bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Psychological Distress Among Individuals with Specific Learning Disabilities: The Impact of Age, Gender, and Disability Type
Authors: Polak Nimrod , Grossman Ephraim S.
Published: December 2024

Background: Extensive research indicates that individuals with learning disabilities are more prone to experiencing heightened levels of emotional difficulties and distress. Objective: This study aimed to investigate the relationships between specific learning disabilities (SLDs) and psychological distress (PD), particularly focusing on the predictive power of difficulties in reading, writing, and arithmetic on PD, while also considering the effects of gender and age. Methods: The sample consisted of 133 participants (73 male) aged 23–44 years (M = 34.41, SD = 9.69), with 56 officially diagnosed with an SLD. The participants completed an online survey comprising a demographics questionnaire, a self-report SLD questionnaire, and psychological distress assessment using the K6 scale. Results: The participants with SLDs reported higher PD levels than the general population, with significant correlations between difficulties in reading, writing, and arithmetic and PD. Reading and writing difficulties were stronger predictors of PD than arithmetic difficulties. Additionally, the women and younger adults reported more PD, with the SLD severity significantly impacting PD beyond these variables. Conclusion: These findings highlight the strong link between SLDs and PD, particularly emphasizing the role of reading and writing difficulties in contributing to psychological distress. Moreover, the subjective severity of the SLD predicted PD beyond gender and age within the SLD group.
Seating and Mobility Concerns of Adults with Duchenne Muscular Dystrophy
Authors: Rosenberg Lori , Gefen Naomi
Published: December 2024

Background: Appropriate wheelchair and seating systems are key to allow for the participation of people with Duchenne Muscular Dystrophy. There is little research that focuses on their long-term seating issues and what topics they want studied. Methods: This mixed-method study with adults with Duchenne used an Internet-based survey about wheelchair mobility and discomfort, followed by in-depth interviews. Unanticipated remarks in the interviews led to a second survey regarding the effects of cold weather on wheelchair driving. Descriptive and qualitative analyses were performed. Results: Thirty-seven individuals completed the original survey: 78% used a powered wheelchair, 58% were uncomfortable in their wheelchair, and 94% felt the need to change their seating position. In-depth interviews (N = 9) revealed three themes: seating and pain management, caregiver cooperation, and temperature sensitivity. Almost all (8/9) interviewees explained that cold was a barrier to their participation. In the second survey (N = 13), 11 reported that cold affected their driving, with 10 remarking that it prevented them from participating in daily life activities and 11 stating that the effects of cold on wheelchair driving should be studied. Conclusions: It is essential to ask end-users to identify key issues to ensure the relevance of research to people with disabilities.
Olfactory Identification as a Biomarker for Cognitive Impairment: Insights from Healthy Aging, Subjective Cognitive Decline, and Mild Cognitive Impairment
Authors: Bouhaben Jaime , Delgado-Lima Alice Helena , Delgado-Losada María Luisa
Published: December 2024

Introduction: This study aims to investigate the relationship between olfactory identification (OI) and cognitive impairment by examining OI abilities across various stages of cognitive deterioration. Methods: A total of 264 participants were divided into three groups based on cognitive status: cognitively healthy, subjective cognitive, and mild cognitive impairment. All participants were assessed using the Sniffin’ Sticks Olfactory Identification test and a comprehensive neuropsychological test battery. Results: Our results highlight the main effects of age and cognitive status on OI scores. Regarding cognitive abilities, OI is associated with measures of short-term memory, long-term, working memory, and selective attention. Finally, logistic regression models showed that OI is a significant predictor for discriminating SCD from CH, MCI from CH, and MCI from SCD. Discussion: These findings suggest the addition of olfactory identification measures in neuropsychological assessments could improve the early detection of individuals at risk for cognitive impairment.
A New Method to Assess the Reliability and Security of Urban Electrical Substations
Authors: Silva-Ortega Jorge , Ortíz Jesús , Candelo-Becerra John E.
Published: December 2024

This paper presents the application of quantitative and qualitative methods to assess reliability and security in urban electrical substations. The method is a visual technique based on a conceptual analysis of the different substation configurations. We also performed a sensitivity analysis considering the effects of connecting and disconnecting various elements of a power system. The procedure considers evaluating the loadability levels of transformers, buses, and lines, as well as the current state of the individual elements and the number of connected elements. A new index was proposed for urban electrical substations, evaluating the non-attended demand risk. The technique was tested in a power system case study with a meshed subtransmission network and distribution circuits to supply power to the loads. The results showed that the proposed method is a useful qualitative method to obtain a quantitative description of the system during operation in critical cases and the non-attended demand risk. In addition, 30% of the electrical substations showed low reliability indicators for critical cases such as failures in transformers that connect different internal configurations. These findings could be of interest for utilities and operators, as this document provides a simplified and graphic method that can integrate components such as configurations, non-attended demand risk, and loadability indicators as key parameters to identify critical points that affect the reliability and security of power systems. The case study showed that the electrical substations with the highest non-attention demand risk, around 50%, were those with single- and double-bar configurations in their respective switchyards. On the other hand, the substations with the lowest risk of unmet demand, equal to or less than 20%, were electrical substations with a double-bar + bypass switch configuration, a double-bar and ring configuration in the 110 kV switchyard, and a single-bar configuration in the 13.8 kV switchyard. This study showed that those substations that had couplings had a higher probability of withstanding contingencies.
Adaptive Traffic Signal Control Based on Graph Neural Networks and Dynamic Entropy-Constrained Soft Actor–Critic
Authors: Jia Xianguang , Guo Mengyi , Lyu Yingying , Qu Jie , Li Dong , Guo Fengxiang
Published: December 2024

Traffic congestion remains a significant challenge in urban management, with traditional fixed-cycle traffic signal systems struggling to adapt to dynamic traffic conditions. This paper proposes an adaptive traffic signal control method based on a Graph Neural Network (GNN) and a dynamic entropy-constrained Soft Actor–Critic (DESAC) algorithm. The approach first extracts both global and local features of the traffic network using GNN and then utilizes the DESAC algorithm to optimize traffic signal control at both single and multi-intersection levels. Finally, a simulation environment is established on the CityFlow platform to evaluate the proposed method’s performance through experiments involving single and twelve intersection scenarios. Simulation results on the CityFlow platform demonstrate that G-DESAC significantly improves traffic flow, reduces delays and queue lengths, and enhances intersection capacity compared to other algorithms. In single intersection scenarios, G-DESAC achieves a higher reward, reduced total delay time, minimized queue lengths, and improved throughput. In multi-intersection scenarios, G-DESAC maintains high rewards with stable and efficient optimization, outperforming DQN, SAC, Max-Pressure, and DDPG. This research highlights the potential of deep reinforcement learning (DRL) in urban traffic management and positions G-DESAC as a robust solution for practical traffic signal control applications, offering substantial improvements in traffic efficiency and congestion mitigation.
PIMCoSim: Hardware/Software Co-Simulator for Exploring Processing-in-Memory Architectures
Authors: Shin Jinyoung , An Seongmo , Lee Sangho , Lee Seung Eun
Published: December 2024

As the scope of artificial intelligence (AI) expands and the structure becomes more complex, the amount of data for inference and training has increased. In traditional computer architectures, the memory bandwidth limitations have intensified bottlenecks in AI systems, and processing-in-memory (PIM) architectures have been proposed to overcome this issue. PIM is an architecture that performs computations within memory, thereby reducing data movement between the CPU and memory. However, since PIM is difficult to optimize as a general-purpose architecture, it is essential to adopt an architecture suitable for the target application. While various simulators and emulators have been introduced for the design space exploration (DSE) of different PIM architectures, simulators are limited in debugging hardware operations, and emulators face challenges in flexibly modifying the system configuration, as emulators implement the entire architecture in hardware. Therefore, this paper introduces PIMCoSim, a comprehensive hardware–software co-simulator for the DSE of DRAM-PIM systems. This co-simulator partially emulates simplified hardware-implemented processing elements (PEs) and integrates software models for memory operations, facilitating the DSE of PIM systems. To validate PIMCoSim, we analyzed results for different computational workloads by varying PIM structures and operational policies, demonstrating the efficiency of DRAM-PIM systems. The co-simulation approach in PIMCoSim aims to contribute to analyzing DRAM-PIM configurations and adopting optimized structures.
Rapid Vehicle Trajectory Prediction Based on Multi-Attention Mechanism for Fusing Multimodal Information
Authors: Ge Likun , Wang Shuting , Wang Guangqi
Published: December 2024

Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder–decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent–agent interaction information extraction module based on a sparse graph attention mechanism, which enables the efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates the model inference speed by generating the decoding queries in parallel. Comparative experiments with the existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics for the Minimum Average Displacement Error (minADE), the Minimum Final Displacement Error (minFDE), and the Miss Rate (MR) by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by the other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies.
Channel Attention-Based Conditional Diffusion Model Applied to Fault Diagnosis Under Imbalanced Data
Authors: Zhang Ping , Lin Yubo , Cui Haowen , Gu Junhua
Published: December 2024

Issues such as data scarcity and data imbalance have long posed significant difficulties in the field of intelligent fault diagnosis. They lead to reduced diagnostic accuracy and endanger the safety and reliability of industrial equipment. To address these challenges, this study introduces a novel channel attention-based conditional diffusion model (CAC-DM) that recalibrates features through a squeeze-and-excitation process. This enhancement boosts the model’s ability to focus on critical features while suppressing irrelevant information, thereby improving the UNet network’s discrimination capability in handling small-sample faults that are highly similar in nature. Experimental validation demonstrates that CAC-DM performs exceptionally well in scenarios with high class similarity, effectively distinguishing among categories with similar distributions in limited data and generating high-quality samples. Compared to existing generative methods, the CAC-DM exhibits significant advantages in producing distinguishable fault samples, particularly in cases of sample imbalance. This approach offers an effective new solution for fault diagnosis.
Graph Neural Network (GNN) for Joint Detection–Decoder MAP–LDPC in Bit-Patterned Media Recording Systems
Authors: Nguyen Thien An , Lee Jaejin
Published: December 2024

With its high area density, bit-patterned media recording (BPMR) is emerging as a leading technology for next-generation storage systems. However, as area density increases, magnetic islands are positioned closer together, causing significant two-dimensional (2D) interference. To address this, detection methods are used to interpret the received signal and mitigate 2D interference. Recently, the maximum a posteriori (MAP) detection algorithm has shown promise in improving BPMR performance, though it requires extrinsic information to effectively reduce interference. In this paper, to solve the 2D interference and improve the performance of BPMR systems, a model using low-density parity-check (LDPC) coding was introduced to supply the MAP detector with the needed extrinsic information, enhancing detection in a joint decoding model we call MAP–LDPC. Additionally, leveraging similarities between LDPC codes and graph neural networks (GNNs), we replace the traditional sum–product algorithm in LDPC decoding with a GNN, creating a new model, MAP–GNN. The simulation results demonstrate that MAP–GNN achieves superior performance, particularly when using the deep learning-based GNN approach over conventional techniques.
Evaluation of Affordable Agricultural Drones for Small and Medium Farms
Authors: Puška Adis , Nedeljković Miroslav , Štilić Anđelka , Božanić Darko
Published: December 2024

Smart technologies are increasingly used in agriculture, with drones becoming one of the key tools in agricultural production. This study aims to evaluate affordable drones for agricultural use in the Posavina region, located in northern Bosnia and Herzegovina. To determine which drones deliver the best results for small and medium-sized farms, ten criteria were used to evaluate eight drones. Through expert evaluation, relevant criteria were first established and then used to assess the drones. The selected drones are designed for crop monitoring and are priced under EUR 2000. Using the fuzzy A-SWARA (Adapted Step-wise Weight Assessment Ratio Analysis) method, it was determined that the most important criteria for drone selection are control precision, flight autonomy, and ease of use, all of which are technical attributes. The fuzzy MARCOS method revealed that the best-performing drones are also the most affordable. The drones D5, D4, and D8 demonstrated the best results. These findings were confirmed through comparative analysis and sensitivity analysis. Their features are not significantly different from those of more expensive models and can, therefore, be effectively used for smart agriculture. This study demonstrates that drones can be a valuable tool for small farms, helping to enhance agricultural practices and productivity.
Evaporation Dynamics and Dosimetry Methods in Numerically Assessing MDI Performance in Pulmonary Drug Delivery
Authors: Talaat Mohamed , Si Xiuhua , Xi Jinxiang
Published: December 2024

Metered dose inhalers (MDIs) play a crucial role in managing respiratory diseases, but their effectiveness depends on whether the intended dose is delivered to the target, which can be influenced by various factors. Accurate assessment of MDI performance is crucial for optimizing MDI delivery and ensuring drug efficacy. This study numerically examined the role of evaporation dynamics and dosimetry methods in assessing the efficiency of MDI delivery to different regions in a mouth–lung model extending to the eleventh generation (G11) of lung bifurcations. The experimentally determined spray exit speed, applied dose, and droplet size distribution were implemented as the initial/boundary conditions. Large eddy simulations (LES) were used to resolve the transient inhalation flows, and a chemical species model was applied to simulate vapor and temperature variations in the airflow. A multi-component model was used to consider the heat and mass transfer between the droplets and the airflow. The model was validated against literature data and applied to evaluate the impact of evaporation on pulmonary drug delivery using MDI, in comparison to inert particles. Three methods were used to quantify deposition, which were based on the droplet count, the droplet mass, and the drug carried by the droplets. The results demonstrate that evaporation notably alters the spray droplet size distribution and subsequent deposition patterns. Compared to inert particles, evaporation led to significantly more droplets ranging from 1–5 µm entering the pulmonary region. For a given region, large discrepancies were observed in the deposition fraction (DF) using different dosimetry methods. In the lower lung, the count-based DF (33.9%) and mass-based DF (2.4%) differed by more than one order of magnitude, while the drug-based DF fell between them (20.5%). This large difference highlights the need to include evaporation in predictive dosimetry, as well as to use the appropriate method to quantify the delivery efficiency of evaporating droplets.
CO2-Based Encapsulation of Rutin-Rich Extracts from Black Elderberry Waste Using Advanced PGSS Process
Authors: Mutavski Zorana , Vidović Senka , Ambrus Rita , Šavikin Katarina , Baixinho João , Fernández Naiara , Nastić Nataša
Published: December 2024

For the first time, rutin-rich extracts from black elderberry waste (BEW) were encapsulated using the particles from gas-saturated solutions (PGSS) method to improve the preservation of rutin. The extracts used in this study were obtained using five different extraction techniques under optimal conditions, as follows: conventional solid–liquid extraction (SLE) and four non-conventional techniques—ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), enhanced solvent extraction (ESE), and supercritical CO2 pretreatment—followed by ESE (SFE-CO2 + ESE). The PGSS process of the obtained extracts was performed using two amphiphilic carriers, glycerol monostearate (GlyMS) and gelucire (Gel), in a mass ratio of 1:6, in favor of the carrier. The efficiency of the PGSS process was evaluated based on the encapsulation yield (EnY), encapsulation efficiency (EE), and physical properties of the encapsulated extracts. The results showed that the SLE extract encapsulated with GlyMS had the highest EnY (92.47%). The Gel only in combination with the ESE extract exceeded the 50% efficacy threshold, with an EnY of 55.18%. The encapsulated SLE extract with Gel showed excellent flow properties and the highest EE (98.91%). These results emphasize the advantages of the PGSS process, including its efficiency and adaptability to produce encapsulated rutin-enriched BEW extracts for pharmaceutical, nutraceutical, and cosmetic applications.
Effects of Different Plasticizers on the Structure, Physical Properties and Film Forming Performance of Curdlan Edible Films
Authors: Chen Ying , Wang Jing , Xu Liang , Nie Yuping , Ye Yunyue , Qian Jianya , Liu Fengsong , Zhang Liang
Published: December 2024

This study successfully developed edible films with excellent mechanical strength and notable water resistance, utilizing curdlan (CL) as the primary matrix and incorporating various plasticizers, including glycerol (GLY), ethylene glycol (EG), propylene glycol (PRO), xylitol (XY), sorbitol (SOR), and polyethylene glycol (PEG). A comprehensive suite of analytical techniques, including Fourier transform infrared spectroscopy (FTIR), wide-angle X-ray diffraction (XRD), scanning electron microscopy (SEM), dynamic mechanical analysis (DMA), and tensile testing, were employed to evaluate the films’ structural and mechanical properties. After incorporating PEG, the water sensitivity increased slightly, with a contact angle (CA) of 97.6°, and a water solubility (WS) of 18.75%. The inclusion of plasticizers altered the crystalline structure of the CL matrix, smoothing and flattening the film surface while reducing hydrogen-bonding interactions. These structural changes led to a more uniform distribution of amorphous chain segments and a decrease in glass transition temperatures. Among the tested plasticizers, GLY exhibited the highest compatibility with CL, resulting in the smoothest surface morphology and delivering the most effective plasticizing effect. The CL-GLY film showed a dramatic improvement in flexibility, with an elongation at break that was 5.2 times higher than that of the unplasticized film (increasing from 5.39% to 33.14%), indicating significant enhancement in extensibility. Overall, these findings highlight the potential of CL-GLY films as sustainable and effective materials for food packaging applications.
Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression
Authors: Ding Qile , Wang Yiren , Zheng Yu , Wang Fengyang , Zhou Shudong , Pan Donghui , Xiong Yuchun , Zhang Yi
Published: December 2024

Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in a study area in southeast China, we first use random forest regression to predict and optimize variogram parameters. We then use the fractional kriging method to interpolate the data and analyze the variability. We also compare the proposed model with traditional methods, including linear regression, K-nearest neighbors, ordinary kriging, and fractional kriging, using cross-validation metrics. The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. The MSE of the proposed model is 25% lower than that of ordinary kriging and 10% lower than that of fractional kriging. In addition, the execution time of the proposed model is slightly higher than other models. The findings suggest that the proposed model effectively captures complex subsurface spatial relationships, offering a reliable and precise solution for performing spatial interpolation tasks.
Fractional-Order Controller for the Course Tracking of Underactuated Surface Vessels Based on Dynamic Neural Fuzzy Model
Authors: Li Guangyu , Li Yanxin , Li Xiang , Liu Mutong , Zhang Xuesong , Jin Hua
Published: December 2024

Aiming at the uncertainty problem caused by the time-varying modeling parameters associated with ship speed in the course tracking control of underactuated surface vessels (USVs), this paper proposes a control algorithm based on the dynamic neural fuzzy model (DNFM). The DNFM simultaneously adjusts the structure and parameters during learning and fully approximates the inverse dynamics of ships. Online identification and modeling lays the model foundation for ship motion control. The trained DNFM, serving as an inverse controller, is connected in parallel with the fractional-order PIλDμ controller to be used for the tracking control of the ship’s course. Moreover, the weights of the model can be further adjusted during the course tracking. Taking the actual ship data of a 5446 TEU large container ship, simulation experiments are conducted, respectively, for course tracking, course tracking under wind and wave interferences, and comparison with five different controllers. This proposed controller can overcome the influence of the uncertainty of modeling parameters, tracking the desired course quickly and effectively.
High-Temperature Fermentation and Its Downstream Processes for Compact-Scale Bioethanol Production
Authors: Pattanakittivorakul Sornsiri , Kumakiri Izumi , Nutaratat Pumin , Hara Marino , Yokota Morihisa , Murata Masayuki , Kosaka Tomoyuki , Thanonkeo Pornthap , Limtong Savitree , Yamada Mamoru
Published: December 2024

High-temperature fermentation (HTF) of ethanol can reduce costs of cooling, sterilization, and related equipment compared to the costs of general ethanol fermentation. To realize HTF, however, there are various issues to be considered, such as the fermentation temperature upper limit for ethanol-producing thermotolerant yeast, the size of a fermenter that does not require cooling, and the effective temperature for suppressing microbial contamination. This study focused on these issues and also on downstream processes that exploit the advantages of HTF at temperatures exceeding 40 °C. The permissible size of a fermenter without cooling was estimated by simulating heat generation and heat dissipation. Fermentation productivity at high temperatures when using the thermotolerant yeast Kluyveromyces marxianus and the inhibitory effect of high temperatures on the growth of contaminant microorganisms were examined. After fermentation, the recovery and concentration of ethanol were performed by reduced-pressure distillation (RPD) and membrane separation. These experiments demonstrate that efficient HTF can reduce the amount of saccharifying enzymes in simultaneous saccharification and fermentation and can shorten the transition time from the saccharification step to the fermentation step in separate saccharification and fermentation, that RPD at fermentation temperatures enables a smooth connection to the HTF step and can be performed with a relatively weak vacuum, and that membrane separation can reduce the running cost compared to the cost of general distillation on a compact scale.
Land Use Around Airports: Policies and Methods for Third-Party Risk Assessment—A Review
Authors: Di Mascio Paola , Dinu Raducu , Loprencipe Giuseppe , Moretti Laura
Published: December 2024

The development and land use surrounding airports are a concern and interest for airport operators, public communities, business communities, and local authorities. Airport development and operations are governed by both national and international regulations that often extend beyond airport property boundaries. Typical international airports’ regulations, recommendations, and guidance documents (e.g., Noise Exposure and Obstacle Limitation Surfaces) and their national counterparts focus on airport land-use planning. Individual third-party risk assessment of airport operations serves as a complementary tool to these regulations, providing means to assess and manage land-use compatibility and control activities near airport perimeters. Developing robust risk assessment models is essential for defining and validating public safety areas and Runway Protection Zones to ensure land-use compatibility and public safety. Although several quantitative risk assessment models exist, significant differences remain in their methodologies and applications. Over the past 20 to 35 years, most models have evolved based on historical data from aircraft accidents. This article provides a comprehensive review of risk analysis methods for areas surrounding airports and presents a quantitative comparison of two specific approaches, the ENAC/Sapienza and ACRP methods, along with their associated calculation software.
Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems
Authors: Sathyamangalam Imran Mohammed Irshadh Ismaaeel , Awasthi Satyesh Shanker , Khayyat Michael , Arrigoni Stefano , Braghin Francesco
Published: December 2024

In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving stack is proposed. The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). The framework is designed to assess the performance of the algorithm under localization uncertainties, while performing obstacle vehicle-overtaking, vehicle-following, and safe-stopping maneuvers.
Implications of Tropical Cyclone Rainfall Spatial–Temporal Variability on Flood Hazard Assessments in the Caribbean Lesser Antilles
Authors: Nabukulu Catherine , Jetten Victor. G. , Ettema Janneke
Published: December 2024

Tropical cyclones (TCs) significantly impact the Caribbean Lesser Antilles, often causing severe wind and water damage. Traditional flood hazard assessments simplify TC rainfall as single-peak, short-duration events tied to specific return periods, overlooking the spatial–temporal variability in rainfall that TCs introduce. To address this limitation, a new user-friendly tool incorporates spatial–temporal rainfall variability into TC-related flood hazard assessments. The tool utilizes satellite precipitation data to break down TC-associated rainfall into distinct pathways/scenarios, mapping them to ground locations and linking them to specific sections of the storm’s rainfall footprint. This approach demonstrates how different areas can be affected differently by the same TC. In this study, we apply the tool to evaluate rainfall patterns and flood hazards in St. George’s, Grenada, during Hurricane Beryl in 2024. The scenario representing the 75th quantile in Spatial Region 2 (S2-Q0.75) closely matched the actual rainfall observed in the study area. By generating multiple hazard maps based on various rainfall scenarios, the tool provides decision-makers with valuable insights into the multifaced flood hazard risks posed by a single TC. Ultimately, island communities can enhance their early warning and mitigation strategies for TC impacts.
Measuring Stereotypes in Interprofessional Education: A Pilot High-Fidelity Simulation Study Among Postgraduate Nursing and Physician Students in a Spanish University
Authors: Cánovas-Pallarés Juan Manuel , Nieto-Caballero Sergio , Baeza-Mirete Manuel , Párraga-Ramírez Manuel José , Rojo-Rojo Andrés
Published: December 2024

Background/Objectives: Nursing professionals are often subject to social stereotypes that can hinder effective teamwork with other healthcare professionals and limit their professional growth. Interprofessional education (IPE) enhances teamwork skills and promotes a better understanding of other professional groups. This study aimed to identify the presence of stereotypes associated with nursing among postgraduate nursing and student physicians specializing in emergency medicine and to assess the applicability of simulation as an IPE strategy. Methods: A pilot study using high-fidelity simulation activity focusing on interdisciplinary collaboration was designed for students in the master’s programs in emergency nursing and emergency medicine at the Catholic University of Murcia. The activity took place in May 2024 and involved 52 participants (24 postgraduate nursing students and 28 postgraduate student physicians). A mixed-method descriptive study was conducted using a 16-item self-administered questionnaire. Data were analyzed using the Shapiro–Wilk test for normality, Fisher’s F test, and the Mann–Whitney U test to evaluate the relationship between variables (p < 0.05). Results: A total of 22 questionnaires were collected (16 from nurses postgraduate student and 6 from postgraduate physicians). Positive attitudes toward nursing stereotypes were found in 9 of the 13 items. No statistically significant differences were observed between the groups regarding most stereotypes, except for one. Negative stereotypes about nursing leadership, professional autonomy, and patient relations were more prominent among nursing students. Conclusions: Stereotypical perceptions exist among both postgraduate nursing and postgraduate student physicians, particularly in nursing leadership and autonomy. Most participants expressed satisfaction with the simulation-based IPE activity, indicating its value in improving the understanding of other professionals’ roles. IPE should be incorporated into health sciences education.
The Evolving Role of the Oncology Nurse in the United States of America—A Survey Exploring Their Perspective
Authors: Grenon Nina N. , Waldrop Karen S. , Pinheiro Natasha , Prosdocimo Brittni
Published: December 2024

Background: The role of the oncology nurse has evolved since the COVID-19 pandemic to continuously meet patient needs, resulting in an increased virtual presence. However, there is little information about whether these roles have continued since the pandemic and how it is affecting nurses. Methods: The aim of this study, conducted via an electronic survey questionnaire, was to examine the perspective of oncology nurses in the United States of America regarding how their role has changed, the adaptation to telehealth, challenges, and needs. Results: Over 5 months, 197 respondents from 17 different states completed the survey. They were all registered nurses working in oncology with different roles, such as being nurse practitioners (23%), nurse navigators (20%), infusion nurses (23%), and working in outpatient settings (93%). The findings of the survey highlight the multifaceted responsibilities of nurses in providing care across the cancer care continuum, emphasizing patient-centered care, communication, education, and empowerment, in addition to expected duties such as the administration of anticancer therapy, monitoring of side effects, and symptom management. A total of 82.6% of United States of America oncology nurses feel their workload has increased. The role of nurses in oncology is continuously evolving and the impact of the COVID-19 pandemic, in certain areas such as telehealth, is here to stay. Conclusions: The results of the study allow a better understanding of the emergent roles of oncology nursing. The importance of self-care initiatives and education are emphasized as critical to support nurses in their complex, busy, and emotionally charged work environment and to help retain existing nurses and attract new individuals into the profession. The study ultimately seeks to inform policy and drive professional development in oncology nursing in the United States of America.
Moving from Principles to Practice: A Scoping Review of Value-Based Healthcare (VBHC) Implementation Strategies
Authors: de Mattia Egidio , Angioletti Carmen , D’Agostino Melissa , Paoletti Filippo , de Belvis Antonio Giulio
Published: December 2024

Background/Objectives: The principles of value-based healthcare (VBHC) have received widespread endorsement, leading healthcare organizations worldwide to shift their strategies towards them. However, despite growing recognition and acceptance, the actual implementation of value-based approaches varies widely. This research aims to identify studies that address the implementation of VBHC at different levels (healthcare policymakers, hospital administrators, and healthcare providers), focusing on each level’s relative strategies. Methods: To this end, a scoping review was conducted in accordance with the PRISMA extension for the scoping reviews checklist. The electronic databases of Web of Science, PubMed, MEDLINE, and Scopus were searched to identify relevant publications in English from January 2006 to 31 July 2023. Results: We identified 30 eligible studies. Findings are organized into four main macro strategic levels, each comprising specific dimensions and operational approaches. Fourteen articles analyzed the role of government commitment in VBHC implementation, while six articles focused on regional integrated care systems. The role of hospitals was described in sixteen records. Conclusions: Our study suggests that a comprehensive approach is necessary for the successful implementation of VBHC. Hospitals emerge as pivotal in this shift, requiring organizational and attitudinal changes among healthcare professionals. However, a complete transition towards VBHC that ensures seamless patient management throughout the entire care delivery value chain necessitates government involvement in terms of state legislation, reimbursement methods, and hospital networking.
Association of HOMA-IR Versus TyG Index with Diabetes in Individuals Without Underweight or Obesity
Authors: Kim Bokun , Kim Gwon-Min , Huh Up , Lee Juhyun , Kim Eunji
Published: December 2024

Background: There are higher numbers of Asian individuals with metabolically obese, normal weight (MONW) who are susceptible to diabetes than other ethnic groups. The homeostatic model assessment for insulin resistance (HOMA-IR) has been used for years to evaluate IR; however, the triglyceride-glucose (TyG) index has been highlighted as a reliable and cost-effective insulin resistance indicator. Thus, this study explored the association of the HOMA-IR versus the TyG index with diabetes in individuals without underweight or obesity. Methods: This cross-sectional study included 10,471 Korean individuals whose body mass index was ≥18.5 and <25.0 kg/m2. Depending on metabolic syndrome criteria, subjects with no conditions, one or two conditions, and three or more conditions were assigned to the metabolically healthy and normal weight (MHNW), pre-MONW, and MONW groups, respectively. Diabetes was diagnosed based on HbA1C and medication information. Results: The HOMA-IR and TyG index discriminated between each group, showing an increasing trend from the MHNW group to the MONW group. However, this trend was more robust for the TyG index. The cut-off values in the TyG index and HOMA-IR were >8.9 and >1.6 in males and >8.7 and >1.6 in females, respectively. The TyG index’s area under the curve was higher than that of the HOMA-IR in both sexes. MONWs distinguished by the cut-off values of the TyG index and HOMA-IR were 2.370 and 1.726 for males and 2.249 and 1.710 for females times more likely to develop diabetes. Conclusions: The TyG index showed a stronger association with diabetes in Korean individuals without underweight or obesity than the HOMA-IR.
Duralumin Alloys in World War II Heritage Aircraft: Correlations Between Manufacturing Parameters and Alloys’ Properties
Authors: Duclos Agathe , Bernardi Elena , Robbiola Luc , Deshayes Christophe , de Souza Machado Luiza , Martini Carla , Chiavari Cristina , Balbo Andrea , Escobar Cesar , Guilminot Elodie , Malard Benoît , Brunet Magali
Published: December 2024

Duralumin-type alloys have been employed for structural and non-structural parts of aircraft since the early 1920s up to now. In the European project “PROtection and Conservation of Heritage AirCRAFT” (PROCRAFT), up to 34 aircraft wrecks from World War II from different nations (United States of America, United Kingdom, France, Germany, Italy) were identified. On most of them, the collection of numerous fragments was possible, thus constituting a large sampling group. A metallographic analysis was carried out, and elemental composition, microstructure and hardness were measured. This article focuses firstly on the elemental composition of this group of historical Al alloys. From these data, some fine characteristics specific to the manufacturing countries will be investigated through principal component analysis. Then, on a restricted group constituted of German alloys, we will discuss how some manufacturing parameters, such as micro-alloying and thermo-mechanical treatments, influence the mechanical properties of the assessed aircraft components. Other influencing parameters, linked to the archaeological nature of the parts, are considered and addressed based on a specific study of an alloy collected on a crashed JU88 aircraft originating from the German company Junkers.
Predicting Peritoneal Dialysis Failure Within the Next Three Months Based on Deep Learning and Important Features Analysis
Authors: Hsu Fang-Yu , Hwang Ren-Hung , Tsai Ming-Hsien , Wang Jing-Tong
Published: December 2024

This study aims to develop a deep learning model to predict peritoneal dialysis (PD) failure within the next three months using data from the preceding three months. Background: PD patients typically perform treatments at home and visit the clinic only once per month, leading to significant gaps in clinical care and increased risks of PD failure, which may necessitate a transition to hemodialysis (HD). Current studies on PD patients largely focus on predicting PD failure, mortality risk, and hospitalization through traditional machine learning methods, with limited application of deep learning for this purpose. Methods: We collected comprehensive patient data, including demographic information, comorbidities, medication history, biochemical test results, dialysis prescriptions, and peritoneal equilibrium test outcomes. After preprocessing, we employed time-series deep learning models to train and make predictions. Results: The model achieved a prediction accuracy of 89% and an AUROC of 92%, outperforming previous methods used for PD failure prediction. Conclusion: This approach not only improves prediction accuracy but also identifies key features that can aid clinicians in developing more precise treatment plans and enhancing patient care.
Evaluation of Evolux™ Intraocular Lenses in Cataract Surgery: Clinical Outcomes and Patient Satisfaction
Authors: Cano-Ortiz Antonio , Sánchez-Ventosa Álvaro , González-Cruces Timoteo , Villalba González Marta , López Pérez María Dolores , Díaz-Ramos José Carlos , Prados Carmona Juan J. , Tejerina Fernández Victor , Elies Amat Daniel , Villarrubia Alberto
Published: December 2024

Objective: This study aimed to evaluate the initial clinical outcomes of and patient satisfaction with the newly introduced extended monofocal Evolux™ intraocular lens following cataract surgery. Methods: A retrospective study was conducted, involving 18 patients (36 eyes) who received Evolux™ lenses bilaterally. The inclusion criteria comprised individuals over 40 years old with no active eye diseases, excluding cataracts, and no postoperative complications. Various parameters were evaluated, including distance, intermediate, and near visual acuity; refraction; defocus curves; dependency on spectacles; and patient satisfaction. Patient satisfaction and visual quality of life were measured using the Catquest-9SF and QOV questionnaires. Results: The average age of the patients was 72.7 years, with the majority being women (77.8%). The results demonstrated excellent postoperative visual acuity at different distances. The mean logMAR values for uncorrected visual acuity were −0.04 for distance vision, 0.15 for intermediate vision, and 0.35 for near vision. The defocus curve indicated good tolerance, with visual acuity exceeding 20/20 in significant defocus ranges. Most patients expressed high satisfaction with their vision without spectacles, particularly for distance and intermediate vision. The need for spectacles for near vision was notably reduced. Furthermore, there was a low incidence of photic phenomena like halos and glare, contributing to the overall high patient satisfaction. Conclusions: Evolux™ lenses were found to be an effective choice for cataract surgery, providing excellent visual acuity at various distances and high patient satisfaction.
Clinical Reasoning and Knowledge Assessment of Rheumatology Residents Compared to AI Models: A Pilot Study
Authors: Kayacan Erdoğan Esra , Babaoğlu Hakan
Published: December 2024

Background: The integration of artificial intelligence (AI) in medicine has progressed from rule-based systems to advanced models and is showing potential in clinical decision-making. In this study, the psychological impact of AI collaboration in clinical practice is assessed, highlighting its role as a support tool for medical residents. This study aimed to compare clinical decision-making approaches of junior rheumatology residents with both trained and untrained AI models in clinical reasoning, pre-diagnosis, first-line, and second-line management stages. Methods: Ten junior rheumatology residents and two GPT-4 models (trained and untrained) responded to 10 clinical cases, encompassing diagnostic and treatment challenges in inflammatory arthritis. The cases were evaluated using the Revised-IDEA (R-IDEA) scoring system and additional case management metrics. In addition to scoring clinical case performance, residents’ attitudes toward AI integration in clinical practice were assessed through a structured questionnaire, focusing on perceptions of AI’s potential after reviewing the trained GPT-4’s answers. Results: Trained GPT-4 outperformed residents across all stages, achieving significantly higher median R-IDEA scores and superior performance in pre-diagnosis, first-line, and second-line management phases. Residents expressed a positive attitude toward AI integration, with 60% favoring AI as a supportive tool in clinical practice, anticipating benefits in competence, fatigue, and burnout. Conclusions: Trained GPT-4 models outperform junior residents in clinical reasoning and management of rheumatology cases. Residents’ positive attitudes toward AI suggest its potential as a supportive tool to enhance confidence and reduce uncertainty in clinical practice. Trained GPT-4 may be used as a supplementary tool during the early years of residency.