bluuebunny/crossref_metadata_2025 huggingface dataset:
Gustatory-Visual Interaction in Human Brain Cortex: fNIRS Study
Authors: Jezierska Karolina , Cymbaluk-Płoska Aneta , Zaleska Justyna , Podraza Wojciech
Published: July 2024

Background: many studies, on for example taste-visual dissonance, have shown that the influence of the visual cortex on taste sensation is enormous. The aim of the presented work is to investigate, using fNIRS, whether a taste stimulus, in this case the taste of bitter, also causes stimulation of the visual cortex in fNIR study. Methods: 51 participants (204 examinations, 9 996 records) were examined with fNIRS, which collects signals from whole left hemisphere. Differences between the maximum and minimum changes in oxyHb concentrations (ΔoxyHb) for the areas of the brain cortex considered responsible for recording visual and gustatory signals were analyzed. Protocol I, II, III and IV – activation with distillate water, coffee with lower concentration, reference (no stimulation) and coffee with higher concentration respectively, were used. Results: We recorded high signals for teste activation on channels covered gustatory cortex, which confirms the correctness of the choice of research method. What seems important, and somewhat surprising, is the fact that we also received high signals for the channels 45-49, which cover the visual cortex. The statistical analysis does not show any statistical differences between the protocols I, II and IV (different taste activation – water, coffee A and coffee B) for specific channels. As a results of the analysis of the correlation between the subjective bitterness assessment solutions, and the signal ΔoxyHb height, it was observed that statically important correlation, although weak, occurs only for 14 and gustatory channels, only for coffee with a higher concentration. Additionally we recorded the only statistically significant difference between women and men for protocol I (water), where the ΔoxyHb signal was twice as high for women. Conclusions: In conclusion, we can clearly state that the senses of sight and taste work closely together. Moreover, this cooperation is not one-sided - not only the visual activation affects the perception of taste, but interestingly, the taste stimulus can generate a hemodynamic response - activity in the visual cortex.
Influence of Early Life Factors on the Breast Milk and Fecal Microbiota of Mother-Newborn Dyads
Authors: Cervantes-Monroy Emmanuel , Zarzoza-Mendoza Imelda C. , Canizales-Quinteros Samuel , Moran-Ramos Sofia , Villa-Morales Judith , López-Contreras Blanca , Carmona-Sierra Fairt V. , Rodríguez-Cruz and Maricela
Published: July 2024

Maternal gut and breast milk (BM) are key in vertically transmission bacteria to infants, shaping their gut microbiota in early life. Although the establishment of early gut microbiota is known, the role of the combined influence of maternal factors and newborn characteristics are not explored. In this study we aimed to assess the influence of maternal BMI and total body fat, age, delivery mode, and newborn sex on the diversity and composition of the BM and gut microbiota (GM) in mother- newborn dyads. In this cross-sectional study, of the 986 pregnant women candidates, 53 participated, and finally, 40 mother-newborn dyads exclusively breastfeeding at 20-28 days post-partum were included. Metataxonomic profiling of DNA extracted from BM and fecal samples was conducted using 16S rRNA sequencing. Globally, the findings offer valuable insights that excessive adiposity, age and C-section delivery influence on a lower abundance of specific taxa in the BM, maternal gut, and gut newborns. Also, the simultaneous analysis of maternal factors and newborn characteristics shows that maternal age and newborn sex explain an important variation in the microbiota composition. These results add to understanding of the intricate interplay between maternal factors and the microbial communities that influence early-life gut and BM microbiota.
Data-Driven Multi-objective Optimization With Fairness Constraints: Balancing Efficiency With Equity in Algorithmic Decision-Making
Authors: Idowu Emmanuel
Published: July 2024

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.
In-Context Learning in Large Language Models: A Comprehensive Survey
Authors: Highmore Clyde
Published: July 2024

This survey provides a comprehensive overview of in-context learning (ICL) in large language models (LLMs), a phenomenon where models can adapt to new tasks without parameter updates by leveraging task-relevant information within the input context. We explore the definition and mechanisms of ICL, investigate the factors contributing to its emergence, and discuss strategies for optimizing and effectively utilizing ICL in various applications. Through a systematic review of recent literature, we first clarify what ICL is, distinguishing it from traditional fine-tuning approaches and highlighting its unique characteristics. We then delve into the underlying causes of ICL, examining theories ranging from implicit meta-learning during pre-training to the emergence of task vectors in LLMs. The survey also covers various approaches to enhance ICL performance, including prompt engineering techniques, demonstration selection strategies, and methods for improving generalization across diverse tasks. Additionally, we discuss the limitations and challenges of ICL, such as its sensitivity to demonstration ordering and potential biases. By synthesizing findings from numerous studies, we aim to provide researchers and practitioners with a clear understanding of the current state of ICL research, its practical implications, and promising directions for future investigation. This survey serves as a valuable resource for those seeking to leverage ICL capabilities in LLMs and contributes to the ongoing discourse on the remarkable adaptability of these models.
Hausdorff Spaces in Bermejo Algebras: The Birth of Treonic Manifold Construction
Authors: Bermejo Valdés Alejandro Jesús
Published: July 2024

We conducted a topological analysis of the novel, non-associative, and unital algebraic structure known as Bermejo Algebras, developed by Alejandro Bermejo, which includes the Algebra B and Treon Algebra. This algebras can define Lie and Malcev algebras when their product operations are derived from the Bermejo Algebras product. Central to this study are treons, complex entities that arise as isomorphisms of Algebra B when the field is real. We define the vector spaces associated with Bermejo Algebras to establish equivalence classes and a quotient topology, resulting in Hausdorff spaces that do not depend on traditional norms, inner products, or metrics. Our findings lay the groundwork for constructing new types of differential manifolds, opening new avenues for advanced mathematical research.
Energy Consumption Minimization with SNR Constraint for Wireless Powered Communication Networks
Authors: Shih Kuei-Ping , Tsai Yu-Sheng , Chen Yen-Da , Wang San-Yuan
Published: July 2024

The paper addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a DT-SWIPT-assisted time allocation scheme, named DaTA, such that the wireless station can share the remaining energy after transmission to the HAP to those who have not transmitted to the HAP to minimize the energy consumption of the network. In addition to proposing a new frame structure, the paper also considers the SNR (Signal-to-Noise) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the paper, the energy consumption minimization problem is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. Simulation results showed that the proposed scheme can have better performance than the related work in terms of energy consumption and energy efficiency.
Research on Acid Aging and Damage Pattern Recognition of GFRP Oil-Gas Gathering Pipeline Based on Acoustic Emission
Authors: Bi Haisheng , Zhang Yuhong , Zhang Chen , Ma Chunxun , Li Yuxiang , Wang Guang , Miao Jiaxu , Cheng Haoran
Published: July 2024

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues on steel pipes, the demand for glass fiber reinforced plastics (GFRP) pipes is increasing in the oil and gas gathering and transmission systems. However, the medium transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-、Ca2+、Mg2+、SO42-、CO32- and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped GFRP pipes with depths of 2mm and 5mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.
Vitronectin Levels in Plasma of Neuroblastoma Patients and Culture Media of 3D Models: A Prognostic Circulating Biomarker?
Authors: López-Carrasco Amparo , Vieco-Martí Isaac , Granados-Aparici Sofía , Acevedo-León Delia , Estañ-Capell Nuria , Portugal Raquel , Huerta-Aragonés Jorge , Cañete Adela , Navarro Samuel , Noguera Rosa
Published: July 2024

Vitronectin is a glycoprotein, present in plasma and extracellular matrix, implicated in cell migration. The high amount of vitronectin found in neuroblastoma biopsies was associated with poor prognosis. However, there are almost no studies of vitronectin levels in neuroblastoma cell culture media nor in plasma of neuroblastoma patients. Vitronectin concentration in culture media of four neuroblastoma cell lines grown in monolayer and in 3D-models, and in plasma of 114 neuroblastoma patients, was quantified using ELISA. Three of the neuroblastoma cell lines secreted vitronectin to culture media when cultured in monolayer and 3D-models. Vitronectin release was higher by neuroblastoma cells cultured in 3D-models than in monolayer, and was still great when cells grown in 3D-scaffolds with cross-linked vitronectin. Vitronectin secretion occurred independently of cell numbers in cultures. Its concentration in plasma of neuroblastoma patients ranged between 52.4 and 870µg/mL (median, 218µg/mL). A ROC curve was used to establish a cutoff of 361µg/mL, above which patients over 18-months-old had worse prognosis (p=0.0018). Vitronectin could be considered a new plasma prognostic biomarker in neuroblastoma, and warrants confirmation in collaborative studies. Drugs inhibiting vitronectin interaction with cells and/or extracellular matrix could represent a significant improvement in survival for neuroblastoma patients.
$2^m - 1$ as the Integer Formulation to Govern the Dynamics of Collatz-Type Sequences
Authors: Goyal Gaurav
Published: July 2024

It is discovered that writing an odd integer as $2^m - 1$ for $m \geq 1$ helps in understanding the dynamics of the Collatz-type sequences. Starting with the original Collatz sequence $3n + 1$, it is found that when the odd step is applied to an odd integer $2^m - 1$, an even integer $2^{m+1} + 2^m - 2$ is obtained, which is exactly once divisible by 2. This implies that the sequence alternates between odd and even steps $m$ times. This governs the dynamics of the Collatz-type sequences because: (i) for the sequence to diverge to infinity, $m$ must be infinite, and (ii) the value of $m$ determines the number of times the integer can be divided by 2 in each even step. This formulation allows construction of odd integers like $\sum 2^m - 1$ to follow specific patterns of odd and even steps. Applying this understanding to the modified Collatz sequence $5n + 1$ reveals that for $m = 3$, the integer $2^{m + 1} + 2^m - 1$ is obtained, indicating that any integer formulation ending with $2^3 - 1$ in $5n + 1$ will diverge to infinity.
Artisan Cheese Cream Fermented with Kefir Grains
Authors: Freitas Denise Rossi , Kamimura Eliana Setsuko , Mazalli Mônica Roberta
Published: July 2024

This is the first study that investigates the effect of kefir with an emphasis on the production of short-chain fatty acids (SCFA) during the fermentation process in food products. The products developed and characterized were an artisanal cream cheese without cream and with added cream and for the analysis of the fatty acid profile, both cream cheeses were compared with commercial cream cheese. The artisan cream cheese had a high amount of lactic acid bacteria characterizing the product formed by lactobacilli and a low concentration of lactose due to the fermentation process. Compared to commercial cream cheese, our products without and with added cream had a higher concentration of short-chain fatty acids (SCFA), especially butyric acid which is important for the health of the gastrointestinal tract and oleic fatty acid which has been associated with the prevention and control of some diseases. Overall, the artisan cream cheese cream with fermented cream with kefir grains is a functional product with an innovative character compared to current products on the market and was well accepted by the younger public. This new product comes as an option for those who need to change their eating habits and maintain a healthy lifestyle.
A Vehicle Classification Method Based on Machine Learning
Authors: Li Xinjin , Chang Jinghao , Li Tiexin , Fan Wenhan , Ma Yu , Ni Haowei
Published: July 2024

In this article, we propose a machine learning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent transportation systems by combining multi-scale feature extraction, cross modal fusion, and attention mechanisms. Our model demonstrated excellent performance on the Stanford Cars-196 dataset. The innovation of this study lies in its advanced multi-scale feature extraction, cross modal fusion, and attention mechanism. It not only improves classification accuracy, but also lays the foundation for further research on intelligent transportation systems and autonomous vehicle technology.
Dynamic Covalent Boronic Acid-Functionalized Alginate/PVA Hydrogels for pH and Shear-Responsive Drug Delivery
Authors: Urbano Bruno F , Oyarzun Yessenia , Ceballos Matias , Ulloa Jose
Published: July 2024

Herein, we investigated hydrogels composed of boronic Acid functionalized alginate and blended with polyvinyl alcohol of different molecular weights to control the release of metoclopramide hydrochloride (MTC) as a function of pH and shear stress. The functionalization of alginate introduced dynamic covalent bonding and pH-responsive properties that can modulate network connectivity. The study investigated the viscoelastic properties of the hydrogels, their drug release profiles, and their responsiveness to changes in pH and shear forces. The results showed higher PVA molecular weight and alkaline pH conditions increased hydrogel viscosity and stiffness due to a more stable and interconnected network structure than acidic pH. Metoclopramide release revealed that the hydrogels exhibited pH-responsive drug release behavior. The drug was more readily released under acidic conditions due to the instability of sp2-hybridized boronate ester bonds. The influence of shear forces on the release of metoclopramide was also investigated at shear rates of 1, 10, and 100 s-1, revealing their effect on matrix stiffening. The research shows that AlgBA/PVA hydrogels have unique properties, such as dynamic covalent bonding, that make them sensitive to external mechanical forces. This sensitivity makes them ideal for applications where physiological conditions trigger drug release
Enhanced Medical Data Extraction: Leveraging LLMs for Accurate Retrieval of Patient Information from Medical Reports
Authors: Garcia-Carmona Angel M , Prieto Maria-Lorena , Puertas Enrique , Beunza Juan-Jose
Published: July 2024

This study presents a strategic approach to developing applications focused on implementing Large Language Models using the Langchain framework in Python. Three language models are highlighted: GPT-3.5 (turbo mode), LLaMA 2, and Vicuna 7B, each with their distinctive features and capabilities. The methodology used is described in detail, including data extraction from medical reports using zero-shot prompting data extraction techniques, interaction with language models, and structured storage of results. The performance of the models in data extraction is evaluated, presenting metrics such as precision, recall, and F1 score. The results demonstrate high model capability in extracting information, although areas for improvement are identified, particularly in data extraction precision. In conclusion, the efficacy of the models in extracting information from medical histories is not considerably acknowledged, with an emphasis on the importance of improving precision and increasing the volume of trained data for future research in healthcare digitalization.
Association of Polyamine and Other Dietary Component Intake and Feces Content of N-acetyl Putrescine, and Cadaverine with Patients’ Colorectal Lesions.
Authors: Barreiro-Alonso Eva , Castro-Estrada Paula , Sanchez Manuel , Peña-Iglesias Pablo , Suarez Lorena , Cantabrana Begoña
Published: July 2024

Colorectal cancer (CRC) is the second leading cause of cancer death worldwide. Early detection and the modification of risk factors, such as diet, can reduce its incidence. Among food components the polyamines are important for maintaining gastrointestinal health and are metabolites of gut microbiota. Their disruption is linked to CRC, making polyamines a potential marker of the disease. This study analyzed the relationship between dietary components, including polyamines, and the presence of polyamines in feces to determine whether their presence could contribute to predicting the occurrence of colorectal lesions in patients. Fifty-nine participants of both sexes (aged 50 to 70 years) who had undergone colonoscopy screening for CRC (18 without and 41 with colorectal lesions) participate in the study. A nutritional survey and determination of fecal polyamine content were performed. Specific dietary components and putrescine levels were higher in patients with colorectal lesions. The diet ratio of putrescine/spermidine and the fecal content of N-acetyl putrescine and cadaverine were elevated in patients with precancerous lesions and adenocarcinomas, showing a potential predictive value for the presence of colorectal lesions. These findings suggest that N-acetyl putrescine and cadaverine could be complementary markers for the diagnosis of suspected colorectal lesions.
SPECIFICS AND COMPLEXITY OF THIRD-PARTY LIBRARY MIGRATIONS IN IT-PROJECTS
Authors: Lysenko Alexander , Kononenko Igor
Published: July 2024

An analysis has been conducted on the relevance of the problem of migrating third-party tools in IT projects, which is characterized by regularity and presents challenges for developers in terms of decision-making. The analysis shows that the migration of third-party tools requires developers not only to have technical knowledge and skills but also a deep understanding of migration management strategies, risk assessment methods, and the ability to integrate new tools into existing projects without negatively impacting the workflow. The necessity of migrating third-party tools in the IT field, which is a crucial aspect for ensuring the relevance, efficiency, and innovation of software in a rapidly changing technological landscape, is directly considered. Main attention is given to the review of modern research and methodologies aimed at simplifying the process of tool migration, reducing development and support costs, and enhancing software security. Proposals regarding comprehensive approaches to managing migrations are considered, including the use of automated systems for analyzing large volumes of data about the history of changes in projects, risk assessment, and effective communication among all project participants. The justification for the significance of migrating third-party tools to ensure the sustainable development of software in a dynamically changing technological environment is discussed. The need for further research in this field is highlighted, aimed at developing new tools and methodologies to optimize the migration process, with the goal of enhancing developer productivity and ensuring the high quality of final software products. The importance of a systematic and comprehensive approach to migrating third-party tools, based on detailed data analysis, a deep understanding of risks, effective communication, and the application of modern technological solutions, is emphasized.
THE WAREHOUSE AND STRUCTURE OF THE INFORMATION SYSTEM FOR ORGANIZING PLANNED PREVENTIVE MAINTENANCE
Authors: Makohonov Anton , Marynych Ivan
Published: July 2024

The work is devoted to the consideration of the information system components, which can be used for effective organization and implementation of planned and preventive repairs in industrial and infrastructure facilities. The rationale for the performance of such works is given. It is also determined that one of the ways to improve their efficiency is the introduction of computerized maintenance management systems. Accordingly, a review of some existing solutions on the market was conducted. Given the advantages and disadvantages of existing solutions are characterized. It is noted that even in Western economies, despite the importance of this tool in service management, the success rate of successfully implementing such systems, even in large, well-resourced organizations, is poor. According to Internet research, the number of successful implementations is only about 25-40% and the number of users using computerized maintenance management systems or enterprise asset management systems to its full potential is only 6-15%. Taking into account the lag in the development of the economy and business processes in Ukraine, this indicator will be even lower. Based on the analysis of these systems, a model of the planned preventive maintenance information system for the Ukrainian market is proposed. The model has the following advantages: ease of understanding and use; accessibility for Ukrainian users; low cost of use. The model includes the following main components: registration and management of companies; creating and managing projects; creation and management of tasks; entering and tracking expenses; functionality for work planning; accountng of sick leave and vacations of employees. The next step in system development is integration with equipment monitoring systems. The collected data on their condition will allow to develop a machine learning model for the implementation of one of the elements of Industry 4.0 - predictive maintenance.
PROBLEM ANALYSIS OF FORECASTING CRYPTOCURRENCY MARKET TRENDS AND MODERN APPROACHES TO ITS SOLUTION
Authors: Moskalenko Valentyna , Fonta Nataliia , Havrylenko Anton , Bezchastnyi Oleksii
Published: July 2024

The current problem of forecasting trends in the cryptocurrency market and modern approaches to solving them are considered. Two main factors have been identified that influence the value of cryptocurrency: the size of the cryptocurrency market and the growth rate of market volumes. The results of research on the prospects of the crypto market are presented, including the fact that Bitcoin in the future may be a protection against the fall of the US dollar for financial market participants. Researchers also view bitcoins not as cash, but as an investment asset. It is concluded that regulation and economic policies related to the use of cryptocurrencies are gradually being strengthened by many countries as its investment attractiveness increases. An analysis of the problem of forecasting the cryptocurrency market trend is presented. An analysis of research and publications on methods for predicting the value of cryptocurrency is presented. Traditional time series forecasting models, such as the ARIMA model, are effective for financial forecasting, but their use is less effective for markets with high volatility, which is typical for cryptocurrencies. Cryptocurrency price forecasting is a time series problem that can be solved using regression and other machine learning techniques. The results of modern research into the potential of machine learning in identifying complex trends and patterns are presented. It has been proven that deep learning methods can be effective in predicting time series with significant fluctuations and almost chaotic and unpredictable behavior. It is concluded that the main aspect is to create flexible models that can adapt to new data and changes in market dynamics. Combining traditional technical factor analysis techniques with innovative machine learning techniques can result in powerful hybrid models. These models use both quantitative and qualitative data to develop better forecasts. The feasibility of developing software systems that implement modern methods of artificial intelligence, including machine learning, deep learning, natural language processing and other technologies to provide market analysis, identify patterns and forecast crypto market trends is substantiated. The use of such software will assist investors in identifying potentially profitable investment opportunities, managing risks and making informed decisions in conditions of high uncertainty.
The Effect of Capital Structure, Growth, and Firm Size on Firm Value
Authors: Selli Ardelia Sarah , Gurendrawati Etty , Armeliza Diah
Published: July 2024

This research aims to determine the impact of capital structure, growth, and firm size affect the infrastructure industry companies stated on the Indonesia Stock Exchange (IDX) from 2019 and 2023 in terms of their firm value. The sample size was determined using purposive sampling. The research employed secondary data, which was sourced from the 2019 until 2023 financial statements of infrastructure companies. The multiple linear regression analysis method was applied in this investigation. Version 26 of SPSS was used to an analyze the data. The findings of this research indicated that a firm's value is influenced by its capital structure, growth, and size.
Qualitative Habitat Evaluation Index for Louisville Streams (QHEILS)
Authors: McKay S. , Athanasakes George , Taylor Sarah , Miller Wolffie , Wagoner Erin , Mattingly Laura
Published: July 2024

Urban stream restoration typically involves multiple objectives addressing different aspects of ecosystem integrity, such as habitat provision, geomorphic condition, watershed connectivity, water quality, and land-use change. Multiple stream assessment tools and models have been developed and applied to inform restoration prioritization, planning, and design. Here, we present the Qualitative Habitat Evaluation Index for Louisville Streams (QHEILS, pronounced “quails”), which was designed as an interdisciplinary assessment method for urban streams in the Louisville, Kentucky, metropolitan region. The model adapts a regional habitat assessment procedure, the Qualitative Habitat Evaluation Index (QHEI), by incorporating additional processes related to geomorphic change and watershed connectivity. The QHEILS was developed in the context of the Beargrass Creek Ecosystem Restoration Feasibility Study, and it provides a rapid procedure for assessing multiobjective benefits associated with proposed restoration actions. This technical note summarizes the model and provides example applications within the Beargrass Creek watershed.
IMAGE CAPTIONING USING TRANSFORMER WITH IMAGE FEATURE EXTRACTION BY XCEPTION AND INCEPTION-V3
Authors: Pardede Jasman , Fandi
Published: July 2024

Image captioning is a task in image processing that involves creating text descriptions that can describe the image content. The formation of the image captioning system model is influenced by image interpretation related to the given image caption. Image interpretation is influenced by the feature extraction used. This research proposes feature extraction with Xception and Inception-V3 by generating an image captioning model using Transformer. Model performance is measured based on BLUE and METEOR values. Based on the results of research conducted on the Flickr8k Dataset, it shows that the best model performance is using Xception feature extraction and batch_size = 256. The image captioning performance of Xception feature extraction for BLUE-1, BLUE-2, BLUE-3, BLUE-4, and METEOR when compared with Inception-V3 achieves increasing of 13.15%, 18.03%, 18.71%, 27.27%, and 15.43% respectively. The performance for Xception feature extraction with batch_size = 256 compared with batch_size = 128, increasing BLUE-1, BLUE-2, BLUE-3, BLUE-4, and METEOR namely 19.81%, 41.84%, 52.23%, 53.14%, and 31.56% respectively.
Digital Agents in Medicine: New Opportunities and Challenges
Authors: Vvedenskaya Elena V. ,   Pirogov Russian National Research Medical University
Published: July 2024

The subject of this article is the new opportunities and challenges that the widespread use of digital agents in medicine brings. ‘Digital agents’ refers to all AI devices, neural net­works, mobile applications, chatbots used for diagnostics and patient monitoring. Along with the advantages of their introduction, there are a number of risks provoked by the loss of the human factor and lack of regulation of new technologies. The article discusses the “ethical pitfalls”, without the elimination of which the effective implementation of digital agents in healthcare is impossible. These include: bias, hallucinations and harmful informa­tion, data subject privacy and consent, as well as transparency and explainability. It has been observed that there is an increasing delegation of diagnosis and treatment decisions to AI, limiting the autonomy of physicians in favor of pre-defined algorithms. This phenomenon of “digital paternalism” entails a loss of trust in qualified physicians. In addition, the article analyzes the problem of social stratification caused by both limiting access to modern tech­nologies for certain segments of the population and creating barriers for people who prefer traditional approaches to medical care. It concludes that it is necessary to thoroughly discuss and develop rules and regulations for the introduction of digital agents in medicine, taking into account the maximum respect for patients’ rights, preservation of human relations “doc­tor–patient” and ethical values.
Distributed Moral Responsibility in the Field of Artificial Intelligence
Authors: Glukhovskii Andrei S. ,   ITMO University
Published: July 2024

The main goal of the article is to justify the optimality of Luciano Floridi’s concept of dis­tributed morality for describing a model of the relationship of responsibility in complex hu­man – AI systems. The high level of its autonomy in functioning does not correspond to the classical instrumentalist approach to technology, as it turns out to be capable of funda­mentally uncontrollable actions in the process of performing the task assigned to it. For the same reason, the classical definition of moral responsibility does not adequately corre­spond to the actual situation in the field of artificial intelligence development, which often leads to problems that hinder the development of this technology. Therefore, the article con­ceptualizes a new technology-appropriate way of describing the relationship of responsibil­ity in the field of artificial intelligence ethics. To achieve this goal artificial intelligence is considered as a participant in various interactions, including social ones, possessing agency, but at the same time as not attributed with true intentionality, motivation, awareness and other properties that are necessary for attributing agency in the classical model of the rela­tionship of responsibility. In addition to referring to Floridi’s description of agency criteria, the justification of such a consideration of AI is based on Annamarie Mol and Marianne de Laet’s concept of fluidity which is developed by them, among others, to denote the ability of technologies to possess agency without having the properties considered necessary for at­tributing agency in the classical model of the role of technologies in social processes. AI technology is analyzed as a complex heterogeneous sociotechnical system made up of both human and non-human agents. The risk of leveling the significance of responsibility in such a system is overcome at the conceptual level by adapting the approach of Floridi’s patient-oriented morality. Changing the emphasis from the subject of action to the object of influence gives moral significance to the entire sum of morally neutral actions that are performed by agents within the framework of a sociotechnical system. Since it produces morally significant effects and impacts, it also acts as a bearer of the relation of responsibi­lity which, as shown in the article, applies to each agent of the system equally thereby inten­sifying moral responsibility in the field of AI.
Histopathological Changes of the Liver in Male White Rats (Rattus Norvegicus) Exposed to Conventional and Herbal Cigarette Smoke
Authors: Nussa Olan Rahayu puji astuti , Kurnianto Ady , Sigit Miarsono
Published: July 2024

This research aimed to determine the condition of hepatocyte cells or liver tissue in the liver organ of male white rats exposed to conventional cigarette smoke and herbal cigarette smoke. The samples consisted of 24 white rats with 3 treatments, P1 as a the negative control consisted of 8 white rats without exposure to cigarette smoke, P2 as the group exposed to conventional cigarette smoke, P3 as the group of white rats exposed to herbal cigarette smoke. The study was conducted for 21 days. On day 22, the rats were euthanatized and necropsied, and part of the liver organ was collected. The liver was put into 10% BNF formalin and prepared for histapathology procedure. Microscopic examination was carried out to observe changes in hepatocyte cells, including degeneration or hemorrhage, inflammatory cells infiltration, and necrosis. The results of this study indicated that exposure to both conventional and herbal cigarette smoke causes damage to hepatocyte cells, cellular changes in hepatocytes, vascularization of the liver, including inflammation, necrosis, and degeneration in the hepatic organ
Effect of Red Dragon Fruit Extract (Hylocereus polyrhizus) on MDA Metabolite Products and CD36 Expression in Artery Carotid Communis Mice (Mus muscullus) Fed a High-Fat Diet
Authors: Larasati Galuh Wiedani Kusuma Dyah , D.K. Wardani Lailia , Legowo Djoko
Published: July 2024

Red Dragon fruit (Hylocereus polyrhizus) has the potential for the prevention of diseases associated with inflammatory and oxidative processes such as cardiovascular diseases due to the presence of their antioxidant compounds including phenols, flavonoids, tocopherol, ascorbic acid, thiamin, niacin, riboflavin, minerals, betacyanin, β-carotene, lycopene, p-coumaric acid, protocatechuic acid, vanillic acid, gallic acid, syringic acid, and p hydroxybenzoic acid. Antioxidant compounds can prevent the atherogenesis process by reducing MDA metabolite products and CD36 expression in artery carotid communis. MDA metabolite products are indicators of lipid peroxidation, while CD36 expression is a scavenger receptor which responsible for the retention and absorption of LDL-Ox in macrophages and endothelial cells. This study aimed to prove the effect of red dragon fruit extract in reducing MDA metabolite products and CD36 expression in artery carotid communis of mice fed a high-fat diet. This research was in vivo experimental study with a randomized post test only control group design. This research used 40 male mice which were divided into five groups, namely negative control K0 (without high fat diet and extract), positive control K1 (high fat diet without extract), P1 (5mg extract/mice/day), P2 (extract 10mg/mice/day), P3 (extract 20mg/mice/day). A high fat diet was given for 56 days. Red dragon fruit extract was given for 28 days. At the end of the study, all mice were euthanized and their artery carotid communis were taken for immunohistochemistry staining. MDA and CD36 expression measurements were carried out using the Immune Reactive Score (IRS), which is the result of multiplying the percentage of immunoreactive cells (A) with the color intensity score (B) of immunoreactive cells.
Novel Methods for Cost-Effectively Generating a Heterogeneous Core Model Based on Scale Change of Nuclear Magnetic Resonance and X-ray Computed Tomography Data
Authors: Zhou Zili , Jia Hu , Zhang Rui
Published: July 2024

In response to the constraint on model size imposed by computational capabilities and the inability to capture the heterogeneity within the core and its dynamic oil displacement characteristics, this paper proposes two novel methods for cost-effectively modeling heterogeneous core models based on scale changes of nuclear magnetic resonance (NMR) and X-ray computed tomography (X-CT) data, respectively. By utilizing NMR and X-CT techniques to characterize information at the subcore scale, we establish a more realistic model at the core scale. First, by using a method of setting up inactive grids, a homogeneous model is established to better represent the actual cross-section of the core. By fitting the core water displacement experimental data, a random heterogeneous core model based on the NMR-T2 spectrum is established by using the modified Schlumberger-Doll Research (SDR) model and complementarity principle. The numerical simulation results show that the random heterogeneous core model partially reflect the heterogeneity of the core, but the simulation results are unstable. Building on this, a deterministic homogeneous core model is established based on X-CT scan data by using the modified Kozeny-Carman model and pore extraction method. Sensitivity analysis results suggest that higher grid accuracy leads to a better fitting effect, with the axial plane grid accuracy impacting the model water-drive process more significantly than that of the end plane. The study paves the way for the rapid and accurate establishment of core models.
Modulation of Large Rhythmic Depolarizations in Human Large Basket Cells by Norepinephrine and Acetylcholine
Authors: Feldmeyer Dirk , Yang Danqing , Qi Guanxiao , Ort Jonas , Witzig Victoria , Bak Aniella , Delev Daniel , Koch Henner
Published: July 2024

Rhythmic brain activity is critical to many brain functions and is sensitive to neuromodulation, but so far very few studies have investigated this activity on the cellular level in vitro in human brain tissue samples. This study reveals and characterizes a novel rhythmic network activity in the human neocortex. Using intracellular patch-clamp recordings of human cortical neurons, we identify large rhythmic depolarizations (LRDs) driven by glutamate release but not by GABA. These LRDs are intricate events made up of multiple depolarizing phases, occurring at ~ 0.3 Hz, have large amplitudes and long decay times. Unlike human tissue, rat neocortex layers 2/3 exhibit no such activity under identical conditions. LRDs are mainly observed in a subset of L2/3 interneurons that receive substantial excitatory inputs and are likely large basket cells based on their morphology. LRDs are highly sensitive to norepinephrine (NE) and acetylcholine (ACh), two neuromodulators that affect network dynamics. NE increases LRD frequency through β-adrenergic receptor activity while ACh decreases it via M4 muscarinic receptor activation. Multi-electrode array recordings show that NE enhances and synchronizes oscillatory network activity, whereas ACh causes desynchronization. Thus, NE and ACh distinctly modulate LRDs, exerting specific control over human neocortical activity.
Epiretinal Membrane Peeling in Eyes with Pseudophakia: Prognostic and Morphological Factors of Visual Outcomes
Authors: Tsen Chui Lien , Lee Tzu Ying , Wu Tsung Tien
Published: July 2024

Background
Epiretinal membrane (ERM) is a sight-threatening disorder, and diagnosis can be delayed. In this study, spectral domain optical coherence tomography (SD-OCT) was used to investigate the predictive factors of visual outcomes in patients with pseudophakic ERM following surgery.
Methods
The records of 61 patients with pseudophakic eyes (61 eyes) with ERM who underwent PPV, ERM peeling, and ILM peeling were retrospectively reviewed. OCT features such as central foveal thickness (CFT), cotton ball sign, ERM foveoschisis, ellipsoid zone (EZ) disruption, external limiting membrane (ELM) disruption, ectopic inner foveal layer (EFIL), disorganization of the retinal inner layers (DRIL), dissociated optic nerve fiber layer (DONFL), and microcystic macular edema (MME), as well as surgical factors, were compared before and after surgery. The correlations between the OCT and surgical factors and change of best-corrected visual acuity (BCVA) before and after the surgery were examined.
Results
The mean follow-up time was 6 months. The BCVA (P < 0.01) and CFT (P < 0.01) showed significant improvements after ERM removal (P < 0.01). Cotton ball sign and DRIL exhibited statistically significant improvement after ERM removal. ERM-foveoschisis (P = 0.015) and MME (P = 0.009) were significantly correlated with less improvement of visual acuity. Better visual acuity before surgery was significantly associated with better improvement of visual acuity (P < 0.001).
Conclusions
Better baseline visual acuity is correlated with better visual acuity improvement after surgery, while ERM foveoschisis and MME identified preoperatively on OCT are associated with relatively less improvement of visual acuity.
Group Integrated Exercise versus Recovery Class for Veterans with Posttraumatic Stress Disorder: A Randomized Clinical Trial
Authors: Neylan Thomas C. , Muratore Laura A. , Williams Chanda L. , Schmitz Martha , Valdez Courtney V. , Maguen Shira , O’Donovan Aoife , Kelley D. Parker , Metzler Thomas J. , Cohen Beth E. , West Anna C. , Phan Jordan D.V. , Antonetti Victor , Mayzel Olga , Hlavin Jennifer A. , Chesney Margaret A. , Mehling Wolf E.
Published: July 2024

There are no reported randomized trials testing exercise versus an active comparator for Posttraumatic Stress Disorder (PTSD). This randomized clinical trial assessed the effectiveness of group exercise versus psychoeducation to improve quality of life and reduces symptomatic severity in Veterans with PTSD. Veterans who met criteria for current PTSD (DSM-5) and/or endorsed moderate levels of PTSD symptoms (CAPS 5 score ≥ 23) were randomly assigned to treatment. Integrative Exercise (IE) combines fitness exercises (aerobics, resistance training, stretching) with mindful body/breath awareness versus Recovery Class (REC) psychoeducation control condition. A total of 84 participants were enrolled of which 41 participants were randomized to IE and 43 participants to REC. There were no significant pre-post differences in change in the WHOQOL Psychological Domain in either group. There was a modest reduction in the total CAPS-5 score in both groups (IE: -8.2 (9.9), p < .001: REC: -7.8 (2.0), p < .001) but no differences across the two conditions. In the IE subsample that was remote, there was a greater improvement in PTSD symptom severity (F[1, 50] = 4.62, p = .036) and in in the WHOQOL Psychological Domain (F(1, 47) = 6.46, p = .014) in those who attended more sessions.
Trial Registration: ClinicalTrials.gov Identifier: NCT02856412 (registration date: February 27, 2017)
Quantitative Assessment of the Environmental Impact of Urban Rail Transits through Carbon Emission Reduction—A Case Study of Beijing Municipality
Authors: Jia Cai , Wang Xudong , Qian Chengyang , Cao Zini , Zhao Long , Lin Luzhou
Published: July 2024

Urban rail transit, as an efficient and eco-friendly mode of transportation, plays a pivotal role in mitigating traffic congestion and lowering urban carbon emissions. Despite the significant contributions by scholars in this area, debates surrounding the quantification of carbon emissions during the operational phase of urban rail transit persist, particularly in assessing its impact on reducing ground traffic congestion. This study focuses on the passenger flows in Beijing during peak morning and evening hours, postulating a shift from urban rail transit to alternative modes, such as buses and taxis. A model predicting traffic congestion states based on passenger flow and other relevant parameters was developed. Through this model, the study calculates the potential congestion times across various scenarios, employing a bottom-up approach to carbon emission estimation to analyze the impact on carbon emissions. Results spanning 2015 to 2021 suggest that substituting urban rail transit with buses could increase congestion by 76–169 minutes and 101–162 minutes during morning and evening peaks, respectively, leading to a 27%-51% and 31%-55% surge in carbon emissions. Conversely, a shift to taxis could result in a 271–291 minutes and 252–312 minutes increment in congestion times, with carbon emissions spiking by 130%-222% and 142%-236%, respectively. These outcomes emphasize the substantial efficacy of urban rail transit in curbing traffic congestion and carbon emissions.