bluuebunny/crossref_metadata_2025 huggingface dataset:
Explained Learning and Hyperparameter Optimization of Ensemble Estimator on the Bio-Psycho-Social Features of Children and Adolescents
Authors: Drobnič Franc , Starc Gregor , Jurak Gregor , Kos Andrej , Pustišek Matevž
Published: October 2023

For decades, projects have been carried out in various countries to assess the developmental status of children and adolescents using anthropometry and specific kinesiological measurements. There is a need for the ability to evaluate this developmental status using a sufficiently simple method or a calculation to be applicable in practice. The most commonly used feature for this purpose is currently body mass index (BMI). From recent experience, this feature may cause problems if used indiscriminately in the developmental phase of life. Therefore, we aimed to find a more suitable feature set. We used data from Artos, the national program monitoring school children and adolescents in Slovenia. The data was analyzed using machine learning (ML) tools to find the most important features to predict a motor efficiency index (MEI), which has been shown to correlate strongly with a person’s health prospects. After data preparation and training a baseline model, a feature selection process was performed, which promoted some features as candidates to predict the motor efficiency index sufficiently. By implementing a hyperparameter optimization, we tuned the ML model to improve its generalization and present the feature interaction more elaborately. We show that besides the single feature’s importance, the features’ interaction should be considered. In the case of MEI, we find that the skin fold thicknesses can complement BMI and contribute to a better development status assessment of children and adolescents.
Incremental Single-Class Fault Detection and Diagnosis Method for Rolling Bearings Based on OS-ELM
Authors: Hao Huijuan , Zhao Yuanyuan , Chen Yu , Zhang Yu , Wang Dan
Published: October 2023

Aiming at the problem of current equipment fault diagnosis models based on deep learning being unable to automatically identify new class faults according to the updated fault data, in this paper we propose an incremental single-class fault diagnosis method based on an online sequential extreme learning machine (OS-ELM). In addition to detecting new types of faults, this method can perform class-incremental learning based on new-class fault data, treating the new-class faults as known faults for ongoing fault detection and diagnosis tasks. This approach first constructs a feature extraction network with a dual-encoder structure to extract data features. Subsequently, the extracted features are used to build a fault diagnosis network based on OS-ELM, where the novelty of new batches of data is determined by the update magnitude of OS-ELM. When a new-class fault is detected, a new OS-ELM representing the current new class is constructed using the new batch of data and added to the fault diagnosis network, thereby achieving incremental model updates. The proposed method is validated through experiments on the CWRU dataset and MFPT dataset. The results demonstrate that the accuracy of this method on the CWRU dataset is 99.62%, while on the MFPT dataset it reaches 98.80%. Compared to other incremental single-class models, this method exhibits excellent fault recognition and diagnosis capabilities.
Research on Global Deterministic Direct Forwarding and Scheduling of Mixed Flow Based on Time-Sensitive Network in Substation
Authors: Shen Zhongyuan , Wang Hao , Wei Min , Wang Ping
Published: October 2023

Time-sensitive networks enable the high-quality mixed transmission of various types of business flows. However, the Time-Aware Scheduler mechanism fails to address the issue of interference in data flows with the same priority. This paper conducts an in-depth analysis of the store-and-forward mechanism in switches, combining it with the characteristics of critical GOOSE and SV-type flows in substations. By introducing methods such as setting offsets and allocating redundant time slots to the data flow of the transmitter in the TAS scheduling mechanism, all factors that cause conflicting interference to the data flow transmission in the TSN network are solved, and the uncertain queuing delay is eliminated. The proposed scheduling algorithm, compared to the TAS scheduling algorithm of the FIFO rule, achieves a maximum reduction of 34.35% in the transmission delay of critical business flows, while the jitter is controlled below 10 μs. Compared to the strict priority algorithm, it reduces the transmission delay by 40.26% while maintaining the standard deviation of delay within 1.59%. The maximum transmission delay and the minimum transmission delay of the data flow scheduled in this paper are between the theoretical boundary values without queuing delay, which satisfies the deterministic transmission of critical business flows under high load conditions, and provides support for future substation integrated networking and high load applications.
Efficient Object Placement via FTOPNet
Authors: Ye Guosheng , Wang Jianming , Yang Zizhong
Published: October 2023

Image composition involves the placement of foreground objects at an appropriate scale within a background image to create a visually realistic composite image. However, manual operations for this task are time-consuming and labor-intensive. In this study, we propose an efficient method for foreground object placement, comprising a background feature extraction module (BFEM) designed for background images and a foreground–background cross-attention feature fusion module (FBCAFFM). The BFEM is capable of extracting precise and comprehensive information from the background image. The fused features enable the network to learn additional information related to foreground–background matching, aiding in the prediction of foreground object placement and size. Our experiments are conducted using the publicly available object placement assessment (OPA) dataset. Both quantitative and visual results demonstrate that FTOPNet effectively performs the foreground object placement task and offers a practical solution for image composition tasks.
Electromagnetic Interference Cancellation in the Frequency Domain Based on the ASEI-VMD Method
Authors: Chen Dongwei , Jin Mengzhe , Liu Jinchao , Liu Weidong , Fang Qingyuan
Published: October 2023

In electromagnetic compatibility (EMC) testing, accurately extracting the radiation emission characteristics from the Equipment Under Test (EUT) in complex electromagnetic environments remains a daunting task. This paper presents a solution by introducing of a frequency-domain electromagnetic interference cancellation method based on the Adaptive Singular Envelope Iterative Variational Mode Decomposition (ASEI-VMD). The process begins with the application of the Adjacent Singular Envelope Entropy Ratio (ASEER) as an evaluation metric to adaptively discern the number of decomposition layers and the penalty factor, allowing the effective decomposition of signals from various channels into modal signals sharing the same center frequency and bandwidth. The singular envelope entropy of each VMD mode is computed as the basis for assessing the efficacy of components in the VMD. This step distinguishes the signals encapsulating the EUT frequency information, which are then isolated for cancellation to retrieve the actual EUT-radiated emission signal. Simulation and experimental data validate the efficacy of this approach. Uniquely, it neither places demands on the testing site nor requires prior conditions for the EUT. The retrieved signal exhibits a correlation of over 96% with the source signal, maintaining a signal-power error below 3 dB. It is suitable for conducting electromagnetic radiation emission testing in complex environments.
Multi-Attention Infused Integrated Facial Attribute Editing Model: Enhancing the Robustness of Facial Attribute Manipulation
Authors: Lin Zhijie , Xu Wangjun , Ma Xiaolong , Xu Caie , Xiao Han
Published: October 2023

Facial attribute editing refers to the task of modifying facial images by altering specific target facial attributes. Existing approaches typically rely on the combination of generative adversarial networks and encoder–decoder architectures to tackle this problem. However, current methods may exhibit limited accuracy when dealing with certain attributes. The primary objective of this research is to enhance facial image modification based on user-specified target facial attributes, such as hair color, beard removal, or gender transformation. During the editing process, it is crucial to selectively modify only the regions relevant to the target attributes while preserving the details of other unrelated facial attributes. This ensures that the editing results appear more natural and realistic. This study introduces a novel approach called MAGAN (Combining GRU Structure and Additive Attention with AGU—Adaptive Gated Units). Moreover, a discriminative attention mechanism is introduced to automatically identify key regions in the input images that are relevant to facial attributes. This mechanism concentrates attention on these regions, enhancing the model’s ability to accurately capture and analyze subtle facial attribute features. The method incorporates external attention within the convolutional layers of the encoder–decoder architecture, facilitating the modeling of linear complexity across image regions and implicitly considering correlations among all data samples. By employing discriminative attention in the discriminator, the model achieves more precise attribute editing. To evaluate the effectiveness of MAGAN, experiments were conducted on the CelebA dataset. The average precision of facial attribute generation in images edited by our model stands at 91.83%. PSNR and SSIM for reconstructed images are 32.52 and 0.957, respectively. In comparison with existing methodologies (AttGAN, STGAN, MUGAN), noteworthy enhancements have been achieved in the domain of facial attribute manipulation.
A Two-Degree-of-Freedom PID Integral Super-Twisting Controller Based on Atom Search Optimizer Applied to DC-DC Interleaved Converters for Fuel Cell Applications
Authors: Saadi Ramzi , Hammoudi Mohamed Yacine , Salah Okba , Laadjal Khaled , Cardoso Antonio J. Marques
Published: October 2023

This paper focuses on the real-time implementation of an optimal high-performance control applied to an interleaved nonisolated DC/DC converter designed for fuel cell applications. Three-phase interleaved boost converters are utilized to minimize input current undulation, increase efficiency, and provide a high output voltage in order to ensure the performance of the FC stack. The proposed control strategy contains an outer loop that generates the reference current based on a two-degree-of-freedom PID controller. This controller provides a robust setpoint tracking and disturbance rejection, which improves the system’s response and efficiency. A fast inner regulation loop based on a super-twisting integral sliding mode (STISM) algorithm is developed to achieve a fixed converter output voltage, equitable phase current sharing, and fast regulation against load disturbances in failure operation. The STISM algorithm exhibits a rapid convergence property of the sliding mode and effectively avoids the chattering phenomena frequently observed in conventional sliding modes. The proposed controller’s gains are determined using the atom search optimization algorithm, which ensures exceptional reliability and a high degree of robustness and stability of the controllers under a variety of operational conditions. This method is inspired from the behavior of atoms and their electrons during the excitation process leading to a one-of-a-kind optimization technique which contributes to the controller’s reliability. Using Matlab-Simulink simulation tools, the efficacy and performance of the designed control have first been evaluated and assessed and compared with other optimization algorithms, and then with a dual loop based on a PID controller. Then, they have been verified by real-time hardware implementation on a 1.2 KW prototype FC converter driven by the dSPACE-1104 card under a variety of tests. The suggested approach offers impressive experimental results in dynamic and steady states.
Tuning Parameters of the Fractional Order PID-LQR Controller for Semi-Active Suspension
Authors: Gao Jin , Li Hui
Published: October 2023

In order to further improve the control effect of proportion integral differential (PID) control and linear quadratic regulator (LQC) control, and improve vehicle ride comfort and enhance body stability, the 7 DOF semi-active suspension model was established, and the fractional order PIλDμ-LQR controller was designed by combining fractional order PIλDμ control theory and LQR control theory. The semi-active suspension model in this paper is more complex, and there are many parameters in the controller. The optimal weighting coefficient of 12 vehicle smoothness evaluation indicators and parameters Kp, Ki, Kd, λ and μ in the controller were founded by NSGA-II algorithm. After optimization, the optimized parameters were brought into the controller for random pavement simulation. Compared to the traditional passive suspension, fractional order PIλDμ individual control and LQR separate control, the simulation results show that the effect of fractional order PIλDμ-LQR control is very significant. The evaluation index of vehicle smoothness has been significantly improved, and the use of fractional order PIλDμ-LQR control has significantly improved the working performance of the suspension and improved the smoothness of the vehicle. At the same time, the adjusting force output of the actuator is very balanced, which inhibits the roll of the body and improves the anti-roll performance. After simulation, the excellent performance of the designed fractional PIλDμ-LQR controller was verified, and the introduced NSGA-II algorithm played an important role in the controller parameter tuning work, which shows that the fractional order PIλDμ-LQR controller and NSGA-II algorithm cooperate with each other to achieve good control effects.
Influence of a PCB Layout Design on the Efficiency of Heat Dissipation and Mutual Thermal Couplings between Transistors
Authors: Górecki Krzysztof , Posobkiewicz Krzysztof
Published: October 2023

This article presents the results of the investigations concerning the influence of the printed circuit board (PCB) layout design on self and transfer transient thermal impedances characterizing thermal phenomena occurring in the network containing two power MOSFETs. The tested devices have the case D2PAK and are soldered to the PCB using the surface mount technology (SMT). The measurement method is described. The tested transistors are presented with the used PCBs on which they are mounted. The obtained measurement results of the mentioned thermal parameters of the tested transistors operating on all the tested PCBs are shown and discussed. The influence of a cooling area of the tested PCBs on the parameters describing self and transfer transient thermal impedances is analyzed.
Blueprint Separable Subsampling and Aggregate Feature Conformer-Based End-to-End Neural Diarization
Authors: Jiao Xiaolin , Chen Yaqi , Qu Dan , Yang Xukui
Published: October 2023

At present, a prevalent approach to speaker diarization is clustering based on speaker embeddings. However, this method encounters two primary issues. Firstly, it cannot directly minimize the diarization error during the training process; secondly, the majority of clustering-based methods struggle to handle speaker overlap in audio. A viable approach for addressing these issues involves adopting end-to-end speaker diarization (EEND). Nevertheless, training this EEND system generally requires lengthy audio inputs, which must be downsampled to allow efficient model processing. In this study, we develop a novel downsampling layer using blueprint separable convolution (BSConv) instead of depthwise separable convolution (DSC) as the foundational convolutional unit, which effectively preserves information from the original audio. Furthermore, we incorporate multi-scale feature aggregation (MFA) into the encoder structure to combine the features extracted by each conformer block to the output layer, consequently enhancing the expressiveness of the model’s feature extraction. Lastly, we employ the conformer as the backbone network to incorporate the proposed enhancements, resulting in an EEND system named BSAC-EEND. We assess our suggested methodology on both simulated and real datasets. The experiment indicates that our proposed EEND system reduces diarization error rate (DER) by an average of 17.3% for two-speaker datasets and 12.8% for three-speaker datasets compared to the baseline.
Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization
Authors: Yue Yaowei , Peng Yun , Wang Duancheng
Published: October 2023

Manually tuning the hyperparameters of a deep learning model is not only a time-consuming and labor-intensive process, but it can also easily lead to issues like overfitting or underfitting, hindering the model’s full convergence. To address this challenge, we present a BiLSTM-TCSA model (BiLSTM combine TextCNN and Self-Attention) for deep learning-based sentiment analysis of short texts, utilizing an improved particle swarm optimization (IPSO). This approach mimics the global random search behavior observed in bird foraging, allowing for adaptive optimization of model hyperparameters. In this methodology, an initial step involves employing a Generative Adversarial Network (GAN) mechanism to generate a substantial corpus of perturbed text, augmenting the model’s resilience to disturbances. Subsequently, global semantic insights are extracted through Bidirectional Long Short Term Memory networks (BiLSTM) processing. Leveraging Convolutional Neural Networks for Text (TextCNN) with diverse convolution kernel sizes enables the extraction of localized features, which are then concatenated to construct multi-scale feature vectors. Concluding the process, feature vector refinement and the classification task are accomplished through the integration of Self-Attention and Softmax layers. Empirical results underscore the effectiveness of the proposed approach in sentiment analysis tasks involving succinct texts containing limited information. Across four distinct datasets, our method attains impressive accuracy rates of 91.38%, 91.74%, 85.49%, and 94.59%, respectively. This performance constitutes a notable advancement when compared against conventional deep learning models and baseline approaches.
Multimodel Collaboration to Combat Malicious Domain Fluxing
Authors: Nie Yuanping , Liu Shuangshuang , Qian Cheng , Deng Congyi , Li Xiang , Wang Zhi , Kuang Xiaohui
Published: October 2023

This paper proposes a novel domain-generation-algorithm detection framework based on statistical learning that integrates the detection capabilities of multiple heterogeneous models. The framework includes both traditional machine learning methods based on artificial features and deep learning methods, comprehensively analyzing 34 artificial features and advanced features extracted from deep neural networks. Additionally, the framework evaluates the predictions of the base models based on the fit of the samples to each type of sample set and a predefined significance level. The predictions of the base models are statistically analyzed, and the final decision is made using strategies such as voting, confidence, and credibility. Experimental results demonstrate that the DGA detection framework based on statistical learning achieves a higher detection rate compared to the underlying base models, with accuracy, precision, recall, and F1 scores reaching 0.979, 0.977, 0.981, and 0.979, respectively. The framework also exhibits a stronger adaptability to unknown domains and a certain level of robustness against concept drift attacks.
Dark Fermentation in the Dark Biosphere: The Case of Citrobacter sp. T1.2D-12
Authors: Gallego-Rodríguez Violeta , Martínez-Bonilla Adrián , Rodríguez Nuria , Amils Ricardo
Published: October 2023

Microbial diversity that thrives in the deep subsurface remains largely unknown. In this work, we present the characterization of Citrobacter sp. T1.2D-1, isolated from a 63.6 m-deep core sample extracted from the deep subsurface of the Iberian Pyrite Belt (IPB). A genomic analysis was performed to identify genes that could be ecologically significant in the IPB. We identified all the genes that encoded the formate–hydrogen lyase and hydrogenase-2 complexes, related to hydrogen production, as well as those involved in glycerol fermentation. This is particularly relevant as some of the substrates and byproducts of this process are of industrial interest. Additionally, we conducted a phylogenomic study, which led us to conclude that our isolate was classified within the Citrobacter telavivensis species. Experimentally, we verified the strain’s ability to produce hydrogen from glucose and glycerol and, thus, of performing dark fermentation. Moreover, we assessed the activity of the nitrate and tetrathionate reductase complexes and the isolate’s ability to tolerate high concentrations of heavy metals, especially Zn. These results suggest that C. telavivensis T1.2D-1 can play a role in the carbon, hydrogen, iron, nitrogen, and sulfur cycles that occur in the deep subsurface of the IPB, making it a candidate worthy of further study for possible biotechnological applications.
Effect of Ultraviolet Irradiation on Vitamin D in Commonly Consumed Mushrooms in Thailand
Authors: Judprasong Kunchit , Chheng Sochannet , Chimkerd Chanika , Jittinandana Sitima , Tangsuphoom Nattapol , Sridonpai Piyanut
Published: October 2023

This study examined the effect and stability of ultraviolet B (UV-B) irradiation and subsequent cooking on vitamin D content in commonly consumed mushrooms in Thailand. Eight varieties of mushrooms were exposed to two-sided UV-B lamps for up to 3 h in a patented cabinet, followed by vitamin D content analysis. Thereafter, the four mushroom varieties with the highest vitamin D content were exposed to UV irradiation, cooked, and analyzed for various forms of vitamin D using LC-MS-MS. The results showed that vitamin D2 in all varieties of mushrooms significantly increased (p < 0.05) after UV-B irradiation according to the exposure time. The highest level of vitamin D2 was found in enokitake mushrooms. In addition, 25-OH D2 and vitamin D4 contents increased after UV-B irradiation in enokitake mushrooms. The vitamin D2 true retention in all cooked mushrooms ranged from 53 to 89% and was highest in stir-fried mushrooms. With economic investment, the two-sided UV-B cabinet has the potential to increase the vitamin D content in commercial mushroom production.
Emerging Processes for Sustainable Processing of Food Ingredients and Products
Authors: Castro-Muñoz Roberto
Published: October 2023

In recent decades, traditional food processing processes, such as homogenization, pasteurization, canning, drying, and smoking, among others, have been successfully applied to obtain, to some extent, acceptable food items [...]
High Pressure and Pasteurization Effects on Dairy Cream
Authors: Machado Fernanda , Duarte Ricardo V. , Pinto Carlos A. , Casal Susana , Lopes-da-Silva José A. , Saraiva Jorge A.
Published: October 2023

Dairy cream, a common ingredient in various dishes and food products, is susceptible to rapid microbial growth due to its high water activity (≈0.97) and pH (≈6.7). Thus, it requires proper processing conditions to ensure food safety and extend shelf life. High-pressure processing (HPP) has emerged as a nonthermal food pasteurization method, offering an alternative to conventional heat-based techniques to obtain tastier, fresh-like, and safe dairy products without undesirable heat-induced alterations. This study assessed the impact of HPP (450 and 600 MPa for 5 and 15 min at 7 °C) and thermal pasteurization (75 °C for 15 s) on the microbiological and physicochemical attributes of dairy cream immediately after processing and throughout refrigerated storage (4 °C). HPP-treated samples remained microbiologically acceptable even on the 51st day of storage, unlike thermally pasteurized samples. Moreover, HPP decreased inoculated Escherichia coli and Listeria innocua counts by more than 6 log units to undetectable levels (1.00 log CFU/mL). pH, color (maximum variation of ΔE* up to 8.43), and fatty acid profiles remained relatively stable under varying processing conditions and during storage. However, viscosity exhibited higher values for HPP-treated samples (0.028 ± 0.003 Pa·s) compared to thermally processed ones (0.016 ± 0.002 Pa·s) by the 28th day of storage. Furthermore, volatile compounds (VOCs) of all treated samples presented a tendency to increase throughout storage, particularly acids and aliphatic hydrocarbons. These findings show HPP’s potential to significantly extend the shelf life of highly perishable dairy cream by at least 15 days compared to thermal pasteurization.
Rehashing Our Insight of Seaweeds as a Potential Source of Foods, Nutraceuticals, and Pharmaceuticals
Authors: Baghel Ravi S. , Choudhary Babita , Pandey Sonika , Pathak Pradeep Kumar , Patel Manish Kumar , Mishra Avinash
Published: October 2023

In a few Southeast Asian nations, seaweeds have been a staple of the cuisine since prehistoric times. Seaweeds are currently becoming more and more popular around the world due to their superior nutritional value and medicinal properties. This is because of rising seaweed production on a global scale and substantial research on their composition and bioactivities over the past 20 years. By reviewing several articles in the literature, this review aimed to provide comprehensive information about the primary and secondary metabolites and various classes of bioactive compounds, such as polysaccharides, polyphenols, proteins, and essential fatty acids, along with their bioactivities, in a single article. This review also highlights the potential of seaweeds in the development of nutraceuticals, with a particular focus on their ability to enhance human health and overall well-being. In addition, we discuss the challenges and potential opportunities associated with the advancement of pharmaceuticals and nutraceuticals derived from seaweeds, as well as their incorporation into different industrial sectors. Furthermore, we find that many bioactive constituents found in seaweeds have demonstrated potential in terms of different therapeutic attributes, including antioxidative, anti-inflammatory, anticancer, and other properties. In conclusion, seaweed-based bioactive compounds have a huge potential to play an important role in the food, nutraceutical, and pharmaceutical sectors. However, future research should pay more attention to developing efficient techniques for the extraction and purification of compounds as well as their toxicity analysis, clinical efficacy, mode of action, and interactions with regular diets.
Non-Saccharomyces Yeasts from Organic Vineyards as Spontaneous Fermentation Agents
Authors: López-Enríquez Lorena , Vila-Crespo Josefina , Rodríguez-Nogales José Manuel , Fernández-Fernández Encarnación , Ruipérez Violeta
Published: October 2023

Currently, non-Saccharomyces yeasts are the subject of interest, among other things, for their contribution to the aromatic complexity of wines. In this study, the characterisation of non-Saccharomyces yeasts was addressed by their isolation during spontaneous fermentations of organic Verdejo grapes, obtaining a total of 484 isolates, of which 11% were identified by molecular techniques as non-Saccharomyces yeasts. Fermentative isolates belonging to the species Hanseniaspora meyeri, Hanseniaspora osmophila, Pichia guilliermondii, Pichia kudriavzevii, Torulaspora delbrueckii, and Wickerhamomyces anomalus were analysed. Significant differences were found in the yeast populations established at the different fermentation stages. Interestingly, W. anomalus stood up as a widely distributed species in vineyards, vintages, and fermentation stages. Several of the strains studied stood out for their biotechnological potential in the production of Verdejo wine, showing the presence of relevant enzymatic activity for the release of varietal aromas and the technological improvement of the winemaking process. Three enzymatic activities were found in an important number of isolates, β-glucosidase, protease, and β-lyase, implicated in the positive aromatic impact on this style of white wine. In that sense, all the isolates of W. anomalus presented those activities. T. delbrueckii isolates were highlighted for their significant β-lyase activity. In addition, T. delbrueckii was outlined because of its potential to achieve an elevated fermenting power, as well as the lack of lag phase. The results obtained highlight the importance of maintaining the microbial diversity that contributes to the production of wines with unique and distinctive characteristics of the production region.
From Waste to Resource: Valorization of Lignocellulosic Agri-Food Residues through Engineered Hydrochar and Biochar for Environmental and Clean Energy Applications—A Comprehensive Review
Authors: Escudero-Curiel Silvia , Giráldez Alba , Pazos Marta , Sanromán Ángeles
Published: October 2023

Agri-food residues or by-products have increased their contribution to the global tally of unsustainably generated waste. These residues, characterized by their inherent physicochemical properties and rich in lignocellulosic composition, are progressively being recognized as valuable products that align with the principles of zero waste and circular economy advocated for by different government entities. Consequently, they are utilized as raw materials in other industrial sectors, such as the notable case of environmental remediation. This review highlights the substantial potential of thermochemical valorized agri-food residues, transformed into biochar and hydrochar, as versatile adsorbents in wastewater treatment and as promising alternatives in various environmental and energy-related applications. These materials, with their enhanced properties achieved through tailored engineering techniques, offer competent solutions with cost-effective and satisfactory results in applications in various environmental contexts such as removing pollutants from wastewater or green energy generation. This sustainable approach not only addresses environmental concerns but also paves the way for a more eco-friendly and resource-efficient future, making it an exciting prospect for diverse applications.
Processing and Utilization of Functional Substances in Edible Plant Products and the Effects on Health Properties
Authors: Zeng Zheling , Yan Xianghui
Published: October 2023

The world’s population is expected to reach 10 billion by 2050, which will pose a threat to the sustainable development of animal-derived foods [...]
What Have We Learned from the Past? An Analysis of Ground Deformations in Urban Areas of Palermo (Sicily, Italy) by Means of Multi-Temporal Synthetic Aperture Radar Interferometry Techniques
Authors: Famiglietti Nicola Angelo , Miele Pietro , Petti Luigi , Guida Domenico , Guadagno Francesco Maria , Moschillo Raffaele , Vicari Annamaria
Published: October 2023

This study focuses on analyzing and monitoring urban subsidence, particularly in the city of Palermo, Italy. Land subsidence, induced by natural and human factors, poses threats to infrastructure and urban safety. Remote sensing (RS), specifically synthetic-aperture radar interferometry (In-SAR), is employed due to its ability to detect ground displacements over large areas with great precision. The persistent scatterer InSAR (PS-InSAR) technique is utilized to identify stable targets and track millimeter-level surface deformations. This research spans from October 2014 to October 2021, using Sentinel-1 satellite data to capture ground deformation from various angles. The findings are integrated into an accessible web app (ArcGIS) for local authorities that could be used aiding in urban planning and enhancing safety measures. This study’s results offer updated deformation maps, serving as an operational tool to support decision-making and community resilience, emphasizing risk awareness and responsible practices. This study highlights that the exponential expansion of urban areas, which does not take into account historical information, can gravely jeopardize both the integrity of urban infrastructure and the well-being of its inhabitants. In this context, remote sensing technologies emerge as an invaluable ally, used in monitoring and safeguarding the urban landscape.
Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol
Authors: Sorici Alexandru , Băjenaru Lidia , Mocanu Irina Georgiana , Florea Adina Magda , Tsakanikas Panagiotis , Ribigan Athena Cristina , Pedullà Ludovico , Bougea Anastasia
Published: October 2023

(1) Objective: We explore the predictive power of a novel stream of patient data, combining wearable devices and patient reported outcomes (PROs), using an AI-first approach to classify the health status of Parkinson’s disease (PD), multiple sclerosis (MS) and stroke patients (collectively named PMSS). (2) Background: Recent studies acknowledge the burden of neurological disorders on patients and on the healthcare systems managing them. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients. (3) Methods: We introduce the data collection journey within the ALAMEDA project, which continuously collects PRO data for a year through mobile applications and supplements them with data from minimally intrusive wearable devices (accelerometer bracelet, IMU sensor belt, ground force measuring insoles, and sleep mattress) worn for 1–2 weeks at each milestone. We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. (4) Results: A novel combination of wearable devices and smartphone applications required for the desired analysis of motor, sleep, emotional and quality-of-life outcomes is introduced. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups (in terms of standard medical test targets). Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.
Vulnerability to Suicide Ideation: Comparative Study between Adolescents with and without Psychosocial Risk
Authors: Brás Marta , Elias Patrícia , Cunha Francisca Ferreira , Martins Cátia , Nunes Cristina , Carmo Cláudia
Published: October 2023

Adolescents are characterized as a risk group for suicide, being the fourth leading cause of death in young people. The main aim was to compare vulnerability to suicidal ideation in a sample of young people with and without psychosocial risk. The total sample consisted of 137 adolescents, aged between 10 and 19 years (M = 14.76; SD = 1.40), and it was composed of two groups—the psychosocial risk group (n = 60) and general population group (n = 77). In both groups, suicidal ideation correlated positively with negative events and negatively with self-esteem and social support satisfaction. When comparing the two groups, the psychosocial risk group presented significantly higher mean values of negative life events (mainly separations/losses and physical and sexual abuse) and significantly lower mean values of satisfaction with social support (particularly with family and social activities). It was also found that, in the psychosocial risk group, negative life events were the only significant predictors of suicidal ideation. This study allowed identifying the role of risk and protective factors in suicidal ideation, according to the psychosocial risk of adolescents. The practical implications of the findings on adolescents’ mental health and the promotion of their well-being are discussed.
Environmental Factors Influencing the Dynamics and Evolution of COVID-19: A Systematic Review on the Study of Short-Term Ozone Exposure
Authors: Popescu Irina-Maria , Baditoiu Luminita Mirela , Reddy Sandhya Rani , Nalla Akhila , Popovici Emilian Damian , Margan Madalin-Marius , Anghel Mariana , Laitin Sorina Maria Denisa , Toma Ana-Olivia , Herlo Alexandra , Fericean Roxana Manuela , Baghina Nina , Anghel Andrei
Published: October 2023

The potential influence of environmental factors, particularly air pollutants such as ozone (O3), on the dynamics and progression of COVID-19 remains a significant concern. This study aimed to systematically review and analyze the current body of literature to assess the impact of short-term ozone exposure on COVID-19 transmission dynamics and disease evolution. A rigorous systematic review was conducted in March 2023, covering studies from January 2020 to January 2023 found in PubMed, Web of Science, and Scopus. We followed the PRISMA guidelines and PROSPERO criteria, focusing exclusively on the effects of short-term ozone exposure on COVID-19. The literature search was restricted to English-language journal articles, with the inclusion and exclusion criteria strictly adhered to. Out of 4674 identified studies, 18 fulfilled the inclusion criteria, conducted across eight countries. The findings showed a varied association between short-term ozone exposure and COVID-19 incidence, severity, and mortality. Some studies reported a higher association between ozone exposure and incidence in institutional settings (OR: 1.06, 95% CI: 1.00–1.13) compared to the general population (OR: 1.00, 95% CI: 0.98–1.03). The present research identified a positive association between ozone exposure and both total and active COVID-19 cases as well as related deaths (coefficient for cases: 0.214; for recoveries: 0.216; for active cases: 0.467; for deaths: 0.215). Other studies also found positive associations between ozone levels and COVID-19 cases and deaths, while fewer reports identified a negative association between ozone exposure and COVID-19 incidence (coefficient: −0.187) and mortality (coefficient: −0.215). Conversely, some studies found no significant association between ozone exposure and COVID-19, suggesting a complex and potentially region-specific relationship. The relationship between short-term ozone exposure and COVID-19 dynamics is complex and multifaceted, indicating both positive and negative associations. These variations are possibly due to demographic and regional factors. Further research is necessary to bridge current knowledge gaps, especially considering the potential influence of short-term O3 exposure on COVID-19 outcomes and the broader implications on public health policy and preventive strategies during pandemics.
Plasma Fibronectin as a Novel Predictor of Coronary Heart Disease: A Retrospective Study
Authors: Peng Longyun , Deng Haiwei , Li Jie , Lu Guihua , Zhai Yuan-Sheng
Published: October 2023

Although fibronectin has been associated with the pathogenesis of atherosclerosis, little is currently known about the relationship between plasma fibronectin and coronary heart disease (CHD). This retrospective study aimed to determine the predictive value of plasma fibronectin for CHD and its severity. A total of 1644 consecutive patients who underwent selective coronary angiography were recruited into the present study. The characteristics and results of the clinical examination of all patients were collected. Logistic regression analyses were performed to determine the predictive value of plasma fibronectin for the presence and severity of CHD. Compared with non-CHD patients, the CHD patients showed significantly higher plasma levels of troponin I and creatine kinase isoenzyme, along with lower plasma levels of fibronectin. However, no significant differences were detected in plasma fibronectin among patients with different grades of CHD. The logistic regression model showed that plasma fibronectin remained an independent predictor of CHD after adjustment with a 1.39-fold increased risk for every 1 SD decrease in plasma fibronectin. Nevertheless, plasma fibronectin could not predict the severity of CHD determined by the number of stenosed vessels and the modified Gensini score. This study demonstrated that lower plasma fibronectin might be an independent predictor of CHD, but it may be of no value in predicting the severity of CHD.
Left Atrial Posterior Wall Isolation with Pulsed Field Ablation in Persistent Atrial Fibrillation
Authors: Gunawardene Melanie A. , Frommeyer Gerrit , Ellermann Christian , Jularic Mario , Leitz Patrick , Hartmann Jens , Lange Philipp Sebastian , Anwar Omar , Rath Benjamin , Wahedi Rahin , Eckardt Lars , Willems Stephan
Published: October 2023

Background: Left atrial posterior wall isolation (LAPWI) may improve rhythm control in addition to pulmonary vein isolation (PVI) in persistent atrial fibrillation (persAF) patients undergoing catheter ablation (CA). However, LAPWI may be challenging when using thermal energy sources. Objective: This study aimed to investigate the efficacy and safety of LAPWI performed by non-thermal pulsed field ablation (PFA) in CA for persAF. Methods: Consecutive persAF patients from two German centers were prospectively enrolled. There were two study cohorts: (1) the LAPWI cohort, which included PFA-guided (re-)PVI with LAPWI for first-time and/or repeat ablation procedures; and (2) a comparative persAF cohort with a PFA PVI-only approach without LAPWI for first-time ablation within the same timeframe. Patients were followed up by routine Holter ECGs. Results: In total, 79 persistent AF patients were included in the study: 59/79 patients were enrolled in the LAPWI cohort, including 16/59 index (27%) and 43/59 repeat ablation procedures (73%). Sixteen patients (16/79; 21%) were in the PVI-only cohort without LAPWI. Of the patients treated with LAPWI, procedure time and fluoroscopy time was 91 ± 30 min and 15 ± 7 min, respectively. The acute PVI rate was 100% in all first-time ablation patients (32 patients (16 PVI only, 16 PVI plus LAPWI), 196/196 PVs). Of the 43 re-do patients in the LAPWI cohort, re-PVI was necessary in 33% (14/43) of patients (27 PVs; 1.9 PV per-patient); in 67% (29/43), all PVs were isolated, and antral ablation of the PV ostia was performed in 48% (14/29). LAPWI was performed successfully in all 59 (100%) patients of the LAPWI cohort. Two minor complications occurred. No esophageal lesion was detected in the LAPWI cohort (n = 33/59 (56%) patients underwent endoscopy). After 354 ± 197 days of follow-up, freedom from atrial arrhythmias was 79.3% (95-CI: 62–95%) in the complete LAPWI cohort (n = 14/59 (24%) on AAD: class Ic n = 9, class III n = 5). There was no difference regarding acute procedural and clinical outcome compared to the PVI-only cohort. Conclusion: LAPWI guided by PFA is feasible and safe in patients undergoing CA for persAF and shows favorable outcomes. In the context of durable PVI, PFA-guided LAPWI may be an effective adjunctive treatment option.
Comparison between In-Hospital and Out-of-Hospital Acute Myocardial Infarctions: Results from the Regional Myocardial Infarction Registry of Saxony-Anhalt (RHESA) Study
Authors: Assaf Mohamad , Costa Daniela , Massag Janka , Weber Christoph , Mikolajczyk Rafael , Lückmann Sara Lena
Published: October 2023

Aims: Risk factors and outcomes of in-hospital ST elevation myocardial infraction (STEMI) are well explored. Recent findings show that non-ST elevation myocardial infarction (NSTEMI) accounts for the majority of in-hospital infarctions (IHMIs). Our aim was to identify differences between IHMI and out-of-hospital myocardial infraction (OHMI) in terms of risk factors, treatment and outcomes, including both STEMI and NSTEMI. Methods: We analyzed the Regional Myocardial Infarction Registry of Saxony-Anhalt dataset. Patient characteristics, treatments and outcomes were compared between IHMI and OHMI. The association between clinical outcomes and myocardial infarction type was assessed using generalized additive models. Results: Overall, 11.4% of the included myocardial infractions were IHMI, and the majority were NSTEMI. Patients with IHMI were older and had more comorbidities than those with OHMI. Compared to OHMI, in-hospital myocardial infarction was associated with higher odds of 30-day mortality (OR = 1.85, 95% CI 1.32–2.59) and complications (OR = 2.36, 95 % CI 1.84–3.01). Conclusions: We provided insights on the full spectrum of IHMI, in both of its classifications. The proportion of IHMI was one ninth of all AMI cases treated in the hospital. Previously reported differences in the baseline characteristics and treatments, as well as worse clinical outcomes, in in-hospital STEMI compared to out-of-hospital STEMI persist even when including NSTEMI cases.
Significance of Hypocapnia in the Risk Assessment of Patients with Pulmonary Hypertension
Authors: Aetou Maria , Wahab Lora , Dreher Michael , Daher Ayham
Published: October 2023

Blood gas analysis is part of the diagnostic work−up for pulmonary hypertension (PH). Although some studies have found that the partial pressure of carbon dioxide (PaCO2) is an independent marker of mortality in individuals with pulmonary arterial hypertension (PH Group 1), there is a lack of data regarding the significance of PaCO2 in individuals with different types of PH based on the new 2022 definitions. Therefore, this study analyzed data from 157 individuals who were undergoing PH work−up, including right heart catheterization, using PH definitions from the 2022 European Society of Cardiology/European Respiratory Society guidelines. At diagnosis, N−terminal pro−B−type natriuretic peptide (NT−pro−BNP) levels were significantly higher, but the time−course of NT−pro−BNP levels during treatment was significantly more favorable in individuals with pulmonary arterial hypertension (PH Group 1) who did versus did not have hypocapnia (p = 0.026 and p = 0.017, respectively). These differences based on the presence of hypocapnia were not seen in individuals with PH Groups 2, 3, or 4. In conclusion, using the new definition of PH, hypocapnia may correlate with worse risk stratification at diagnosis in individuals with pulmonary arterial hypertension. However, hypocapnic individuals with pulmonary arterial hypertension may benefit more from disease−specific therapy than those without hypocapnia.