bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis
Authors: Gehlen Tobias , Joost Theresa , Solbrig Philipp , Stahnke Katharina , Zahn Robert , Jahn Markus , Adl Amini Dominik , Back David Alexander
Published: January 2025

Background and Objective: The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear. Therefore, this study aimed to investigate the performance of existing symptom checker apps in orthopedic and traumatology cases compared to physicians in the field. Methods: 30 fictitious case vignettes of common conditions in trauma surgery and orthopedics were retrospectively examined by four orthopedic and traumatology specialists and four different symptom checker apps for diagnostic accuracy and the recommended urgency of measures. Based on the estimation provided by the doctors and the individual symptom checker apps, the percentage of correct diagnoses and appropriate assessments of treatment urgency was calculated in mean and standard deviation [SD] in [%]. Data were analyzed statistically for accuracy and correlation between the apps and physicians using a nonparametric Spearman’s correlation test (p < 0.05). Results: The physicians provided the correct diagnosis in 84.4 ± 18.4% of cases (range: 53.3 to 96.7%), and the symptom checker apps in 35.8 ± 1.0% of cases (range: 26.7 to 54.2%). The agreement in the accuracy of the diagnoses varied from low to high (Physicians vs. Physicians: Spearman’s ρ: 0.143 to 0.538; Physicians vs. Apps: Spearman’s ρ: 0.007 to 0.358) depending on the different physicians and apps. In relation to the whole population, the physicians correctly assessed the urgency level in 70.0 ± 4.7% (range: 66.7 to 73.3%) and the apps in 20.6 ± 5.6% (range: 10.8 to 37.5%) of cases. The agreement on the accuracy of estimating urgency levels was moderate to high between and within physicians and individual apps. Conclusions: AI-based symptom checker apps for diagnosis in orthopedics and traumatology do not yet provide a more accurate analysis regarding diagnosis and urgency evaluation than physicians. However, there is a broad variation in the accuracy between different digital tools. Altogether, this field of AI application shows excellent potential and should be further examined in future studies.
Finite-Time Path-Following Control of Underactuated AUVs with Actuator Limits Using Disturbance Observer-Based Backstepping Control
Authors: Ebrahimpour MohammadReza , Lungu Mihai
Published: January 2025

This paper presents a three-dimensional (3D) robust adaptive finite-time path-following controller for underactuated Autonomous Underwater Vehicles (AUVs), addressing model uncertainties, external disturbances, and actuator magnitude and rate saturations. A path-following error system is built in a path frame using the virtual guidance method. The proposed cascaded closed-loop control scheme can be described in two separate steps: (1) A kinematic law based on a finite-time backstepping control (FTBSC) is introduced to transform the 3D path-following position errors into the command velocities; (2) The actual control inputs are designed in the dynamic controller using an adaptive fixed-time disturbance observer (AFTDO)-based FTBSC to stabilize the velocity tracking errors. Moreover, the adverse effects of magnitude and rate saturations are reduced by an auxiliary compensation system. A Lyapunov-based stability analysis proves that the path-following errors converge to an arbitrarily small region around zero within a finite time. Comparative simulations illustrate the effectiveness and robustness of the proposed controller.
Utilizing the Finite Fourier Series to Generate Quadrotor Trajectories Through Multiple Waypoints
Authors: Kovryzhenko Yevhenii , Taheri Ehsan
Published: January 2025

Motion planning is critical for ensuring precise and efficient operations of unmanned aerial vehicles (UAVs). While polynomial parameterization has been the prevailing approach, its limitations in handling complex trajectory requirements have motivated the exploration of alternative methods. This paper introduces a finite Fourier series (FFS)-based trajectory parameterization for UAV motion planning, highlighting its unique capability to produce piecewise infinitely differentiable trajectories. The proposed approach addresses the challenges of fixed-time minimum-snap trajectory optimization by formulating the problem as a quadratic programming (QP) problem, with an analytical solution derived for unconstrained cases. Additionally, we compare the FFS-based parameterization with the polynomial-based minimum-snap algorithm, demonstrating comparable performance across several representative trajectories while uncovering key differences in higher-order derivatives. Experimental validation of the FFS-based parameterization using an in-house quadrotor confirms the practical applicability of the FFS-based minimum-snap trajectories. The results indicate that the proposed FFS-based parameterization offers new possibilities for motion planning, especially for scenarios requiring smooth and higher-order derivative continuity at the expense of minor increase in computational cost.
Computational Thinking for Science Positions Youth to Be Better Science Learners
Authors: Cannady Matthew A. , Collins Melissa A. , Hurt Timothy , Montgomery Ryan , Greenwald Eric , Dorph Rena
Published: January 2025

Computational thinking plays a central and ubiquitous role in many science disciplines and is increasingly prevalent in science instruction and learning experiences. This study empirically examines the computational thinking skills that are particular to engaging in science and science learning and then tests if these skills are predictive of science learning over the course of one semester. Using a sample from 600 middle school science students, we provide the psychometric properties of a computational thinking for science assessment and demonstrate that this construct is a consistent predictor of science content learning. The results demonstrate that the relationship between computational thinking for science and science content learning is consistent across variations in students and classrooms, above and beyond other demonstrated predictors—STEM fascination or scientific sensemaking. Further, the analysis also showed that experience with computer programming languages, especially block languages, is associated with higher levels of computational thinking. The findings reveal implications for research, teaching, and learning, including some implications for advancing equitable opportunities for students to develop computational thinking for science. This paper advances knowledge about how to ensure that students have the dispositions, skills, and knowledge needed to use technology-enabled scientific inquiry practices and to position them for success in science learning.
A Risk Preference-Based Optimization Model for User-Side Energy Storage System Configuration from the Investor’s Perspective
Authors: Gao Jinming , Sun Yixin , Su Xianlong
Published: January 2025

To enhance the utilization of emerging energy sources, the application of battery energy storage systems (BESSs) was increasingly explored by investors. However, the immature development of BESS technologies introduced supply–demand imbalances, complicating the establishment of standardized cost analysis frameworks for potential investments. To address this challenge, a hybrid optimization model for a user-side BESS was developed to maximize total net returns over the system’s entire life cycle. The model accounted for factors such as energy storage arbitrage revenue, government tariff subsidies, reductions in electricity transmission fees, delays in grid upgrades, and overall life cycle costs. Conditional value-at-risk (CVaR) was employed as a risk assessment metric to provide investment allocation recommendations across various risk scenarios. An example analysis was conducted to allocate and evaluate the net returns of different battery types. The results demonstrated that the model identified optimal investment strategies aligned with investors’ risk preferences, enabling informed decision-making that balanced returns with operational stability. This approach enhanced the resilience and economic viability of user-side energy storage configurations.
A Comparative Study of Incremental ΔΣ Analog-to-Digital Converter Architectures with Extended Order and Resolution
Authors: Aziz Monica , Kaesser Paul , Ibrahim Sameh , Ortmanns Maurits
Published: January 2025

Incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are one of the best candidates for integrated sensor interface systems when it comes to high resolution and power efficiency. Advanced architectures such as Multistage noise shaping (MASH) or extended counting (EC) I-DS ADCs can be used to achieve a high resolution and fast conversion times and avoid stability issues. Different architectures have been proposed in the state of the art (SoA), but there exists no extensive quantitative or qualitative comparison between them. This manuscript fills this gap by providing a detailed system-level comparison between MASH, EC, and other architectural options in I-DS ADCs, where different performances between these architectures are realized depending on the employed oversampling ratio (OSR) and the chosen number of quantizer bits. Also, for specific MASH designs, the appropriate choice of the digital filter improves the SQNR. The advantages, disadvantages, and limitations of the different architectures are presented including non-idealities such as coefficient mismatch showing that 2-1 MASH-LI is less sensitive to mismatch and provides a high maximum stable amplitude (MSA) relative to the simulated architectures. Furthermore, the 2-1 EC achieves good results and comes with the advantage of a lower noise penalty factor compared to the MASH architectures. This work is intended to assist designers in selecting the most appropriate enhanced I-DS MASH architecture for their specific requirements and applications.
Cooperative Service Caching and Task Offloading in Mobile Edge Computing: A Novel Hierarchical Reinforcement Learning Approach
Authors: Chen Tan , Ai Jiahao , Xiong Xin , Hu Guangwu
Published: January 2025

In the current mobile edge computing (MEC) system, the user dynamics, diversity of applications, and heterogeneity of services have made cooperative service caching and task offloading decision increasingly important. Service caching and task offloading have a naturally hierarchical structure, and thus, hierarchical reinforcement learning (HRL) can be used to effectively alleviate the dimensionality curse in it. However, traditional HRL algorithms are designed for short-term missions with sparse rewards, while existing HRL algorithms proposed for MEC lack delicate a coupling structure and perform poorly. This article introduces a novel HRL-based algorithm, named hierarchical service caching and task offloading (HSCTO), to solve the problem of the cooperative optimization of service caching and task offloading in MEC. The upper layer of HSCTO makes decisions on service caching while the lower layer is in charge of task offloading strategies. The upper-layer module learns policies by directly utilizing the rewards of the lower-layer agent, and the tightly coupled design guarantees algorithm performance. Furthermore, we adopt a fixed multiple time step method in the upper layer, which eliminates the dependence on the semi-Markov decision processes (SMDPs) theory and reduces the cost of frequent service replacement. We conducted numerical evaluations and the experimental results show that HSCTO improves the overall performance by 20%, and reduces the average energy consumption by 13% compared with competitive baselines.
Low-Delay AES Key Expansion Units Based on DDBT Structure
Authors: Zheng Xinxing , Yan Han , Peng Zhiwei , Zhang Xiaoqiang
Published: January 2025

Advanced Encryption Standard (AES) key expansion unit is usually implemented by chain structure with a long critical path length. That makes key expansion unit become the bottleneck of high-speed AES implementations. In this paper, a design method of low-delay AES key expansion unit is proposed. The proposed design method is based on a delay-drive binary tree (DDBT) structure, which has been proven that it has the shortest critical path length. Based on the proposed design method, a low-delay AES encryption key expansion unit and a low-delay AES encryption/decryption unified key expansion unit are designed in this paper. Both hardware complexity analysis and integrated circuit synthesis indicate that our DDBT-structure-based designs can reduce the delay greatly compared to traditional chain structures. Furthermore, compared to previous works, our designs can achieve the largest throughput.
Optimizing 3D Point Cloud Reconstruction Through Integrating Deep Learning and Clustering Models
Authors: Emadi Seyyedbehrad , Limongiello Marco
Published: January 2025

Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step methodology: first, a variational autoencoder reduces features, followed by clustering models to assess and mitigate noise in the point clouds. This study evaluates four clustering methods—k-means, agglomerative clustering, Spectral clustering, and Gaussian mixture model—based on photogrammetric parameters, reprojection error, projection accuracy, angles of intersection, distance, and the number of cameras used in tie point calculations. The approach is validated using point cloud data from the Temple of Neptune in Paestum, Italy. The results show that the proposed method significantly improves 3D reconstruction quality, with k-means outperforming other clustering techniques based on three evaluation metrics. This method offers superior versatility and performance compared to traditional and machine learning techniques, demonstrating its potential to enhance UAV-based surveying and inspection practices.
Characterisation of Harmonic Resonance Phenomenon of Multi-Parallel PV Inverter Systems: Modelling and Analysis
Authors: Peiris Kasun , Elphick Sean , David Jason , Robinson Duane
Published: January 2025

Solar PV inverters require output filters to reduce unwanted harmonics in their output, where LCL filters are a more economical choice than larger inductance-only filters. A drawback of these filters is that they can introduce power quality disturbances, especially at higher frequencies (above 2 kHz). This paper investigates and characterises the resonance phenomenon introduced by different filter types, i.e., LC or LCL, and identifies their behavioural change when combined with multiple parallel grid-tied PV inverter systems. MATLAB/Simulink modelling aspects of PV inverter systems related to resonance phenomenon are presented, including establishing resonance at a specific frequency where potentially large variations in the parameter selection across manufacturers may exist. In addition, a method is developed to establish output filter frequency response through measurements, which is used to develop validated solar PV harmonic models for high-frequency analysis. The low-frequency harmonic models can be used up to the resonant frequency where the current flowing through the filter capacitor is insignificant compared to the current flowing into the electricity network.
Investigation of Energy-Efficient Solutions for a Single-Family House Based on the 4E Idea in Poland
Authors: Ciuman Piotr , Kaczmarczyk Jan , Winnicka-Jasłowska Dorota
Published: January 2025

The paper analyses multi-variant energy simulations carried out in IDA ICE 4.8 software for a newly designed single-family building within the framework of the 4E Idea. This idea assumes the use of energy-saving, ecological, ergonomic, and economic solutions in construction and building operation. Energy simulations were conducted to evaluate the annual energy-saving potential of the developed architectural house concept, which incorporates ergonomic analyses and cost-effective construction solutions. Analyses were conducted to optimise the non-renewable primary energy index by selecting mechanical ventilation system (CAV or VAV) with heat recovery; the configuration of photovoltaic module installation in terms of their location and orientation; the exposure and type of solar thermal collectors (flat and vacuum); and the use of two types of heat pumps (air- and ground-source). The most favourable energy performance of the building was achieved with an HVAC system equipped with a VAV mechanical ventilation system with heat recovery, an on-grid photovoltaic installation, vacuum solar thermal collectors, and a ground-source heat pump with a horizontal heat exchanger. This configuration resulted in a primary energy index value of 2 kWh/m2/year. The results of the analyses carried out for the 4E building concept may serve as a reference point for future energy-efficient building designs aspiring to meet higher standards of sustainable development.
Seasonal Dynamics of Periderm Maintenance and Phellogen Re-Initiation in Aesculus hippocastanum
Authors: Brzostowska Anna , Gola Edyta M. , Myśkow Elżbieta
Published: January 2025

The periderm plays a crucial role in trees, acting as a barrier protecting internal tissues against biotic and abiotic stresses, thus having an impact on tree physiology, ecology, and general performance. It consists of the meristematic phellogen, whose activity gives rise to suberized phellem (cork) cells outwardly and the parenchymatous phelloderm inwardly. Despite the periderm importance, intra-annual and seasonal changes in phellogen activity and phellem and phelloderm differentiation are poorly recognized. Therefore, we aimed to compare periderm development and functioning in successive years in horse chestnut, utilizing standard histological methods. We distinguished six stages of periderm development, including phellogen initiation and the differentiation of its derivatives. In the following years, the phellogen was active for a similar period, but produced fewer derivative cells. Importantly, some phellogen cells lost their meristematic characteristics before the end of the season and differentiated into phellem. To maintain periderm integrity, the remaining phelloderm cells underwent divisions, leading to phellogen re-initiation. Alternatively, when all periderm cells differentiated into the phellem, the new (subsidiary) phellogen originated from the underneath collenchyma. We postulate that phellogen re-initiation could be a mechanism ensuring the functional integrity of the periderm and discuss the role of phelloderm or collenchyma cells in this process.
Synergizing Wood Science and Interpretable Artificial Intelligence: Detection and Classification of Wood Species Through Hyperspectral Imaging
Authors: Qi Yicong , Zhang Yin , Tang Shuqi , Zeng Zhen
Published: January 2025

With the increasing demand for wood in the wood market and the frequent trade of high-value wood, the accurate identification of wood varieties has become essential. This study employs two hyperspectral imaging systems—visible and near-infrared spectroscopy (VNIR) and short-wave infrared spectroscopy (SWIR)—in combination with a deep learning model to propose a method for wood species identification. Spectral data from wood samples were obtained through hyperspectral imaging technology, and classification was performed using a combination of convolutional neural networks (CNNs) and Transformer models. Multiple spectral preprocessing and feature extraction techniques were applied to enhance data quality and model performance. The experimental results show that the full-band modeling is significantly better than the feature-band modeling in terms of classification accuracy and robustness. Among them, the classification accuracy of SWIR reaches 100%, the number of model parameters is 1,286,228, the total size of the model is 4.93 MB, and the Floating Point Operations (FLOPs) is 1.29 M. Additionally, the Shapley Additive Explanation (SHAP) technique was utilized for model interpretability, revealing key spectral bands and feature regions that the model emphasizes during classification. Compared with other models, CNN-Transformer is more effective in capturing the key features. This method provides an efficient and reliable tool for the wood industry, particularly in wood processing and trade, offering broad application potential and significant economic benefits.
Harnessing the Influence of Pressure and Nutrients on Biological CO2 Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches
Authors: Chatzis Alexandros , Kontogiannopoulos Konstantinos N. , Dimitrakakis Nikolaos , Zouboulis Anastasios , Kougias Panagiotis G.
Published: January 2025

The biological methanation process has emerged as a promising alternative to thermo-catalytic methods due to its ability to operate under milder conditions. However, challenges such as low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain methane production yield. This study investigates the combined effects of trace element concentrations and applied pressure on biological methanation, addressing their synergistic interactions. Using a face-centered composite design, batch mode experiments were conducted to optimize methane production. Response Surface Methodology (RSM) and Artificial Neural Network (ANN)—Genetic Algorithm (GA) approaches were employed to model and optimize the process. RSM identified optimal ranges for trace elements and pressure, while ANN-GA demonstrated superior predictive accuracy, capturing nonlinear relationships with a high R² (>0.99) and minimal prediction errors. ANN-GA optimization indicated 97.9% methane production efficiency with a reduced conversion time of 15.9 h under conditions of 1.5 bar pressure and trace metal concentrations of 25.0 mg/L Fe(II), 0.20 mg/L Ni(II), and 0.02 mg/L Co(II). Validation experiments confirmed these predictions with deviations below 5%, underscoring the robustness of the models. The results highlight the synergistic effects of pressure and trace metals in enhancing gas–liquid mass transfer and enzymatic pathways, demonstrating the potential of computational modeling and experimental validation to optimize biological methanation systems, contributing to sustainable methane production.
Blockchain and Artificial Intelligence for Decentralized Edge Environments
Authors: Qiao Yuansong
Published: January 2025

Edge environments typically involve multiple stakeholders and the management of decentralized devices and resources, presenting significant challenges related to the assurance of data security, real-time processing, and efficient coordination [...]
Exploring Financial Literacy in Higher Education with the Help of FinTech: A Bibliometric Analysis of Linkages to Access, Behavior, and Well-Being Through Digital Innovation
Authors: Croitoru Ionut Marius , Dragan (SPIRIDON) Paula-Paraschiva , Ignat Nicoleta Daniela , Jumanca Romanita
Published: January 2025

This study explores the dynamic interaction between financial literacy and higher education, focusing on the critical role of financial education in improving individual financial well-being. Using bibliometric analysis and the VOSviewer software, this research examines thematic clusters in financial literacy, categorized into access, behavior, health, and education. By analyzing 469 articles from the Web of Science database (2020–2024), this study identifies trends and key linkages between financial literacy and societal well-being, highlighting the role of digital innovation. While FinTech is discussed as a facilitator of financial inclusion and education, the primary focus lies in understanding how financial literacy drives behavioral change, capacity building, and economic resilience. This paper provides information for policymakers and educators to design inclusive, behaviorally focused educational programs that address specific demographic needs, ultimately contributing to societal and economic resilience.
Safe Treatment of Surface Coalfield Fires Above Shallow-Buried Goaf in Steeply Dipping Coal Seams
Authors: Zhang Pihong , Chen Ruchang , Zhu Guoqing , Yang Dezhi , Li Xin , Jiang Wei , Liu Hao , Zhang Zhiyi
Published: January 2025

Xinjiang is a region of China that suffers severe energy resource loss and air pollution resulting from long-term coalfield fires in near-surface inclined coal seams. Beneath these fire areas, abandoned mined-out goaf is common. Accidents easily occur during the treatment of such fire areas owing to the instability of strata overlying the goaf. Here, we carried out non-destructive exploration of the goaf below a fire area using the airborne transient electromagnetic method, accurately identifying the locations and sizes of 21 goaf areas. We then established a stratigraphic model using the thermal-solid coupling function in UDEC software. Our simulations showed that under the combined action of high temperature generated by coal combustion and high pressure generated by fire-fighting machinery, the maximum displacement and vertical stress in strata overlying the goaf were 1.42 m and 36 MPa, respectively. Such large displacement and stress values inevitably lead to the destabilization of overlying strata via turning, sliding, and tipping, seriously threatening the safety of mining personnel and machinery. In the field, the rock layer above the goaf was first accurately blasted, and then fire extinguishing was carried out after the overlying rock had collapsed and compacted.
A Facile Electrode Modification Approach Based on Metal-Free Carbonaceous Carbon Black/Carbon Nanofibers for Electrochemical Sensing of Bisphenol A in Food
Authors: Wang Jin , Yang Zhen , Gu Shuanghuan , Pan Mingfei , Xu Longhua
Published: January 2025

Bisphenol A (BPA) is a typical environmental estrogen that is distributed worldwide and has the potential to pose a hazard to the ecological environment and human health. The development of an efficient and sensitive sensing strategy for the monitoring of BPA residues is of paramount importance. A novel electrochemical sensor based on carbon black and carbon nanofibers composite (CB/f-CNF)-assisted signal amplification has been successfully constructed for the amperometric detection of BPA in foods. Herein, the hybrid CB/f-CNF was prepared using a simple one-step ultrasonication method, and exhibited good electron transfer capability and excellent catalytic properties, which can be attributed to the large surface area of carbon black and the strong enhancement of the conductivity and porosity of carbon nanofibers, which promote a faster electron transfer process on the electrode surface. Under the optimized conditions, the proposed CB/f-CNF/GCE sensor exhibited a wide linear response range (0.4–50.0 × 10−6 mol/L) with a low limit of detection of 5.9 × 10−8 mol/L for BPA quantification. Recovery tests were conducted on canned peaches and boxed milk, yielding satisfactory recoveries of 86.0–102.6%. Furthermore, the developed method was employed for the rapid and sensitive detection of BPA in canned meat and packaged milk, demonstrating comparable accuracy to the HPLC method. This work presents an efficient signal amplification strategy through the utilization of carbon/carbon nanocomposite sensitization technology.
Pomegranate Juices: Analytical and Bio-Toxicological Comparison of Pasteurization and High-Pressure Processing in the Development of Healthy Products
Authors: Cairone Francesco , Cesa Stefania , Arpante Irene , Di Simone Simonetta Cristina , Mendez Alejandro Han , Ferrante Claudio , Menghini Luigi , Filippi Antonello , Fraschetti Caterina , Zengin Gokhan , Carradori Simone , Gallorini Marialucia , Mannina Luisa , Spano Mattia
Published: January 2025

Two different produced and packaged commercial typologies of pomegranate juice were analyzed for their physicochemical, nutritional, and biological properties. The effects of classical pasteurization (PJ) and high-pressure processing (HP), applied during the productive cycle, were evaluated through several advanced analytical methods, such as CIEL*a*b* colorimetry, HPLC-DAD, DI-ESI-MS and MS/MS, and NMR analyses. Moreover, the exerted biological activity of the two pomegranate juices was monitored through Total Phenolic and Total Flavonoid Contents, antiradical, antioxidant and chelating activity. The potential inhibition of key enzymes of degenerative processes (cholinesterases, tyrosinase) and diabetes (amylase, glucosidase), the allelopathy toward Cichorium intybus, Dicondra repens, and Diplotaxis tenuifolia, and the in vivo toxicity on brine shrimp were also evaluated. The two different applied processing techniques analyzed impacted the bioactive compound’s preservation differently, modifying the phytocomplex profile. HP significantly degrades punicalins and punicalagins, better preserving anthocyanins, if compared to PJ’s impact. Sensory qualities, antioxidant activity, enzymatic inhibition, and ecotoxicological potential were differently impacted by the two applied processes. The obtained results can be beneficial for finding the optimal processing conditions that balance microbial safety with nutritional value preservation, contributing to the development of healthy pomegranate juice products.
Efficacy of Household and Commercial Washing Agents in Removing the Pesticide Thiabendazole Residues from Fruits
Authors: Du Xinyi , Ho Lauren , Li Sisheng , Doherty Jeffery , Lee Junghak , Clark John M. , He Lili
Published: January 2025

Pesticide residues on fruits pose a global food safety concern, emphasizing the need for effective and practical removal strategies to ensure safe consumption. This study investigates the efficacy of household ingredients (corn starch, all-purpose flour, rice flour and baking soda) and four commercial fresh produce wash products in eliminating a model pesticide thiabendazole with and without a model non-ionic surfactant Alligare 90® from postharvest fruits. Surface-enhanced Raman spectroscopy (SERS) was employed for the rapid, in situ quantification of residue removal on apple surfaces. Soaking in 2% corn starch followed by soaking in 5% baking was the most effective homemade strategy, removing 94.13% and 91.78% of thiabendazole with and without the surfactant. Among commercial washing agents, soaking in 2% Product 4 demonstrated the highest efficiency, removing 95.3% and 95.99% of thiabendazole with and without surfactant. These results suggested that the non-ionic surfactant did not affect removal efficiency. Both protocols were effective across various fruits (apples, grapes, lemons, strawberries), validated by liquid chromatography–mass spectrometry (LC-MS/MS) analyses. However, safety concerns regarding the composition of Product 4 highlighted the benefits of homemade strategies. Overall, this work offers practical guidelines for reducing pesticide residues on fruits and enhancing food.
Sustainable Protein Sources: Functional Analysis of Tenebrio molitor Hydrolysates and Attitudes of Consumers in Poland and Spain Toward Insect-Based Foods
Authors: Maciejewska Marcelina , Dąbrowska Anna , Cano-Lamadrid Marina
Published: January 2025

This study explores the potential of Tenebrio molitor protein hydrolysates as functional food ingredients, evaluating their bioactivity and consumer acceptance of the incorporation of edible insects into food across Poland and Spain. By aligning technical advancements with consumer preferences, this research bridges the gap between laboratory innovation and market feasibility, contributing to the development of sustainable functional foods. The study optimized the process of enzyme hydrolysis using serine protease from Cucurbita ficifolia, thereby enhancing DPPH scavenging capacity increased from 3.15 ± 0.53 to 8.17 ± 0.62 µM Trolox/mL and ABTS decolorization capacity increased from 4.29 ± 0.01 to 10.29 ± 0.01 µM Trolox/mL after 5 h of hydrolysis. Consumer surveys incorporating the Food Neophobia, Insect Phobia, and Entomophagy Scales revealed demographic and cultural influences on entomophagy acceptance. Among respondents, 27.1% in Poland and 25.7% in Spain had previously consumed insect-based products, while Polish participants showed a higher willingness to adopt insect-enriched foods. The study confirmed that hydrolysis enhances the antioxidant activity of T. molitor protein hydrolysates and that demographic and cultural factors significantly influence consumer acceptance of insect-based foods.
Discrete Fractional-Order Modeling of Recurrent Childhood Diseases Using the Caputo Difference Operator
Authors: Madani Yasir A. , Ali Zeeshan , Rabih Mohammed , Alsulami Amer , Eljaneid Nidal H. E. , Aldwoah Khaled , Muflh Blgys
Published: January 2025

This paper presents a new SIRS model for recurrent childhood diseases under the Caputo fractional difference operator. The existence theory is established using Brouwer’s fixed-point theorem and the Banach contraction principle, providing a comprehensive mathematical foundation for the model. Ulam stability is demonstrated using nonlinear functional analysis. Sensitivity analysis is conducted based on the variation of each parameter, and the basic reproduction number (R0) is introduced to assess local stability at two equilibrium points. The stability analysis indicates that the disease-free equilibrium point is stable when R0<1, while the endemic equilibrium point is stable when R0>1 and otherwise unstable. Numerical simulations demonstrate the model’s effectiveness in capturing realistic scenarios, particularly the recurrent patterns observed in some childhood diseases.
Genomic Rewilding of Domestic Animals: The Role of Hybridization and Selection in Wolfdog Breeds
Authors: Báčová Alžběta , Lucas Lledó José Ignacio , Eliášová Kristýna , Neradilová Silvie , Stronen Astrid Vik , Caniglia Romolo , Galaverni Marco , Fabbri Elena , Mattucci Frederica , Boyko Adam , Hulva Pavel , Černá Bolfíková Barbora
Published: January 2025

Background/Objectives: The domestication of the grey wolf (Canis lupus) and subsequent creation of modern dog breeds have significantly shaped the genetic landscape of domestic canines. This study investigates the genomic effects of hybridization and breeding management practices in two hybrid wolfdog breeds: the Czechoslovakian Wolfdog (CSW) and the Saarloos Wolfdog (SAW). Methods: We analyzed the genomes of 46 CSWs and 20 SAWs, comparing them to 12 German Shepherds (GSHs) and 20 wolves (WLFs), which served as their ancestral populations approximately 70–90 years ago. Results: Our findings highlight that hybridization can increase genetic variability and mitigate the effects of inbreeding, as evidenced by the observed heterozygosity levels in both wolfdog breeds. However, the SAW genome revealed a higher coefficient of inbreeding and longer runs of homozygosity compared to the CSW, reflecting significant inbreeding during its development. Discriminant Analysis of Principal Components and fixation index analyses demonstrate that the CSW exhibits closer genetic proximity to the GSH than the SAW, likely due to differences in the numbers of GSHs used during their creation. Maximum likelihood clustering further confirmed clear genetic differentiation between these hybrid breeds and their respective ancestors, while shared ancestral polymorphism was detectable in all populations. Conclusions: These results highlight the role of controlled hybridization with captive-bred wolves and peculiar breeding strategies in shaping the genetic structure of wolfdog breeds. To ensure the long-term genetic health of these breeds, it is recommended to promote adequate and sustainable breeding practices to maintain genetic diversity, minimize inbreeding, and incorporate the careful selection of unrelated individuals from diverse lineages, while avoiding additional, uncontrolled crossings with wild wolves.
Genome-Wide Identification and Characterization of the CDPK Family of Genes and Their Response to High-Calcium Stress in Yinshania henryi
Authors: An Liangfeng , Fang Huihui , Zhang Ximin , Tang Jing , Gong Jiyi , Yi Yin , Tang Ming
Published: January 2025

Background/Objectives: Calcium-dependent protein kinases (CDPKs) are a crucial class of calcium-signal-sensing and -response proteins that significantly regulate abiotic stress. Yinshania henryi is a member of the Brassicaceae family that primarily grows in the karst regions of southwestern China, with a notable tolerance to high-calcium soils. Currently, the function of the CDPK family of genes in Y. henryi has yet to be explored. Methods: This study employed a comprehensive approach starting with bioinformatic methods to analyze the whole-genome sequencing data of Y. henryi and identified YhCDPK genes by combining phylogenetic characteristics and protein domain analysis. Results: It then delved into the physicochemical properties, gene structure, chromosomal localization, phylogenetic tree, and promoter cis-acting elements of these YhCDPK genes. Subsequently, RNA-seq data and qRT-PCR analysis were utilized to understand the expression patterns of YhCDPK genes. Twenty-eight YhCDPK genes were found to be unevenly distributed across six chromosomes; these can be classified into four subfamilies, with many cis-acting elements in their promoter regions involved in plant growth and stress responses. Furthermore, the differential expression patterns of YhCDPK genes under different concentrations of calcium treatments were investigated using RNA-seq data and qRT-PCR analysis. Conclusions: These results are a critical first step in understanding the functions of YhCDPK genes, and they lay a foundation for further elucidating the adaptability and response mechanism of YhCDPK genes in Y. henryi to the karst environment.
The Effect of an Improved Environment According to Watson’s Theory of Human Care on Sleep, Anxiety, and Depression in Patients Undergoing Open Heart Surgery: A Randomized Controlled Trial
Authors: Azizoğlu Hatice , Gürkan Zeynep , Bozkurt Yasemin , Demir Canan , Akaltun Hatice
Published: January 2025

Background/Objectives: According to Watson’s Human Care Theory, an improved environment influences patients’ care processes. The purpose of this study was to examine the effect of an improved environment, according to Watson’s Human Care Theory, on sleep quality, anxiety, and depression in patients undergoing open heart surgery. Methods: Upon admission to the ward from the postoperative intensive care unit, the experimental group underwent environmental remediation for three days. The environmental arrangements ensured that the patient’s room maintained an appropriate temperature range of 18–26 °C and humidity values of 30–50%. Monitoring took place at 21:00, 22:00, and 23:00 on Days 1, 2, and 3, at which times, the brightness of the patient rooms gradually decreased. On the morning of the fourth day, the patients were interviewed face to face, and research questionnaires were filled out (ClinicalTrials.gov identifier of the manuscript: NCT06744023). Results: After the implementation of an improved environment in accordance with Watson’s Human Care Theory, the sleep duration of the patients in the experimental group (5.91 h) was higher than that of the control group (4.1 h). At the same time, the mean sleep quality score was measured as 300 ± 15.33 in the experimental group and 116.33 ± 14.94 in the control group. In addition, anxiety and depression levels were lower in the experimental group (5.63 ± 0.59; 4.53 ± 0.42) compared with the control group (12.03 ± 0.85; 10.03 ± 0.82). Conclusions: We recommend implementing improved environmental arrangements in accordance with Watson’s Human Care Theory to improve sleep quality and reduce anxiety and depression levels in patients undergoing open heart surgery.
The Impact of a Healthy Lifestyle on Lower Urinary Tract Symptoms and Erectile Function: A Prospective Study
Authors: Lombardo Riccardo , Romagnoli Matteo , Splendore Chiara , Sarcinelli Luca , Turchi Beatrice , Gallo Giacomo , Tema Giorgia , Franco Antonio , Nacchia Antonio , Fusco Ferdinando , Al Salhi Yazan , Fuschi Andrea , Pastore Antonio , Cicione Antonio , Tubaro Andrea , De Nunzio Cosimo
Published: January 2025

Background and Objectives: The impact of lifestyle on lower urinary tract symptoms has been deeply evaluated in recent years; however, studies in the young population are missing. The aim of this study is to evaluate the impact of alcohol intake, tobacco and cannabinoid smoking, physical activity, and dietary regime on urinary symptoms and sexual function in young adults under 30 years of age. Methods: A prospectively enrolled population of healthy young adults of both sexes under 30 years of age was selected. Young people with comorbidities were excluded. All participants were assessed by completing an anonymous questionnaire which included questions on medical history, lifestyle, smoking and alcohol intake, urinary symptoms, and sexual function only in male subjects. The questionnaire was postponed in case there was an acute pathology. Results: Overall, 802 young adults were prospectively enrolled, of whom 44% were male and 56% female, with a median age of 26 (23/28) years. In our population, 580/818 (70.9%) subjects presented an IPSS ≥ 3. In the analysis of the association between urinary symptoms and smoking, smokers presented urinary symptoms more frequently than nonsmokers (76% vs. 61%; p < 0.05). No association between urinary symptoms and alcohol intake, cannabinoid smoking, physical activity, and dietary regimen was recorded. On multivariable analysis, smokers had an almost doubled risk of urinary symptoms compared to nonsmokers (OR: 1.78; p = 0.001). Conclusions: In conclusion, we demonstrated how even in the young population there can be a correlation between LUTSs and different lifestyles.
Cultural Competence of Obstetricians/Gynecologists and Midwives Providing Midwifery Care to Roma Women in Western Greece
Authors: Chinoporou Chrysoula , Diamanti Athina , Asimaki Eleni , Nanou Christina , Varela Pinelopi , Vivilaki Victoria , Deltsidou Anna
Published: January 2025

Background: Providing midwifery care to Roma women is a significant public health issue due to their status as a vulnerable population, often facing unique challenges and discrimination in accessing healthcare. Cultural competence refers to the ability of maternity providers to understand and incorporate cultural factors within the broader healthcare system. Objective: This study aimed to investigate the cultural competence of obstetricians/gynecologists and midwives working in Western Greece who provide midwifery care to Roma women. Methods: A cross-sectional quantitative study was conducted using a questionnaire from the Roma Women’s Empowerment and Fight against discrimination in Access to Health (REACH) project, which focuses on empowering Roma women and combating healthcare access discrimination. The questionnaire covered three areas: the cultural competence of maternity professionals, their knowledge of Roma women’s lifestyle, and participants’ demographics. The sample included 100 maternity professionals from hospitals and health centers in Western Greece. Results: Cultural competence was found to be moderate to high, with a mean score of 6.9 (SD = 2.2) for the ability to provide adequate care. In the past six months, 33% had provided care to 1–5 Roma women with communication issues, and 53% frequently faced challenges in service delivery. Common problems included Roma women not understanding the information provided (72.9%), and not having necessary documents (41.7%). Obstetricians/gynecologists had higher knowledge scores compared to midwives, and higher educational attainment correlated with better knowledge. Older age was associated with higher cultural competence (p = 0.048). Conclusions: Cultural competence was positively correlated with knowledge levels, with obstetricians/gynecologists exhibiting higher competence than midwives. Enhancing cultural competence among perinatal care providers is crucial to addressing health disparities faced by Roma women. The study’s cross-sectional design and reliance on self-reported data may limit the generalizability and introduce bias. Enhancing cultural competence through targeted training programs can help address healthcare disparities faced by Roma women.
When I Receive Too Much Social Support: The Effect of Social Support Overload on Users’ Life Burnout and Discontinuance in Fitness Apps
Authors: Li Ruihan , Wang Shuang , Wu Tailai
Published: January 2025

Background/Objectives: As fitness apps increasingly incorporate social interaction features, users may find themselves overwhelmed by an excess of received support, struggling to effectively manage it. Highlighting a novel recipient-centric perspective, we aim to investigate the impact of social support overload on users’ life burnout and discontinuance within fitness apps. Methods: Utilizing Social Support Theory and Basic Psychological Needs Theory, we develop a model to examine how emotional, network, and informational support overload affect life burnout and discontinuance through the frustration of basic psychological needs: autonomy, competence, and relatedness. A total of 443 fitness app users were included in our study, and we employed Structural Equation Modeling (SEM) to empirically test this model. Results: The results highlight the significant mediating role of the frustration of basic psychological needs between social support overload and life burnout/discontinuance. Network and informational support overload positively correlate with frustration of all needs, whereas emotional support overload shows a complex relationship. All need frustrations are linked to life burnout, but only autonomy and relatedness frustrations significantly lead to discontinuance. Additionally, gender and app use proficiency are significant control variables impacting discontinuance. Conclusions: This study adopts a novel recipient-centric perspective to explore social support overload, examining its effects on life burnout and discontinuance and offering practical implications for both users and app managers.
Antioxidant Potential and Its Changes Caused by Various Factors in Lesser-Known Medicinal and Aromatic Plants
Authors: Skrovankova Sona , Mlcek Jiri
Published: January 2025

The review focuses on the evaluation of antioxidant potential and its changes by various factors such as growing conditions, the use of fertilizers, the analyzed part of the plant, the solvent used, the extraction method, purifying procedures, and the determination method for selected medicinal and aromatic plants that are lesser-known as antioxidant sources. The lesser-known representatives of Lamiaceae family (Lamium album, Leonurus cardiaca, Hyssopus officinalis, Scutellaria baicalensis), Asteraceae family (Artemisia absinthium), Myrtaceae family (Pimenta dioica), and Rosaceae family (Crataegus laevigata) were selected. The most important factors affecting antioxidant potential are the used solvent and its polarity (water and its temperature, ethanol, mixture of these solvents, methanol, n-butanol, and ethylacetate), extraction techniques, essential oil preparation, and the type and conditions of antioxidant activity (AA) determination method (DPPH, ABTS, FRAP, etc.). The plant composition and the occurrence of biologically active compounds (BACs), such as phenolics (phenolic acids and flavonoids) that participate in their biological impacts and deactivate reactive oxygen species, are also described. This work thus provides a summary of this issue and an extension of information focused on factors that affect plant components’ presence and thus have an impact on the overall antioxidant potential (total polyphenol content TPC, antioxidant activity) of lesser-known plant representatives with antioxidant effect.