bluuebunny/crossref_metadata_2025_split huggingface dataset:
A connection method for a defeasible extension of ALCH
Authors: Fernandes Renan , Freitas Fred , Varzinczak Ivan , Farias Pedro P. M. 
Published: August 2024

Abstract

The development of proof methods for defeasible description logics (DLs), following those for classical DLs, is mainly based on semantic tableaux.However, the literature offers equally viable alternatives for automated theorem proving, such as the connection method.It consists of a goal-oriented direct proof search method that searches for connections (complementary pairs of literals) in a set of literals organized in clauses called a matrix.This paper presents a connection method for an exception-tolerant family of DLs.In that regard, (i) we use the language of ALCH extended with a typicality operator on concepts and another one on roles; (ii) we revisit the definition of a matrix representation of a knowledge base and establish the conditions for a given axiom to be provable from this matrix with a new normal form (Bi-Typicality Normal Form); (iii) we show how to handle term unification and define a suitable blocking condition in the presence of typicality operators; and (iv) we establish correctness, completeness, and termination of our algorithm.
Osteoporosis and Fractures in Patients with Acromegaly: A Two-Center Cross-Sectional Study
Authors: Alvarez Mauricio , Rincon Juliana , Ulloa Maria , Rincon Oswaldo , Mejia Liliana , Alvarado Alejandra , Bernal Monica , Galvis Alba 
Published: August 2024

Abstract

Purpose
This cross-sectional study aimed to assess the prevalence of vertebral fragility fractures, non-vertebral fragility fractures, hip fragility fractures, and osteoporosis in patients diagnosed with acromegaly.
Methods
Data were collected on age, sex, body mass index (BMI), time from diagnosis of acromegaly, insulin-like growth factor (IGF-1) levels, disease control, pharmacological management, risk factors for osteoporosis, vertebral fragility fractures, non-vertebral fragility fractures, hip fragility fractures, and osteoporosis.
Results
A total of 124 patients with acromegaly were included (67 men and 57 women). The mean age at diagnosis was 44 ± 12 years; the mean time from diagnosis was 12 ± 8 years; and the mean BMI was 27 ± 4 kg/m². Fragility fractures were found in 27 patients (21%). There were no significant differences in the presence of osteoporosis or fragility fractures according to age, sex, BMI, duration of acromegaly, or IGF-1 levels at diagnosis. A higher percentage of patients with osteoporosis were treated with somatostatin analogs compared to those without osteoporosis (46% vs. 15%; p < 0.05)
Conclusion
A high prevalence of osteoporosis and fragility fractures was found in patients with acromegaly, regardless of age, sex, BMI, time from diagnosis, IGF-1 levels, and disease control. More patients with osteoporosis were treated with somatostatin analogs compared to those without osteoporosis. Taken together, our results suggest that the severity of the disease and the need for second-line therapies, may be associated with the increased risk of osteoporosis.
Sustainability considerations of nuclear power in space transportation and infrastructures
Authors: Paek Sung Wook , Park Eugene Seungho , Brooks Samuel , Roy Rajkumar 
Published: August 2024

Abstract

Nuclear thermal propulsion (NTP) and nuclear electric propulsion (NEP) systems are considered to be potential enablers for exploring Mars and beyond. The fission surface power can provide continuous heat and electricity, which complements solar power on the Moon and Mars. Nuclear propulsion and power are attracting spacefaring nations and stakeholders again after the Space Race era due to their advantages and envisioned applications. This paper examines space nuclear power from a few sustainability perspectives. First, from the space logistics and transportation perspective, we identify potential operational regimes where nuclear space propulsion could complement or outperform chemical rocket propulsion. Second, we identify self-engineering technologies that may enhance the reliability of nuclear power systems in orbit or on the surface. Lastly, we discuss extraterrestrial infrastructures, inspired by the design approaches and technologies adopted in the architecture, engineering, and construction (AEC) industry, which could enhance the sustainability of nuclear power in space and contribute to the cislunar circular economy.
A tailored internet-delivered mindfulness-based interventions for patients with hepatocellular carcinoma after TACE: A qualitative Study
Authors: Liu Zengxia , Li Min , Jia Yong , Zheng Lufang , Chen Li 
Published: August 2024

Abstract

Purpose
The purpose of this study was to explore the true experiences of hepatocellular carcinoma (HCC) patients after TACE who participated the tailored internet-delivered mindfulness-based interventions (iMBIs).
Methods
From November 2020 to December 2022, 11 HCC patients after TACE who participated the tailored iMBIs were recruited from a tertiary hospital in Jilin Province by purposive sampling. Semi-structured interviews were performed to collect data, and qualitative content analysis was conducted to analyze the data.
Results
We identified five main categories from interviews, including mindfulness mindset, improvement of physical discomfort, resistance to mindfulness practice, support and encouragement, and accessibility and convenience. Each category can be divided into several general categories.
Conclusions
Participants reported subjective psychological and physical benefits. However, participants also had negative experiences in the process of mindfulness practice. While iMBIs gives participants a sense of convenience and accessibility, there are also issues with device usage limitations.
A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing
Authors: Giallorenzo Saverio , Sinagra Emanuele , Trotta Angelo , Vergolini Loris , Zanellati Andrea 
Published: August 2024

Abstract

We present a framework to quantitively measure social inclusion and suggest actions to increase the integration of more isolated agents. An important aim of our framework is to lower the barriers to entry for researchers in the field. Hence, we show how one can assemble, program, and deploy affordable, over-the-counter IoT devices to work as reliable means for detecting the frequency and duration of social interactions. On this data, we propose an ad-hoc algorithm to infer social inclusion from interactions. We define measures, rooted in network analysis, to estimate the level of social inclusion of the agents under study and propose sustainable interventions to increase the integration of agents—in a way that maximally aligns with the natural tendency of the agents in the network to establish interactions, lowering the risk of the network returning to its previous state. To validate our proposal, we conduct an in-field case study on a class of primary school students. We report on the experience of using the devices with children—whose social interactions might be more difficult to investigate via questionnaires or interviews than more senior subjects—and the results of applying our measures to identify isolated students and suggest integration interventions.
Perceptions, Challenges, and Improvement Strategies for the Standardized Use of Intravenous Infusion in Chinese Medical Institutions: A Qualitative Interview Study
Authors: Zhong Xiaoyan , Huang Yuexi , Wei Qiming , Chen Kun , Wang Shurong , Huang Yilan 
Published: August 2024

Abstract

Purpose
This study aims to evaluate the awareness of standardized intravenous infusion use among medical professionals and inpatients in Chinese medical institutions and to explore effective strategies to improve the rate of standardized use, thereby enhancing patient safety and medical quality.
Methods
Utilizing a qualitative research design, the study conducted semi-structured interviews to deeply understand the perceptions and challenges faced by medical professionals (pharmacists, doctors, nurses) and patients regarding standardized intravenous infusion use. The sample was selected through purposive sampling to ensure diversity. The interviews covered perceptions, challenges, and improvement strategies, with data analysis performed using Nvivo 12 plus.
Results
The study found variations in medical professionals' awareness of standardized intravenous infusion use. Physicians' clinical decisions were influenced by patient expectations and medical insurance policies, while nurses believed that standardization could reduce their workload, and pharmacists faced difficulties in implementation. Patient attitudes toward intravenous infusions were diverse, with some preferring them. The study also revealed that education and training, medical insurance policies, pharmacists' professional development, clinical pathways, specialized drug management, and inter-departmental collaboration significantly impact the standardization of intravenous infusion use.
Conclusion
This study uncovers the multi-dimensional challenges of standardized intravenous infusion use among inpatients in Chinese medical institutions and emphasizes the importance of strengthening education, training, and inter-departmental collaboration. It is recommended that relevant departments engage in joint discussions to ensure the consistency of medical insurance policies with clinical practices, promoting the efficient use of medical resources and insurance funds.
Representation in Science and Trust in Scientists in the United States
Authors: Druckman James , Ognyanova Katherine , Safarpour Alauna , Schulman Jonathan , Trujillo Kristin Lunz , Uslu Ata Aydin , Green Jon , Baum Matthew , Mathé Alexi Quintana , Qu Hong , Perlis Roy , Lazer David 
Published: August 2024

Abstract

American scientists are notably unrepresentative of the population. The disproportionately small number of scientists who are women, Black, Hispanic or Latino, from rural areas, religious, and from lower socioeconomic backgrounds has consequences. Specifically, it means that, relative to their counterparts, individuals who identify as such are more dissimilar and more socially distant from scientists. These individuals, in turn, have less trust in scientists, which has palpable implications for health decisions and, potentially, mortality. Increasing the presence of underrepresented groups among scientists can increase trust, highlighting a vital benefit of diversifying science. This means expanding representation across several divides—not just gender and race but also rurality and socioeconomic circumstances.
A General Linear Model to Describe Observed Global Warming as a Function of the Atmospheric Surface Concentrations of Greenhouse Gases and Greenhouse Constants
Authors: Friberg Rasmus 
Published: August 2024

Abstract

This is the first paper in a series of papers with the overall aim of analyzing and deriving theoretical models to indirectly measure and test the root cause of observed global warming (GW) and the greenhouse effect (GHE) at the earth’s surface due to the atmospheric concentration of greenhouse gases (GHGs) in general and the mass of anthropogenic CO2 emissions (MACE) in particular. The objective of this specific paper is to present a new theoretical model, which is the framework theory and the starting point for explaining the relationship between observed global warming (GW) and the atmospheric concentrations of greenhouse gases (GHGs). It is derived from the theory of Lambert–Beer–Bouger’s law, Planck’s law and the heat balance over the atmospheric surface layer, where the GW is measured. The result is called the general linear model of GW (GLMGW). Consequently, the GLMGW indicates that there is a linear relationship between the observed GW (or GAST-global atmospheric surface temperature) and the GHG atmospheric surface concentrations. The characteristic constants of the GLMGW are the greenhouse constants for each GHG, which is a measure of how much the temperature rises per atmospheric concentration of a given greenhouse gas. The claims originating from the GLMGW were tested directly or indirectly in subsequent papers (Evidences I-V) based on the global data series of temperature and atmospheric GHG concentrations obtained from NOAA, ECMWF and GCP. Several new practical validated measurement models are available for calculating and forecasting GW as a function of the dominant GHGs. The different models can be used with high measurement certainty and forecasting capability to estimate several different quantities in the context of the GW; for example, the global atmospheric surface temperature anomaly (GASTA) can be used as a function of the atmospheric CO2 concentration, and the remaining time and remaining MACE can be used to breach the 1.5K and 2.0K limits.
Dust Surrounding Mars Detected by MAVEN
Authors: Ye Shengyi , Wang Guangzhou , Wu Han , Wang Jian 
Published: August 2024

Abstract

Mars is one of the most extensively explored planet in our solar system. However, previous research on the dust environment surrounding Mars remains quite limited. We used the method of detecting signals generated by high-speed dust impacts on the spacecraft and filtered out over 86000 dust impact events from the burst mode data recorded by the Langmuir Probe and Wave instrument (LPW) payload of the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft during the period from October 2014 to November 2022. This direct detection method for dust particles has a large effective observational area, allowing for the observation of space environment with low dust number density. Through calculations, we discovered a complex dust environment around Mars. We analyzed three potential sources: interplanetary dust, dust released by Martian moons, and the dust that transported from the Martian surface. We observed a significant increase in dust impact signal detection rates near the orbital altitude of Phobos. This could be the first effective observation proving that the moon of Mars is releasing dust. Additionally, we found correlations between Martian global dust storm event in Martian Year 34 and the variation of dust in Martian space environment. This may suggest that Martian dust storms are capable of lifting dust particles from the surface to high altitudes previously unanticipated. Our findings can help to understand the sources of interplanetary dust within the solar system and the ways in which Martian dust storms influence Mars' atmosphere and space environment.
Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management
Authors: Hooda Akash 
Published: August 2024

Abstract

Real-time big data analytics is crucial for industries that handle high-velocity data streams, such as IoT, finance, e-commerce, and social media. Traditional big data processing frameworks often struggle to adapt to the dynamic nature of real-time data, leading to inefficiencies, increased latency, and higher operational costs. This research proposes an Adaptive Real-Time Big Data Processing Framework that integrates a Random Forest model for load prediction with Q-learning for dynamic resource allocation. The framework adjusts in real-time based on predictive insights and learned policies, optimizing system performance while minimizing costs.
Experimental results demonstrate that the framework reduces latency by 15%, improves throughput by 20%, compared to traditional methods. The Random Forest model achieved an efficiency of 93.62%, while the Q-learning approach further achieved 94.86%, reflecting superior resource utilization. These improvements indicate the framework's effectiveness in dynamically adapting to real-time data, ensuring high performance and cost-efficiency.
Adaptive AI-Driven Earthquake Simulation Leveraging Real-Time Geospatial Data and Advanced Machine Learning Models
Authors: Tanaka Akira , Matsumoto Yuki 
Published: August 2024

Abstract

The burgeoning field of earthquake simulation has profound implications for advancing our understanding of seismic phenomena and enhancing disaster preparedness. Recent advances in machine learning and geospatial data analytics have opened new avenues for more accurate and adaptive predictive models. This paper addresses the critical research question: How can adaptive AI-driven models, synergized with real-time geospatial data, improve the accuracy and responsiveness of earthquake simulations? To tackle this, we develop a hybrid simulation framework that integrates advanced machine learning algorithms, such as convolutional neural networks and recurrent neural networks, with dynamically updated geospatial datasets. These datasets include seismic activity records, topographical features, and human-made infrastructure information, updated in near real-time. Our main contributions include the introduction of a novel adaptive learning mechanism that continuously refines model parameters in response to new data inputs, leading to enhanced predictive reliability and reduced false alarm rates. We conduct extensive experiments using historical earthquake data and contemporary geospatial information to validate our approach. The results demonstrate marked improvements in simulation fidelity, with a 30% increase in accuracy and a 25% reduction in predictive latency compared to existing methods. Through this innovative integration of AI and geospatial data, our research not only advances the state-of-the-art in earthquake simulation but also provides a scalable framework adaptable to other geospatial and predictive modeling challenges.
Scenting Relief-Aromatherapy for alleviating Primary Dysmenorrhea in Adolescents and Young Women: A systematic review and meta-analysis
Authors: Tahir Hasab Nawaz , Rehman Naureen , Tahir Mursala , Alotaibi Nemer , Al-Dhubaibi Mohammed Saleh , Mohamed Noureldaim Elnoman Elbadawi , Zaheer Imran , Ali Yousaf 
Published: August 2024

Abstract

Background Primary dysmenorrhea (PD) is prevalent among adolescents and young women, significantly impacting their quality of life. Aromatherapy has emerged as a promising non-pharmacological intervention for managing PD, but its age-specific efficacy, mode of application, and type of aroma oils remain underexplored. Thus, this systematic review evaluate aromatherapy's effectiveness in alleviating PD symptoms among adolescents and young women aged 10–24.Methods Data were sourced from PubMed, Google Scholar, ScienceDirect, Cochrane, Bielefeld Academic Search Engine (BASE), and through cross-referencing. Results were synthesized by conducting meta-analysis using random-effects models, and assessing heterogeneity using I² statistics. Quality assessment of the included studies was also carried out.Results A total of 15 studies were included for meta-analysis of which 4 were RCTs and 11 were non-RCTs. Aromatherapy significantly reduced PD pain score across studies, with a standardized mean difference (SMD) of -3.19 (CI -5.09, -1.28; I² = 98%) and − 0.98 (CI-1.40, -0.57; I² = 64%) in quasi-experimental studies and RCTs, respectively. In addition, moderate to severe pain was measured categorically (RR: 0.39; CI 0.25, 0.60; I² = 36%).. Adolescents exhibited the most significant benefit (SMD: -3.64; CI -6.24, -1.03; I² 98%; RR: 0.39; CI 0.25, 0.60; I² = 36%), compared to young women (SMD: -1.04; CI -1.35, -0.72; I² 53%). Inhalation aromatherapy (SMD − 3.09; CI -5.06, -1.12; I² 98% ) showed superior efficacy over massage (SMD − 1.20; CI -1.45, -0.96; I² 7%). Rose essential oil, cinnamon, and peppermint oil demonstrated significant efficacy, while lavender and lemon oils showed varying outcomes.Conclusion This study supports aromatherapy's effectiveness in reducing PD symptoms, particularly among adolescents. Standardized protocols and well-designed trials are needed to optimize aromatherapy's therapeutic potential for managing menstrual pain across diverse age groups, addressing current gaps in research methodology and oil-specific efficacy.
Strategic development of rural halal tourism using the Business Model Canvas framework: A case study from East Java, Indonesia
Authors: Ukhrowi Mukhamad Zahid 
Published: August 2024

This article highlights the significance of villages in local governance, especially their potential in tourism, which can drive economic benefits and community development. The purpose of this research is to analyze the development strategy of Sidomulyo Village towards halal tourism using the Business Model Canvas (BMC) approach. The methodology involves a qualitative field research design with data collected through structured interviews, direct observations, and documentation reviews in Sidomulyo Village, East Java. The findings reveal that Sidomulyo Village has significant potential as a halal tourism destination due to its rich natural resources, cultural heritage, and established tourist attractions such as educational tours, batik houses, and coffee industry tours. The village’s strategic use of BMC elements such as value propositions, customer segments, channels, and key partnerships can enhance its appeal to both local and international tourists. Implications of this study suggest that Sidomulyo Village can achieve sustainable economic growth and community welfare by leveraging strategic planning and cost management, thus positioning itself as a unique halal tourism destination.Keywords: Halal tourism; village development; Business Model Canvas (BMS); Sidomulyo Village; economic growth.