bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction Models for Elevated Cardiac Biomarkers from Previous Risk Factors and During the COVID-19 Pandemic in Residents of Trujillo City, Peru
Authors: Caballero-Vidal Joao , Díaz-Ortega Jorge Luis , Yupari-Azabache Irma Luz , Castro-Caracholi Luz Angélica , Alva Sevilla Juan M.
Published: November 2024

Cardiac troponin serum concentration is a marker of myocardial injury, but NT-pro BNP is a marker of myocardial insufficiency. The purpose of this study was to determine binary logistic regression models to verify the possible association of cardiovascular risk indicators, pre-pandemic history, the number of times participants were infected with SARS-CoV-2, and vaccination against these biomarkers. A total of 281 residents of Trujillo city (Peru) participated between September and December 2023. We found a high prevalence of abdominal obesity of 55.2%; glycemia > 100 m/dL in 53%; hypercholesterolemia in 49.8%; low HDL in 71.9%; and LDL > 100 mg/dL in 78.6%. A total of 97.5% were vaccinated against COVID-19, and 92.2% had three or more doses. Also, 2.5% had cTnI > 0.05 ng/mL, and 3.3% had NT-proBNP > 125 pg/mL. The number of COVID-19 infections versus cTnI > 0.05 ng/mL presented an OR = 3.513 (p = 0.003), while for NT-proBNP > 125 pg/mL, the number of comorbidities presented an OR = 2.185 (p = 0.025) and LDL an OR = 0.209 (p = 0.025). A regression model was obtained in which there is an association between a higher number of COVID-19 infections and elevated cTnI values and a model implying an association of the number of comorbidities and LDL with the NT-proBNP level in a direct and inverse manner, respectively. Both models contribute to the prevention of cardiac damage.
Impact of Physician Specialty on Treatment Costs of Invasive Melanoma
Authors: Garcia John P. , Ho Olivia A. , Haider Syed Ali , Borna Sahar , Gomez-Cabello Cesar A. , Forte Antonio Jorge , Spaulding Aaron C.
Published: November 2024

Introduction: Melanoma is a deadly type of skin cancer that develops from melanocytes and can manifest on the skin or other regions of the body. Its incidence is increasing rapidly, with approximately 100,000 diagnoses and 7000 deaths per year in the US alone. We conducted a cross-sectional study with the aim of determining an association between the cost of care for invasive melanoma and the specialty involved in the treatment to adequately guide future treatment. Methods: We analyzed data from 3817 patients (2013–2018) using the Florida inpatient/outpatient dataset, CMS cost reports, and the National Plan and Provider Enumeration System. Covariates included age, sex, race/ethnicity, insurance type, region, county rurality, the number of procedures, the comorbidity index, obesity, metastatic cancer presence, hospital size, and physician volume. Multivariable mixed linear regression was used to analyze the data, and the cost was adjusted to the 2019 USD. Results: Dermatology had the largest decrease in the overall and outpatient costs compared to general surgery, followed by plastic surgery. The inpatient costs for dermatology and plastic surgery were lower than those for general surgery, but not significantly so. Conclusions: The costs associated with surgical procedures may vary depending on the specialty of the physician treating the patient. Dermatology was associated with lower treatment costs for invasive melanoma compared to other specialties, indicating that physician specialty influences the cost of care.
Prognostic Implications of Initial Radiological Findings of Pulmonary Fibrosis in Patients with Acute SARS-CoV-2 Infection: A Prospective Multicentric Study
Authors: Cîrjaliu Roxana-Elena , Gurrala Sri Vidhya , Nallapati Balaji , Krishna Vamsi , Oancea Cristian , Tudorache Emanuela , Marc Monica , Bratosin Felix , Bogdan Iulia , Rosca Ovidiu , Barata Paula Irina , Hangan Laurentiu Tony , Chirilă Sergiu Ioachim , Fildan Ariadna-Petronela
Published: November 2024

Pulmonary fibrosis detected during the acute phase of SARS-CoV-2 infection may significantly influence patient prognosis. This study aimed to evaluate the prognostic value of initial high-resolution computed tomography (HRCT) findings of pulmonary fibrosis in hospitalized COVID-19 patients and to examine how these findings relate to disease severity and clinical outcomes, with a particular focus on the development and validation of predictive scoring systems. In this multicentric prospective cohort study from January 2023 to January 2024, 120 adult patients with confirmed SARS-CoV-2 infection requiring hospitalization were enrolled from two Romanian university hospitals. Patients were categorized based on the presence (n = 60) or absence (n = 60) of pulmonary fibrosis signs on admission HRCT scans, identified by reticular opacities, traction bronchiectasis, honeycombing, and architectural distortion. Biochemical analyses, severity scores (SOFA, APACHE II, NEWS 2), and novel compound scores combining clinical and radiological data were assessed. Patients with HRCT evidence of pulmonary fibrosis had significantly higher severity scores and worse clinical outcomes. The HRCT score alone was a strong predictor of severe COVID-19 (area under the ROC curve [AUC] = 0.885), with a best cutoff value of 9.72, yielding 85.7% sensitivity and 79.8% specificity. Compound Score 1, integrating SOFA, APACHE II, and HRCT scores, demonstrated excellent predictive performance with an AUC of 0.947, sensitivity of 92.5%, and specificity of 88.9%. Compound Score 2, combining systemic inflammation markers (SIRI, SII) and NEWS 2, also showed a strong predictive capability (AUC = 0.913), with 89.2% sensitivity and 85.7% specificity at the optimal cutoff. Regression analysis revealed that Compound Score 1 had the highest hazard ratio for severe COVID-19 outcomes (HR = 4.89; 95% CI: 3.40–7.05), indicating its superior prognostic value over individual markers and traditional severity scores. Initial HRCT findings of pulmonary fibrosis are significantly associated with increased disease severity in hospitalized COVID-19 patients. The HRCT score is a valuable prognostic tool, and, when combined with clinical severity scores into Compound Score 1, it enhances the prediction of severe COVID-19 outcomes with high sensitivity and specificity. These compound scores facilitate the early identification of high-risk patients, guiding clinical decision-making and optimizing patient management to improve outcomes.
Association Between Frequency of Away-From-Home Meals and Prevalence of Inflammatory Sinonasal Diseases: A Population-Based Cross-Sectional Study
Authors: Han Munsoo , Jeong Yujin , Kwak Sooun , Shin Jaemin , Kim Taehoon
Published: November 2024

At the beginning of the COVID-19 pandemic, people had to stay at home due to quarantine, and the food delivery industry grew significantly. Concerns have been raised regarding the popularity of away-from-home (AFH) meals and their impact on health. In this study, we evaluated the association between the frequency of AFH meals and the prevalence of allergic rhinitis (AR) and chronic rhinosinusitis (CRS). In this cross-sectional study, the Korean National Health and Nutrition Examination Survey was reviewed from 2010 to 2019. The frequency of AFH meals was assessed as how often the respondents ate AFH meals in an average week. Those who ate AFH meals less than once per week were designated as group 1, one to four times as group 2, and five times or more as group 3. The diagnoses of AR and CRS were evaluated, and symptoms, endoscopic findings, and serum immunoglobulin E (IgE) levels were assessed. Logistic regression analyses were performed. A total of 48,515 participants were eligible. In multivariate logistic regression analysis for AR, when compared to group 1, the odds ratios (ORs) for AR in participants of group 2 (OR = 1.226, 95% confidence interval [CI] = 1.136–1.324) and group 3 (OR = 1.227, 95% CI = 1.126–1.337) were significantly higher (p < 0.0001). For CRS, group 2 (OR = 1.139, 95% CI = 1.029–1.260) and group 3 (OR = 1.210, 95% CI = 1.078–1.358) showed a significantly higher risk than group 1 (p = 0.0044). Individuals who consume AFH meals frequently might suffer less from AR or CRS if they change their dietary habits and prepare meals more often at home.
The Temporomandibular Joint and the Human Body: A New Perspective on Cross Talk
Authors: Abbass Marwa M. S. , Rady Dina , El Moshy Sara , Ahmed Radwan Israa , Wadan Al-Hassan Soliman , Dörfer Christof E. , El-Sayed Karim M. Fawzy
Published: November 2024

Background: As a unique joint that facilitates the articulation of the upper and lower jaws, the temporomandibular joint (TMJ) is concerned with several critical functions, such as speech and mastication. Pain that can become incapacitating is a result of temporomandibular disorders (TMDs), which are complex disorders affecting the masticatory muscles and the TMJ. Several anomalies and TMDs have an interdisciplinary relationship. Complementary and concurrent disorders may be caused by occlusal anomalies, psychological disorders, and changes in spine posture. Methods: This article examines the clinical characteristics of TMDs, their classification, their etiological factors, and the impact of TMJ disorders on the human body with reference to their anatomies and histological structures. Results: The clinical picture of some TMJ pathologies may be unknown, so certain biomarkers, such as cytokines, may be useful for an accurate diagnosis as they are frequently seen in TMJ disorders. Furthermore, novel therapeutic approaches that target pro-inflammatory cytokines and treat TMDs by using tissue engineering and regenerative medicine while permitting TMJ cartilage and bone regeneration may offer numerous benefits that require clinical translation. Conclusions: Implementation of recent modalities such as microvesicles and platelet-rich plasma in growth factors may provide a promising approach to enhance bone formation. In addition, we target different biological markers that give insights into the introduction of new pharmaceutical agents for therapy.
Modeling Analysis for Downlink RIS-UAV-Assisted NOMA over Air-to-Ground Line-of-Sight Rician Channels
Authors: Li Suoping , Liu Xiangyu , Gaber Jaafar , Pan Guodong
Published: November 2024

This paper proposes a drone-assisted NOMA communication system equipped with a reconfigurable intelligent surface (RIS). Given the Line-of-Sight nature of the Air-to-Ground link, a more realistic Rician fading environment is chosen for the study of system performance. The user’s outage performance and secrecy outage probability of the RIS-UAV-assisted NOMA downlink communication under the Rician channels are investigated. Jointly considering the Line-of-Sight and Non-Line-of-Sight links, the closed-form expressions of each user’s outage probability are derived by approximating the composite channels as Rician distributions to characterize the channel coefficients of the system’s links. Considering the physical layer security in the presence of the eavesdropper, the secrecy outage probability of two users is further studied. The relationship between the system outage performance and the Rician factor of the channel, the number of RIS elements, and other factors are analyzed. The results of this study show that compared with Rayleigh fading, the Rician fading is more practical with the actual Air-to-Ground links; the user’s outage probability and the secrecy outage probability are lower over the Rician channels. The number of RIS elements and the power allocation factor by the base station for the users are inversely proportional to the user’s outage probability, and RIS element number, path loss index, and distance factor also have a greater impact on the outage probability. Compared with OMA, NOMA has a certain enhancement to the system performance.
Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment
Authors: Salas Christian , Durán Orlando , Vergara José Ignacio , Arata Adolfo
Published: November 2024

In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system’s ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems.
A Systematic Review of Living Labs in the Context of Sustainable Development with a Focus on Bioeconomy
Authors: Lakatos Elena Simina , Pacurariu Roxana Lavinia , Bîrgovan Andreea Loredana , Cioca Lucian Ionel , Szilagy Andrea , Moldovan Alina , Rada Elena Cristina
Published: November 2024

The living lab (LL) concept has a significant potential to drive sustainable development, particularly in the bioeconomy sector, by assisting communities in improving their quality of life whilst also considering the environmental impact. In this context, the main research question was the following: is there any specific mechanism through which living labs operate under the sustainable development concept? We reviewed 120 papers addressing this topic and performed a bibliometric analysis on the linkage between sustainability and living labs by applying the methodology of a systematic review. We concluded that living labs can drive innovation and experimentation in sustainability, which can be transposed into tangible solutions to economic, environmental, and social problems. The role of living labs in the transition to a bioeconomy is discussed and further research directions are presented.
Assessing the Establishment of American Mink (Neogale vison) Escapees from the Fur Industry in Bulgaria
Authors: Nikova Polina K. , Kachamakova Maria , Koshev Yordan
Published: November 2024

Mink farming has seen a resurgence in Bulgaria since 2013, and this has led to a high risk of American mink escaping and establishing feral populations. The largest active commercial farm in the country has a capacity of nearly 130,000 animals. The aims of this study were to gather first-hand evidence of the presence of mink in the wild around the farm, assess their level of establishment, and document the native species and local communities for future impact assessment. Surveys were conducted using camera traps within a 3.7 km radius around the farm in the period 2020–2021 at ten stations with 1943 realised trap-nights. Some early signs of the establishment of the American mink in Bulgaria were documented. A large number of registrations was made, as frequently as the Eurasian otter and golden jackal and more frequently than other mustelids in the study area. Mink were observed throughout the two-year study, and escaped mink have been registered in the region as early as 2017. These factors are interpreted as signs of the early stages of establishment. When considering environmental, economic, public health, and social factors, we recommend that mink farming should be banned in Bulgaria and further monitoring and management actions must be undertaken for the individuals in the wild.
Towards Computer-Supported Functional Modelling in Engineering Design Education
Authors: Martinec Tomislav , Valjak Filip , Horvat Nikola , Bojčetić Nenad
Published: November 2024

The growing need for solutions that can support the computer-based and distant assessment of functional models has resulted in ad hoc implementations of various diagramming tools. These tools are typically not intended for the purpose of functional modelling and lack the flexibility and efficiency of the traditional pen-and-paper approach. This paper reports on an experimental study of 42 students who were introduced to functional modelling through either printed vocabulary materials for pen-and-paper modelling or a specifically developed software application for computer-based modelling. All participants received an identical task—model an electric citrus juicer—with a brief description of how one operates and a photograph of a commercial example. The results show no significant difference in their total scores. However, the pen-and-paper group performed significantly better when it came to the selection of appropriate functions and creating plausible function–flow pairs. These results suggest that the current version of the software alters the functional modelling process in which the students typically engage. Also, it has been hypothesised that the software tool’s lack of flexibility and dynamism in presenting the predefined function vocabulary, when compared to the traditional printouts, might result in earlier fixation and the selection of less appropriate functions. On the other hand, the computer-supported approach can be better controlled and is less prone to critical errors, such as disregarding functional modelling conventions.
Integrated Extraction of Entities and Relations via Attentive Graph Convolutional Networks
Authors: Gao Chuhan , Xu Guixian , Meng Yueting
Published: November 2024

For information security, entity and relation extraction can be applied in sensitive information protection, data leakage detection, and other aspects. The current approaches to entity relation extraction not only ignore the relevance and dependency between name entity recognition and relation extraction but also may result in the cumulative propagation of errors. To solve this problem, it is proposed that an end-to-end joint entity and relation extraction model based on the Attention mechanism and Graph Convolutional Network (GCN) to simultaneously extract named entities and their relationships. The model includes three parts: the detection of entity span, the construction of an entity relation weighted graph, and the inference of entity relation type. Firstly, the detection of entity spans is viewed as a sequence labeling problem, and a multi-feature fusion approach for word embedding representation is designed to calculate all entity spans in a sentence to form an entity span matrix. Secondly, the entity span matrix is employed in the Multi-Head Attention mechanism for constructing the weighted adjacency matrix of the entity relation graph. Finally, for the inference of entity relation type, considering the interaction between entities and relations, the entity span matrix and relation connection matrix are simultaneously fed into the GCN for integrated extraction of entities and relations. Our model is evaluated on the public NYT dataset, attaining a precision of 66.4%, a recall of 63.1%, and an F1 score of 64.7% for joint entity and relation extraction, significantly outperforming other approaches. Experiments demonstrate that the proposed model is helpful for inferring entities and relations, considering the interaction between entities and relations through the Attention mechanism and GCN.
Proposed Explainable Interference Control Technique in 6G Networks Using Large Language Models (LLMs)
Authors: Tahir H. Ahmed , Alayed Walaa , Hassan Waqar Ul , Haider Amir
Published: November 2024

After the advent of 5th generation (5G) and 6th generation (6G) cellular networks, the complexity of managing real-time signal interference has increased in dense and dynamic environments. Traditional interference techniques, such as frequency reuse and allocation, while effective, lack robust adaptability and transparency needed to reduce interference in advanced communication networks. This paper introduces a novel approach that fuses large language models (LLMs) and Explainable Artificial Intelligence (XAI) to mitigate interference and enhance interference management in the mathematical foundations of 6G networks. The proposed approach provides accurate interference predictions, which the LLM balances with its complex architecture, necessary to meet the demands of beyond 5G and 6G networks, along with interpretable explanations to ensure transparency in decision-making. The proposed framework has been evaluated across various performance metrics. Interference latency consistently achieves lower rates of 0.95 s, compared to traditional techniques, which average around 1 s. Furthermore, the confidence score of the LLM shows a stable value of 0.87 throughout the system, compared to 0.85 in techniques without LLMs. Overall, the XAI-driven LLM demonstrates the potential of incorporating LLMs and XAI into wireless networks to improve resilience in next-generation networks. This proof of concept introduces a novel framework that offers new dimensions in wireless communication, particularly for interference management, prediction, and mitigation.
Threshold Filtering for Detecting Label Inference Attacks in Vertical Federated Learning
Authors: Ding Liansheng , Bao Haibin , Lv Qingzhe , Zhang Feng , Zhang Zhouyang , Han Jianliang , Ding Shuang
Published: November 2024

Federated learning, as an emerging machine-learning method, has received widespread attention because it allows users to train locally during the training process and uses relevant cryptographic knowledge to safeguard the privacy of data during model aggregation. However, existing federated learning is also susceptible to privacy breaches, e.g., label inference attacks against vertical federated learning scenarios, where an adversary is able to reason about the labels of other participants based on the trained model, leading to serious privacy breaches. In this paper, we design a detection method for label inference attacks in vertical federated learning scenarios, which is able to detect the attacks based on the principles of the attacks. We design a threshold-filtering detection method based on the principle of attack to determine that the model is under attack when the threshold value is greater than a set parameter. Furthermore, we have created six threat model classifications based on different a priori conditions of the adversary to comprehensively analyze the adversary’s attacks. In addition to the detection method of attacks, the extent of attacks on the model and the effectiveness of the defense can also be evaluated. The evaluation module will experimentally measure the changes in the relevant metrics such as the accuracy of the attack, the F1 score, and the change in the accuracy after the defense method. For example, detection in the full connected neural network model assesses the attack and defense effectiveness of the model with an attack accuracy of 86.72% in the breast cancer Wisconsin dataset and an F1 score of 0.743, which is reduced to 36.36% after dispersed training. This ensures that users have an overall grasp of the extent to which the training model is under attack before deploying the model.
Nets4Learning: A Web Platform for Designing and Testing ANN/DNN Models
Authors: Mudarra Antonio , Valdivia David , Ducange Pietro , Germán Manuel , Rivera Antonio J. , Pérez-Godoy M. Dolores
Published: November 2024

Nowadays, any research discipline is interested in tackling its problems with artificial intelligence and, therefore, is demanding knowledge and frameworks with the aim of developing and using intelligent methods. Within this scenario, neural networks stand out for the important results they have achieved. This paper introduces Nets4Learning, a web platform for designing, training and testing artificial/deep neural network models. The application deals with some of the most popular tasks in the data science field such as tabular classification, regression, image classification and object detection. Nets4Learning has been designed so that any researcher from any discipline can easily develop neural network models without special programming or digital skills. In fact, the user does not have to install anything as the application is publicly available and can be accessed from any device. The site also has manuals, glossaries, etc., and all this code is available on GitHub.
Design and Equivalent Circuit Model Extraction of a Fractal Slot-Loaded 3–40 GHz Super Wideband Antenna
Authors: Alamro Wasan , Seet Boon-Chong , Wang Lulu , Parthiban Prabakar
Published: November 2024

In this paper, we present the design and equivalent circuit model (ECM) of a fractal slot-loaded super wideband (SWB) antenna for compact and high-performance applications operating in the 3–40 GHz range. The proposed antenna features a compact dimension of 40 × 35 × 1.57 mm³, a measured bandwidth ratio of 13:1, a peak gain of 9.7 dBi, an average radiation efficiency of 94%, and a low cross-polarization level across the entire bandwidth. The presented ECM is derived using transmission line theory and incorporates the individual behavior of each constituting element of the antenna. A dual sequential optimization approach is employed to determine the optimal element values. The ECM results show good agreement with both simulated and measured results in terms of the magnitude of reflection coefficient |S11| and both real and imaginary impedances with low mean absolute percentage errors of 4.9%, 7.5%, and 7.7%, respectively, demonstrating the model’s ability to accurately predict the antenna’s performance.
Overbounding the Model Uncertainty for Kalman Filter-Based Advanced Receiver Autonomous Integrity Monitoring in the Presence of Time Correlation by the Hybrid Evolutionary Algorithm
Authors: Zhang Hengwei , Jiang Yiping
Published: November 2024

Overbounding the integrity risk is a significant challenge for Kalman filter (KF)-based advanced receiver autonomous integrity monitoring (ARAIM) when the measurement error has an uncertain time correlation. Thus, this paper presents a method that addresses this challenge by effectively bounding the integrity risk for KF-based ARAIM while considering the uncertainty in the model of the time-correlated error. Firstly, the recursive equation for covariance is derived, establishing a direct mathematical expression that links the integrity risk and the correlation time constant. Subsequently, a min–max optimization model is constructed, utilizing the obtained expression as the objective function, to simultaneously bound the integrity risk and reduce conservatism. To effectively address the current min–max optimization problem, a hybrid evolutionary algorithm is proposed, which conducts global searching followed by local searching. The simulation result demonstrates that it outperforms other algorithms, enabling rapid attainment of the minimum upper bound on the integrity risk.
Review on the Application of Living PSA in Nuclear Power
Authors: Zhang Min , Xu Zhao , Zhang Guichang , Wang Binbin , Zhang Bin , Liu Yilong
Published: November 2024

With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants.
Electrochemical Evaluation of Choline Bromide-Based Electrolyte for Hybrid Supercapacitors
Authors: Ayaganov Zhanibek , Malchik Fyodor , Bakenov Zhumabay , Mansurov Zulkhair , Maldybayev Kaiyrgali , Kurbatov Andrey , Ng Annie , Pavlenko Vladimir
Published: November 2024

Choline bromide (ChBr) has been less explored as an electrolyte material. This work demonstrates the promising potential of ChBr as a novel aqueous electrolyte for hybrid supercapacitors. At its optimized concentration of 3.5 M, ChBr solution exhibits a maximum conductivity of 79.56 mS cm−1 at room temperature, along with a viscosity of 3.15 mPas and a density of 1.14 g cm−3. A reduction in water activity of the optimized ChBr electrolyte concentration extends the electrochemical stability window (ESW), enabling operation up to 1.9 V for two-electrode cells. When the current densities increase from 0.5 to 5 A g−1, the hybrid supercapacitor based on ChBr electrolyte with the optimized mass ratio of electrodes composed of commercial microporous carbon (Maxsorb) demonstrates impressive specific energy and capacitance retention from 41 to 36 Wh kg−1 and from 330 to 300 F g−1 (per mass of one electrode), respectively. The experimental results obtained from this work demonstrate possibilities for further development and applications of ChBr-based hybrid systems in energy storage devices.
A Leaf Chlorophyll Content Estimation Method for Populus deltoides (Populus deltoides Marshall) Using Ensembled Feature Selection Framework and Unmanned Aerial Vehicle Hyperspectral Data
Authors: Chen Zhulin , Wang Xuefeng , Qiao Shijiao , Liu Hao , Shi Mengmeng , Chen Xingjing , Jiang Haiying , Zou Huimin
Published: November 2024

Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R2 of 0.848, RMSE of 1.454 μg/cm2 and MAE of 1.121 μg/cm2. Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks.
Source–Sink Structural Coupling Within Forest-Clustered Landscapes Drives Headstream Quality Dynamics in Mountainous Sub-Watersheds: A Case Study in Chongqing, China
Authors: Lin Li , Qin Kunrong , Yan Chen , Ren Wei , Zhu Haoxiang , Shu Chengji , Lai Xiaohong , Li Fangying , Liao Lingyun , Lan Siren , Li Mingyang , Wang Haiyang
Published: November 2024

Water environment quality is profoundly driven by a series of landscape characteristics. However, current knowledge is limited to the independent response of water quality to single landscape elements; this has led to poor knowledge of the potential role of structural coupling within landscapes in driving water quality changes, especially in those agroforestry-mixed mountainous watersheds with highly embedded forest-clustered landscapes and abundant headstreams. Given this fact, this study aims to evaluate whether and how the source–sink coupling structure of forest-clustered landscapes systematically drives headstream quality dynamics. We first systematically assessed the association pattern of source and sink structures within forest-clustered landscapes, and then innovatively proposed and constructed a functional framework of source–sink coupling structure of landscapes across 112 agroforestry-mixed mountainous sub-watersheds in Chongqing, China. On this basis, we further evaluated the driving pattern and predictive performance of the source–sink coupling structure of landscapes behind headstream quality dynamics. We report three findings: (1) headstream quality varied across agroforestry-mixed sub-watersheds, mapping out the source–sink structures and functions of landscapes; (2) there was significant functional coordination between source–sink structures of the forest-clustered landscapes, which significantly drove headstream quality dynamics; (3) the structural positioning and differences of the forest-clustered landscapes along the multivariate functional axes directly corresponded to and predicted headstream quality status. These findings together highlight a key logic that the response of water quality dynamics to landscapes is essentially that to the functional coupling between the source–sink structures of landscapes, rather than the simple combination of a single landscape contribution. This is the first study on the landscape–runoff association from the perspective of source–sink structural coupling, which helps to deepen understanding of the correlation mechanism between water dynamics and landscape systems, and provides a new functional dimension to the development of future landscape ecological management strategies from a local to a global scale.
Investigating the Effects of Elevation on Microbial Communities and Soil Properties at Fanjing Mountain, China
Authors: Wang Juncai , Xiao Shengyang , Hayat Kashif , Liao Xiaofeng , Chen Jingzhong , Zhang Lanyue , Xie Yuangui
Published: November 2024

Elevation is one of the most influential factors affecting soil characteristics and microbial communities in forest ecosystems. Nevertheless, there is no consensus on how soil characteristics, soil microbials, and their relationships response to the elevation of the mountain ecosystem. We investigated the soil physicochemical characteristics, the activity of soil enzymes, and the microbial community at elevational sites from 600 to 2400 m above sea level (asl) in the western slopes of the Fanjing Mountain ecosystem, China. The soil microbial communities were determined by high throughput 16S rRNA and ITS amplicon sequencing. The results demonstrated that soil total nitrogen (TN) showed a slight decrease, whereas total phosphorus (TP) and total potassium (TK) gradually tended to increase with increasing elevation. The large macroaggregates (>2 mm) accounted for the largest proportion of the aggregate fraction (66.23%–76.13%) in the 0–10 cm soil layer with elevation. The average values of the soil electrical conductivity (EC), soil organic carbon (SOC), and cation exchange capacity (CEC) concentration in the 0–60 cm layer undulated with increasing elevation, and the highest values were observed at 1500–1800 m asl and 1800–2100 m asl, respectively. The activities of soil urease, sucrase, acid phosphatase, and catalase clearly differed (p < 0.05) with increasing elevation, and the minimum values were found at 2100–2400 m asl. Interesting, with increasing soil depth, the values of these factors tended to decrease, indicating surface aggregation. In addition, the soil microbial (bacterial and fungal) community diversity exhibited a single-peak pattern with elevation. Our results also revealed that the soil bacterial and fungal communities varied significantly at different elevation sites. The bacterial communities were dominated by the phyla Acidobacteria, Pseudomonadota, and Chloroflexi, and the phyla Basidiomycota and Ascomycota dominated the fungal communities. The Pearson and redundancy analyses revealed that the SOC, TP, four soil enzymes, and soil aggregates were significant factors influencing the soil microbial community. In conclusion, soil properties and enzyme activities jointly explained the elevational pattern of the soil microbial community in the Fanjing Mountain. The results of this study provide insights into the influence of elevation on soil characteristics, microbial communities, and their relationships in the Fanjing Mountain ecosystem.
Effect of Adding Winemaking By-Product on the Characteristics of Petit Suisse Cheese Made with A2A2 Milk and Probiotic
Authors: Weis Cláudia Moreira Santa Catharina , Gomes Márcia Miss , Vicenzi Bárbara Geremia , Fabiano Giovanna Alexandre , Lopes Jean de Oliveira , Santos Patrícia Daniele da Silva dos , Tormen Luciano , Santos Oscar Oliveira , Bezerra Rosangela Maria Neves , Antunes Adriane Elisabete Costa , Bertan Larissa Canhadas , Costa Giselle Nobre , Yamazaki Ricardo Key
Published: November 2024

By-products generated in the winemaking industry contain compounds with health-promoting properties, which can be reintroduced into the food production chain. This study evaluated the use of a by-product from the industrial processing of grapes as an ingredient in the manufacture of Petit Suisse cheese, made with A2A2 milk and the addition of the probiotic Bifidobacterium animalis subsp. lactis HN019. Two Petit Suisse formulations were made in three independent batches: a control formulation without the addition of the by-product (F0) and a formulation containing 10% of the by-product (F1). The proximate composition of the cheeses was characterized on the first day after manufacturing them. The addition of the by-product led to an increase in ash, lipids, and carbohydrates and a reduction in moisture and protein contents. The physicochemical characterization and the texture profile analysis showed no changes throughout the product’s shelf life. The probiotic counts remained abundant (~eight log CFU/g) in both formulations with no changes seen throughout the shelf life period. Scanning electron microscopy images showed the added bacteria had typical structures. No differences were observed in the fatty acid profiles of the formulations, and both exhibited a total of 18 fatty acids, including saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs). Additionally, the by-product conferred antioxidant activity to the F1 formulation. The addition of the by-product in fresh cheese may be an interesting approach in regards to the processing technology used, its microbiological safety, and its nutritional value. The use of A2A2 milk and a probiotic culture thus enhanced the Petit Suisse cheese, resulting in a healthier product.
Acidogenic Fermentation of Organic Residual Solids: Effect of Different Alkaline Sources on pH, Alkalinity, and Fermentation Performance
Authors: Ramos-Suarez Maria , Zhang Yue , Heaven Sonia
Published: November 2024

Acidogenic fermentation (AF) of wastes provides a biological route to volatile fatty acids (VFA) production as an alternative to fossil hydrocarbons. As VFA are acidic in nature, the pH in AF typically drops, affecting the fermentation performance. The effect of different alkaline substances such as urea, NaOH, or CaCO3 on AF of organic residual solids (ORS), a food waste-rich fraction of municipal solid waste (MSW), was studied. Enzymes were used as an additive in simultaneous hydrolysis and fermentation (SHF) experiments. Use of urea (66 mg Urea g−1 TS) in SHF resulted in a VFA yield of 0.14 g VFA g−1 VS after 6 days, 177% higher than the control (no urea addition). pH and ammonia concentrations significantly influenced the bacterial population distribution. In SHF using NaOH/CaCO3, added alkalinity of 11 g CaCO3 L−1 did not influence VFA yields or pH control with NaOH addition at pH 7 or higher. This study demonstrates that, while increasing pH up to 8.5 using different alkaline substances improves VFA production, higher pH is detrimental to SHF due to enzyme inhibition. Finally, the use of urea as an alkaline source must be considered carefully as the acidic pH naturally reached by AF can inhibit urea hydrolysis.
Assessing the Role of Forest Grazing in Reducing Fire Severity: A Mitigation Strategy
Authors: Lovreglio Raffaella , Lovreglio Julian , Satta Gabriele Giuseppe Antonio , Mura Marco , Pulina Antonio
Published: November 2024

This study investigates the role of prescribed grazing as a sustainable fire prevention strategy in Mediterranean ecosystems, with a focus on Sardinia, an area highly susceptible to wildfires. Using FlamMap simulation software, we modeled fire behavior across various grazing and environmental conditions to assess the impact of grazing on fire severity indicators such as flame length, rate of spread, and fireline intensity. Results demonstrate that grazing can reduce fire severity by decreasing combustible biomass, achieving reductions of 25.9% in fire extent in wet years, 60.9% in median years, and 45.8% in dry years. Grazed areas exhibited significantly lower fire intensity, particularly under high canopy cover. These findings support the integration of grazing into fire management policies, highlighting its efficacy as a nature-based solution. However, the study’s scope is limited to small biomass fuels (1-h fuels); future research should extend to larger fuel classes to enhance the generalizability of prescribed grazing as a fire mitigation tool.
The Formation of Protein–Chitosan Complexes: Their Interaction, Applications, and Challenges
Authors: Xie Yufeng , Ding Jiaqi , Li Yue , Wei Pengfei , Liu Shiying , Yang Rui
Published: November 2024

Protein–polysaccharide interactions have been a subject of considerable interest in the field of food science. Chitosan is the most prominent and naturally occurring polysaccharide with a positive charge, and its hydroxyl and amino groups facilitate protein–chitosan interactions due to their diverse biochemical activities. The complexation of chitosan enables the modification of proteins, thereby enhancing their value for applications in the food and nutrition industry. This paper presents a summary of the complexes formed by chitosan and different proteins, such as lactoglobulin, egg white protein, soybean isolate protein, whey isolate protein, and myofibrillar protein, and systematically describes the modes of interaction between proteins and chitosan. The effects of protein–chitosan interactions on functional properties such as solubility, emulsification, antioxidant activity, and stability are outlined, and the potential applications of protein–chitosan complexes are discussed. In addition, the current challenges associated with the formation of protein–chitosan complexes and potential solutions to these challenges are highlighted. This paper provides an overview of the current research progress on the interaction of proteins with chitosan and its derivatives in the food industry.
The Anthropological Process of Identifying Missing Persons as a Teaching Method for Increasing Awareness in Legal and Forensic Dentistry in the Republic of Cyprus
Authors: Giannakopoulos Kostis , Lambrou Persefoni , Kaklamanos Eleftherios G. , Aristotelous Anna
Published: November 2024

Forensic Odontology is a critical field within forensic science, with significant importance in legal contexts and the identification of victims in mass disasters or cases of abuse. This article focuses on the efforts made by the European University Cyprus (EUC) School of Dentistry to raise awareness among dental students regarding legal and forensic dentistry practices. Through voluntary visits to the Anthropological Laboratory of the Committee on Missing Persons (CMP) in Cyprus, students gain firsthand experience and insights into anthropological analysis for identifying missing individuals. These visits provide students with practical application opportunities, emphasizing record-keeping practices, oral tissue morphology analysis, and unique aspects of restorative work during identification processes. Feedback from participating students highlights the value of experiential learning in increasing awareness about forensic odontology’s importance and relevance in real-world scenarios. The initiatives undertaken by the EUC School of Dentistry contribute to enhancing legal and forensic dentistry awareness among future dental professionals in Cyprus.
Vehicular Traffic Flow Detection and Monitoring for Implementation of Smart Traffic Light: A Case Study for Road Intersection in Limeira, Brazil
Authors: dos Santos Ximenes Talía Simões , de Oliveira Silva Antonio Carlos , de Martino Guilherme Pieretti , Emiliano William Machado , Menzori Mauro , Meyer Yuri Alexandre , Molina Júnior Vitor Eduardo
Published: November 2024

This paper proposes the development of a smart traffic light prototype based on vehicular traffic flow measurement in the stretch between two avenues in the city of Limeira, SP, Brazil, focusing on the stretch towards UNICAMP’s School of Technology. To this end, we initially developed a Python code using the OpenCV library in order to detect and count vehicles. With the counting in operation, programming logic was inserted, aiming at preparing traffic light timers based on vehicular traffic. Finally, the traffic lights were added to display video via a code change to show the ongoing color changes, also obtaining a code for identifying vehicles and flow, in addition to the virtual traffic light system itself in the system. Vehicle counting accuracy was 75% for large vehicles, 90% for passenger cars, and 100% for motorcycles. The simulation of a smart traffic light implementation worked satisfactorily according to the post-processing of the video recorded for validation.