bluuebunny/crossref_metadata_2025 huggingface dataset:
The Automated Inspection of Precast Utility Tunnel Segments for Geometric Quality Based on the BIM and LiDAR
Authors: Guo Zhigang , Wang Gang , Liu Zhengxiong , Liu Lingfeng , Zou Yakun , Li Shengzhen , Yang Ran , Hu Xin , Li Shenghan , Wang Daochu
Published: August 2024

The quality inspection of each precast utility tunnel segment is crucial, especially the cross-sectional dimensions and surface smoothness, since they influence the assembly precision at the construction site. Traditional manual inspection methods are not only time-consuming and costly but also limited in accuracy. In order to achieve a high-precision and high-efficiency geometric quality inspection for multi-type precast utility tunnel segments, this paper proposes an automated inspection method based on the Building Information Model (BIM) and Light Detection and Ranging (LiDAR). Initially, the point cloud data (PCD) of the precast utility tunnel segment are acquired through LiDAR and preprocessed to obtain independent point clouds of the precast utility tunnel segment. Then, the shape of the precast utility tunnel segment is identified using the proposed Cross-Sectional Geometric Ratio Feature Identification (CSGRFI) algorithm. Subsequently, the geometric features of the components are extracted based on preset conditions, and the geometric dimensions are calculated. Finally, the quality inspection results are obtained by comparing with the design information provided by the BIM. The proposed method was validated in a real precast component factory. The results indicate that the method achieved a 100% success rate in identifying the cross-sectional shapes of the segments. Compared with the manual measurement method, the proposed method demonstrated a higher accuracy in the geometric quality assessment and an improved time efficiency by 44%. The proposed method enables the efficient geometric quality inspection of tunnel segments, effectively addressing the construction industry’s need for large-scale, high-quality tunnel projects.
Wind-Induced Aerodynamic Responses of Triangular High-Rise Buildings with Varying Cross-Section Areas
Authors: Yadav Himanshu , Roy Amrit Kumar
Published: August 2024

This study investigated the aerodynamic behavior and structural responses of prismatic and tapered high-rise buildings under extreme wind conditions, focusing on peak wind-induced forces and moments. Using Computational Fluid Dynamics (CFD) simulations with a hybrid RANS/LES approach, the analysis explored the effects of turbulent inflow on the mean pressure coefficients, vortex dynamics, and force coefficients at different wind incidence angles (0°, 30°, and 60°). The results revealed significant differences in peak aerodynamic loads between prismatic and tapered building shapes. The tapered models experienced larger vortex formations and greater suction effects, particularly at two-thirds of the building height, with peak across-wind forces occurring at a 30° wind incidence angle. In contrast, the prismatic model showed the highest peak in along-wind forces and base overturning moments at a 60° wind incidence angle, with Karman vortex shedding and horseshoe vortices prominently captured. The study also highlighted the importance of unsteady inflow conditions in accurately predicting peak dynamic responses, particularly in the wake flow, where vortices significantly influence aerodynamic loads.
Temperature Influence on the Synthesis of Pt/C Catalysts for Polymer Electrolyte Membrane Fuel Cells
Authors: Kim Gayoung , Lee Dong-Hyun , Park Gyungse , Sun Ho-Jung , Kim In-Tae , Park Sehkyu , Rim Hyung-Ryul , Lee Hong-Ki , Shim Joongpyo
Published: August 2024

To reduce the manufacturing cost of polymer electrolyte membrane fuel cells (PEMFCs), tests targeting the decrease of reaction temperature and the amount of reducing agent in the polyol method for the synthesis of Pt/C catalysts were conducted. The reaction temperature in the polyol method was changed from 50 to 160 °C. Through XRD and TGA, it was determined that the reduction of platinum ions by the oxidation of ethylene glycol started at 70 °C. Below a 60 °C reaction temperature, Pt (1 1 1) peaks in XRD were barely visible, indicating that no deposition occurred. TEM revealed that Pt particles were well-dispersed above a 100 °C reaction temperature. For manufacturing platinum catalysts using the polyol method, it was found that 100 °C is the optimal synthesis temperature. Additionally, it was found that similar performance can be achieved by adding water to decrease the amount of ethylene glycol during synthesis. Finally, considering various analyses, it is evident that the dispersion, size, and crystallinity of platinum particles had the most significant impact on performance.
RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing
Authors: Kesavapillai Ahalya R. , Aslam Shabnam M. , Umapathy Snekhalatha , Almutairi Fadiyah
Published: August 2024

The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images.
Potential of Carbohydrate Antigen 19-9 and Serum Apolipoprotein A2-Isoforms in the Diagnosis of Stage 0 and IA Pancreatic Cancer
Authors: Hanada Keiji , Shimizu Akihiro , Tsushima Ken , Kobayashi Michimoto
Published: August 2024

Apolipoprotein A2-ATQ/AT (apoA2-ATQ/AT) is a new biomarker for diagnosing pancreatic cancer (PC). In this study, the value of blood carbohydrate antigen 19-9 (CA19-9) and apoA2-ATQ/AT levels in diagnosing stage 0 and IA PC was evaluated. During 2014–2021, 12 patients with stage 0 PC and 12 patients with IA PC (average age: 73.8 years) underwent resection at JA Onomichi General Hospital. In addition, the data of 200 healthy controls were collected from a community-based cohort study. Levels of two apoA2-isoforms were measured using enzyme-linked immunosorbent assay (ELISA) with specific antibodies to calculate the apoA2-i Index as a surrogate value for apoA2-ATQ/AT. The cutoff value for the apoA2-i Index was determined to be 62.9 μg/mL. CA19-9 levels were also measured through ELISA. Among all 24 patients with PC, the positivity rates for apoA2-i and CA19-9 were 33.3% and 25.0%, respectively. The positivity rates for apoA2-i and CA19-9 were 16.7% and 8.3% in patients with stage 0 PC and 50.0% and 41.7% in those with stage IA, respectively. For CA19-9-negative patients, the apoA2-i positivity rate was 9.1% in stage 0 and 42.9% in stage IA. The combined positivity rate for both markers was 16.7% in stage 0 and 66.7% in stage IA. Imaging findings in apoA2-i- and CA19-9-positive patients included pancreatic duct dilatation (87.5%/100%), duct stenosis (75.0%/50%), and atrophy (87.5%/66.7%). The imaging findings of this study suggest that apoA2-i may enhance the sensitivity for detecting CA19-9-negative stage 0 and IA PC, and complementary measurements with CA19-9 may be valuable for diagnosing early-stage PC. Therefore, minute PC with pancreatic duct dilation, duct stenosis, and atrophy may exhibit a high positivity rate, aiding differential diagnosis.
Aerodynamic Interaction Minimization in Coaxial Multirotors via Optimized Control Allocation
Authors: Berra Andrea , Trujillo Soto Miguel Ángel , Heredia Guillermo
Published: August 2024

Coaxial multirotors, characterized by overlapping rotors, represent a common solution to increasing payload capacity while maintaining a compact platform size. However, the overlap between motors generates airflow disturbances that, if not taken into account properly, may decrease the system’s overall performance. In this paper, aerodynamic interactions for coaxial multirotors are analyzed and characterized. Two rotor models are introduced, which account for the aerodynamic interaction between the upper and the lower rotor. Each model is accompanied by its corresponding mixer design and analyzed with respect to the state-of-the-art mixer solution for classical multirotor systems. The proposed approaches are tested through rotor stand experiments, simulations, and implementation on an actual coaxial platform. The results demonstrate the effectiveness of these models in mitigating the adverse aerodynamic effects, thereby improving the performance and efficiency of coaxial multirotor systems.
ELECTRE-TRI Multicriteria Approach for Measuring Performance of Rural Co-Operatives in Southwest Paraná, Brazil
Authors: Telles Leomara Battisti , Macedo Luciano Medina , Bittencourt Juliana Vitória Messias
Published: August 2024

The maintenance of a satisfactory quality of life in rural areas is fundamental for sustainable development. One of the ways to improve quality of life indicators is through the gathering of rural workers in solidarity economy organizations as these enterprises aim to integrate development with economic, social, and environmental sustainability. Because solidarity economy organizations have a robust social purpose, their performance must be evaluated based on both social and financial indicators. The objective of this article is to propose a performance evaluation model for rural solidarity economy enterprises, aiming to support decision making in these enterprises based on multicriteria decision analysis (MCDA), particularly the ELECTRE-TRI methodology. In order to demonstrate the applicability of the developed model and to perform sensitivity analyses, the model was applied to a group of eight family agriculture co-operatives in the southwest state of Paraná, Brazil. All the participating co-ops were considered part of the solidarity economy, and they served 2500 rural producer families across at least 15 municipalities. The results showed the applicability and stability of the model, enabling us to identify the dimensions in which each co-op should concentrate their efforts to improve not only their performance but also the outcomes for the farmers that they serve. Based on these results, organizational and improvement activities can be developed and implemented. This analysis contributes to economic and social indicators by offering improvement strategies for the professionalization and strategic management of RSEEs, thus strengthening these enterprises and, consequently, family agriculture.
Green Biobased Polyethylene Terephthalate (bioPET) Composites Reinforced with Different Lengths of Basalt Fiber for Technical Applications
Authors: Kuciel Stanisław , Rusin-Żurek Karina
Published: August 2024

This paper presents the modification results and effects of reinforcing green polyethylene terephthalate matrix composites (bioPET ECOZEN® T120) with basalt fibers of two different lengths. Five types of composites with two filling levels of 7.5 and 15 wt% of each fiber were produced by injection molding. Basic mechanical and processing properties, microstructure photographs, and reinforcement effects were analyzed and low- and high-cycle fatigue tests were performed. A significant increase in strength and stiffness was observed (especially for short fibers) proportional to the amount of fibers; longer fibers would also increase the deformation capacity of the composite. Furthermore, longer fibers would reduce relaxation processes (creep) but would not increase the dissipation capacity and mechanical energy. Predictability of fatigue effects enables optimal environmentally friendly materials to be designed.
Spirulina/Arthrospira/Limnospira—Three Names of the Single Organism
Authors: Sinetova Maria A. , Kupriyanova Elena V. , Los Dmitry A.
Published: August 2024

Recent advances in research techniques have enabled rapid progress in the study of spirulina, an ancient edible cyanobacteria. Nowadays, spirulina species are classified into three genera: Spirulina, Arthrospira, and Limnospira. The latter now refers to industrially manufactured spirulina strains. Whole-genome sequencing revealed gene clusters involved in metabolite production, and the physiology of spirulina. Omics technologies demonstrated the absence of hazardous compounds in spirulina cells, confirming the safety of this biomass as a food product. Spirulina is a good source of different chemicals used in food manufacturing, food supplements, and pharmaceuticals. Spirulina’s enrichment with inherent biologically active substances makes it a potential supplier of natural products for dietary and pharmaceutical applications. Spirulina is also a prospective component of both terrestrial and space-based life support systems. Here, we review current breakthroughs in spirulina research and clarify fallacies that can be found in both professional literature and public media.
Genome Sequencing of Three Pathogenic Fungi Provides Insights into the Evolution and Pathogenic Mechanisms of the Cobweb Disease on Cultivated Mushrooms
Authors: Lan Yufei , Cong Qianqian , Yu Qingwei , Liu Lin , Cui Xiao , Li Xiumei , Wang Qiao , Yang Shuting , Yu Hao , Kong Yi
Published: August 2024

Fungal diseases not only reduce the yield of edible mushrooms but also pose potential threats to the preservation and quality of harvested mushrooms. Cobweb disease, caused primarily by fungal pathogens from the Hypocreaceae family, is one of the most significant diseases affecting edible mushrooms. Deciphering the genomes of these pathogens will help unravel the molecular basis of their evolution and identify genes responsible for pathogenicity. Here, we present high-quality genome sequences of three cobweb disease fungi: Hypomyces aurantius Cb-Fv, Cladobotryum mycophilum CB-Ab, and Cladobotryum protrusum CB-Mi, isolated from Flammulina velutipes, Agaricus bisporus, and Morchella importuna, respectively. The assembled genomes of H. aurantius, C. mycophilum, and C. protrusum are 33.19 Mb, 39.83 Mb, and 38.10 Mb, respectively. This is the first report of the genome of H. aurantius. Phylogenetic analysis revealed that cobweb disease pathogens are closely related and diverged approximately 17.51 million years ago. CAZymes (mainly chitinases, glucan endo-1,3-beta-glucosidases, and secondary metabolite synthases), proteases, KP3 killer proteins, lipases, and hydrophobins were found to be conserved and strongly associated with pathogenicity, virulence, and adaptation in the three cobweb pathogens. This study provides insights into the genome structure, genome organization, and pathogenicity of these three cobweb disease fungi, which will be a valuable resource for comparative genomics studies of cobweb pathogens and will help control this disease, thereby enhancing mushroom quality.
On the Optimal Combination of Elliptically Distributed Biomarkers to Improve Diagnostic Accuracy
Authors: Dong Shiqi , Li Zhaohai , Li Yuanzhang , Liu Aiyi
Published: August 2024

Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, as most biomarkers follow distributions that deviate from normality. While the likelihood ratio combination is recognized to be the optimal approach, it is complicated to calculate. To achieve a more accurate and effective combination of biomarkers, especially when these biomarkers deviate from normality, we propose using a receiver operating characteristic (ROC) curve methodology based on the optimal combination of elliptically distributed biomarkers. In this paper, we derive the ROC curve function for the elliptical likelihood ratio combination. Further, proceeding from the derived best combinations of biomarkers, we propose an efficient technique via nonparametric maximum likelihood estimate (NPMLE) to build empirical estimation. Simulation results show that the proposed elliptical combination method consistently provided better performance, demonstrating its robustness in handling various distribution types of biomarkers. We apply the proposed method to two real datasets: Autism/autism spectrum disorder (ASD) and neural tube defects (NTD). In both applications, the elliptical likelihood ratio combination improves the AUC value compared to the multivariate normal likelihood ratio combination and the best linear combination.
Arterial Thoracic Outlet Syndrome—A Case Study of a 23-Year-Old Female Patient Diagnosed Using a Thermal Imaging Camera
Authors: Żołnierczuk Michał , Skołozdrzy Tomasz , Donotek Maciej , Szlosser Zbigniew , Prowans Piotr , Król Małgorzata , Opałka Bianka , Orczyk Kamil , Surówka Anna
Published: August 2024

We present the case of a 23-year-old woman who reported weakness in the left upper limb, decreased warmth, numbness in the fingers, pain in the clavicular region, and a severe cold sensation in the limb. A thermal imaging camera examination was performed for diagnostic purposes, which guided further diagnostic and therapeutic management towards arterial thoracic outlet syndrome (aTOS). Following surgery and rehabilitation procedures, significant remission of symptoms was achieved and the patient’s condition improved. This is the first report on the diagnosis of aTOS using thermal imaging, paving the way for further clinical research into this effective, rapid, and radiation-free method of diagnostic imaging. Conclusion: Thermal imaging is one of the most effective, readily available, and patient-safe methods for diagnosing vascular disease associated with flow disruption.
Gaps in the Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector and Key Recommendations
Authors: Palaniappan Kavitha , Lin Elaine Yan Ting , Vogel Silke , Lim John C. W.
Published: August 2024

Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There are five key regulatory issues that need to be addressed: (i) data security and protection—measures to cover the “digital health footprints” left unknowingly by patients when they access AI in health services; (ii) data quality—availability of safe and secure data and more open database sources for AI, algorithms, and datasets to ensure equity and prevent demographic bias; (iii) validation of algorithms—mapping of the explainability and causability of the AI system; (iv) accountability—whether this lies with the healthcare professional, healthcare organisation, or the personified AI algorithm; (v) ethics and equitable access—whether fundamental rights of people are met in an ethical manner. Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight. AI services that enhance their functionality over time need to undergo repeated algorithmic impact assessment and must also demonstrate real-time performance. Harmonising regulatory frameworks at the international level would help to resolve cross-border issues of AI in healthcare services.
Navigating Immovable Assets: A Graph-Based Spatio-Temporal Data Model for Effective Information Management
Authors: Syafiq Muhammad , Azri Suhaibah , Ujang Uznir
Published: August 2024

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling.
Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach
Authors: Wahbeh Abdullah , Al-Ramahi Mohammad , El-Gayar Omar , Nasralah Tareq , Elnoshokaty Ahmed
Published: August 2024

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom’s benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom.
Remote Monitoring: How to Maximize Efficiency through Appropriate Organization in a Device Clinic
Authors: Maines Massimiliano , Tomasi Giancarlo , Poian Luisa , Simoncelli Marzia , Zeni Debora , Santini Monica , Del Greco Maurizio
Published: August 2024

Introduction: Remote device monitoring is indicated under class I A standard of care according to the latest HRS/EHRA/APHRS/LAHRS Expert Consensus Statement on Practical Management of the Remote Device Clinic. Despite this strong endorsement and the supporting data, the adoption of remote monitoring practices remains lower than expected. One cause of the underutilization of telemonitoring devices is work overload. Thus, a crucial point for improving the adoption of remote monitoring systems is ensuring their sustainability. Materials and Method: After analyzing the resources necessary to manage a device telemonitoring clinic, we initiated a process to reduce redundant transmissions: 1. eliminated scheduled loop recorder transmissions, retaining only alert transmissions; 2. reduced the frequency of the scheduled transmissions of pacemakers from four to one per year and the scheduled transmissions for defibrillators from four to two per year; and 3. optimized and customized the programming of device alerts with two primary interventions. Results: These strategies allowed us to significantly reduce the number of transmissions/patient/year from 7.3 to 4.7. The first change was made in January 2020, which eliminated scheduled transmissions for loop recorders, reduced transmissions per patient from 14 to 10.4 for loop recorders, and decreased global transmissions per patient from 7.6 to 6.5. The subsequent adjustment in January 2021, which reduced the scheduled transmissions of pacemakers and defibrillators, further lowered transmissions per patient from 6.5 to 5.2 for pacemakers and from 4.7 to 3.1 for defibrillators. Additionally, enhanced attention to device reprogramming starting in January 2022 resulted in a further reduction in transmissions per patient from 5 to 4.7. Conclusion: Carrying out some simple changes in the number of scheduled transmissions and optimizing the programming of the devices made it possible to reduce the number of transmissions and make the remote monitoring of the devices more sustainable
Clinical Trial: A Pragmatic Randomised Controlled Study to Assess the Effectiveness of Two Patient Management Strategies in Mild to Moderate Ulcerative Colitis—The OPTIMISE Study
Authors: Danese Silvio , Fiorino Gionata , Vicaut Eric , Paridaens Kristine , Ugur Asiya , Clark Brian , Vanasek Tomas , Stepek David , D’Amico Ferdinando , West Rachel , Gilissen Lennard P. L. , Wisniewska Jarosinka Maria , Drobinski Piotr , Fronik Grzegorz , Fic Mirosław , Walczak Michał , Kowalski Maciej , Korczowski Bartosz , Wiatr Michal , Peyrin-Biroulet Laurent
Published: August 2024

Background: Current management of mild-to-moderate ulcerative colitis (UC) involves monitoring clinical markers of disease activity, such as stool frequency (SF) and rectal bleeding (RB), and adjusting treatment accordingly. Our aim was to assess whether targeting treatment based on faecal calprotectin (FC) levels (treat-to-target; T2T) provides greater UC disease control versus a symptom-based approach. Methods: This was a pragmatic, randomised (1:1) controlled study of patients with mild-to-moderate UC (global Mayo score 2–6) treated with ≤2.4 g/day 5-aminosalicylic acid that compared the effectiveness of two management strategies with (interventional arm) and without (reference arm) FC home monitoring over 12 months of follow-up. Treatment was optimised in the interventional arm using FC values and clinical symptoms (PRO-2), while the reference arm used only PRO-2. Results: 193 patients completed the study. No significant difference was found for the primary endpoint (Mayo Endoscopic Subscore [MES] = 0 at 12 months). A numerical advantage for the interventional arm over the reference arm for the primary endpoint (37.0% vs. 33.4%, respectively) and for MES ≤ 1, RB = 0, and SF ≤ 1 at 12 months was found following imputation for missing data. The composite endpoint of MES = 0, RB = 0, and SF ≤ 1 at 12 months was achieved at a significantly higher rate in the interventional arm than the reference arm (effect size [ES]: 0.17, 95% CI 0.02–0.32; p < 0.05). A similar result was obtained for MES ≤ 1, RB = 0 and SF ≤ 1 (ES: 0.22; 95% CI 0.07–0.37; p < 0.05). Conclusions: T2T using FC monitoring was effective in patients with mild-to-moderate UC at 12 months. Further longer-term studies are required to confirm the results.
Age-Related Outcomes in Heart Failure with Mildly Reduced Ejection Fraction
Authors: Reinhardt Marielen , Schupp Tobias , Behnes Michael , Lau Felix , Schmitt Alexander , Abel Noah , Akin Muharrem , Rusnak Jonas , Akin Ibrahim , Weidner Kathrin
Published: August 2024

Objective: This study investigates age-related differences and outcomes in patients hospitalized with heart failure with a mildly reduced ejection fraction (HFmrEF). Background: The characterization of patients with HFmrEF and the prognostic value of age has rarely been investigated. Methods: Patients with HFmrEF were retrospectively included at one institution between 2016 and 2022. The distribution of HF aetiology and prognostic outcomes were investigated comparing patients with ≤40, >40 to ≤60, >60 to ≤80, and >80 years of age. The primary endpoint was long-term all-cause mortality. Kaplan–Meier and multivariable Cox proportional regression analyses were applied for statistics. Results: For the present study, 2184 patients with HFmrEF with a median age of 76 years were included. Non-ischemic cardiomyopathy was the most common HF aetiology in patients <40 years of age, whereas patients with 60–80 years of age (60.2%) and >80 years of age (58.2%) had the higher rates of ischemic cardiomyopathies. The risk of long-term all-cause mortality at 30 months was highest in patients with >80 years of age (HR = 2.167; 95% CI 1.928–2.436; p = 0.001), even after multivariable adjustment. Furthermore, patients with >80 years of age had the highest risk of HF-related rehospitalization (HR = 1.529; 95% CI 1.293–1.807; p = 0.001). Conclusions: Ischemic cardiomyopathy represents the most common cause of HF in elderly patients with HFmrEF, whereas younger patients were more likely to suffer from non-ischemic HF aetiologies. Increasing age was an independent predictor of long-term all-cause mortality in patients hospitalized with HFmrEF.
Street Design Elements That Influence Mental Well-Being: Evidence from Southern Chile
Authors: Zumelzu Antonio , Heskia Cristóbal , Herrmann-Lunecke Marie Geraldine , Vergara Gastón , Estrada Mariana , Jara Constanza
Published: August 2024

The aim of this article is to assess which elements of street design impact subjective well-being in the central area of a city in southern Chile, based on residents’ perceptions. Fifty-six semi-structured walking interviews were conducted to obtain records of pedestrians’ self-reported perceptions of their environment. To categorize the emotions reported in the interviews, the Circumplex Model of Affect was used to organize and classify the declared emotions. The results revealed that street design elements such as heritage buildings with well-maintained facades with intense colors in their coatings, spacious front gardens, wooden facades, low fences, wide sidewalks, soft or rubberized floors, and trees with colorful fruits and leaves promote a greater dominance of high-intensity positive emotions such as enjoyment, joy, happiness, liking, and pleasure. In contrast, neglected or abandoned building facades, blind fronts with graffiti or murals, high fences, tall buildings, treeless or vegetation-free sidewalks, untrimmed bushes, and narrow and poorly maintained sidewalks promote negative emotions of both high and low activation such as fear, anger, dislike, rage, unsafety, discomfort, and stress. The article concludes with the development of an emotional map of momentary experiences, identifying places of well-being and discomfort in public spaces. The value of this map is discussed as a tool to inform urban design in the promotion of healthier pedestrian environments in Latin American cities.
“She’ll Never Be a Man” A Corpus-Based Forensic Linguistic Analysis of Misgendering Discrimination on X
Authors: Sevilla Requena Lucia
Published: August 2024

Misgendering is a form of microaggression that reinforces gender binarism and involves the use of incorrect pronouns, names or gendered language when referring to a transgender and gender non-conforming (TGNC) individual. Despite growing awareness, it remains a persistent form of discrimination, and it is crucial not only to understand and address misgendering but also to analyse its impact within online discourse towards the TGNC community. The present study examines misgendering directed at the TGNC community present on platform X. To achieve this, a representative sample of 400 tweets targeting two TGNC individuals is compiled, applying an annotation scheme to manually classify the polarity of each tweet and instances of misgendering, and then comparing the manual annotations with those of an automatic sentiment detection system. The analysis focuses on the context and frequency of intentional misgendering, using word lists to examine the data. The results confirm that misgendering perpetuates discrimination, tends to co-occur with other forms of aggression, and is not effectively identified by automatic sentiment detection systems. Finally, the study highlights the need for improved automatic detection systems to better identify and address misgendering in online discourse and provides potentially useful tools for future research.
Comparison of the Skin Microbiota in the Periocular Region between Patients with Inflammatory Skin Diseases and Healthy Participants: A Preliminary Study
Authors: Ferček Iva , Ozretić Petar , Tambić-Andrašević Arjana , Trajanoski Slave , Ćesić Diana , Jelić Marko , Geber Goran , Žaja Orjena , Paić Josipa , Lugović-Mihić Liborija , Čivljak Rok
Published: August 2024

(1) Background: Periocular or periorbital dermatitis is a common term for all inflammatory skin diseases affecting the area of skin around the eyes. The clear etiopathogenesis of periocular dermatitis is still not fully understood. Advances in molecular techniques for studying microorganisms living in and on our bodies have highlighted the microbiome as a possible contributor to disease, as well as a promising diagnostic marker and target for innovative treatments. The aim of this study was to compare the composition and diversity of the skin microbiota in the periocular region between healthy individuals and individuals affected by the specific entity of periocular dermatitis. (2) Methods: A total of 35 patients with periocular dermatitis and 39 healthy controls were enrolled in the study. After a skin swab from the periocular region was taken from all participants, DNA extraction and 16S rRNA gene amplicon sequencing using Illumina NovaSeq technology were performed. (3) Results: Staphylococcus and Corynebacterium were the most abundant bacterial genera in the microbiota of healthy skin. Analysis of alpha diversity revealed a statistically significant change (p < 0.05) in biodiversity based on the Faith’s PD index between patients and healthy individuals. We did not observe changes in beta diversity. The linear discriminant analysis effect size (LEfSe) revealed that Rothia, Corynebacterium, Bartonella, and Paracoccus were enriched in patients, and Anaerococcus, Bacteroides, Porphyromonas, and Enhydrobacter were enriched in healthy controls. (4) Conclusions: According to the results obtained, we assume that the observed changes in the bacterial microbiota on the skin, particularly Gram-positive anaerobic cocci and skin commensals of the genus Corynebacterium, could be one of the factors in the pathogenesis of the investigated inflammatory diseases. The identified differences in the microbiota between healthy individuals and patients with periocular dermatitis should be further investigated.
Multiplex-PCR Detection of Clostridium tyrobutyricum, Clostridium butyricum, and Clostridium sporogenes in Raw Milk for Cheesemaking
Authors: Floris Irene , Martucci Francesca , Romano Angelo , Marello Giuseppina , Ligotti Carmela , Bianchi Daniela Manila
Published: August 2024

Late blowing defects in semi-hard and hard cheeses caused by spore-forming clostridia (e.g., Clostridium tyrobutyricum, Clostridium butyricum, Clostridium sporogenes) pose a major issue for the dairy industry. With this study, we applied a multiplex PCR for the rapid and simultaneous detection of clostridia in raw milk for cheese production. Spore detection in milk usually relies on culture-dependent methods, among which the most probable number (MPN) technique is sensitive but time-consuming and nonspecific. We tested two PCR-based protocols: the one entailed direct milk analysis with results obtained within 24 h; the other included an enrichment step and gave results within 72 h. The second protocol was found to be more sensitive; it detected concentrations as low as 100 cells/L for C. sporogenes and C. butyricum and 800 cells/L for C. tyrobutyricum. Both protocols were applied to field samples (211 samples underwent protocol no. 1; 117 samples underwent protocol no. 2) collected from four dairy processing plants in Piedmont. The prevalence of C. butyricum (protocol no. 1: 9.5%; protocol no. 2: 23%) was higher than either C. sporogenes (0%; 9.4%) or C. tyrobutyricum (0%; 6.8%). Protocol no. 2 detected multiple targets in eight samples, indicating that more than one microorganism was present. Our findings underscore the importance of implementing preventive measures and early detection strategies to mitigate the risk of cheese spoilage due to clostridial contamination.
Superficial Vein Thrombosis in an Asymptomatic Case of Cholangiocarcinoma with Recent History of COVID-19
Authors: Ciobica Mihai-Lucian , Sandulescu Bianca-Andreea , Sotcan Mihai Alexandru , Dumitrescu Lucian-Marius-Florin , Eftimie Lucian-George , Calin Cezar-Ionut , Iordache Mihaela , Cuzino Dragos , Carsote Mara , Nistor Claudiu , Radu Ana-Maria
Published: August 2024

The COVID-19 pandemic brought into prominence several emergent medical and surgical entities, but, also, it served as trigger and contributor for numerous apparently unrelated ailments such as arterial and venous thromboembolic complications. Additional risk factors for these thrombotic traits may be concurrent (known or unknown) malignancies, including at hepatic level. Among these, cholangiocarcinoma (CCA), a rare cancer of intra- and extra-hepatic biliary ducts, represents a very aggressive condition that typically associates local and distant advanced stages on first presentation requiring a prompt diagnosis and a stratified management. This neoplasia has been reported to present a large spectrum of paraneoplastic syndromes in terms of dermatologic, renal, systemic, neurologic, endocrine, and cardiovascular settings, that, overall, are exceptional in their epidemiologic impact when compared to other cancers. Our aim was to introduce a most unusual case of CCA-associated distant thrombosis in a male adult who initially was considered to experience COVID-19-related thrombotic features while having a history of obesity and bariatric surgery. This is a hybrid type of paper: this clinical vignette is accompanied by two distinct sample-focused analyses as a basis for discussion; they each had different methods depending on their current level of statistical evidence. We only included English-published articles in PubMed, as follows: Firstly, we conducted a search of reports similar to the present case, regarding distant vein thrombosis in CCA, from inception until the present time. We performed a literature search using the keywords “cholangiocarcinoma”, “thrombosis”, and “Trousseau’s syndrome” and identified 20 cases across 19 original papers; hence, the current level of evidence remains very low Secondly, we searched for the highest level of statistical evidence concerning the diagnosis of venous thrombosis/thromboembolism in patients who underwent COVID-19 infection (key search terms were “COVID-19”, alternatively, “coronavirus”, and “SARS-CoV-2”, and “thrombosis”, alternatively, “thromboembolism”) and included the most recent systematic reviews and meta-analyses that were published in 2024 (from 1 January 2024 until 8 July 2024). After excluding data on vaccination against coronavirus or long COVID-19 syndrome, we identified six such articles. To conclude, we presented a probably unique case of malignancy with an initial manifestation consisting of recurrent superficial vein thrombosis under anticoagulation therapy, with no gastrointestinal manifestations, in a patient with a notable history for multiple episodes of SARS-CoV-2 infection and a prior endocrine (gastric) surgery. To our knowledge, this is the first identification of a CCA under these specific circumstances.
Moving beyond Flow Factors: Modeling Full Film Lubrication with Representative Surface Topography Using Heterogeneous Multiscale Methods
Authors: Montgomery Joshua , Hammersley Camille , Wilson Mark C. T. , Bryant Michael , de Boer Gregory
Published: August 2024

Lubrication modeling has long been dominated by the well-established Patir and Cheng flow factors method. The flow factors approach allows for accurate estimates of macroscale parameters (such as friction) in a reasonable amount of time. These methods are stochastic representations of microscale interactions and are not able to predict local scale (pressure, film thickness) phenomena with a suitable degree of accuracy. This contrasts with a deterministic approach, where a numerical grid must be applied that fully defines the microscale surface topography across the contact. The mesh resolution required leads to prohibitively long execution times and lacks scalability to engineering systems, but provides accurate predictions of local scale phenomena. In this paper, heterogeneous multiscale methods (HMM) are expanded to model varying and are, therefore, more representative of surface topography within lubricated contacts. This representative topography is derived from measured data, thereby allowing the accuracy of deterministic methods to be achieved with the speed of a flow factor method. This framework is then applied to compare key performance characteristics (pressure, film thickness, etc.) when idealized, Gaussian, and measured surface topography are modeled. The variations in microscale geometry are defined by measurements from across two tilted-pad bearings, demonstrating the ability of the expanded HMM framework to model representative surface topography. A comparison with a deterministic method is included as validation, and outputs of the HMM are discussed in the context of the lubrication across multiple length scales.
GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset
Authors: Rai Hari Mohan , Yoo Joon , Dashkevych Serhii
Published: August 2024

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike.
Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data
Authors: Sarkar Reetika , Sun Jianping
Published: August 2024

Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as “science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems”. The purpose of safety signal detection is to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. While post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals, there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS) demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.
Study of Cayley Digraphs over Polygroups
Authors: Sanjabi Ali , Jafarpour Morteza , Hoskova-Mayerova Sarka , Aghabozorgi Hossien , Vagaska Alena
Published: August 2024

In this paper we introduce Cayley digraphs associated to finitely generated polygroups, where the vertices correspond to finite products of the generators of polygroups and the edges to multiplication by vertices and generators. We investigate some properties of the Cayley digraphs, emphasizing connectivity and existence of cycles for each vertex of the Cayley digraphs. Particularly, we identify Cayley digraphs on polygroups derived from conjugate classes of dihedral groups. Moreover, we examine some fundamental illustrative examples of Cayley digraphs through the class of gmg-polygroups.
Correction: Stanojković-Sebić et al. Response of Arugula to Integrated Use of Biological, Inorganic, and Organic Fertilization. Microorganisms 2024, 12, 1334
Authors: Stanojković-Sebić Aleksandra , Miladinović Vladimir , Stajković-Srbinović Olivera , Pivić Radmila
Published: August 2024

In the original publication [...]