bluuebunny/crossref_metadata_2025 huggingface dataset:
Hematological Reference Intervals of Mangrove Parrots (Amazona amazonica) Kept in a Wildlife Management Establishment in the Amazon Biome, Pará, Brazil
Authors: Santos¹ Dallyth Maia da Costa , Lima Francisco Wanderson Bizerra , Neri Júnior Nailson de Andrade , Casari Milena Barbosa , Serruya Analiel , da Silva Thyele Chaves , Costa Leonardo Carvalho , Guerreiro Anna Klara de Matos , Moura Luis Edson Silva , Gripp Mayra Coelho , Carneiro Mário José Costa , de Barros Cintya Renata Santana , Magalhães Matos Paulo Cesar , Thiesen Roberto , Lopes Cinthia Távora de Albuquerque , Silveira Natália da Silva e Silva
Published: September 2024

Amazona amazonica, popularly known as the mangrove parrot, belongs to the order Psittaciformes and is widely distributed across South America, occurring in all regions of Brazil. Although this species is commonly admitted to veterinary care, detailed hematological data are scarce. These data are crucial for guiding clinical interventions, monitoring specimens in human care environments, and providing research references. Hematological reference intervals were established for man-grove parrots kept in a fauna management establishment located in the Amazon biome in the state of Pará, Brazil. The study included 32 Amazona amazonica parrots. Average values for the analyzed analytes in the complete blood count (CBC) were: red blood cells (RBC) 2.42 million/µL; hemoglobin 15.92 g/dL; hematocrit 50.31%; MCV 205.47 fL; MCHC 31.7%; MCH 66.73 pg; white blood cells (WBC) 6,281.4 cells/µL; heterophils 2,671.1 cells/µL; lymphocytes 3,452.27 cells/µL; eosinophils 0 cells/µL; basophils 22.63 cells/µL; monocytes 106.41 cells/µL; thrombocytes 29.44; PPT 6.71 g/dL. Morphological characteristics of the cells were similar to those of other parrots. This research is pioneering in deter-mining hematological reference intervals for mangrove parrots under human care in a holding facility in the Amazon biome. These data can be used as a reference for animals of the species raised under similar conditions.
Ten-Year Observational Study of Patients with Lung Adenocarcinoma: Clinical Outcomes, Prognostic Factors, and Five-Year Survival Rates
Authors: Ziora Paweł , Kiczmer Paweł , Zaboklicka Natalia , Wypyszyńska Julia , Stachura Maria , Sito Zuzanna , Rydel Mateusz , Czyżewski Damian , Drozdzowska Bogna
Published: September 2024

Background/Objectives: Lung carcinoma is the leading cause of cancer-related deaths globally, with lung adenocarcinoma being the most prevalent subtype. This study aims to review the clinical data and survival outcomes of patients diagnosed with lung adenocarcinoma who underwent surgical treatment. Methods: We conducted a retrospective analysis of 471 patients who had surgery for lung adenocarcinoma at our institution. Their medical histories, including previous neoplasms, comorbidities, tumor characteristics, and symptoms, were thoroughly reviewed. We calculated the overall survival rate and evaluated the impact of tumor grading and spread through air spaces (STAS) on patient outcomes. Results: The survival rate for the entire cohort was 76.23%. There was no significant difference in survival rates between patients classified as G1 and G2, suggesting that a simplified two-tier grading system may be more effective. Patients with STAS had a lower survival rate than those without STAS. Conclusions: Our findings indicate that a simplified grading system may improve prognostic evaluations for lung adenocarcinoma patients. Furthermore, STAS is a crucial factor affecting survival rates and should be considered in future treatment strategies. Expanding research in this area is essential to enhance treatment approaches for lung adenocarcinoma patients.
Association Between Nutrition-Related Indicators and Non-Alcoholic Fatty Liver Disease and Advanced Liver Fibrosis: Evidence from NHANES 2017–2020
Authors: Wei Shouxin , Yu Sijia , Yang Ningbo , Jia Yindong , Lan Yunshen
Published: September 2024

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide, and its incidence has been rising in parallel with the prevalence of obesity and diabetes. A critical marker for the progression of NAFLD is advanced hepatic fibrosis (AHF), which can result in cirrhosis and hepatocellular carcinoma. This study sought to investigate the potential associations between five nutrition-related indices—the geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), controlled nutritional score (CONUT), triglyceride-total cholesterol-body mass index (TCBI), and albumin/globulin ratio (AGR)—and the progression of NAFLD and AHF. Methods: This study analyzed the association between five nutrition-related indicators and the occurrence of NAFLD and AHF by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03. After adjusting for demographic, lifestyle, clinical, and laboratory-related factors, the associations were examined employing various statistical techniques, including multivariate logistic regression, subgroup analysis, and smooth curve fitting analysis. Results: This study included 5,514 subjects, of whom 2,088 were diagnosed with NAFLD and 359 with AHF. After adjusting for potential confounding factors using multivariate logistic regression analysis, the results revealed that GNRI, PNI, and TCBI were positively associated with the incidence of NAFLD, while CONUT and AGR demonstrated negative associations with NAFLD. Additionally, GNRI, CONUT, and TCBI were positively associated with the incidence of AHF, while AGR demonstrated a negative association with AHF.Conclusions: The study demonstrates that nutritional status plays a dual role in the progression of NAFLD and AHF, serving as both a risk and protective factor. These findings offer a scientific foundation for the early identification of high-risk populations and the development of individualized nutritional intervention strategies.
Assessing the Multiplier Effect of National Parks: A Case Study of Buiratau State National Nature Park in Kazakhstan
Authors: Sapiyeva Akmaral , Yessenov Meirzhan , Aktymbayeva Aliya , Nuruly Yeldar , Sakypbek Mereke , Razdobudko Olesya , Assipova Zhanna
Published: September 2024

This article examines the complex interactions of the multiplier effect within the realm of tourism, with a specific emphasis on national parks. The main objective is to evaluate the multiplier effect within national parks, specifically focusing on Buiratau State National Nature Park in Kazakh-stan as a case study. The multiplier effect is a fundamental principle in the field of economic analysis. Economic multiplier effect is the term used to describe the occurrence when an initial infusion of spending in a certain industry leads to subsequent rounds of extra and sustainable economic activity. In the context of national parks, this phenomenon arises when visitor expendi-tures result in several favorable consequences, including increased employment, infrastructure advancement, and general economic expansion. The article seeks to measure the multiplier effect of tourism activities in Buiratau State National Nature Park through a thorough assessment. This entails examining several variables and datasets pertaining to visitor expenditure and its influ-ence. The study contributes to the existing literature by examining a recently constructed national park in a developing country. It compares the findings of this park with well-established national parks in other areas, emphasizing the distinct problems and opportunities it presents. Further-more, the research presents a methodological framework for evaluating the multiplier effect of national parks, which may be utilized in comparable contexts. This research ultimately enhances our comprehension of the economic implications of national parks and emphasizes their function as drivers for local and regional growth.
Bioactive and Its Chondroprotective Roles in Ameliorating Osteoarthritis: A Focus on Nanoparticle Therapy
Authors: Hridayanka Kota Sri Naga , Duttaroy Asim K. , Basak* Sanjay
Published: September 2024

Bioactive like resveratrol, epigallocatechin gallate, curcumin, and other polyphenols often target various signalling pathways, including NFκB, TGFβ, and Wnt/β-catenin in osteoarthritis (OA) pathogenesis, by executing epigenetic-modifying activities. Epigenetic modulation by bioactive is gaining traction with the onset of nutrigenomics. Such modulations can target genes by histone modifications, promoter DNA methylation, and non-coding RNA expression, some of which are directly involved in disease pathophysiology but less explored in OA. OA patients often explore options that can improve the quality of their life in addition to existing treatment with nonsteroidal anti-inflammatory drugs (NSAIDs). Although bioactive and natural compounds exhibited therapeutic potential against OA, several disadvantages loom over them, like insolubility and poor bioavailability. Nanoformulated bioactives promise a better way to alleviate OA since they also control systemic events, including metabolic, immunological, and inflammatory responses, by modulating host gut microbiota that can regulate OA pathogenesis. Recent data suggest gut dysbiosis in OA. However, limited evidence is available on the role of bioactive as epigenetic and gut modulators in ameliorating OA. Moreover, the question of whether polyphenolic bioactive modulates gut microbial response that results in epigenetic-controlling activities of OA pathogenesis is not known. This narrative review explored the interplay of nanoformulated bioactive and host gut microbiota on chondrocyte growth, metabolism, and epigenetic alteration linked with OA progression and pathogenesis.
Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis
Authors: Mendoza Paz Santiago , Willems Patrick , Villazón Gómez Mauricio F.
Published: September 2024

We assessed machine learning techniques (MLTs) for downscaling global climate model’s precipitation to local level in Bolivia. The skill, assumptions and uncertainty of the process are analyzed. Additionally, future projections are delivered. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machines (SVM), is used on four zones (highlands, Andean slopes, Amazon lowlands and Chaco lowlands). The downscaled series’ skill is evaluated in terms of relative errors. The uncertainty is analyzed through variance decomposition. MLTs’ skill is adequate in most cases, especially for the highlands and the Andean slopes. Moreover, RF tends to outperform SVM. Stationary assumptions are robust in the highlands and Andean slopes. Perfect prognosis assumption is poor in the highlands and Andean slopes; topographical complexity appears to be the reason. Regarding uncertainties, MLTs are the dominant source. In future projections, the highlands show shorter dry spell lengths with more frequent but less intense high rainfall events and higher annual rainfall. The Andean slopes exhibit an increased annual rainfall with a reduction in the high precipitation intensities but an increased frequency. The Amazon lowlands present a decrease in annual rainfall, and the Chaco lowlands an increase.
Diagnostic Accuracy of Glutamate Dehydrogenase Enzyme (GDH) for Clostridium difficile Infection: A Meta-Analysis
Authors: Desai Dev , Patel Manav , Shah Heer , Thaker Pankil
Published: September 2024

Introduction: Clostridium difficile is a gram-positive, spore-forming anaerobe and a leading cause of healthcare-associated diarrhea globally, primarily linked to antibiotic use. Its enzyme, Glutamate Dehydrogenase (GDH), plays a critical role in the metabolism of glutamate, and its detection has become a key diagnostic marker for C. difficile infection (CDI). This meta-analysis evaluates various diagnostic modalities used for GDH detection, focusing on their sensitivity, specificity, and clinical utility. Methods: A systematic search of PubMed, Google Scholar, and Cochrane Library was conducted. Studies comparing GDH testing with gold-standard methods (PCR, toxigenic culture) for CDI diagnosis were included. Data were extracted from eligible studies, and statistical analyses were performed to calculate pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Results: The pooled sensitivity and specificity of GDH testing were 93% and 92%, respectively. The DOR was calculated to be 30.98, and the AUC was 0.9794, indicating high diagnostic accuracy. GDH testing proved effective in identifying both infected and non-infected individuals, reducing false positives and negatives. Conclusion: GDH testing is a highly sensitive and specific diagnostic tool for CDI, supporting its role in clinical settings for early detection and intervention. Despite its strengths, variability in test performance suggests the need for further research to refine GDH-based protocols and ensure consistent outcomes across diverse healthcare environments.
A Secure Cloud Service for Managing User's Crucial Data Using NLP, Blockchain, and Smart Contracts
Authors: Ramaswamy Arun Kumar Banavara , Rangappa Komala , Prasad Mahadeshwara , Kumar Shreyas Arun
Published: September 2024

The management of user data in the cloud is easily poised to become a giant issue for any business, with so much digital information floating around these days that open it up as an easy target when companies aren’t vigilant. In this paper, a novel cloud-based service is proposed that employs various advanced NLP and encryption methods with the use of blockchain. These techniques are combined to provide a solid solution to secure fundamental data such as credit card numbers, passports or any government identity cards etc. Using a hybrid NLP model, integrating Transformer Models and Named Entity Recognition (NER), to automatically catagorzing data as critical vs non-critical. Only the most important data is encrypted by a user’s cryptographic wallet before being divided into multiple chunks and stored on an exclusive cloud cluster; metadata then takes turns managing securely through blockchain to provide traceable means of retaining integrity. Smart contracts provide strict access control measures and change the cryptographic nonce if need be to prevent illegal entrance into a specific zone, thus create security. This all-inclusive strategy maintains well-known high security standards for protecting the confidentiality, availability and integrity of your sensitive data on a global scale—delivering you simple yet scalable secure world-class cloud-based data management. A proposed framework developed to fulfil the security requirements for current cloud services, which is a beneficial contribution in context of data protection and cloud Security.
Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility
Authors: Chintalapati Akhil , Enkhbat Khashbat , Annamalai Ramanathan , Amali Geraldine Bessie , Ozaydin Fatih , Noel Mathew Mithra
Published: September 2024

In the evolving digital landscape, the pervasive influence of Artificial Intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the research ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the Quantum Sentinel is presented - an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment.
The Relationship Between Feeling of Emptiness and Non-Suicidal Self-Injury in Borderline Personality Disorder Symptoms: The Mediating Role of the Inner Strengths
Authors: Sripunya Piangdao , Wongpakaran Tinakon , Wongpakaran Nahathai
Published: September 2024

Background and Objectives: Fifty percent of individuals with borderline personality disorder (BPD) experience Non-Suicidal Self-Injury (NSSI). One of the crucial factors related to NSSI is the feeling of emptiness. Although some inner strengths, such as five precepts, meditation, and equanimity, have been identified as potential buffers against negative mental health outcomes in BPD, their role in mediating the relationship between emptiness and NSSI is not well-documented. This study aimed to explore how these inner strengths mediate the relationship between feelings of emptiness and NSSI in individuals with BPD symptoms. Materials and Methods: A total of 302 participants with BPD symptoms completed several assessments: the SCID-II Personality Disorder (Borderline PD) to assess feelings of emptiness and NSSI, the Inner-Strength-Based Inventory (i-SBI) to evaluate five precepts, meditation, and equanimity, and the Outcome Inventory Depression (OI-Dep) to assess depression. Mediation analysis was performed using IBM SPSS and AMOS, with NSSI as the outcome variable, feelings of emptiness as the predictor, and the inner strengths as mediators. Results: The participants had a mean age of 36.56, with 65.4% being female. The analysis showed that five precepts, meditation, and equanimity significantly mediated the relationship between feelings of emptiness and NSSI, with a standardized coefficient of β = 0.534 (95% CI = 0.417 to 0.647, p < 0.001). The indirect effect of emptiness through these inner strengths was significant (β = 0.034, 95% CI = 0.009 to 0.075, p = 0.005). The mediation model explained 38% of the variance in NSSI with a 3% increase, albeit small but significant. Conclusions: The study highlights a negative mediating role of inner strengths in the relationship between feelings of emptiness and NSSI meaning that as these inner strengths increase, the direct impact of feelings of emptiness on NSSI decreases. This suggests that these inner strengths act as protective factors, reducing the likelihood that someone with feelings of emptiness will resort to NSSI as a coping mechanism. These findings suggest that targeting these inner strengths could be a valuable strategy in developing interventions aimed at reducing NSSI by addressing the underlying emotional challenges associated with BPD.
Interpretable Regression Models for Predicting and Optimizing Hydrogen Production in Co-Gasification Process
Authors: Vaiyapuri Thavavel
Published: September 2024

Renewable energy is essential to environmental sustainability and ranks high among the United Nations strategic goals. In this context, as opposed to conventional biomass gasification, co-gasification emerges as a promising technological pathway to effectively combine the distinct benefits of various gasification feedstocks in order to generate hydrogen-rich syngas. The goal of achieving net-zero emissions has further raised the demand for biomass co-gasification. However, the complicated reactions involved in co-gasification process pose challenge in optimizing the process parameters for increased productivity and performance. To the author’s knowledge, there is no consensus regarding the most suitable machine learning (ML) based regression models for optimizing biomass-plastics co-gasification process, as no prior research has examined the effectiveness of different regression models for this process. Further, the practical application of ML models is adversely affected by their black box nature and lack of interpretability. To address these gaps, the objective of the currect research is two-fold: Firstly, to model the co-gasification process using seven different ML algorithms. Secondly, to develop an evaluation framework and assess model interpretability to select an ideal model for co-gasification interpretation and optimization. The support vector regression(SVR) model demonstrated the most reliable predictive performance. The study employed learning curve analysis and cross validation analysis to substantiate the model predictive performance and exhibit its potential to generalize. Beyond performance analysis, Shapely additive explanation, (SHAP) is first explored in the realm of cogasification to interpret the prediction of the studied models from global and local perspective. The research findings are expected to provide valuable insights to better understand and optimize biomass-plastics cogasification process for H2-rich syngas production.
Regulation Cybersecurity in Financial Technology: Insight into Challenges, Compliance, and Best Practices
Authors: Tiwari Shashank
Published: September 2024

While the financial technology (FinTech) sector has revolutionized conventional banking services with technology-driven solutions, it also faces considerable cybersecurity hurdles to guard against data protection and sustain the overall financial performance. The main purpose of this paper is to identify the key cybersecurity challenges encountered by FinTech companies, such as data confidentiality concerns, complex cyber-attacks, difficulties with regulatory compliance and third-party risks or technological innovations. This also assesses the regulatory responses to those challenges, such as data privacy regulations, macroprudential regimes, industry-specific guidance and international regulatory cooperation. Through this analysis, the paper attempts to synthesize current state of cybersecurity domain within FinTech and provides suggestions for strengthening data shelter and financial stability by implementing best practices with regulatory strategies.
Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application
Authors: Ekpetere Kenneth Okechukwu , Mehta Amita V. , Coll James Matthew , Liang Chen , Onochie Sandra Ogugua , Ekpetere Michael Chinedu
Published: September 2024

A web application – IMERG Precipitation Extractor (IPE) was developed that relies on the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data available at a global coverage. The IPE allows users to query, visualize, and download time series satellite precipitation data for various locations, including points, watersheds, country extents, and digitized areas of interest. It supports different temporal resolutions ranging from 30 minutes to 1 week. Additionally, the IPE facilitates advanced analyses such as storm tracking and anomaly detection, which can be used to monitor climate change through variations in precipitation frequency and intensity. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2,360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region (e.g., Nevada), the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of -22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash-Sutcliffe Efficiency (NSE) of 0.80, and a Kling-Gupta Efficiency (KGE) of 0.52. In the wet region (e.g., Louisiana), the average CC was 0.93, the PRB was 24.82%, the RMSE was 0.96, the MBR was 0.79, the NSE was 0.80, and the KGE was 0.18. Median RAI indices from both IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS, advocating for further global climate change studies.
Evaluating Short-Term Intensive Insulin Intervention in Newly Diagnosed Type 2 Diabetes: A Meta-Analysis Perspective
Authors: Tunuguntla Mounika Nagarani , Yadav Rukesh , Obed Reddy Prashant , Chanti Pranathi , Nitish Reddy Ummadi Venkata , Alhussain Haitham , Kaushal Akshita , Panjiyar Binay
Published: September 2024

Background: Oral hypoglycemic agents are typically the first-line treatment for type 2 diabetes mellitus (T2DM). This study examines the benefits of initiating insulin as the first-line therapy for newly diagnosed T2DM patients. Methods: A PubMed search using keywords ‘Type 2 Diabetes Mellitus’ and ‘Early STIIT’ included RCTs assessing beta cell function, insulin resistance, glycemic control, and lipid profiles. Revman 5.4.1 software was used for meta-analysis, with outcomes represented as mean difference (MD) and 95% confidence intervals (CI). Results: Significant reductions in fasting blood glucose (FBG: MD -4.75, 95% CI -7.07, -2.43), HbA1c (MD -1.84, 95% CI -3.01, -0.68), and postprandial glucose (PPG: MD -9.02, 95% CI -10.48, -7.56) were observed with early STIIT therapy compared to baseline. Beta cell function improved (HOMA-B: MD 53.70, 95% CI 27.78, 79.63) and insulin resistance decreased (HOMA-IR: MD -1.84, 95% CI -3.65, -0.04). Lipid profile improvements included reductions in total cholesterol (TC), triglycerides (TG), and LDL, with no change in HDL. BMI remained stable (MD -0.15, 95% CI -0.82, 0.51). Most patients achieved long-term remission and glycemic control without oral hypoglycemic agents. Conclusion: Early STIIT in newly diagnosed T2DM patients significantly reduces glycemic markers and maintains long-term glycemic control, preserving beta cell function. The therapy also improves lipid profiles, reducing TC, TG, and LDL. Initiating insulin as a first-line treatment shows promising results for glycemic control and overall metabolic health.
Shape Memory Polymer Foam Based on Nanofibrillar Composites of Polylactide/Polyamide
Authors: Elumalai Dhanumalayan , Hosseinnezhad Ramin , Bondarenko Vladislav , Morawiec Jerzy , Vozniak Iurii , Galeski Andrzej
Published: September 2024

This paper presents the novel development of a shape memory polymer foam based on polymer-polymer nanocomposites. Herein, polylactide (PLA)/biosourced polyamide (PA) foams are fabricated by in-situ fibrillation of polymer blends and subsequent supercritical CO2 foaming technique. In this system, PLA serves as a shape memory polymer to endow this foam with a shape memory effect (SME), and in-situ generated PA nanofibers are employed to reinforce the PLA cell walls and provide an additional permanent phase. Concentration of PA, 5 wt.%, is chosen to form an entangled nanofibrillar network. Foams of PLA/PA nanoblends with the same content of constituents are fabricated to reveal the effect of minor phase morphology on the cell structure and shape memory behavior of polymer foams. Profiting from the reinforcing effect of PA nanofibers, the PLA/PA nanocomposite foam exhibits smaller foam cells, a narrower cell size distribution and a comparable cell concentration than the PLA/PA nanoblend foam. In addition, PA nanofibers, unlike PA nanodroplets, favor the shape fixation ratio and recovery ratio and shorten the shape recovery time.
Climate Impacts, Changing Patterns of Land Use and Loss of Traditional Fulani Cattle Management Practices: Drivers of Antimi-Crobial Resistance in Kwara State, Nigeria
Authors: Cole Jennifer , Adetona Mutiat A. , Basiru Afisu , Jimoh Wasiu A. , Adulsalami Somrat , Ade-Yusuf Rodhiat O. , Babalola Karimat A. , Adetunji Victoria O. , Ahmed Akeem O. , Adeyemo Ismail A. , Olajide Abiola M. , Aremu Abdulfatai , Odetokun Ismail A. , Eltholth Mahmoud
Published: September 2024

This paper presents a case study of Fulani herdsmen in Nigeria, whose traditional ethnoveteri-nary practices risk being lost as the country transitions to more intensive and enclosed livestock practices. We use a planetary health framing to make visible the value of indigenous practices that are less damaging to the environment, animal welfare and human health. Through ethnographic observation, focus group discussions (FGD) and key stakeholder interviews, we show that the Fulani use a complex system of herbal medicines and traditional herding practices to maintain herd health, manage and treat animal disease when it arises. However, their traditions often sit uncomfortably with commercial farming practices. As traditional Fulani grazing lands are eroded, dispossessed Fulani take employment from businessmen farmers but both parties’ inex-perience with shed hygiene, artificial feed and less environmentally resilient crossbreeds leads to increased incidence of infectious disease. This in turn drives higher use of antibiotics. There is a ‘causal chain’ of underlying drivers that leads, through poorer environmental, animal and hu-man health, to increased use of antibiotics. The antibiotic resistance that emerges from this chain threatens human health now and into the future. Through a planetary health framing we advocate for more respect for Fulani herdsmen and their traditional ethnoveterinary practices as an alter-native to increasing antibiotic use.
Instrument Selection in Panel Data Models with Endogeneity: A Bayesian Approach
Authors: Herce Postigo Álvaro , Salvador Manuel
Published: September 2024

This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of credit worthiness.
Antiaging Properties of Kalanchoe blossfeldiana Ethanol Extract – Ex Vivo and In Vitro Studies
Authors: Stefanowicz-Hajduk Justyna , Nowak Anna , Hering Anna , Kucharski Łukasz , Graczyk Piotr , Kowalczyk Mariusz , Sulikowski Tadeusz , Szymańska Anna Muzykiewicz
Published: September 2024

Species of the genus Kalanchoe have a long history of therapeutic use in ethnomedicine linked to their remarkable medical properties. One of them is Kalanchoe blossfeldiana succulents occurring in tropical regions. Despite the great interest in this plant, there are no reports about its therapeutic effects on the skin. In this study, the antioxidant properties and skin permeation of a topical hydrogel containing K. blossfeldiana ethanol extract was assessed. Additionally, the content of active compounds in the K. blossfeldiana extract was evaluated by UHPLC-MS and HPLC-UV. The extract was analysed with three antioxidant assays: ABTS, DPPH and FRAP. Furthermore, antielastase and antihialuronidase properties of the tested extract were assessed. The ex vivo penetration studies were performed using the Franz diffusion cells. The obtained results show that antioxidant properties of K. blossfeldiana extract were similar to ascorbic acid, while antielastase and antihialuronidase tests indicated strong antiaging and anti-inflammatory activity (IC50 was 26.8 ± 0.13 and 77.31 ± 2.44 µg/mL, respectively). Moreover, active ingredients contained in K. blossfeldiana extract penetrated through human skin and accumulate in it. In conclusion, the hydrogel containing K. blossfeldiana extract can be considered as an interesting and new alternative to dermatologic and cosmetic preparations.
Estimating Agglomerate Particle Degradation Rates in Treated Produced Water and Hydraulic fracturing fluid during Hydraulic Fracturing: Insights from the Bakken Formation Using d90 Metrics and Pump Speed.
Authors: Epie Njie Claudius , Ling Kegang , Jakaria Md
Published: September 2024

This study examines the degradation of agglomerate particles in treated produced water used for hydraulic fracturing in the Bakken Formation. By analyzing d90 values and pump speeds from different manufacturers, the research quantifies particle size reduction rates, revealing how degradation contributes to pore throat blocking in tight formations. The study found that agglomerate particles in hydraulic fracturing fluids ranged from 9 to 85 microns in size, with degradation occurring most rapidly within the first five minutes of pump action. As time progresses, degradation slows, indicating that pore clogging is most likely at the beginning of the fracturing process. The research emphasizes that pump speed is a critical factor in determining the rate of particle degradation. To minimize formation damage, it is recommended to scale up particle size and use coarser particles in suspension. However, both large and small particles pose risks: larger particles can reduce porosity, while smaller particles may penetrate pore throats and form internal filter cakes. The study highlights the importance of selecting the appropriate pump speed and particle size distribution to optimize fracturing fluid design, maintain formation permeability, and reduce the risk of formation damage. Lower pump speeds and fewer coarse particles are advised to avoid pore clogging during the fracturing process.
The Changing Definition Of Human Nature From Traditional to Contemporary Approaches: A View Of Human Nature In The Philosophy Of Informatıon
Authors: Kantar Nesibe
Published: September 2024

The issue of what human nature is an important issue for understanding the present and building the future. The place of human beings in the universe as well as their duty, their relationship with society, and their environment has been shaped by the meaning attributed to human nature. This article examines the transformation that the understanding of human nature in traditional philosophy has undergone after the information revolution from the perspective of information philosophy. Information technologies, which have integrated into every area of our lives after the information revolution, has deeply affected our scientific, economic, intellectual, and cultural activities. With the information revolution, human nature has been redefined with its characteristics that participate in design and creation activities, thanks to its interaction abilities, which are information-processing capacities, beyond biological and spiritual characteristics. The understanding of human nature has been addressed in different historical periods since the mythological period. The subject here is to evaluate the understanding of human nature from the traditional to the present. The anthropocentric understanding of humans, traditionally defined in the context of biological, physiological, and spiritual characteristics, is defined in the philosophy of information in a holistic context as an agent with information processing capacity, an informational object. As an entity that carries and processes information, humans are evaluated in the same category as information technologies such as cybernetic systems and artificial intelligence, although they have different characteristics, as informational objects. In addition, this study aims to provide a new perspective to the literature by examining the change process in the understanding of human nature.
“Es mi única responsabilidad, y hacerlo mal ¿Te imaginas?”: imaginario social y subjetivo de género en hombres trabajadores de una transnacional minera en el estado de Oaxaca
Authors: Cortés Ramón , Nazar Beutelspacher Austreberta
Published: September 2024

The objective of this work is to analyze, from a feminist perspective, the formation of the social and subjective gender imaginary of working men in the extractive corporation Compañía Minera Cuzcatlán, in the municipality of San José del Progreso, Valles Centrales region, in the state of Oaxaca. Through semi-structured interviews, the findings show that being a man is closely linked to economic provision and protection of the family. Both of these are combined as a mandate that sustains masculine subjectivity and the social imaginary of gender, as well as the assigned identity and social role. Likewise, mining work is associated with the strength and endurance that the male body supposedly harbors in this work environment, whose conditions are adverse. The strength and resistance of men’s bodies distinguishes the work they do compared to women, even when the technology used does not demand the work traditionally performed by miners; this assumption prevails even in men who perform administrative tasks and in areas other than where the mineral is extracted. What this idea produces is a reinforcement and hierarchy between genders in current mining.
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysis
Authors: Williams-Lekuona Mikel , Cosma Georgina
Published: September 2024

In the field of Image-Text Retrieval (ITR), recent advancements have leveraged large-scale Vision-Language Pretraining (VLP) for Fine-Grained (FG) instance-level retrieval, achieving high accuracy at the cost of increased computational complexity. For Coarse-Grained (CG) category-level retrieval, prominent approaches employ Cross-Modal Hashing (CMH) to prioritise efficiency, albeit at the cost of retrieval performance. Due to differences in methodologies, FG and CG models are rarely compared directly within evaluations in the literature, resulting in a lack of empirical data quantifying the retrieval performance-efficiency tradeoffs between the two. This paper addresses this gap by introducing the \texttt{FiCo-ITR} library, which standardises evaluation methodologies for both FG and CG models, facilitating direct comparisons. We conduct empirical evaluations of representative models from both subfields, analysing precision, recall, and computational complexity across varying data scales. Our findings offer new insights into the performance-efficiency trade-offs between recent representative FG and CG models, highlighting their respective strengths and limitations. These findings provide the foundation necessary to make more informed decisions regarding model selection for specific retrieval tasks and highlight avenues for future research into hybrid systems that leverage the strengths of both FG and CG approaches.
Cold and humid climatic conditions over the last millennium decreased the carbon accumulation in peatlands of the subtropical monsoon region
Authors: Liu Hanxiang
Published: September 2024

Carbon accumulation in most northern peatlands is generally positively correlated with temperature under natural climate change. In the subtropical monsoon region of China, the climate differs from that of most northern peatlands, where a significant number of peatlands have developed in mountainous areas. However, it remains unclear how the carbon dynamics of these subtropical peatlands respond to climate change. Here, we reconstructed the net carbon fluxes of a typical mountainous peatland in Tianmu Mountain, eastern China, over the past millennium. Climate records in the subtropical monsoon zone indicate fluctuating and declining temperatures alongside increasing humidity over the past thousand years. Drought and higher winter temperatures have facilitated the terrestrialization of waterlogged depressions and triggered the peatland formation in this region. The net carbon accumulation in the peatland has generally shown a downward trend due to the progressively decreasing winter temperature and increasing humidity. When winter temperatures decrease, the growing season for vegetation is shortened, resulting in less litter production and reduced carbon accumulation. Increased humidity leads to greater surface waterlogging and prolonged flooding of surface vegetation, which hampers vegetation growth, reduces litter production, and consequently lowers carbon accumulation. Despite the decline in carbon accumulation over the last millennium, the peatland’s net carbon balance remains in a 'carbon sink' state. This suggests that the risk of carbon release from the peatland carbon pool under natural climate change conditions is not substantial in the subtropical monsoon area.
A neural network-based modeling approach for transient performance prediction of gas turbine engines
Authors: Wu Xiaohua , Hu Xiaoan , Xiang Xin , Lin Shengzhi
Published: September 2024

Considering the difficulties in obtaining the specific component maps and highly iterative performance requirements when analyzing the transient performance, modeling the transient process is quite a complicated task. With few but sufficient experimental data, this study establishes the dataset-driven neural network models to predict thrust and exhaust gas temperature for the transient process of gas turbine engines. In addition, the transient parameters calculated from GasTurb13 common models are performed. Three neural network models, including convolutional neural network (CNN), long-short term memory neural network (LSTM), and CNN-LSTM, are built, trained, and tested. Compared with the numerical and experimental results, the LSTM model established in this research has a quite significant performance and an ability for forecasting the key parameters by inputting the other relevant parameters. According to the overall validation analysis, the recommended method could donate accurate results using few experimental data and small hardware resources, demonstrating awesome potential that completes the intelligent control of the aircraft and engine, and evaluates the dangerous influence of transient mechanical stress.
Establishment and Characterization of Three Gemcitabine-Resistant Human Intrahepatic Cholangiocarcinoma Cell Lines
Authors: Li Jiandong , Hu Yanxin , Zhang Jiayao , Zhang Weiguang , Yu Jianhua , Lu Baochun
Published: September 2024

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant liver tumor associated with a dismal prognosis, largely due to chemotherapy resistance. However, the mechanisms underlying gemcitabine (GEM) resistance in ICC remain poorly understood. In this study, we established three GEM-resistant cell models and evaluated their resistance by assessing cell proliferation, cell cycle arrest, and DNA damage. The results disclosed that GEM-resistant cells exhibited significant tolerance to GEM-induced growth inhibition, reduced cell cycle arrest, and decreased DNA damage compared to parental cells. We then explored potential resistance mechanisms and found that pathways and targets such as EMT, PI3K/Akt, p53R2, and IGF-1R did not show a significant correlation with ICC resistance. Interestingly, our findings suggested that reactive oxygen species (ROS) might promote GEM resistance in ICC. In conclusion, we characterized a GEM-resistant ICC model, which can be employed to investigate alternative resistance mechanisms and explore new treatment approaches.
Kinetic Liquid Metal Synthesis of Flexible 2D Conductive Oxides for Multimodal Wearable Sensing
Authors: Rahman Md Saifur , Agnew Simon A. , Ong Samuel , Scheideler William J.
Published: September 2024

Transparent conducting oxides (TCOs) are crucial for high-performance displays, solar cells, and wearable sensors. However, their high process temperatures and brittle nature have hindered their use in flexible electronics. We report an approach to overturn these limitations by harnessing the physics Cabrera Mott native oxidation to fabricate large-area, two-dimensional transparent electrodes via liquid metal printing. Our robotic, solvent-free and vacuum-free process deposits ultrathin (2–10 nm thick) 2D indium tin oxide (ITO) with exceptional flexibility, high transparency (> 95%) and superior conductivity (> 1300 S/cm) for wearable bioelectrodes. In a significant advance over previous work, we utilize hypoeutectic In-Sn alloys to print 2D ITO at < 140 ºC on flexible polymers. Our detailed materials characterization and microscopy reveal the efficacy of Sn-doping and high crystallinity with large, platelike grains formed by the liquid metal reaction environment. The ultrathin nature of 2D ITO yields significant enhancement to bending strain tolerance, scratch resistance exceeding durability of traditional PEDOT, and low contact impedance to skin comparable to Ag/AgCl. Finally, we utilize the conductivity and transparency of 2D ITO for synchronous, multimodal measurements via electrocardiography (ECG) and pulse plethysmography (PPG). This order-of-magnitude improvement to printed TCOs could enable new wearable biometrics and display-integrated sensors.
Exploring the Thermal and Mechanical Properties of Semi Metallic XCr₂S₄ (X: Ag, Au) Monolayers
Authors: Tagani Meysam Bagheri
Published: September 2024

The synthesis of non-layered two-dimensional materials is crucial due to the ability to precisely control their composition and structural features. Recently, a non-layered two-dimensional material with the empirical formula AuCrS₂ was synthesized [Nano Letters, 24 (2024) 2308]. Inspired by this experimental report, we predict two thermodynamically and dynamically stable two-dimensional materials with the empirical formula XCr₂S₄ (X=Ag, Au) using density functional theory. These materials are semi-metallic and exhibit high mechanical resistance, with a Young's modulus of 200 N/m. Utilizing machine learning, we calculated the phonon thermal conductivity of these materials. At room temperature, their thermal conductivity is approximately 45 W/mK, indicating a lower phononic thermal conductivity compared to transition metal dichalcogenide monolayers and Maxene. Given their semi-metallic nature, we also investigated their electronic thermal conductivity by calculating the transmission coefficient in the linear response regime. Our findings indicate that the electron contribution to heat transfer is about 5%. The combination of high mechanical flexibility, semi-metallic nature, and low thermal conductivity suggests these materials have significant potential for use in rechargeable ion batteries, electrodes, and sensors.