bluuebunny/crossref_metadata_2025 huggingface dataset:
More than Spicy, Moringa oleifera as a Feedstock for Functional biochar@ZnO
Authors: Msaadi Radhia , Bhakta Arvind K. , Sassi Wafa , Tang Mengqi , Mascarenhas Ronald J. , Mekhalif Zineb , Ammar Salah , CHEHIMI MOHAMED
Published: October 2024

Water treatment is one of the most research-challenging areas, and continuously requires the development of efficient sustainable materials for catalytic total mineralization of organic compounds. Herein, Moringa oleifera biomass was converted to "black gold" biochar loaded with ZnO nanoparticles (MOFB@ZnO) by slow pyrolysis, at 500 °C under an inert atmosphere, (MOFB@ZnO). This composite catalyst was used for the total degradation of Congo Red, a model organic pollutant. SEM confirmed the uniform distribution of ZnO nanoparticles, while XPS and XRD indicated the presence of ZnO, primarily in the wurtzite crystalline structure. Optimization was conducted using the central composite design (CCD) method and STATISTICA 12.0 software. The composite catalyst was applied in the electrochemical degradation of Congo Red dye, using a platinum anode and a carbon felt cathode to generate H2O2 from O2 reduction. The ZnO-decorated Moringa oleifera biochar catalyst permitted to achieve of 98% removal of total organic carbon in less than 6h. It is 10-fold more efficient than pristine biochar and can be recycled up to 4 times without any significant loss. The process is highly efficient producing sustainable low-cost catalysts for water purification from organics, and contributes to sustainable chemical processes therefore addressing SDGs 6, 9, and 13.
Eco-Friendly TiO2 Nanoparticles: Harnessing Aloe Vera for Superior Photocatalytic Degradation of Methylene Blue
Authors: De Luca Agnese , De Benedetto Angeloantonio , Cascione Mariafrancesca , De Matteis Valeria , Di Corato Riccardo , Corrado Massimo , Rinaldi Rosaria
Published: October 2024

In recent years, the contamination of aquatic environments by organic chemicals has become an increasing concern. To effectively remove toxic pollutants and biologically resistant compounds, a combination of advanced technologies must complement conventional methods. Indeed, among environmental pollutants, dyes such as methylene blue (MB), congo red and crystal violet persist in the environment because they are difficult to biodegrade. Advanced oxidation processes (AOPs) are widely employed to treat various types of wastewaters, often in conjunction with bi-ological treatments. Among these technologies, heterogeneous photocatalysis stands out as one of the most extensively studied, with Titanium dioxide being the most researched photocatalyst due to its high photoactivity. In this study, Titanium dioxide nanoparticles (NPs) were synthe-sized using both a green method, leveraging the natural properties of Aloe vera leaf extract, and a conventional approach. The resulting NPs were thoroughly characterized using XRD, TEM, and ζ-potential analysis. Their photocatalytic performance was assessed by measuring the degrada-tion of MB under UV light. The TiO2 NPs synthesized via the green method demonstrated a degradation efficiency of (50 ± 3) % after 180 minutes, significantly higher than the (16 ± 3)% achieved by NPs synthesized through the conventional route. Moreover, the reaction rate con-stant for the green-synthesized TiO2 NPs was found to be approximately five times greater than that of the conventionally synthesized NPs. These results open new scenario in the pollution removing strategy research.
CNN-based Multi-Object Detection and Segmentation in 3D LiDAR Data for Dynamic Industrial Environments
Authors: Schneider Danilo Giacomin , Stemmer Marcelo Ricardo
Published: October 2024

Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. In this paper, we propose a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using Bird’s Eye View (BEV) maps derived from 3D LiDAR data. Our method aims to enable mobile robots to localize movable objects and their occupancy, crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in real-world. Furthermore, we integrate our CNN-based detection and segmentation model into a ROS2-based framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to the advancement of perception systems by proposing a solution for multi-source multi-object tracking and mapping.
An Anti-Mouse CXCR5 Monoclonal Antibody (Cx5Mab-3) Shows High Affinity for Flow Cytometry
Authors: Ishikawa Kenichiro , Suzuki Hiroyuki , Tanaka Tomohiro , Kaneko Mika K. , Kato Yukinari
Published: October 2024

The CXC chemokine receptor 5 (CXCR5) is a member of the G protein-coupled receptor family that is highly expressed in B cells and a subset of T cells such as T follicular helper cells. Various types of cancers, including non-small cell lung cancer, breast cancer, and prostate cancer also express CXCR5; therefore, antibodies that specifically bind to CXCR5 could be useful for clarification of the mechanisms of cancer progression. In this study, we aimed to develop high-affinity monoclonal antibodies (mAbs) for flow cytometry against mouse CXCR5 (mCXCR5). The established anti-mCXCR5 mAb, Cx5Mab-3 (rat IgG2b, kappa), reacted with mCXCR5-overexpressed Chinese hamster ovary (CHO)-K1 (CHO/mCXCR5) by flow cytometry. Kinetic analyses using flow cytometry indicated that the dissociation constants (KD) of Cx5Mab-3 for CHO/mCXCR5 is 7.2 × 10−10 M, indicating that Cx5Mab-3 is useful for detecting mCXCR5 by flow cytometry with high affinity.
Identifying Anthropogenic versus Natural Submerged Prehistoric Landscapes: Two Case Studies from the Sicilian Channel
Authors: Galili Ehud , Horwitz Liora Kolska , Patania Ilaria , Bar Amir , Ogloblin Isaac Ramirez
Published: October 2024

In submerged landscapes, distinguishing anthropogenic features versus natural ones, is often challenging. We have developed a set of criteria to validate the identification of submerged anthropogenic remains that include, examining the geological context, sea-level considerations, associated archaeological finds (including coastal survey), and documenting the broader archaeological context. Furthermore, our experience demonstrates that, while progress has been made in applying remote-sensing technologies to detect anthropogenic features on the seabed, there is no substitute for direct, visual assessment by an underwater archaeologist for verification of their anthropogenic status. We have applied these criteria to examine two published case studies detailing suspected anthropogenic stone features on the seabed in the Sicilian Channel. Our examination has led us to conclude that both localities are not anthropogenic features. The Pantelleria Vecchia Bank features represent natural outcrops on a submerged paleo-landscape that were shaped by depositional and erosional processes, during transgression and regression periods. The suspected Lampedusa cultic site, comprises natural features that are located on a submerged neo-landscape formed due to erosion and retreat of the coastal cliff since the Mid -Holocene, when sea level reached its present level.
RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications
Authors: Sathupadi Kaushik , Avula Ramya , Velayutham Arunkumar , Achar Sandesh
Published: October 2024

AI-driven applications are rapidly growing, and more applications are joining the market competition. As a result, the AI-as-a-Service (AIaaS) model is experiencing rapid growth. Many of these AIaas-based applications are not properly optimized initially. Once they start experiencing a large volume of traffic, different challenges start revealing themselves. One of these challenges is maintaining a profit margin for the sustainability of the AIaaS application-based business model, which depends on the proper utilization of computing resources. This paper introduces the Resource Award Predictive (RAP) model for AIaaS cost optimization called RAP-Optimizer. It is developed by combining a Deep Neural Network (DNN) with the simulated annealing optimization algorithm. It is designed to reduce resource underutilization and minimize the number of active hosts in cloud environments. It dynamically allocates resources and handles API requests efficiently. The RAP-Optimizer reduces the number of active physical hosts by an average of 5 per day, leading to a 45% decrease in server costs. The impact of the RAP-Optimizer has been observed over a 12-month period. The observational data show a significant improvement in resource utilization. It effectively reduces operational costs from $2,600 to $1,250 per month. Furthermore, the RAP-Optimizer increases the profit margin by 179%, from $600 to $1,675 per month. The inclusion of the Dynamic Dropout Control (DDC) algorithm in the DNN training process mitigates overfitting, achieving a 97.48% validation accuracy and a validation loss of 2.82%. These results indicate that the RAP-Optimizer effectively enhances resource management and cost-efficiency in AIaaS application, making it a valuable solution for modern cloud environments.
Stability Analysis of Boost PFC Considering the External Impedance
Authors: Yang Donghyuk , Choi Deokkwan
Published: October 2024

This stability analysis method is simply represented and understood on a Bode plot of two impedances without complex equations. The external impedance of the charging system is properly quantified and designed based on the stability criteria by analyzing .
Tackling the Challenge of Visceral Leishmaniasis: A Severe Threat to Public Health
Authors: Mohan Aswin , Naisam Shahanas , Sreekumar Nidhin
Published: October 2024

Visceral leishmaniasis (VL), or kala-azar, is a severe and potentially fatal form of leishmaniasis, a disease classified under Neglected Tropical Diseases (NTDs). VL primarily affects the internal organs, with symptoms ranging from chronic fever and weight loss to hepatosplenomegaly and anemia. The disease is transmitted by sandflies, with Leishmania donovani and Leishmania infantum being the primary pathogens. Despite various treatment options like liposomal amphotericin B and miltefosine, challenges remain due to drug toxicity, high costs, and limited availability. Alternative therapeutic approaches, particularly those involving phytocompounds, offer promising potential for overcoming these limitations and improving outcomes for affected populations.
Evaluating the Role of Organizational and Management Factors in Enhancing the Performance of Sidama Bunna Football Club
Authors: Sentie Wasihun Abate , Tiruneh Getinet
Published: October 2024

This study aimed to assess the contribution of organizational and management factors to the performance of Sidama Bunna Football Club. For this study, a cross-sectional survey design was adopted. The study's target population were Sidama Bunna football club players, coaches and club managers (n=44) selected using a comprehensive sampling technique. Key informant interviews and questionnaires were used as instruments. Pearson Product Moment Correlation Coefficient and multiple regression were used to analyze the data at the 0.05 significant level. The result shows that there was a strong positive significant relationship between club performance and human resource (r=0.774**, p<0.01), materials resources (r= 0.697**, p<0.01), finance (r= 0.765**, p<0.001), strategic factors (r= 0.788**, p<0.001), communication (r= 0.779**, p<0.001), whereas there were no significant relationships between club performance and club structure (r=0.263, p>0.05). The findings also confirmed that The R Square value of 0.794 indicates that 79.4% of the variance in club performance is explained by these factors, highlighting the importance of a comprehensive approach that considers multiple dimensions. The Adjusted R Square of 0.760 further supports the model's robustness. The ANOVA results (F (6, 37) = 23.745, p < 0.001) confirm the significant influence of these variables on club performance. In this combined model, Strategic Factors (B = 0.360, p = 0.002) and Finance (B = 0.196, p = 0.054) remain significant, reinforcing their critical roles. However, communication, structure, human resources, and materials resources are not statistically significant in this combined model, indicating that their influence may be overshadowed by the more dominant factors of strategic planning and finance.
A Cross-Layer Secure and Energy-Efficient Framework for Internet of Things: A Comprehensive Survey
Authors: Mustafa Rashid , Sarkar Nurul I. , Mohaghegh Mahsa , Pervez Shahbaz
Published: October 2024

This survey delves into cross layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. In this paper, we provide a comprehensive review and analysis of secure and energy efficient cross-layer framework for IoT.We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field ofcross-layer design for secure and energy efficient IoT networks. We expect this study will stimulate further research endeavors to build a secure and energy-efficient framework for next-generation IoT systems.
Visitors’ Behaviors and Perceptions of Spatial Factors of the Uncultivated Internet-Famous Sites in Urban Riverfront Public Spaces: A Case Study in Changsha, China
Authors: Zheng Bohong , Huang Yuanyuan , Guo Rui
Published: October 2024

This article takes the representative uncultivated riverfront internet-famous sites (the uncultivated RIFSs) in Changsha city, China as an example, to explore the internal mechanism of its formation, and finds out that they are closely related to "urban subculture" and "informality of urban public space". In terms of methodology, through questionnaire surveys and in-depth interviews, this study investigates the behavioral characteristics of on-site visitors, overall perceptions and satisfaction of public spaces, and perceptions of spatial and humanistic elements of visitors. It also compares the online-offline differences of the spatial perceptions on the uncultivated RIFSs between this study and previous research, that is, instead of focus on the urban physical spaces, the online social media users pay more attention to their self-presentation. Meanwhile, the visitors place greater emphasis on the functionality, practicality, and experiential activities of the urban physical spaces. Finally, this article proposes the optimization strategies for the uncultivated RIFSs from two aspects: planning and governance, and public space design. Firstly, it is necessary to protect this special type of spaces, avoiding the binary opposition between "formal spaces" and "informal spaces”, achieving a gradual integration of "subculture spaces" and "mainstream culture spaces", and highlighting the city's inclusiveness. Secondly, human-centric space design should be demonstrated, that is, paying practical attention to people's diverse lifestyles and value needs, strengthening the composite utilization of riverside spaces to enhance diverse vitality.
Quantitative Analysis of the Fractional Fokker–Planck Levy Equation via a Modified Physics-Informed Neural Network Architecture
Authors: Fazal Fazlullah , Sulaiman Muhammad , Bassir David , Alshammari Fahad Sameer , Laouini Ghaylen
Published: October 2024

An innovative approach is utilized in this paper to solve the Fractional Fokker–Planck–Levy (FFPL) equation. A hybrid technique is designed by combining the finite difference method (FDM), Adams numerical technique, and physics-informed neural network (PINN) architecture, namely, the FDM-APINN, to solve the fractional Fokker‒Planck‒Levy (FFPL) equation numerically. Two scenarios of the FFPL equation are considered by varying the value of α (i.e., 1.75,1.85). Moreover, three cases of each scenario are numerically studied for different discretized domains with 100,200 and 500 points in x∈[-1,1] and t∈[0,1]. For the FFPL equation, solutions are obtained via the FDM-APINN technique via 1000,2000, and 5000 iterations. The errors, loss function graphs, and statistical tables are presented to validate our claim that the FDM-APINN is a better alternative intelligent technique for handling fractional-order partial differential equations with complex terms. FDM-APINN can be extended by using nongradient-based bioinspired computing for higher-order fractional partial differential equations.
Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis
Authors: Ahmad Farooq , Finos Livio , Guidolin Mariangela
Published: October 2024

Hydroelectric power is one of the most essential renewable energy sources in the world. In addition to generating electricity, hydropower offers other benefits such as flood control, irrigation assistance, and clean drinking water. In this study, we examine the evolution of hydropower in the context of energy transition with a forecasting analysis. We analyze time series data of hydropower generation from 1965 to 2023 and apply Innovation Diffusion Models as well as other models such as Prophet and ARIMA for comparison. The models are evaluated for different geographical regions, namely the North, South, and Central American countries, the European countries, and the Middle East with Asian countries, to determine their effectiveness in predicting trends in hydropower generation. The models' accuracy is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Through this analysis, we find that, on average, the GGM model outperforms the Prophet and ARIMA models, and is more accurate than the Bass Model. This analysis underscores the critical role of precise forecasting in energy planning and suggests further research to validate these results and explore other factors influencing the development of hydroelectric generation.
Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming
Authors: Sotiropoulos Dionisios , Koronakos Gregory , Solanakis Spyridon V.
Published: October 2024

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited insights available on both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score's calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in Genetic Programming (GP). Here, Occam's razor principle guides evolutionary bias towards simpler, more interpretable models. To ascertain our method's efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multi-Layer Perceptrons (MLPs), Regression Trees and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers comparable accuracy with these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.
Application of Artificial Neural Networks for Optimizing Enhanced Heavy Oil Recovery in the Niger-Delta
Authors: ODO JUDE EMEKA , MMADU DANIEL CHIDI
Published: October 2024

The decline of conventional oil reserves in Nigeria's Niger Delta region necessitates the exploration of alternative heavy oil recovery methods, particularly given that heavy oil constitutes nearly 20% of the region's estimated crude oil reserves. This study investigates the optimization of enhanced heavy oil recovery through the application of Artificial Neural Networks (ANN) and innovative hot chemical flooding techniques. The experimental phase involved the development of a novel chemical mix comprising dissolved liquid soap, scent leaf extract, bitter leaf extract, palm frond ash, xanthan gum, dry gin, and a unique alkali-surfactant mixture (DG + DPFA). These chemicals were tested at varying injection temperatures, and their respective recovery efficiencies were evaluated.
For the ANN model development, key reservoir and operational parameters—porosity, permeability, oil specific gravity, and injection temperature—were utilized as input layers, with three hidden layers and one output layer representing the recovery efficiency. The ANN model successfully correlated the input parameters to recovery efficiencies, providing a robust prediction framework for enhanced heavy oil recovery. Software simulations conducted in parallel with the experimental work offered insights into future recovery potential and anticipated water cut profiles. Furthermore, the study presented mathematical correlations for each chemical flooding process, linking the input variables to recovery efficiency.
The integration of ANN modelling and experimental validation establishes a comprehensive methodology for optimizing heavy oil recovery in the Niger Delta, suggesting that upstream companies should consider adopting hot chemical flooding strategies to enhance production rates and economic returns. These findings underscore the importance of leveraging advanced technologies and local resources to address the challenges of heavy oil recovery in declining conventional reservoirs.
Death Profiles Due to Infectious Endocarditis in Brazil and Regions
Authors: Cordeiro João Vitor Fazzio de Andrade , Raposo Leticia Martins , Godoy Paulo Henrique
Published: October 2024

Background: Studies on infectious endocarditis (IE) are mostly limited to institutions, which highlights the need for more comprehensive epidemiological studies. Objective: To verify the profile of deaths due IE and the relationship with deaths from other causes and conditions in Brazil and regions. Method: Study in database from the Brazilian Mortality Information System, from 2000 to 2019. Mentions of deaths due to IE, other causes and conditions were identified according to ICD-10. Cluster analysis was performed to verify the relationship between variables. Results: 52,055 deaths from IE were found, with a predominance of males, aged 60 to 79 years, in the Southeast, with deaths from the circulatory system most frequent. Three profiles were identified: 1 - age equal to or greater than 80 years, women, deaths due endocrine, circulatory and metabolic diseases, in the South and Southeast; 2 - also in the South and Southeast, age between 30 and 79 years, men, deaths due infectious and genitourinary diseases; 3 - 0 to 29 years, no genders differences, deaths due diseases of the respiratory system, in the North, Northeast and Central-West. Conclusion: Cluster analysis showed diversity in the profiles of deaths due IE, indicating heterogeneity in regions of the country.
The Role of Manufacturing Operations in SMEs Performance: A Systematic Review
Authors: Mudau Munei C. , Moshapo Lavhelani W. , Monyela Taryn M. , Thango Bonginkosi A.
Published: October 2024

The critical role of manufacturing operations in enhancing the performance of Small and Medium Enterprises (SMEs) has become increasingly evident in recent years. This systematic review examines how manufacturing operations influence the performance of SMEs, analysing findings from 69 diverse research papers published between 2014 and 2024. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key operational strategies, including lean manufacturing and advanced manufacturing technologies, assessing their effectiveness in improving business outcomes such as efficiency, productivity, and financial performance. The methodology included a thorough review of the research methods used in the studies, identifying potential biases, gaps, and highlighting the need for diverse approaches. While manufacturing operations have led to improved productivity and increased efficiency for many SMEs, challenges such as limited resources, lack of technical expertise, and resistance to change remain prevalent. Notably, a reporting bias was observed, with 48% of the literature composed of quantitative studies, 30% being mixed-method studies, and only 20% and 1% representing qualitative and exploratory studies, respectively. This imbalance suggests an overemphasis on quantitative methods, limiting the range of ideas and understanding available. Key strategies such as lean manufacturing and advanced technologies were shown to enhance productivity by 15% to 25% in SMEs. By adopting a broad approach, this review ensures that the findings are applicable across various sectors and geographies, offering valuable insights into best practices and challenges across diverse economic contexts. The broad scope also allows for the identification of under-researched areas, fostering a more comprehensive understanding of how manufacturing operations contribute to SME success. The review provides a decision-making framework for business leaders and policy recommendations to foster SME growth in a competitive, globalized market.
The Aggregation-Induced Emission Enhancement Properties of Heteroaryl Fused Triazapentalens Synthesis and Fluorescence Characteristics
Authors: Wang Yingchun , Wu Junfeng , Dehaen Wim
Published: October 2024

A pyridine-fused triazapentalene shows weak fluorescence in solution, and is readily accessible via nitrene-mediated cyclization. In this research, a modified Cadogan reaction was used to synthesize HetATAP1. Palladium-catalyzed reactions have been demonstrated as post-functionalization methods. Interestingly, Suzuki cross-coupling with various boronic acids resulted in poor to moderate yields of the products HetATAP2-5 arylated on the azole moiety. Direct CH arylation of HetATAP1 gave the products with the same regiochemistry in satisfactory yields. The structures of HetATAP2-5 were confirmed using NMR analysis. In addition, the fluorescence properties of HetATAP2-5 were studied in the solid-state and solution at room temperature, and the photophysical properties of HetATAP3 and 4 in the aggregated state were both measured in ACN/water and THF/water mixtures, respectively.
Analysis of Disease Activity in Axial Spondyloarthritis Reveals That the Obesity-Activity Association Could Vary Depending on the Activity Index Chosen for This Purpose
Authors: Queiro Rubén , Alonso-Castro Sara , Braña Ignacio , Loredo Marta , Pardo Estefania , Burger Stefanie , Chiminazzo Valentina , Alperi Mercedes
Published: October 2024

Background and aims: Obesity has been related to increased disease activity in axial spondyloarthritis (axSpA), but this association might vary depending on the composite index chosen to assess disease activity. We aim to check that possibility. Methods: Three hundred and thirty consecutive patients were recruited from the monographic axSpA unit of a university center. To assess disease activity, BASDAI and ASDAS-CRP measurements were collected. The factors associated with the different disease activity thresholds of these instruments were analyzed using univariate and multivariate logistic regression models. Results: The study included 127 women and 203 men, mean age 47.6 (SD 12.9) years, median disease duration 8 years [IQR: 4-16], and 63% on biologic therapies. Most patients met therapeutic goals with a BASDAI < 4 in 187 (56.7%) and ASDAS inactive/low category in 182 (55.2%). Being male was associated with BASDAI remission (OR 2.63), but smoking reduced this likelihood (OR 0.28). Similar findings were found for ASDAS inactive disease (male: OR 2.09; smoking: OR 0.39). The variables associated with BASDAI ≥ 4 in the multivariate logistic model were male (OR 0.36), age (OR 1.02), smoking (OR 2.39), and obesity (OR 2.94), whereas associations with active/very active ASDAS categories were male (OR 0.49), age (OR 1.02), and smoking (OR 2.34). However, obesity was not associated with these higher ASDAS categories (p = 0.183). Conclusions: While the association between smoking and increased disease activity was consistent across all composite activity indices, the obesity-activity relationship was only apparent through the BASDAI.
Non-Markovian Gravitational Decoherence and the Black Hole Information Paradox
Authors: Li Yuanxin
Published: October 2024

An isolated system always evolves according to unitary evolution and maintain coherence. However the system inevitably interacts with the environment, information about the relative phases between the quantum states leaks into the environment and becomes delocalized, known as environment-induced decoherence. Here we consider the gravitational decoherence of a quantum system near the event horizon of a Schwarzschild black hole. We show that the gravitational decoherence is non-Markovian, which is consistent with information conservation and the Pagecurve-like entanglement dynamics. Moreover, taking the point of view that information on the collapsed matter is stored as the quantum fluctuation of the horizon, the horizon can be regarded as an “uncertain” quantum object instead of random fluctuations of graviton noise. Now the Hawking radiation bath plays the role of the environment, leading to the decoherence of the horizon. Therefore, information about the location of the horizon, or equivalently information about the collapsed matter, is carried away by the Hawking radiation. The memory time of the non-Markovian process and the Page time are estimated.
Identifying Drugs That Target Glucokinase for Treating Primary Amoebic Encephalitis (PAM) &Amp; Diabetes
Authors: Abraham Jason , Chauhan Neha , Ray Supriyo
Published: October 2024

Naegleria fowler, also known as brain-eating amoeba is a thermophilic microorganism that causes primary amoebic encephalitis (PAM) having >98% mortality rate. To date, there are no specific drugs identified to treat this disease. N. fowleri expresses only one form of hexokinase i.e. glucokinase (nGLK) unlike humans that express four isozymes of hexokinase including glucokinase (hexokinase IV) or hGLK that is critical for the first step of glycolysis. Until now, there have not been many studies on glucokinase as a drug target to treat PAM, hence, this study provides an exhaustive analysis of this relatively novel drug target. Thus, it was hypothesized that amoebic glucokinase can arrest carbohydrate metabolism and assist regulating its virulence. To find suitable antagonists to inhibit the activity of nGLK, we first tested the present recommended drugs by the CDC on their ability to bind nGLK. Interestingly our studies show that many of the present recommended drugs can inhibit both amoebic (nGLK) and human (hGLK) homologs of glucokinase. We identified new inhibitors for nGLK and further explored suitable hGLK antagonists for the potential treatment of diabetes. Despite significant structural differences between nGLK and hGLK, it was interesting to note that all our screened inhibitors bound with both nGLK and hGLK. Levomefolic acid; Ziprasidone; and Risperidone were found to be more suited to treat PAM while, Simeprevir; Indacaterol; Crizotinib and Mocetinostat were found to be suited for treating diabetes. The selections were based on multiple criteria including binding affinity; ADME scores; absorption; and its ability to cross the blood brain barrier. This study identifies multiple drugs that have potential not only to treat PAM but also to treat diabetes and also use for lab based biochemical assays for glycolysis inhibitors.
Porcine Airway Organoid-Derived Well-Differentiated Epithelial Cultures as a Tool for Characterization of Swine Influenza a Virus Strains
Authors: Gerhards Nora M. , Vrieling Manouk , Dresken Romy , Nguyen-Van Oort Sophie , Bordes Luca , Wells Jerry M. , de Swart Rik L.
Published: October 2024

Swine influenza A viruses (IAVsw) are important causes of disease in pigs but also constitute a public health risk. IAVsw strains show remarkable differences in pathogenicity. We aimed to generate airway organoids from the porcine lower respiratory tract and use these to establish well-differentiated airway epithelial cell (WD-AEC) cultures grown at air-liquid interface (ALI) for in vitro screening of IAVsw strain virulence. Epithelial cells were isolated from bronchus tissue of juvenile pigs, and airway organoids were cultured in extracellular matrix in medium with human growth factors. Single cell suspensions of these 3D organoids were seeded on transwell filters and differentiated at ALI to form a pseudostratified epithelium containing ciliated cells, mucus-producing cells and tight junctions. Inoculation with a low dose of IAVsw in a low volume inoculum resulted in virus replication without requiring addition of trypsin, and was quantified by detection of viral genome loads in apical washes. Interestingly, inoculation of an H3N2 strain known to cause severe disease in pigs induced greater reduction in trans-epithelial resistance and more damage to tight junctions than H1N2 or H1N1 strains associated with mild disease in pigs. We conclude that the porcine WD-AEC model is useful to assess the virulence of IAVsw strains.
Characterization of Stages of CO2-EOR Process in Low-Permeability Oil Reservoirs Based on Core Flooding Experiments
Authors: Zhu Yutong , Wang Xinwen , Kang Yulong , Guo Chaobin , He Qingcheng , Li Cai
Published: October 2024

Understanding the CO2 displacement mechanism in ultra-low permeability reservoirs is essential for improving oil recovery. In this research, a series of displacement experiments were conducted on sandstone core samples from the Chang 6 reservoir in the Huaziping area, using a multifunctional core displacement apparatus and Nuclear Magnetic Resonance (NMR) technology under conditions of constant pressure, variable pressure, and constant effective confining stress. The results revealed that the distribution characteristics of pore structure in the rock samples were critical factors influencing CO2 displacement efficiency. Under the same conditions, cores with more large pores exhibited higher oil recovery. Though fractures can facilitate CO2 flowing through pores, but the confining pressure applied during displacement caused partial closure of fractures, resulting in reduced rock permeability. Based on the oil-to-gas ratio and oil recovery in the outlet section of fractured rock samples, the CO2 displacement process exhibited five stages of no gas，a small amount of gas, gas breakthrough, large gas channeling, and gas fluctuation. Although the displacement stage of different cores is different, it is in the breakthrough stage in the range of 2PV.
Crisis Management and PR Recovery: Navigating the E. Coli Outbreak and Strategies for Rebuilding Public Trust
Authors: Karimova Nigar
Published: October 2024

This paper analyzes Chipotle Mexican Grill’s public relations strategy in response to the 2015 E. coli outbreak that severely impacted its reputation and financial standing. The company’s delayed initial response exacerbated the crisis, leading to a significant decline in stock prices and consumer trust. However, through leadership changes, enhanced safety protocols, strategic marketing campaigns, and customer loyalty programs, Chipotle gradually rebuilt its image and began to recover financially. The paper explores the short- and long-term effects of the crisis on Chipotle and outlines key lessons for companies facing similar PR challenges, emphasizing the importance of swift crisis management, transparency, and proactive communication in restoring public confidence.
Neoliberal Urbanism in Chile: A Review
Authors: Vergara-Perucich Francisco
Published: October 2024

This paper examines the impact of neoliberal urbanism on socio-spatial inequalities in Chile, focusing on housing commodification and urban fragmentation. Utilizing a thematic analysis of key academic literature from the past two decades, the study explores the ethical challenges faced by urban practitioners in reconciling profit-driven urban development with the right to the city. The findings highlight how neoliberal policies have intensified segregation in cities like Santiago, where wealth and resources are concentrated in affluent areas, while peripheral, impoverished regions suffer from limited access to services. This dynamic perpetuates cycles of exclusion and marginalization, challenging efforts toward spatial justice. The paper also considers potential alternative frameworks for urban governance and policymaking that could foster more inclusive, equitable urban environments. Ultimately, the study calls for a redefinition of urban planning strategies to prioritize social equity over market imperatives, advocating for a more participatory and democratic approach to urban development.
Toxicity of Formaldehyde, and the Harmful and Aroma Compounds Derived from It
Authors: Sun Xiaoyan , Yang Chunmin , Zhang Weiyue , Zheng Jie , Li Yixin , Ou Juanying , Ou Shiyi
Published: October 2024

Formaldehyde is generated via the biochemical and chemical pathways, and it widely exists in foods. Its chronic exposure causes carcinogenic, neurotoxic, reproductive, allergic, immunological, genetic and respiratory toxicities. However, food-sourced formaldehyde is usually neglected due to its rapid metabolism after intake. Compared with its residual level in processed foods, a large proportion of formaldehyde disappears because of its high reactivity. Nevertheless, its elimination reaction mechanism and the potential consequences remain unclear. In this review, the disappearing pathway of formaldehyde from two aspects, the formation of formaldehyde-derived toxins and aromas, was reviewed. This review article may help food scientists take appropriate strategies to use the beneficial reactions of formaldehyde or divert its harmful effects to beneficial ones.
Diagnosis of Enlarged Vestibular Aqueduct using Wideband Tympanometry
Authors: Ganaha Akira , Nojiri Nao , Nakamura Takeshi , Higa Teruyuki , Kondo Shunsuke , Tono Tetsuya
Published: October 2024

Background: Wideband tympanometry (WBT) has the potential to distinguish various mechanical middle ear and inner ear pathologies noninvasively. This study investigated the diagnostic value of WBT in the diagnosis of enlarged vestibular aqueduct (EVA). Methods: The absorbance and resonance frequency (RF) of patients with EVA (40 ears, 25 patients) and matched population controls (39 ears, 28 subjects) were compared, alongside receiver operating characteristic (ROC) analysis. Correlations between VA width and RF were also examined. Results: Patients with EVA had higher absorbance at low frequencies (226–917 Hz) and lower absorbance at high frequencies (2,520–4,896 Hz) compared to controls. The RF of the EVA group was significantly lower versus controls (751 [391–1,165] vs. 933 [628–1,346] Hz). ROC analysis revealed area under the curve values of 0.771 and 0.801 respectively, for absorbance and RF. RF had a sensitivity, specificity, positive predictive value, and negative predictive value of 74.4%, 82.5%, 76.7%, and 80.6%, respectively, for diagnosing EVA. In the EVA group, the VA midpoint width (r = −0.334) and VA petrous width (r = −0.402) both significantly correlated with RF. Conclusion: Our findings support the utility of WBT for diagnosing EVA, with RF as the optimal index used.
A New Easy-to-Perform Flow Cytometry Assay for Determining Bacterial and Viral Infection-Induced Polymorphonuclear Neutrophil and Monocyte Membrane Marker Modulation in Febrile Patients
Authors: la Sorda Marilena , De Lorenzis Desy , Battaglia Alessandra , Fiori Barbara , Graffeo Rosalia , Santangelo Rosaria , D’inzeo Tiziana , De Pascale Gennaro , Schinzari Giovanni , Pedone Romina Rose , Rossi Ernesto , Sanguinetti Maurizio , Sali Michela , Fattorossi Andrea
Published: October 2024

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Role of the Foliar Endophyte Colletotrichum in the Resistance of Invasive Ageratina adenophora to Disease and Abiotic Stress
Authors: Yang Ai-Ling , Li Yu-Xuan , Zeng Zhao-Ying , Zhang Han-Bo
Published: October 2024

Plant‒fungal associations often drive plant invasion success by increasing host growth, disease resistance, and tolerance to environmental stress. In this study, to determine whether the numerically dominant foliar endophyte Colletotrichum can directly enhance the resistance of the invasive plant Ageratina adenophora to disease and environmental stressors, we planted sterile A. adenophora seedlings, inoculated them with three endophytic fungal strains of Colletotrichum with distinct genetic backgrounds and compared their role in the resistance of A. adenophora to the pathogenicity of three foliar pathogens from the Didymellaceae family, as well as to abiotic stress, including drought and nutrient stress. We observed that the foliar endophyte Colletotrichum reduced photosynthesis-related physiological indicators, increased resistance-related indicators, and decreased the biomass of A. adenophora in the absence of pathogen attack, drought and nutrient stress. However, some endophytic Colletotrichum strains exhibit positive effects on resistance to certain foliar pathogen attacks, and some endophytes increase the biomass of A. adenophora under drought or nutrient stress. Our results revealed that there are certain positive effects of foliar Colletotrichum endophytes on A. adenophora in response to biotic and abiotic stresses, which may be beneficial for its invasion.
Assessment of the American College of Surgeons-Surgical Risk Calculator (ACS-SRC) for Prediction of Early Postoperative Complications in Patients Undergoing Cytoreductive Surgery for Ovarian Peritoneal Carcinomatosis
Authors: Kabeya Cedric , Khaled Charif , Karaki Jocelyne , Polastro Laura , Moreau Michel , Bucella Dario , Fastrez Maxime , Liberale Gabriel
Published: October 2024

Ovarian cancer is diagnosed at a locally advanced stage in two-thirds of cases. The first line of treatment consists of cytoreductive surgery (CRS) combined with neoadjuvant and/or adjuvant chemotherapy. However, CRS can be associated with high rates of postoperative complications (POCs) and detection of fragile patients at high risk of POCs is important. The American College of Surgeons Surgical Risk Calculator (ACS-SRC) provides a predictive model for early POCs (30 days) for any given surgical procedure. This study aimed to evaluate the performance of the ACS-SRC in predicting the occurrence of early POCs for patients undergoing CRS for ovarian cancer. This was a retrospective study that included patients undergoing CRS for advanced ovarian cancer between January 2010 and December 2022. Early POCs were reviewed and the rate of POCs was compared with those predicted by the ACS-SRC to evaluate its accuracy (i.e., discrimination and calibration). A total of 218 patients were included, 112 of whom underwent extensive surgery/resection. A total of 94 complications were recorded. This cohort demonstrated correct calibration of the ACS-SRC for the prediction of surgical site infection (SSI), readmission, and the need for nursing care post-discharge (NCPD; transfer to revalidation center or need for nursing care at home). Using both the discrimination and calibration methods, the score only predicted NCPD. In this study, the ACS-SRC was shown to be of little value for patients undergoing cytoreductive surgery for ovarian peritoneal carcinomatosis, as it only accurately predicted NCPD.
Complexity Evaluation for Urban Intersection Scenarios in Autonomous Driving Tests: Method and Validation
Authors: Jiangkun Li , Ruixue Zong , Ying Wang , Weiwen Deng
Published: October 2024

Complexity is a crucial metric for evaluating urban intersection scenarios. However, existing complexity evaluations are often derived indirectly from the driving risk of autonomous vehicles, leading to subjective and incomplete evaluations. Therefore, this study undertakes a comprehensive evaluation of intersection scenarios from a systems perspective by quantifying the number of vehicles and the characteristics of vehicle interactions within the intersection.Specifically, we propose an objective framework for assessing complexity. In this framework, the number of vehicles serves as the foundation for assessment, while three interaction characteristics—interaction density, interaction disorder, and interaction risk—are modeled as scaling factors of complexity. In particular, by combining logistic and hyperbolic tangent functions, we accurately capture the nonlinear effects of vehicle numbers and interaction characteristics on complexity.The experimental results demonstrate that the evaluation outcomes of the proposed method are highly correlated with the risk and efficiency performance of autonomous vehicles at intersections, thereby validating the method. Additionally, this method is utilized to guide the generation of highly complex urban intersection scenarios, achieving double the generation efficiency compared to the random sampling method.
Models For The Design And Optimization Of The Multi-stage Wiredrawing Process Of Znal15% Wires For Spray Metallization
Authors: del Rey Juan Carlos , Guerrero-Vacas Guillermo , Comino Francisco , Rodríguez-Alabanda Óscar
Published: October 2024

Key findings include the modelling of the material behavior when ZnAl15% wires were subjected to the tensile test at different speeds, with strain rate sensitivity coefficient m = 0.0128, demonstrating that this type of alloy is especially sensitive to the strain rate. Besides, the optimal friction coefficient (µ) for the drawing process of this material was experimentally identified as µ = 0.28, the ideal drawing die angle was determined to be 2α = 10°, and the alloy's deformability limit has been established by a reduction ratio r ≤ 22.5%, that indicates good plastic deformation capacity. The experimental results confirmed that the proposed models are feasible for the design and optimization of industrial processes to improve the efficiency and quality of ZnAl15% alloy wire production.