or maintains patient quality of life and promotes patient health empowerment. However, there is a great lack of knowledge on the part of society and health administrations regarding the functions of these nurses.
Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions
Authors: Ginzburg-Ganz Elinor , Segev Itay , Balabanov Alexander , Segev Elior , Kaully-Naveh Sivan , Machlev Ram , Belikov Juri , Katzir Liran , Keren Sarah , Levron Yoash
Published: October 2024

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand what is the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics, and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what are the currently open theoretical and numerical challenges in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided to ``model-based'' methods and ``model-free'' methods, in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles and energy storage.
Exploring the Application of Wearable Technologies in Creating Emotional Connections Between Human Brands and Customers: An Exploratory Study
Authors: Ghorbani Asiabar Mojtaba , Ghorbani Asiabar Morteza , Ghorbani Asiabar Alireza
Published: October 2024

This exploratory study investigates the role of wearable technologies in fostering emotional connections between human brands and customers. Given the rapid growth of the wearable technology market and the increasing importance of emotional connections in marketing, this research aims to uncover the potential of these technologies in strengthening the affective bond between brands and consumers. The mixed-method approach included semi-structured in-depth interviews with 25 marketing experts and an online survey of 300 consumers. Data analysis was conducted using thematic coding and statistical tests including Pearson correlation and multiple regression. Results indicated that wearable technologies can enhance emotional connections through personalized interactions (r=0.72, p<0.001), creating a sense of proximity (β=0.65, p<0.01), and facilitating two-way communications (β=0.58, p<0.01). These findings offer new perspectives for marketing strategies and customer relationship management in the digital age, highlighting the potential of wearable technologies as a powerful tool for building and maintaining emotional bonds between human brands and their audiences. The study contributes to the growing body of literature on technology-mediated brand relationships and provides practical implications for marketers seeking to leverage wearable technologies in their brand communication strategies.
Bacterial Resistance to Mercury: A Mini-Review
Authors: de Mattos D’Avila Daniel , G. Ferrari Rafaela , Rodrigues Paloma , Neves Gabriel , Ramos Filho Alexandre , Mano Rami Fanticelli , Conte-Junior Carlos
Published: October 2024

With the increase in mercury pollution around the world, several bacteria have been identified that are capable of resisting mercury toxicity. With this in mind, the aim of this review was to determine which genes are involved in mercury resistance, which bacterial genera exhibit this resistance and which bacterial isolation sources have been most reported. To answer these questions, the PICO method (population, intervention, comparison and outcome) was used, three databases were searched and 17 relevant articles were included. As a result, resistance is due to a set of mer genes that transcribe mer proteins. The most important genes identified were merA and merR, and their proteins confer resistance by reducing Hg to Hg+2 or Hg0. Among the bacteria studied, those of the genera Pseudomonas, Escherichia and the phylum Cyanobacteria stand out, the most important being Escherichia coli and Synechocystis sp. which are highly efficient and fast at reducing Hg. Based on the results, Escherichia coli and Synechocystis sp. are promising candidates for reducing environmental Hg, especially in aquatic environments. However, there is a lack of studies on the mechanism of bioremediation carried out by cyanobacteria and the influence of abiotic factors on the presence and/or expression of mer genes.
Changes in the Abundance and Distribution of Benthic Mollusks in Polluted Sediments of a Shallow Subtropical Estuary
Authors: Stark Rachael H. , Johnson Kevin B.
Published: October 2024

To quantify the ecological impacts of organic sediments and environmental dredging, benthic mollusks were chosen to serve as bioindicators of environmental change, measured as sediment organic content and associated parameters. Data on species richness, biodiversity, and abundances were collected alongside sediment and near-bottom water quality data before, during, and after environmental dredging. Organic sediment content was found to have an inverse logarithmic relationship with benthic mollusk biodiversity, species richness, and abundance. Sediments with lower organic content had higher biodiversity (up to 1.337), while sediments with higher organic content had low biodiversity (0-0.6667). Post-hoc analyses found that percent dissolved oxygen, which correlates with sediment organic content, was responsible for 29.31-34.12% of the benthic mollusk community variation.
Effect of the Complex Allele p.[Ile148Thr;Ile1023_Val1024del] 2 in Cystic Fibrosis and Tracing of a Founder Effect in Mexican 3 Families
Authors: Mendiola-Vidal Namibia Guadalupe , Contreras-Cubas Cecilia , Barajas-Olmos Francisco , Villafan-Bernal José Rafael , Yañez-Felix Ana Lucia , García-Ortiz Humberto , Centeno-Cruz Federico , Mendoza-Caamal Elvia , Alaez-Verson Carmen , Jiménez-Ruíz Juan Luis , Monge-Cázares Tulia , Lieberman Esther , Baca Vicente , Lezana José Luis , Martínez-Hernández Angélica , Orozco Lorena
Published: October 2024

Cystic fibrosis (CF) diagnosis can be a challenge due to the large spectrum of pathogenic variants in the CFTR gene and the effects of complex alleles. Next-generation sequencing has improved our understanding of the contribution of these complex alleles to the wide spectrum of CF clinical symptoms and to the response to medications. Herein, we studied nine CF patients from six unrelated families carrying the complex allele p.[Ile148Thr;Ile1023_Val1024del] with a frequency of 0.18%. All the patients were from Central Mexico. This complex allele was found in trans with Class I and II pathogenic variants (PVs) such as [c.1521_1523del, p.(Phe508del), and c.3231_3232del, p.(Phe1078Profs*77)]. A targeted search of a dataset of 2,217 exomes from healthy individuals revealed that eight individuals (0.18%) carried the p.(Ile148Thr) variant, but only one (0.022%), who was also born in Central Mexico, was a carrier of the complex allele. These findings led us to hypothesize a founder effect for the p.[Ile148Thr;Ile1023_Val1024del] complex allele in Mexican patients with CF, probably through migration from the French-Canadian population in Mexico. Finally, protein modeling revealed that this complex allele disrupts the secondary structure of the CFTR protein and might alter the ion flow.
Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region
Authors: Nevrlý Václav , Dostál Michal , Bitala Petr , Klecka Vit , Sléžka Jiří , Polach Pavel , Nevrlá Katarína , Barabášová Melánie , Langová Růžena , Bernatikova Sarka , Martiníková Barbora , Vašínek Michal , Nevrlý Adam , Lazecký Milan , Suchánek Jan , Chaloupecká Hana , Kiča David , Wild Jan
Published: October 2024

This study examines the varying performance of low-cost sensors during seasonal smog events, particularly those resulting from solid fuel combustion linked to residential or industrial heating and other pollution sources. A prototype low-cost sensor node developed within this work was collocated for several months with reference equipment at an air quality monitoring station in the city of Ostrava (Moravian-Silesian region, Czech Republic). Deterioration of air quality during a pair of smog alert events, namely wintertime "Polish smog" episode and springtime "Saharan dust storm", which occurred through the extended period of co-location, was characterized quantitatively based on particulate matter and carbon monoxide concentrations measured by the low-cost sensors. These datasets were compared with reference data and model predictions provided by the Copernicus Atmospheric Monitoring Service. Selected low-cost sensors demonstrated linear response during Polish smog episode with highly correlated time series of carbon monoxide and particulate matter concentrations. However, when compared to reference data, the Sensirion SPS30 sensor is found to overestimate coarse particle (PM10) concentrations by the factor of two during winter evaluation period. On the other hand, oppositely biased PM10 response (underestimation by the factor of five) was determined from co-location measurement during Saharan dust storm event. This observation was attributed to combination of specific detection efficiency of the given low-cost sensor and shifts in particle size distribution due to seasonal change of ambient conditions.
Evaluation of Cell-free DNA Long Fragments in the Triage of FIT+ Patients Enrolled in a Colorectal Cancer Screening Program: An Italian Prospective, Cross-Sectional Study
Authors: Scimia Mauro , Pepe Francesco , Russo Gianluca , Malapelle Umberto , Scimia Simone , Alfieri Annalaura , Olivieri Valentina , Chuang Rachel , Tanaka Hiromi , Sha Michael , Chen David , Shen Shuo , Scimone Claudia , Palumbo Lucia , Gavrilov Yulia , Edelstein Stav , Bianco Maria Antonia , Troncone Giancarlo
Published: October 2024

Background: Colorectal Cancer screening programs are effective in reducing incidence and mor-tality. In Europe, every FIT+ patient is referred to colonoscopy. Available data shows that ~75.0% of these patients are negative. It is desirable to select patients at greater risk of having a positive colonoscopy.

Methods: 711 subjects, aged 50-74, attending the screening program of ASL-NA-3-SUD (Na-ples, Italy), were enrolled in a cross-sectional study to evaluate the performance of QuantiDNA™ test and non-inferiority of an alternative approach (AAP). This evaluation is based on FIT+ and QuantiDNA™+ patients referred to colonoscopy, compared to Standard of Care (SOC), colon-oscopy following a FIT+ test alone. A non-inferiority margin for Colorectal Neoplasia (CN) and Advanced Adenomas (AA) was set at -10% and at -3.8% for CRC.

Results: The odds ratio was 1.76 (p-value= 0.009). The detection rate of AAP was 15.9% for colorectal neoplasia (CN), 13.0% for advanced adenoma (AA), and 3.0% for CRC. The risk dif-ference between AAP and SOC was -5.07% (95% C.I -9.23, -0.90) for CN, -4.02% (95% C.I. -7.89, -0.16) for AA, and -1.04% (95% C.I. -3.16, 1.07) for CRC. This data suggests that AAP is non-inferior to SOC for detecting CN, AA, and CRC. The expected decrease in colonoscopies is 33.4%.

Conclusions: The QuantiDNA™ test is straightforward, non-invasive, and well-tolerated. Data from this study indicates it is effective in the reduction of the need for colonoscopy examina-tions (-33.4%) and is non-inferior to SOC in the detection of significant colorectal lesions.
A Comprehensive Approach for Mapping Ecological System Types Using Sentinel‐2 Satellite Imagery
Authors: Sunde Michael , Diamond David , Elliott Lee
Published: October 2024

The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.
A Study of the Ecological Security Based on the DPSIR (Driver Pressure State Impact Response) Framework Fit for Marine Environmental Protection Evaluation Approach
Authors: Shih Yi-Che
Published: October 2024

This research uses the DPSIR framework to analyse and anticipate Taiwan's marine ecosystems' ecological security. The goal is to understand how human-induced pressures including pollution, overfishing, habitat loss, and climate change affect Taiwan's marine habitats. For Taiwanese maritime ecological security assessments, the DPSIR framework, case studies, and assessment models are used. The DPSIR model classifies human activities, environmental pressures, marine ecosystem states, effects, and reactions. Assessment models complement the DPSIR framework for systematic and quantitative evaluation, while case studies are practical. This systematic literature review follows PRISMA guidelines for thoroughness and organisation. The research shows the DPSIR framework's adaptability and efficacy in analysing Taiwan's complex marine biological processes. Human-induced stressors on marine states are revealed via a comprehensive examination of causes, pressures, states, consequences, and responses, contributing to ecological security discourse. The DPSIR model helps Taiwan identify environmental change sources, analyse pressures, and provide sustainable marine environmental management solutions. The research concludes that the DPSIR framework is crucial to Taiwan's maritime environmental preservation. Despite its strengths, the DPSIR framework has to be refined and customised to Taiwan's unique maritime environments. Taiwan's maritime protection policies are shaped by the study's evidence-based findings. The report also recommends combining the DPSIR framework with SWOT analysis to improve evaluation and decision-making in Taiwan's marine environmental protection setting.
Learning A-Share Stock Recommendation From Stock Graph and Historical Price Simultaneously
Authors: Chen Hanyang , Wang Tian , Konpang Jessada , Sirikham Adisorn
Published: October 2024

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision-making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.