bluuebunny/crossref_metadata_2025 huggingface dataset:
Market Introduction and Sensory Evaluation of a Traditional, Revitalized “Back to the Future” Greek Cheese (Kashkaval Pindou)
Authors: Siafaka Sotiria , Chatzitheodoridis Fotios , Skalkos Dimitris
Published: November 2024

Kashkaval Pindou is a traditional semi-hard cheese produced in Greece since the beginning of last century. Its production was halted in the last decades but has recently been revitalized using modern techniques. In this study, the quality and organoleptic criteria for this “recently introduced to the market” cheese in the dishes of selected restaurants and hotels was studied. Research was conducted in the city of Ioannina, Greece using two types of questionnaires via a survey which was carried out in person between September and December 2023, distributed to 18 experts, chefs, and owners of restaurants utilizing the Kashkaval Pindou cheese. The statistical analysis used was an approach of the Delphi methodology, which seeks consensual agreement within a group of experts. Following the two rounds of survey, the experts evaluated the 11 quality criteria for the use of the cheese in order of their preference (10–9 mean values): “Greek Tradition”, “ease of consumption”, “quality, healthy and safe properties”, “easy to be used product” and “ease of integration in the menu”. Of medium preference (9–8 mean values) was the cheese’s “geographic origin”, “producers’ and suppliers’ capacity”, “enhancement of the kitchen’s creativity”, and “value for money”. Last in the order of selection (8–7 mean values) was the cheese “reputation in the market” and “attractive packaging”. Regarding the organoleptic evaluation, the experts indicated the cheese’s pleasant taste, creamy texture, uniformity of the color, and buttery, milky foam aroma as high in their preference. These results, in agreement with findings in the existing literature, prove that Kashkaval Pindou fulfils the required quality and organoleptic criteria to become an appealing new cheese in the global market.
CFD-FEM Analysis for Functionality Prediction of Multi-Gear Pumps
Authors: Zharkevich Olga , Reshetnikova Olga , Nikonova Tatyana , Berg Alexandra , Berg Andrey , Zhunuspekov Darkhan , Nurzhanova Oxana
Published: November 2024

A comprehensive model for evaluating the functionality of a multi-gear pump has been developed. The integrated model for assessing the functionality of a multi-gear pump contains a computational fluid dynamics analysis (CFD) model combined with a finite element method (FEM)-based strength model. Two submodels were linked: a CFD submodel to evaluate the internal pressure distribution of the pump and a structural FEM submodel to calculate the stresses and structural displacements of the pump due to fluid pressure. Finite element analysis in SolidWorks 2016 was used to evaluate the strength of the gear joints of the pump gears. As the pressure of the working fluid increases from 6 to 20 MPa, a linear increase in Mises stresses is observed. At the shaft, these stresses increase to 226.2 MPa, and at the tooth mouths, they reach a maximum value of 205.5 MPa. With the increase in torque on the drive shaft from 100 to 500 N·m, there is a significant increase in Mises stresses localized in the contact zones of the shaft with the drive gear. Analysis of the data obtained showed that the displacements caused by the pressure of the working fluid are insignificant compared to the displacements arising under the action of torque. With increasing pressure and torque, there is a tendency to decrease the safety factor, which indicates a decrease in the safety factor of the design of the multi-gear pump. The safety factor is not provided at a torque of 400–500 N·m. The simulation results are confirmed by correlation analysis. The average approximation error is 5–7%.
UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning
Authors: Liu Yaxi , Li Xulong , He Boxin , Gu Meng , Huangfu Wei
Published: November 2024

Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter.
Structural Decomposition of the Passivity-Based Control System of Wind–Solar Power Generating and Hybrid Battery-Supercapacitor Energy Storage Complex
Authors: Shchur Ihor , Lis Marek , Kuzyk Rostyslav-Ivan
Published: November 2024

Wind–solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out, which was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object presented in the form of a port-Hamiltonian system, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant, and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers according to the interconnections and damping assignment (IDA) method of PBC, which locally performs the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multi-physical systems.
Establishing a Collaborative Genomic Repository for Adult Burn Survivors: A Burn Model System Feasibility Study
Authors: Sibbett Stephen , Oh Jamie , Carrougher Gretchen , Muffley Lara , Ashford Nathaniel , Pacleb Maiya , Mandell Samuel , Schneider Jeffrey , Wolf Steven , Stewart Barclay , Gibran Nicole S.
Published: November 2024

In this study, we aimed to integrate a genetic repository with an existing longitudinal national burn database. We set out two primary objectives, namely (1) to develop standard operating procedures for genetic sample collection and storage, DNA isolation, and data integration into an existing multicenter database; and (2) to demonstrate the feasibility of correlating genetic variation to functional outcomes in a pilot study, using the catechol-O-methyltransferase (COMT) gene. Dubbed the worrier/warrior gene, COMT variants have been associated with varying phenotypes of post-traumatic stress, wellbeing, and resilience. Between August 2018 and July 2020, COMT variants were identified for 111 participants from three sites and correlated with their outcome data. We found no association between COMT variants and functional outcomes, likely due to the inadequate sample size. We also asked all potential participants why they consented to or refused genetic analysis. A thematic analysis of responses revealed altruism and personal interest/enthusiasm in the study as top reasons for consenting. Privacy concerns were the most common reason for refusal. In conclusion, we successfully developed standard operating procedures for genetic sample collection and storage, DNA isolation, and data integration into an existing database, and we demonstrated the feasibility of conducting a multicenter collaborative study using a centralized lab location.
Analysis of Shielding Performance in Double-Layered Enclosures with Integrated Absorbers
Authors: Kwon Jong Hwa , Hyoung Chang-Hee , Park Hyun Ho
Published: November 2024

Generally, various technologies, including waveguide below cutoff (WBC), gasket sealing, and bonding, are employed in metallic enclosures to achieve the high electromagnetic shielding performance required for EMP protection and EMC countermeasures in shielding structures or facilities. While the shielding structure or facility is properly constructed and maintained according to design specifications, its electromagnetic shielding performance can remain at the required level, effectively protecting internal electrical and electronic equipment from external electromagnetic interference. However, unintended apertures often occur during the construction or maintenance of shielding facilities, compromising their shielding performance. Therefore, it is crucial to develop technologies that prevent shielding effectiveness degradation caused by both intentional and unintentional apertures. This paper proposes a structure incorporating a composite absorber (made of dielectric and magnetic absorber) within a double metal panel of enclosure featuring an aperture, aimed at maintaining and improving the facility’s shielding performance. The effectiveness of the proposed structure was validated through numerical simulation.
DPFANet: Deep Point Feature Aggregation Network for Classification of Irregular Objects in LIDAR Point Clouds
Authors: Zhang Shuming , Xu Dali
Published: November 2024

Point cloud data acquired by scanning with Light Detection and Ranging (LiDAR) devices typically contain irregular objects, such as trees, which lead to low classification accuracy in existing point cloud classification methods. Consequently, this paper proposes a deep point feature aggregation network (DPFANet) that integrates adaptive graph convolution and space-filling curve sampling modules to effectively address the feature extraction problem for irregular object point clouds. To refine the feature representation, we utilize the affinity matrix to quantify inter-channel relationships and adjust the input feature matrix accordingly, thereby improving the classification accuracy of the object point cloud. To validate the effectiveness of the proposed approach, a TreeNet dataset was created, comprising four categories of tree point clouds derived from publicly available UAV point cloud data. The experimental findings illustrate that the model attains a mean accuracy of 91.4% on the ModelNet40 dataset, comparable to prevailing state-of-the-art techniques. When applied to the more challenging TreeNet dataset, the model achieves a mean accuracy of 88.0%, surpassing existing state-of-the-art methods in all classification metrics. These results underscore the high potential of the model for point cloud classification of irregular objects.
Analysis of Molding Defection in IC Packaging and Testing Process
Authors: Chang Bao Rong , Tsai Hsiu-Fen , Chen Chen-Chia
Published: November 2024

Molding injects a molding compound into a mold to form a protective shell around the wafer. During the injection process, overflow may occur, leading to mold flash, which reduces yield and causes significant manufacturing cost losses. This paper proposes a deep-learning-based method for detecting and predicting the occurrence of mold flash probability to address this issue. First, the paper conducts random forest importance analysis and correlation analysis to identify the key parameters that significantly impact mold flash. This paper uses these key parameters as input signals for the prediction model. The paper introduces an HLGA Transformer to construct an ensemble meta-learning model that predicts the probability of molding defects, achieving a prediction accuracy of 98.16%. The ensemble meta-learning approach proposed in this paper outperforms other methods in terms of performance. The model predictions can be communicated to the system in real time, allowing it to promptly adjust critical machine operation parameters, thereby significantly improving the molding process yield and reducing substantial manufacturing cost losses.
Hierarchical Optimization Configuration Strategy of Synchronous Condenser in High Penetration Wind Power Sending Systems
Authors: Sun Zhenglong , Qiao Jianhua , Meng Xin , Pan Chao , Li Zewei , Belikov Juri , Levron Yoash
Published: November 2024

The increasing deployment of large-scale wind turbines in place of conventional generators is expected to lead to the dominance of asynchronous power sources in future power systems, further accelerating the trend toward grid electrification. As a result, the ability of power sources to support system voltage and frequency is gradually diminishing. Synchronous condensers (SCs), which are synchronous machines operating without prime movers, serve as effective devices for providing both dynamic voltage support and inertia. They can significantly enhance the system’s capacity to maintain voltage and frequency stability. However, most existing studies on the optimization of synchronous condenser configurations tend to focus on only one aspect at a time rather than addressing both simultaneously, limiting the full potential of these devices. Optimizing either the voltage or frequency in isolation often results in suboptimal improvements in the other. Moreover, the simultaneous optimization of both voltage and frequency can lead to non-convergent outcomes, complicating the search for an optimal solution. To address this, the paper proposes a hierarchical optimization strategy for synchronous condenser configuration aimed at enhancing both voltage and frequency stability. First, the connection sites for the synchronous condensers are determined based on short-circuit ratio (SCR) constraints. Next, an outer layer optimization model is developed to minimize the total installed capacity of the condensers while taking into account the SCR and transient overvoltage levels as constraints. Following this, an inner layer optimization model is introduced, incorporating a rate of change in the frequency fRoCoF and maximum frequency deviation fnadir as constraints. The model is solved using the bacterial foraging optimization algorithm (BFOA). Finally, a case study of a power grid with a high proportion of wind power validates the effectiveness of the proposed synchronous condenser configuration strategy. Compared to traditional methods, the total required capacity of synchronous condensers was significantly reduced.
Robust Distributed Load Frequency Control for Multi-Area Power Systems with Photovoltaic and Battery Energy Storage System
Authors: Lan Yunrui , Illindala Mahesh S.
Published: November 2024

The intermittent power generation of renewable energy sources (RESs) interrupts the balance between power generation and demand load due to the increased frequency fluctuation, which challenges the frequency stability analysis and control synthesis of power generation systems. This paper proposes a robust distributed load frequency control (DLFC) scheme for multi-area power systems. Firstly, a multi-area power system is constructed by integrating photovoltaic (PV) and battery energy storage systems (BESSs). Then, by employing the linear matrix inequality (LMI) technique, the sufficient condition capable of ensuring that the proposed controller satisfies H∞ robust performance in the sense of asymptotic stability is derived. Finally, testing is conducted on a four-area renewable power system, and results verify the strong robustness of the proposed controller against load disturbance and intermittence of RESs.
A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery
Authors: Kumar Shahil , Raj Krish Kumar , Cirrincione Maurizio , Cirrincione Giansalvo , Franzitta Vincenzo , Kumar Rahul Ranjeev
Published: November 2024

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.
Evaluation of a Peer-to-Peer Smart Grid Using Digital Twins: A Case Study of a Remote European Island
Authors: Buckley Niall , Bo Claudia , Delkhah Faezeh , Byrne Niall , Shearcaigh Avril Ní , Brennan Stephanie , Correa Dayanne Peretti
Published: November 2024

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.
Migration and Transformation of Heavy Metals During the CO2-Assistant Thermal Treatment of Oily Sludge
Authors: Li Jiantao , Lin Fawei , Guo Xuan , Luan Chujun , Yu Hongdi , Li Rundong , Yan Beibei , Chen Guanyi
Published: November 2024

Thermal treatment has significant advantages in resource recovery for oily sludge (OS). However, the instability of heavy metals (HMs) within the residue poses a considerable risk of secondary pollution. This study explored the migration and transformation of HMs from OS under varying conditions (i.e., temperature, constant-temperature duration time, and different ratios of O2 and CO2). The elevation of the pyrolysis temperature augmented the decomposition of organic matter and total petroleum hydrocarbons (TPHs). However, the increased temperature also diminished the stabilization of HMs, and facilitating the HM’s transfer to oil and gas, particularly for HMs (i.e., As and Pb) with low boiling points. The constant-temperature duration time exhibited a weak impact on HM transformation, but the internal heating mechanism of microwave pyrolysis promoted the stabilization of HMs through vitrification. The existing O2 with oxidizing properties facilitated the oxidation of organic matter and TPHs to CO2 and H2O, which also promoted the transformation of HMs into oxidized states for stabilization. Comparatively, CO2 promoted the thermal cracking and disrupted the stability of HMs to a certain extent. Above all, this work revealed the migration and transformation of HMs in OS varied with the thermochemical methods and possessed an important significance for the immobilization and stabilization of HMs.
A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms
Authors: Wu Guodong , Hu Diangang , Zhang Yongrui , Bao Guangqing , He Ting
Published: November 2024

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.
A Microgrid Stability Improvement Method by Applying Virtual Adaptive Resistor Paralleling with a Grid-Connected Inverter
Authors: Shi Mingming , Zheng Xian , Fei Juntao , Xie Wenqiang , Yu Jianyu
Published: November 2024

An increase in renewable energy generation in the microgrid can cause voltage oscillation problems. To address this issue, an equivalent circuit of the microgrid was established, including a synchronous generator, grid-connected inverter, and constant power load. Then, the impact of different renewable energy generation ratios, different direct current (DC) voltage loops, and phase-locked loop control bandwidths of the grid-connected inverter on microgrid stability were analyzed. The results indicate that an increase in the renewable energy generation ratio leads to a decrease in the stability margin of the microgrid. A microgrid stability improvement method involving the parallel connection of a virtual resistor with the grid-connected inverter was proposed. The resistance value of the virtual resistor was obtained through an adaptive algorithm. This method ensures the stable operation of the microgrid under different renewable energy generation ratios.
Simulating Agricultural Water Recycling Using the APEX Model
Authors: Doro Luca , Wang Xiuying , Jeong Jaehak
Published: November 2024

Irrigation plays a vital role in many agricultural crop production regions. Drainage water recycling (DWR) is a popular irrigation water management system that collects excess water drained from cropland fields and stores it in on-site reservoirs for reuse. The efficacy of these systems varies by location, climate, irrigation frequency, and crop demands. Simulating this system would be beneficial for assessing the impact of water and land management practices on agriculture and natural resources. This study presents the development of computational algorithms for DWR simulation with the Agricultural Policy Environmental eXtender (APEX) model, along with the results for 39 testing sites where both reservoir and drainage systems are adopted. Simulating a DWR system with the revised reservoir module, the APEX model simulates irrigation water reuse ranging between 29% and 93%; sediment reduction of around 66%; nitrogen loss reduction of 23% and 73% for the mineral and organic forms, respectively; and phosphorus loss reduction of 22% and 79% for the soluble and sediment-transported forms, respectively. In conclusion, the results provided by the APEX model for sediment loss reduction align with field data, but discrepancies for nitrogen and phosphorus losses emerged from this test.
How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study
Authors: Brignone Stefano , Minazzi Luca , Molina Christophe , Putelli Tiziano , Volta Pietro
Published: November 2024

Fish movement into large, deep lakes has been rarely investigated due to the complexity and extent of such ecosystems. Among the different monitoring methods available, acoustic telemetry enables the study of the spatial ecology and behavior of aquatic organisms in lentic environments. In this study, the movement of 69 hatchery-reared adult brown trout (size 43–61 cm) marked with acoustic transmitters was monitored in the large and deep subalpine Lake Lugano (Switzerland and Italy). Trout were tracked for six consecutive months by seven acoustic receivers (March–August 2022), positioned in a non-overlapping array. Trout movement was reconstructed using R packages specific for acoustic telemetry (actel and RSP), which also allowed us to translate tracking information into utilization distribution (UD) areas for each fish. The effects of different environmental variables (rainfall, water discharge of the two main tributaries of Lake Lugano, atmospheric pressure, cloud coverage, and moon phases) on trout movement were tested, but none of these variables seemed to significantly correlate with fish movement. After release, most of the tagged fish exhibited reiterative movements during the initial month, with some maintaining this behavior throughout the entire study period. This spatial behavior can be particularly evident in hatchery-reared fish due to their aggressive and bold attitude. The association of these behavioral traits, shaped by domestication, could expose hatchery-reared fish to high risks and post-release mortality in the wild. Indeed, within a few months after the release, most of the tagged fish were no longer detected by the acoustic receivers. In addition, 26% of the total tagged fish were caught by recreational or professional fishermen.
Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
Authors: Rajendran Venushini , Ramasamy R Kanesaraj
Published: November 2024

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem.
Study on the Structural Characteristics and Foaming Properties of Ovalbumin—Citrus Pectin Conjugates Prepared by the Maillard Reaction
Authors: Zhang Shanshan , Liu Yibo , Wu Wenhui
Published: November 2024

This study explored the structural features and foaming properties of ovalbumin (OVA) and its glycosylated conjugates with citrus pectin (CP) formed through the Maillard reaction. The results demonstrated that OVA and CP were successfully conjugated, with the degree of grafting increasing to 43.83% by day 5 of the reaction. SDS-PAGE analysis confirmed the formation of high-molecular-weight conjugates. Fourier-transform infrared (FT-IR) and fluorescence spectroscopy further revealed alterations in the secondary and tertiary structures of OVA, including an enhanced β-sheet content, a reduced β-turn content, and the depletion of tryptophan residues. Moreover, the surface hydrophobicity of the OVA–CP conjugates significantly increased, enhancing foaming properties. Furthermore, the analysis of foaming properties exhibited that the Maillard reaction improved the foaming capacity of OVA to 66.22% and foaming stability to 81.49%. These findings highlight the potential of glycosylation via the Maillard reaction to significantly improve the foaming properties of OVA, positioning it as a promising novel foaming agent.
Chemical Profile of Kumquat (Citrus japonica var. margarita) Essential Oil, In Vitro Digestion, and Biological Activity
Authors: Vrca Ivana , Fredotović Željana , Jug Blaž , Nazlić Marija , Dunkić Valerija , Jug Dina , Radić Josip , Možina Sonja Smole , Restović Ivana
Published: November 2024

Kumquat is one of the smallest citrus fruits (from the Rutaceae family), and its essential oil’s biological effects have not yet been sufficiently researched, in contrast to the essential oils of its relatives. Therefore, the aim of this large-scale study was to investigate the chemical profile of kumquat essential oils (KEOs) isolated by microwave-assisted distillation (MAD) and Clevenger hydrodistillation using GC-MS analysis. To test the bioaccessibility of their bioactive components, in vitro digestion with commercially available enzymes was performed. The final step of this research was to test their cytotoxic activity against a cervical cancer cell line (HeLa), a human colon cancer cell line (HCT116), a human osteosarcoma cell line (U2OS), and a healthy cell line (RPE1). Two methods were used to test the antioxidant activity: DPPH (2,2-diphenyl-1-picrylhydrazyl) and ORAC (oxygen radical absorbance capacity). The antibacterial activity was tested in relation to the growth and adhesion of Escherichia coli and Staphylococcus aureus on a polystyrene surface. The GC-MS analysis showed that the major compound in both kumquat essential oils was limonene, which was stable before and after in vitro digestion (>90%). The results showed that the cytotoxic activity of the KEOs in all three cancer cell lines tested was IC50 1–2 mg/mL, and in the healthy cell line (RPE1), the IC50 value was above 4 mg/mL. The antibacterial activity of the KEOs obtained after MAD and Clevenger hydrodistillation was 4 mg/mL against E. coli and 1 mg/mL against S. aureus. The KEOs after MAD and Clevenger hydrodistillation reduced the adhesion of E. coli by more than 1 log, while there was no statistically significant effect on the adhesion of S. aureus to the polystyrene surface. Both KEOs exhibited comparable levels of antioxidant activity using both methods tested, with IC50 values of 855.25 ± 26.02 μg/mL (after MAD) and 929.41 ± 101.57 μg/mL (after Clevenger hydrodistillation) for DPPH activity and 4839.09 ± 91.99 μmol TE/g of EO (after MAD) and 4928.78 ± 275.67 μmol TE/g of EO (after Clevenger hydrodistillation) for ORAC. The results obtained show possible future applications in various fields (e.g., in the food, pharmaceutical, cosmetic, and agricultural industries).
Spectroscopy of a Sample of RV Tauri Stars Without IR Excess
Authors: Puķītis Kārlis , Korenika Karina
Published: November 2024

We observed high-resolution optical spectra of 11 RV Tauri stars without IR excess, with the primary goal of searching for chemical depletion patterns. Using equivalent widths of absorption lines, we calculated the photospheric parameters and chemical element abundances for five stars in the sample: HD 172810, V399 Cyg, AA Ari, V457 Cyg, and V894 Per. Only the abundance pattern of V457 Cyg suggests depletion. In the spectrum of this star, TiO lines are also observed in the emission, in addition to metal emissions. V457 Cyg is likely a binary system that was once surrounded by a circumbinary disc. In the spectrum of V894 Per, we find a set of spectral lines that appear to belong to another star, corroborating that it is an eclipsing variable rather than an RV Tauri star. The high overabundance of sodium may result from mass transfer within the binary system.
Formulation and Evaluation of Clotrimazole Mucoadhesive Vaginal Globules
Authors: Jadach Barbara , Otworowska Michalina
Published: November 2024

The aim of this study was to prepare vaginal suppositories with mucoadhesive properties to prolong the action of antifungal component clotrimazole (CLO). This was achieved by preparing vaginal pessaries on a hydrophilic gel base composed of gelatin and gelatin enriched with PEG 400 (in a 1:1 ratio), and then checking the properties of the obtained vaginal drugs. The prepared globules, containing 100 mg of CLO, were characterized in terms of mass and swelling index, organoleptic analysis was also prepared. In addition, a texture analysis and a study of the dissolution of clotrimazole were performed. On the basis of the obtained results, it was concluded that the modification of the composition of the gelatin–glycerin base by the addition of macrogol had a positive effect on the mucoadhesive properties of the globules. In addition, due to the presence of PEG 400, the globules were stiffer. It was also observed that the presence of CLO reduces the value of the force needed for compression during the texture analysis study. Comparing the CLO release profiles of the prepared globules and commercially available clotrimazole tablets, the release profile for the globules was slower than for the tablets, which indicates the possibility of using mucoadhesive globules as a form of a drug that releases the medicinal substance more slowly at the site of administration.
Impact of Respiratory Syncytial Virus (RSV) in Adults 60 Years and Older in Spain
Authors: Jimeno Ruiz Sara , Peláez Adrián , Calle Gómez Ángeles , Villarreal García-Lomas Mercedes , Martínez Silvina Natalini
Published: November 2024

Background/Objectives: Respiratory illnesses frequently lead to hospitalization in adults aged 60 and older, especially due to respiratory viral infectious (RVI). This study investigates hospitalization patterns and characteristics of RVI at HM Hospitals from October 2023 to March 2024; Methods: We retrospectively explored hospitalizations of patients aged 60 years and older with RVIs, gathering data on demographics, clinical profiles, comorbidities, and treatments. Outcomes included hospitalization, ICU admissions, and mortality, and independent factors associated with outcomes were identified using a multi-state model; Results: From October 2023 to March 2024, from a total of 3258 hospitalizations, 1933 (59.3%) were identified as positive for RVIs. Overall, SARS-CoV-2 was the most prevalent (52.6%), followed by influenza (32.7%), and RSV (11.8%). Most RVI involved single infections (88.2%). Hospitalization rates increased with age for SARS-CoV-2 (333.4 [95% CI: 295.0–375.2] to 651.6 [95% CI: 532.1–788.4]), influenza (169.8 [95% CI: 142.6–200.7] to 518.6 [95% CI: 412.1–643.1]), and RSV (69.2 [95% CI: 52.2–90.0] to 246.0 [95% CI: 173.8–337.5]), with SARS-CoV-2 showing the highest rate, followed by influenza and RSV. In the multi-state model, RSV infection significantly increased ICU admission risk (HR: 2.1, 95%, p = 0.037). Age on admission (HR: 1.1, 95%, p < 0.001) and Charlson score (HR: 1.4, 95%, p = 0.001) were associated with transitioning from admission to death. ICU to death risks included age at admission (HR: 1.7, 95%, p < 0.001); Conclusions: RVI in adults 60 years and older are associated with high hospitalization and mortality rates, primarily driven by influenza and SARS-CoV-2, followed by RSV. Age and comorbidities significantly impact disease severity, emphasizing the need for targeted prevention and management strategies for RSV in this vulnerable population.
Assessing Changes in Vascular Inflammation and Urate Deposition in the Vasculature of Gout Patients After Administration of Pegloticase Using Positron Emission Tomography and Dual-Energy Computed Tomography—A Pilot Study
Authors: Khanna Ira , Mani Venkatesh , Pyzik Renata , Kaufman Audrey , Chi Weiwei , Bagiella Emilia , Robson Philip , Ali Yousaf
Published: November 2024

We assessed changes in vascular inflammation and monosodium urate (MSU)-coded deposits after administration of Pegloticase in the vasculature of tophaceous gout patients using 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/computed tomography (PET/CT) and dual-energy CT (DECT). Ten patients with tophaceous gout, intolerant or refractory to urate-lowering therapy (ULT), were treated with Pegloticase every two weeks for six months. 18F-FDG PET/CT and DECT were performed at baseline and after Pegloticase therapy to detect vessel wall inflammation (Standard uptake value, SUVmean, and SUVmax) and vascular MSU-coded deposition (MSU volume). Data were summarized using means and standard deviations. Baseline and follow-up values were compared for each variable using mixed-effect models. Significant decreases in SUVmean (p = 0.0003) and SUVmax (p = 0.009) were found with a trend towards a decrease in vessel wall MSU volume after treatment. There was a significant decrease in serum urate, correlating with reduction in SUVmean (R2 = 0.65), with a trend towards a decrease in CRP and blood pressure in all patients. Despite the small sample size, we were able to demonstrate a decrease in vessel wall inflammation and a trend towards a decrease in MSU volume by intensively lowering serum urate. These findings suggest that MSU-coded deposits and hyperuricemia may play a role in vascular wall inflammation. It remains to be seen whether this correlates with a decrease in adverse cardiovascular outcomes.
Assessment of Functioning and the Integration of Patients After Traumatic Brain Injury in Their Homes and Social Environments Following Inpatient Rehabilitation
Authors: Hrastar Neža , Grabljevec Klemen , Vidmar Gaj
Published: November 2024

Introduction: Patients with head injuries usually return to their home environments after completing rehabilitation, which is an additional challenge in the case of functional, motor or cognitive impairment. In order to identify the problems that such patients face and to objectify their problems with integration, we used the Community Integration Questionnaire-Revised (CIQ-R). At the same time, the validity of the translation of the questionnaire into Slovenian was checked. Methods: The CIQ-R was translated, and patients were telephoned twice, 14 days apart. In addition, they were assessed using the Functional Independence Measure (FIM) during the first call. The reliability of the CIQ-R in terms of internal consistency was assessed using Cronbach’s alpha and Guttman’s lambda-2. The reliability in terms of repeated measures was assessed using intraclass correlation (ICC). The association between FIM and CIQ-R scores was assessed using Pearson’s correlation. Results: Internal consistency of the CIQ-R was high (α = 0.8, λ = 0.85), and there was a very high re-test stability of the overall CIQ-R score (ICC = 0.95). The cognitive component of the FIM in the home environment was linearly associated with the CIQ-R score (r = 0.8, p ≤ 0.001). Conclusions: The translation of the CIQ-R questionnaire into Slovenian was successfully validated, and we proved its potential suitability for clinical use.
Factors Influencing Nurses’ Decisions to Leave or Remain in the Home and Community Care Sector During the COVID-19 Pandemic
Authors: Connelly Denise M. , Guitar Nicole A. , Van Belle Travis A. , McKay Sandra M. , King Emily C.
Published: November 2024

Background/Objectives: Home and community care (HCC) nurses experienced increased occupational challenges during the COVID-19 pandemic, including increased workloads, job stressors, and occupational risks, like virus exposure. The objective of this study was to elucidate what factors influenced nurses’ decisions to stay in their role, take a temporary leave, or exit HCC during the COVID-19 pandemic. Methods: A secondary analysis of data collected using a cross-sectional online open survey distributed among HCC Registered Practical Nurses across Ontario between June and September 2022 was conducted. The factors contributing to nurses’ decision to remain in HCC, temporarily leave, or exit the sector were evaluated using multinomial logistic regression (p < 0.05). Results: Of the 664 participants, 54% (n = 357) stayed in the HCC sector, 30% (n = 199) temporarily left, and 16% (n = 108) exited the sector. Nurses with greater years of experience working in HCC and those who avoided infection were more likely to stay in their role in HCC, which may reflect strong relationships with long-term clients, opportunity and accumulated experience to increase income, and maintenance of good health. Nurses with higher levels of emotional intelligence were more likely to take leaves and exit HCC, suggesting that stepping away may have been a strategy to safeguard themselves. Conclusions: HCC leadership should prioritize the development of solutions to support nurses in the HCC workforce, including those with fewer years of experience. This may promote nurses’ participation in the sector, particularly during times of heightened occupational challenges and crises, like COVID-19.
A Comparison of Using Cuffed and Uncuffed Face Masks for Providing Manual Bag Ventilation in Elderly Patients with Obesity
Authors: Ratajczyk Paweł , Wasiak Krzysztof , Kluj Przemysław , Gaszyński Tomasz
Published: November 2024

Background: With the improvement of healthcare, the number of elderly individuals, including those with obesity, is increasing. The accumulation of various ventilation problems associated with the use of face masks in both these patient groups can pose a challenge even for an experienced anesthesiologist. The main aim of this study was to evaluate the ventilation of elderly patients with obesity using face masks, uncuffed or cuffed, and compare it with values obtained among patients with obesity who are not elderly. The secondary aim of the study was to demonstrate which of the two masks tested is better for elderly patients with android and gynoid obesity. Methods: This study was conducted at University Clinical Hospital No. 1 in Lodz among 108 patients with obesity, 50 elderly and 58 non-elderly. Patients’ BMIs ranged from 35.0 to 59.0. For the study, the uncuffed Intersurgical Eco Mask II and cuffed Ambu Ultra Seal face masks were used. Expiratory tidal volume and leakage obtained during the use of both types of masks were examined. The obtained data were analyzed using the Kolmogorov–Smirnov test and supplemented with Wilcoxon test values. Results: In elderly patients with obesity, especially those with gynoid obesity, the use of the Intersurgical Eco Mask II is associated with better ventilation parameters than the Ambu Ultra Seal mask. Only in the case of elderly patients with android obesity did the use of the Ambu Ultra Seal mask yield similar results to the Intersurgical Eco Mask II. Conclusions: Uncuffed face masks provide better ventilation parameters during manual bag ventilation in elderly patients with obesity.
Parental Stress in Autistic Children with Poor Oral Hygiene: A Pilot Study to Develop and Validate a Measurement Scale
Authors: Alegría Pablo López , Landim Síbila Floriano , Pérez Valdés Vidal Antonio , Martínez Escudero Natalia , Botelho Juliana Nunes , Branco Braulio Henrique Magnani , Villagrán Francisca , Sandoval Cristian , Marques Déborah Cristina de Souza , Parrón Carreño Tesifon , González Manuel Martín
Published: November 2024

Background/Objectives: Research indicates that children with autism spectrum disorder (ASD) exhibit a deficiency in skills and initiative when it comes to adhering to daily oral hygiene routines. This, in turn, increases the likelihood of oral pathologies, thereby placing a significant emotional strain on their parents. In addition to the typical stress they already experience, parents of children with ASD are also burdened with pediatric oral health issues. However, a review of the literature reveals a lack of studies measuring stress in parents of children whose oral health affects their autistic condition. This research aimed to design and to validate a stress scale for parents of autistic children with poor oral hygiene. Methods: The study used an exploratory, non-experimental design to validate and determine the reliability of the scale. Scale reliability was assessed through a cross-sectional pilot test to evaluate internal scale consistency, with a focus on item similarity. We used Aiken’s V to estimate the validity of the scale, and Cronbach’s α for calculating scale reliability. Calculations, estimations, and statistical analysis were conducted using SPSS. Results: Expert validation, a pilot test, and a cross-sectional, non-experimental design established the reliability of the scale. Conclusions: We conclude that the 20-item scale exhibits validity (0.95) and reliability (0.965), ensuring its applicability in future research.