bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibacterial Activity of a Bioactive Tooth-Coating Material Containing Surface Pre-Reacted Glass in a Complex Multispecies Subgingival Biofilm
Authors: Tanaka Caio Junji , Rodrigues José Augusto , Pingueiro João Marcos Spessoto , Macedo Tatiane Tiemi , Feres Magda , Shibli Jamil Awad , Bueno-Silva Bruno
Published: June 2023

Bioactive materials were developed with the ability to release fluoride and provide some antimicrobial potential, to be widely used in dentistry today. However, few scientific studies have evaluated the antimicrobial activity of bioactive surface pre-reacted glass (S-PRG) coatings (PRG Barrier Coat, Shofu, Kyoto, Japan) on periodontopathogenic biofilms. This study evaluated the antibacterial activity of S-PRG fillers on the microbial profile of multispecies subgingival biofilms. A Calgary Biofilm Device (CBD) was used to grow a 33-species biofilm related to periodontitis for 7 days. The S-PRG coating was applied on CBD pins from the test group and photo-activated (PRG Barrier Coat, Shofu), while the control group received no coating. Seven days after treatment, the total bacterial counts, metabolic activity, and microbial profile of the biofilms were observed using a colorimetric assay and DNA–DNA hybridization. Statistical analyses were applied; namely, the Mann–Whitney, Kruskal–Wallis, and Dunn’s post hoc tests. The bacterial activity of the test group was reduced by 25.7% compared with that of the control group. A statistically significant reduction was observed for the counts of 15 species: A. naeslundii, A. odontolyticus, V. parvula, C. ochracea, C. sputigena, E. corrodens, C. gracilis, F. nucleatum polymorphum, F. nucleatum vincentii, F. periodonticum, P. intermedia, P. gingivalis, G. morbillorum, S. anginosus, and S. noxia (p ≤ 0.05). The bioactive coating containing S-PRG modified the composition of the subgingival biofilm in vitro, thereby decreasing colonization by pathogens.
Gut Microbiota-Mediated Pharmacokinetic Drug–Drug Interactions between Mycophenolic Acid and Trimethoprim-Sulfamethoxazole in Humans
Authors: Dukaew Nahathai , Thongkumkoon Patcharawadee , Sirikaew Nutnicha , Dissook Sivamoke , Sakuludomkan Wannachai , Tongjai Siripong , Thiennimitr Parameth , Na Takuathung Mingkwan , Benjanuwattra Juthipong , Kongthaweelert Prachya , Koonrungsesomboon Nut
Published: June 2023

Mycophenolic acid (MPA) and trimethoprim-sulfamethoxazole (TMP-SMX) are commonly prescribed together in certain groups of patients, including solid organ transplant recipients. However, little is known about the pharmacokinetic drug–drug interactions (DDIs) between these two medications. Therefore, the present study aimed to determine the effects of TMP-SMX on MPA pharmacokinetics in humans and to find out the relationship between MPA pharmacokinetics and gut microbiota alteration. This study enrolled 16 healthy volunteers to take a single oral dose of 1000 mg mycophenolate mofetil (MMF), a prodrug of MPA, administered without and with concurrent use of TMP-SMX (320/1600 mg/day) for five days. The pharmacokinetic parameters of MPA and its glucuronide (MPAG) were measured using high-performance liquid chromatography. The composition of gut microbiota in stool samples was profiled using a 16S rRNA metagenomic sequencing technique during pre- and post-TMP-SMX treatment. Relative abundance, bacterial co-occurrence networks, and correlations between bacterial abundance and pharmacokinetic parameters were investigated. The results showed a significant decrease in systemic MPA exposure when TMP-SMX was coadministered with MMF. Analysis of the gut microbiome revealed altered relative abundance of two enriched genera, namely the genus Bacteroides and Faecalibacterium, following TMP-SMX treatment. The relative abundance of the genera Bacteroides, [Eubacterium] coprostanoligenes group, [Eubacterium] eligens group, and Ruminococcus appeared to be significantly correlated with systemic MPA exposure. Coadministration of TMP-SMX with MMF resulted in a reduction in systemic MPA exposure. The pharmacokinetic DDIs between these two drugs were attributed to the effect of TMP-SMX, a broad-spectrum antibiotic, on gut microbiota-mediated MPA metabolism.
Instant Formulation of Inhalable Beclomethasone Dipropionate—Gamma-Cyclodextrin Composite Particles Produced Using Supercritical Assisted Atomization
Authors: Wu Hsien-Tsung , Lin Han-Cyuan , Tu Yi-Jia , Ng Kim Hoong
Published: June 2023

Medical composites derived from Gamma-cyclodextrin (γ-CD) and beclomethasone dipropionate−gamma-cyclodextrin (BDP−γ-CD) are synthesized over supercritical-assisted atomization (SAA) herein. Carbon dioxide, which serves the dual function of spraying medium and co-solute, is incorporated in this process along with the ethanolic solvent. Results indicate that, for fine spherical particles, optimized aerosol performance could be obtained with 50.0% (w/w) ethanolic solvent, precipitator, and saturator at 373.2 K and 353.2 K, respectively, and carbon dioxide-to-γ-CD flow ratio of 1.8 in the presence of 10 wt% leucine (LEU) as dispersion enhancer. It is also noted that γ-CD solution at low concentration typically renders better aerosol performance of the particles. During drug particle-derivation, the solubility of drug BDP elevated considerably due to the formation of inclusion complexes, further assisted by the ethanolic solvent which increases the lipophilicity of BDP. Meanwhile, the in vitro aerosolization and dissolution performance of drug composites derived from varied γ-CD-to-BDP mass ratio (Z) were also evaluated. It was found that high Z promises higher fine particle fraction in the obtained drug composite while the dissolution rate of active ingredient (BDP) exhibits positive correlation to the content of water-soluble excipient (γ-CD) in the formulation. This study offers a new avenue for instant drug formulation with promising pulmonary delivery over the SAA technique.
Tensile and Surface Wettability Properties of the Solvent Cast Cellulose Fatty Acid Ester Films
Authors: Kallakas Heikko , Kattamanchi Tanuj , Kilumets Catherine , Tarasova Elvira , Krasnou Illia , Savest Natalja , Ahmadian Iman , Kers Jaan , Krumme Andres
Published: June 2023

Thermoplastic cellulose esters are promising materials for bioplastic packaging. For that usage, it is important to understand their mechanical and surface wettability properties. In this study, a series of cellulose esters are prepared, such as laurate, myristate, palmitate, and stearate. The aim of the study is to investigate the tensile and surface wettability properties of the synthesized cellulose fatty acid esters to understand their suitability as a bioplastic packaging material. Cellulose fatty acid esters are first synthesized from microcrystalline cellulose (MCC), then dissolved in pyridine solution, and after the solvent cast into thin films. The cellulose fatty acid ester acylation process is characterized by the FTIR method. Cellulose esters hydrophobicity is evaluated with contact angle measurements. The mechanical properties of the films are tested with the tensile test. For all the synthesized films, FTIR provides clear evidence of acylation by showing the presence of characteristic peaks. Films’ mechanical properties are comparable to those of generally used plastics such as LDPE and HDPE. Furthermore, it appears that with an increase in the side-chain length, the water barrier properties showed improvement. These results show that they could potentially be suitable materials for films and packaging materials.
Potentiometric Sensor Arrays Based on Hybrid PFSA/CNTs Membranes for the Analysis of UV-Degraded Drugs
Authors: Parshina Anna , Yelnikova Anastasia , Safronova Ekaterina , Kolganova Tatyana , Bobreshova Olga , Yaroslavtsev Andrey
Published: June 2023

The degradation of drugs is a substantial problem since it affects the safety and effectiveness of pharmaceutical products, as well as their influence on the environment. A novel system of three potentiometric cross-sensitive sensors (using the Donnan potential (DP) as an analytical signal) and a reference electrode was developed for the analysis of UV-degraded sulfacetamide drugs. The membranes for DP-sensors were prepared by a casting procedure from a dispersion of perfluorosulfonic acid (PFSA) polymer, containing carbon nanotubes (CNTs), whose surface was preliminarily modified with carboxyl, sulfonic acid, or (3-aminopropyl)trimethoxysilanol groups. A correlation between the sorption and transport properties of the hybrid membranes and cross-sensitivity of the DP-sensor to sulfacetamide, its degradation product, and inorganic ions was revealed. The analysis of the UV-degraded sulfacetamide drugs using the multisensory system based on hybrid membranes with optimized properties did not require a pre-separation of the components. The limits of detection of sulfacetamide, sulfanilamide, and sodium were 1.8 × 10−7, 5.8 × 10−7, and 1.8 × 10−7 M. The relative errors of the determination of the components of the UV-degraded sulfacetamide drugs were 2–3% (at 6–8% relative standard deviation). PFSA/CNT hybrid materials provided the stable work of the sensors for at least one year.
Hybrid Device Fabrication Using Roll-to-Roll Printing for Personal Environmental Monitoring
Authors: Phung Thanh Huy , Gafurov Anton Nailevich , Kim Inyoung , Kim Sung Yong , Kim Kyoung Min , Lee Taik-Min
Published: June 2023

Roll-to-roll (R2R) printing methods are well known as additive, cost-effective, and ecologically friendly mass-production methods for processing functional materials and fabricating devices. However, implementing R2R printing to fabricate sophisticated devices is challenging because of the efficiency of material processing, the alignment, and the vulnerability of the polymeric substrate during printing. Therefore, this study proposes the fabrication process of a hybrid device to solve the problems. The device was created so that four layers, composed of polymer insulating layers and conductive circuit layers, are entirely screen-printed layer by layer onto a roll of polyethylene terephthalate (PET) film to produce the circuit. Registration control methods were presented to deal with the PET substrate during printing, and then solid-state components and sensors were assembled and soldered to the printed circuits of the completed devices. In this way, the quality of the devices could be ensured, and the devices could be massively used for specific purposes. Specifically, a hybrid device for personal environmental monitoring was fabricated in this study. The importance of environmental challenges to human welfare and sustainable development is growing. As a result, environmental monitoring is essential to protect public health and serve as a basis for policymaking. In addition to the fabrication of the monitoring devices, a whole monitoring system was also developed to collect and process the data. Here, the monitored data from the fabricated device were personally collected via a mobile phone and uploaded to a cloud server for additional processing. The information could then be utilized for local or global monitoring purposes, moving one step toward creating tools for big data analysis and forecasting. The successful deployment of this system could be a foundation for creating and developing systems for other prospective uses.
Biomass 3D Printing: Principles, Materials, Post-Processing and Applications
Authors: Li Yongxia , Ren Xueyong , Zhu Lin , Li Chunmiao
Published: June 2023

Under the background of green and low-carbon era, efficiently utilization of renewable biomass materials is one of the important choices to promote ecologically sustainable development. Accordingly, 3D printing is an advanced manufacturing technology with low energy consumption, high efficiency, and easy customization. Biomass 3D printing technology has attracted more and more attentions recently in materials area. This paper mainly reviewed six common 3D printing technologies for biomass additive manufacturing, including Fused Filament Fabrication (FFF), Direct Ink Writing (DIW), Stereo Lithography Appearance (SLA), Selective Laser Sintering (SLS), Laminated Object Manufacturing (LOM) and Liquid Deposition Molding (LDM). A systematic summary and detailed discussion were conducted on the printing principles, common materials, technical progress, post-processing and related applications of typical biomass 3D printing technologies. Expanding the availability of biomass resources, enriching the printing technology and promoting its application was proposed to be the main developing directions of biomass 3D printing in the future. It is believed that the combination of abundant biomass feedstocks and advanced 3D printing technology will provide a green, low-carbon and efficient way for the sustainable development of materials manufacturing industry.
Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms
Authors: Shahabi Himan , Ahmadi Reza , Alizadeh Mohsen , Hashim Mazlan , Al-Ansari Nadhir , Shirzadi Ataollah , Wolf Isabelle D. , Ariffin Effi Helmy
Published: June 2023

Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management.
Areca Yellow Leaf Disease Severity Monitoring Using UAV-Based Multispectral and Thermal Infrared Imagery
Authors: Xu Dong , Lu Yuwei , Liang Heng , Lu Zhen , Yu Lejun , Liu Qian
Published: June 2023

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R2 value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value < 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R2 value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants.
Unlocking the Potential of Data Augmentation in Contrastive Learning for Hyperspectral Image Classification
Authors: Li Jinhui , Li Xiaorun , Yan Yunfeng
Published: June 2023

Despite the rapid development of deep learning in hyperspectral image classification (HSIC), most models require a large amount of labeled data, which are both time-consuming and laborious to obtain. However, contrastive learning can extract spatial–spectral features from samples without labels, which helps to solve the above problem. Our focus is on optimizing the contrastive learning process and improving feature extraction from all samples. In this study, we propose the Unlocking-the-Potential-of-Data-Augmentation (UPDA) strategy, which involves adding superior data augmentation methods to enhance the representation of features extracted by contrastive learning. Specifically, we introduce three augmentation methods—band erasure, gradient mask, and random occlusion—to the Bootstrap-Your-Own-Latent (BYOL) structure. Our experimental results demonstrate that our method can effectively improve feature representation and thus improve classification accuracy. Additionally, we conduct ablation experiments to explore the effectiveness of different data augmentation methods.
Correction: Witanto et al. Distributed Data Integrity Verification Scheme in Multi-Cloud Environment. Sensors 2023, 23, 1623
Authors: Witanto Elizabeth Nathania , Stanley Brian , Lee Sang-Gon
Published: June 2023

The authors make the following corrections to the published paper [...]
Development of Optical Label-Free Biosensor Method in Detection of Listeria monocytogenes from Food
Authors: Fernández Blanco Ana , Hernández Pérez Manuel , Moreno Trigos Yolanda , García-Hernández Jorge
Published: June 2023

The present work describes an alternative method for detecting and identifying Listeria monocytogenes in food samples by developing a nanophotonic biosensor containing bioreceptors and optical transducers. The development of photonic sensors for the detection of pathogens in the food industry involves the implementation of procedures for selecting probes against the antigens of interest and the functionalization of the sensor surfaces on which the said bioreceptors are located. As a previous step to functionalizing the biosensor, an immobilization control of these antibodies on silicon nitride surfaces was carried out to check the effectiveness of in plane immobilization. On the one hand, it was observed that a Listeria monocytogenes-specific polyclonal antibody has a greater binding capacity to the antigen at a wide range of concentrations. A Listeria monocytogenes monoclonal antibody is more specific and has a greater binding capacity only at low concentrations. An assay for evaluating selected antibodies against particular antigens of Listeria monocytogenes bacteria was designed to determine the binding specificity of each probe using the indirect ELISA detection technique. In addition, a validation method was established against the reference method for many replicates belonging to different batches of meat-detectable samples, with a medium and pre-enrichment time that allowed optimal recovery of the target microorganism. Moreover, no cross-reactivity with other nontarget bacteria was observed. Thus, this system is a simple, highly sensitive, and accurate platform for L. monocytogenes detection.
Design and Evaluation of the Extended FBS Model Based Gaze-Control Power Wheelchair for Individuals Facing Manual Control Challenges
Authors: Zhang Xiaochen , Li Jiazhen , Jin Lingling , Zhao Jie , Huang Qianbo , Song Ziyang , Liu Xinyu , Luh Ding-Bang
Published: June 2023

This study addresses the challenges faced by individuals with upper limb disadvantages in operating power wheelchair joysticks by utilizing the extended Function–Behavior–Structure (FBS) model to identify design requirements for an alternative wheelchair control system. A gaze-controlled wheelchair system is proposed based on design requirements from the extended FBS model and prioritized using the MosCow method. This innovative system relies on the user’s natural gaze and comprises three levels: perception, decision making, and execution. The perception layer senses and acquires information from the environment, including user eye movements and driving context. The decision-making layer processes this information to determine the user’s intended direction, while the execution layer controls the wheelchair’s movement accordingly. The system’s effectiveness was validated through indoor field testing, with an average driving drift of less than 20 cm for participates. Additionally, the user experience scale revealed overall positive user experiences and perceptions of the system’s usability, ease of use, and satisfaction.
Affective Neural Responses Sonified through Labeled Correlation Alignment
Authors: Álvarez-Meza Andrés Marino , Torres-Cardona Héctor Fabio , Orozco-Alzate Mauricio , Pérez-Nastar Hernán Darío , Castellanos-Dominguez German
Published: June 2023

Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.
Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
Authors: Kinman Garrett , Žilić Željko , Purnell David
Published: June 2023

This paper explores the use of low earth orbit (LEO) satellite links in long-term monitoring of water levels across remote areas. Emerging sparse LEO satellite constellations maintain sporadic connection to the ground station, and transmissions need to be scheduled for satellite overfly periods. For remote sensing, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our online learning-based approach combines Monte Carlo and modified k-armed bandit approaches, to produce an inexpensive scheme that is applicable to scheduling any LEO satellite transmissions. We demonstrate its ability to adapt in three common scenarios, to save the transmission energy 20-fold, and provide the means to explore the parameters. The presented study is applicable to wide range of IoT applications in areas with no existing wireless coverages.
Application of Topical Hyperbaric Oxygen Therapy and Medical Active Dressings in the Treatment of Arterial Leg Ulcers—A Pilot Study
Authors: Pasek Jarosław , Szajkowski Sebastian , Cieślar Grzegorz
Published: June 2023

Leg ulcers are a very serious worldwide medical problem. When the ulcer is extensive and deep the prognosis is usually unfavorable. The treatment requires comprehensive solutions that take into account modern specialized medical dressings, and more and more often, selected methods in the field of physical medicine. The study included 30 patients (13 women—43.4% and 17 men—56.6%) with chronic arterial ulcers of the lower limbs. The mean age of the treated patients was 65.63 ± 8.77 years. Patients were randomly assigned to two study groups. In group 1 (16 patients), specialist ATRAUMAN Ag medical dressings and local hyperbaric oxygen therapy treatments were used. In group 2 (14 patients), only specialized ATRAUMAN Ag dressings were used. The treatment was carried out for 4 weeks. The progress of healing ulcers was assessed by using the planimetric method, while the intensity of pain ailments was assessed by the visual analog VAS scale. In both study groups, a statistically significant reduction in the mean surface area of the treated ulcers was obtained, respectively, from 8.53 ± 1.71 cm2 to 5.55 ± 1.11 cm2 in group 1 (p < 0.001) and 8.43 ± 1.51 cm2 to 6.28 ± 1.13 cm2 in group 2 (p < 0.001). There was also a statistically significant reduction in the intensity of pain ailments, respectively: 7.93 ± 0.68 points to 5.00 ± 0.63 points in group 1 (p < 0.001) and 8.00 ± 0.67 points to 5.64 ± 0.49 points in group 2 (p < 0.001). The percentage change in ulcer area from baseline in group 1 was 34.6 ± 8.47% and was statistically significantly greater than in group 2 (25.23 ± 6.01%) (p = 0.003). In turn, the percentage assessment of the pain intensity assessed in the VAS scale in group 1 was 36.97 ± 6.36% and was statistically significantly higher compared to group 2 (29.34 ± 4.77%) (p = 0.002). The addition of local hyperbaric oxygen therapy treatments as a supplement to the therapy with the use of specialized medical dressings improves the effectiveness the arterial ulcers treatment of the lower limbs in terms of reducing the ulceration area and reducing pain ailments.
Modelling and Design of Pre-Equalizers for a Fully Operational Visible Light Communication System
Authors: Bostanoglu Murat , Dalveren Yaser , Catak Ferhat Ozgur , Kara Ali
Published: June 2023

Nowadays, Visible Light Communication (VLC) has gained much attention due to the significant advancements in Light Emitting Diode (LED) technology. However, the bandwidth of LEDs is one of the important concerns that limits the transmission rates in a VLC system. In order to eliminate this limitation, various types of equalization methods are employed. Among these, using digital pre-equalizers can be a good choice because of their simple and reusable structure. Therefore, several digital pre-equalizer methods have been proposed for VLC systems in the literature. Yet, there is no study in the literature that examines the implementation of digital pre-equalizers in a realistic VLC system based on the IEEE 802.15.13 standard. Hence, the purpose of this study is to propose digital pre-equalizers for VLC systems based on the IEEE 802.15.13 standard. For this purpose, firstly, a realistic channel model is built by collecting the signal recordings from a real 802.15.13-compliant VLC system. Then, the channel model is integrated into a VLC system modeled in MATLAB. This is followed by the design of two different digital pre-equalizers. Next, simulations are conducted to evaluate their feasibility in terms of the system’s BER performance under bandwidth-efficient modulation schemes, such as 64-QAM and 256-QAM. Results show that, although the second pre-equalizer provides lower BERs, its design and implementation might be costly. Nevertheless, the first design can be selected as a low-cost alternative to be used in the VLC system.
Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning
Authors: Dou Shiqing , Wang Lin , Fan Donglin , Miao Linlin , Yan Jichi , He Hongchang
Published: June 2023

Citrus has become a pivotal industry for the rapid development of agriculture and increasing farmers’ incomes in the main production areas of southern China. Knowing how to diagnose and control citrus huanglongbing has always been a challenge for fruit farmers. To promptly recognize the diagnosis of citrus huanglongbing, a new classification model of citrus huanglongbing was established based on MobileNetV2 with a convolutional block attention module (CBAM-MobileNetV2) and transfer learning. First, the convolution features were extracted using convolution modules to capture high-level object-based information. Second, an attention module was utilized to capture interesting semantic information. Third, the convolution module and attention module were combined to fuse these two types of information. Last, a new fully connected layer and a softmax layer were established. The collected 751 citrus huanglongbing images, with sizes of 3648 × 2736, were divided into early, middle, and late leaf images with different disease degrees, and were enhanced to 6008 leaf images with sizes of 512 × 512, including 2360 early citrus huanglongbing images, 2024 middle citrus huanglongbing images, and 1624 late citrus huanglongbing images. In total, 80% and 20% of the collected citrus huanglongbing images were assigned to the training set and the test set, respectively. The effects of different transfer learning methods, different model training effects, and initial learning rates on model performance were analyzed. The results show that with the same model and initial learning rate, the transfer learning method of parameter fine tuning was obviously better than the transfer learning method of parameter freezing, and that the recognition accuracy of the test set improved by 1.02~13.6%. The recognition accuracy of the citrus huanglongbing image recognition model based on CBAM-MobileNetV2 and transfer learning was 98.75% at an initial learning rate of 0.001, and the loss value was 0.0748. The accuracy rates of the MobileNetV2, Xception, and InceptionV3 network models were 98.14%, 96.96%, and 97.55%, respectively, and the effect was not as significant as that of CBAM-MobileNetV2. Therefore, based on CBAM-MobileNetV2 and transfer learning, an image recognition model of citrus huanglongbing images with high recognition accuracy could be constructed.
TFC-GCN: Lightweight Temporal Feature Cross-Extraction Graph Convolutional Network for Skeleton-Based Action Recognition
Authors: Wang Kaixuan , Deng Hongmin
Published: June 2023

For skeleton-based action recognition, graph convolutional networks (GCN) have absolute advantages. Existing state-of-the-art (SOTA) methods tended to focus on extracting and identifying features from all bones and joints. However, they ignored many new input features which could be discovered. Moreover, many GCN-based action recognition models did not pay sufficient attention to the extraction of temporal features. In addition, most models had swollen structures due to too many parameters. In order to solve the problems mentioned above, a temporal feature cross-extraction graph convolutional network (TFC-GCN) is proposed, which has a small number of parameters. Firstly, we propose the feature extraction strategy of the relative displacements of joints, which is fitted for the relative displacement between its previous and subsequent frames. Then, TFC-GCN uses a temporal feature cross-extraction block with gated information filtering to excavate high-level representations for human actions. Finally, we propose a stitching spatial–temporal attention (SST-Att) block for different joints to be given different weights so as to obtain favorable results for classification. FLOPs and the number of parameters of TFC-GCN reach 1.90 G and 0.18 M, respectively. The superiority has been verified on three large-scale public datasets, namely NTU RGB + D60, NTU RGB + D120 and UAV-Human.
Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism
Authors: Cheng Haoyuan , Zhang Deqing , Zhu Jinchi , Yu Hao , Chu Jinkui
Published: June 2023

Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion network is applied to underwater polarization images; that is, the underwater polarization images are fused with intensity images using the deep learning method. To construct a training dataset, we establish an experimental setup to obtain underwater polarization images and perform appropriate transformations to expand the dataset. Next, an end-to-end learning framework based on unsupervised learning and guided by an attention mechanism is constructed for fusing polarization and light intensity images. The loss function and weight parameters are elaborated. The produced dataset is used to train the network under different loss weight parameters, and the fused images are evaluated based on different image evaluation metrics. The results show that the fused underwater images are more detailed. Compared with light intensity images, the information entropy and standard deviation of the proposed method increase by 24.48% and 139%. The image processing results are better than other fusion-based methods. In addition, the improved U-net network structure is used to extract features for image segmentation. The results show that the target segmentation based on the proposed method is feasible under turbid water. The proposed method does not require manual adjustment of weight parameters, has faster operation speed, and has strong robustness and self-adaptability, which is important for research in vision fields, such as ocean detection and underwater target recognition.
Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
Authors: Hoang Minh Long , Carratù Marco , Paciello Vincenzo , Pietrosanto Antonio
Published: June 2023

The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°.
Influence of Facade Greening with Ivy on Thermal Performance of Masonry Walls
Authors: Pichlhöfer Alexander , Korjenic Azra , Sulejmanovski Abdulah , Streit Erich
Published: June 2023

Heat transfer through building envelopes is a crucial aspect of energy efficiency in construction. Masonry walls, being a commonly used building material, have a significant impact on thermal performance. In recent years, green roofs and walls have gained popularity as a means of improving energy efficiency, reducing urban heat islands, and enhancing building aesthetics. This study aims to investigate the effect of ivy (Hedera helix) greening on heat transfer through masonry walls and their corresponding surface temperatures. Ivy was chosen as a model plant due to its widespread use and ability to cover large surface areas. The results of this study suggest that ivy greening can have a significant impact on the thermal performance of masonry walls. During winter, the heat transfer coefficient of greened walls was found to be up to 30% lower compared to non-greened walls. This indicates that ivy greening can help reduce energy consumption for heating and thus improve the energy efficiency of buildings. In addition, the surface temperature under the ivy was found to be significantly higher than on the bare wall during winter. However, during summer, the surface temperature under the ivy was lower than on the bare wall, which may help reduce cooling energy consumption. The results of this study are consistent with previous research in the field. Overall, this study provides valuable insights into the potential benefits of ivy greening on the thermal performance of masonry walls.
Grid-Connected Renewable Energy Sources: A New Approach for Phase-Locked Loop with DC-Offset Removal
Authors: Bany Issa Mohammad A. , Al Muala Zaid A. , Bello Bugallo Pastora M.
Published: June 2023

Renewable Energy Sources (RES) are widely used worldwide due to their positive effect on the environment, being sustainable, low cost, and controllable. The power generated from RESs must be configured to interface and perfectly synchronize with the grid by using Power Electronics Converters (PEC). A Phase-Locked Loop (PLL) is one of the most popular synchronization techniques used due to its speed and robustness. A growing issue that results in oscillations in the estimated fundamental grid phase, frequency, and voltage amplitude is the DC-offset in the input of the PLL. This study was developed to eliminate the DC-offset in the single-phase grid synchronization using Delay Signal Cancellation (DSC) and a fixed-length Transfer Delay (TD)-based PLL. Then, the small-signal model, stability analysis, and selection of controller gains were discussed. The proposed PLL was simulated using MATLAB/Simulink. Moreover, to evaluate the proposed method, several scenarios were developed in order to compare it with other powerful PLLs in terms of performance indicators such as settling time, frequency, and phase error. As a result, the proposed PLL has the fastest dynamic response, completely rejects the DC-offset effect, and fully synchronizes with the electrical grid.
Predictors of Post-Traumatic Stress Symptoms in Nurses during COVID-19 Pandemic: The Role of Occupational Stressors, Personality Traits, and Availability of Protective Equipment
Authors: Dolić Matea , Antičević Vesna , Dolić Krešimir , Pogorelić Zenon
Published: June 2023

Purpose: During the coronavirus disease 2019 (COVID-19) pandemic, healthcare professionals were more frequently affected by post-traumatic stress disorder than the general population. The purpose of this historical, prospective study was to determine the influence of occupational stressors and personality traits on the magnitude of post-traumatic stress symptoms in nurses. The secondary objective was to examine the mediating role of protective equipment use on the relationship between exposure to pandemic-related stressors and levels of post-traumatic stress symptoms in nurses. Methods: The study was conducted after the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic among nurses working at the University Hospital of Split, Croatia. A total of 380 nurses completed the web-based survey. Among them, 217 (57.1%) worked with COVID-19 patients and 163 (42.9%) worked in non-COVID departments. A quantitative, cross-sectional, descriptive, and comparative design was used. Results: Personality traits (introversion, neuroticism, and openness), along with exposure to work stressors (public criticism, workplace hazards and harms, and work conflicts) and direct exposure to work with patients suffering from SARS-CoV-2 infection, may serve as significant risk factors for the development of post-traumatic stress symptoms (p < 0.05). The use of protective equipment did not alter the effect of exposure to pandemic-related stressors on the amount of trauma experienced during the COVID-19 pandemic. Conclusion: Personality characteristics and levels of work stress are the most important predictors of the development of post-traumatic stress symptoms in nurses who worked during the first wave of the COVID-19 pandemic. Healthcare facilities and their leaders need to make more efforts to provide better psychosocial support services for nurses.
Comparative Study of UV Radiation Resistance and Reactivation Characteristics of E. coli ATCC 8739 and Native Strains: Implications for Water Disinfection
Authors: Duque-Sarango Paola , Romero-Martínez Leonardo , Pinos-Vélez Verónica , Sánchez-Cordero Esteban , Samaniego Esteban
Published: June 2023

In certain countries where fresh water is in short supply, the effluents from wastewater treatment plants are being recycled for other uses. For quality assurance, tertiary disinfection treatments are required. This study aims to evaluate the inactivating efficacy with an ultraviolet (UV) system on fecal bacteria from effluents of urban wastewater treatment facilities and the post-treatment influence of the environmental illumination. The effect from different UV doses was determined for native and standardized lyophilized strains of Escherichia coli right after the irradiation as well as after 24 h of incubation under light or dark conditions. To achieve 3 log-reductions of the initial bacterial concentration, a UV dose of approximately 12 mJ cm−2 is needed for E. coli ATCC 8739 and native E. coli. However, there is a risk of the reactivation of 0.19% and 1.54% of the inactivated organisms, respectively, if the treated organisms are stored in an illuminated environment. This suggests that the post-treatment circumstances affect the treatment success; storing the treated water under an illuminated environment may pose a risk even if an effective inactivation was achieved during the irradiation.
Sustainable Development between Demonstration Farm and Agricultural Labor Productivity: Evidence from Family Farms in the Mountainous Area of Western China
Authors: Huang Tingting , Hu Jiangfeng , Huang Qinghua
Published: June 2023

Agricultural labor productivity is an important indicator that reflects the sustainable development of agriculture and rural areas. Demonstration farms provide an important link between small-scale farmers and modern agriculture in mountainous area of western China, which is playing an important role in improving labor productivity. This paper focuses on using the PSM-logit model and sensitivity analysis to empirically test the causal relationship between demonstration farms and labor productivity, and a micro-large sample of 1823 family farms was adopted. The highlighted findings are as follows: the average labor productivity of the demonstration farm is 2.8 times higher than that of the non-demonstration farm. There is a significant positive correlation between demonstration farms and labor productivity. Utilizing demonstration farms, when all control variables are added, can remarkably promote farm income by CNY301458 on average. In the matched sample, and under scenarios controlling for other covariates, we saw that demonstration farms can significantly enhance the farm income of CNY285108, CNY288509, and CNY291077 on average, respectively, after taking the radius matching, the kernel matching, and the nearest neighbor matching. The research inspired us to enhance the demonstrative establishment of family farms and accelerate the rate at which comprehensive development benefits are derived from demonstration farms. The results of this research could provide a policy reference for the promotion of high-quality development and the sustainable development of family farms in China and serve as an experience reference for promoting sustainable development of agriculture and rural areas globally.
Experimental Study and Modeling of the Fracture Behavior, Mechanical Properties, and Bonding Strength of Oil Well Cement
Authors: Vipulanandan Cumaraswamy , Mohammed Ahmed Salih , Ramanathan Praveen
Published: June 2023

This study aimed to analyze the outcomes of stress intensity factor (KI) and new bond strength tests of oil well cement (class H) with a water-to-cement ratio (w/c) of 0.38. Mechanical properties of the cement paste, such as the compressive and flexural strengths, were tested and qualified at 1, 7, and 28 days of curing. The relationship between the elastic modulus and axial strain using the differential of the Vipulanandan p-q model for the cement paste was obtained. The stress intensity factor of the cement paste was between 0.3 and 0.6 MPa.m, and the crack tip opening displacement (CTOD) was between 2.798 and 6.254 µm at three different ratios between the initial notch height (a) and the thickness of the beam (d) (a/d = 0.3, 0.4, and 0.5). The nonlinear Vipulanandan p-q model was used to model the compressive and flexural stress–strain behavior of the cement at three curing times. The bonding strength between the cement and steel tube representing the casing in the borehole was 0.75, 1.89, and 2.59 MPa at 1, 7, and 28 days respectively.
E-Government 3.0: An AI Model to Use for Enhanced Local Democracies
Authors: Vrabie Catalin
Published: June 2023

While e-government (referring here to the first generation of e-government) was just the simple manner of delivering public services via electronic means, e-gov 2.0 refers to the use of social media and Web 2.0 technologies in government operations and public service delivery. However, the use of the term ‘e-government 2.0’ is becoming less common as the focus shifts towards broader digital transformation initiatives that may include AI technologies, among others, such as blockchain, virtual reality, and augmented reality. In this study, we present the relatively new concept of e-government 3.0, which is built upon the principles of e-government 2.0 but refers to the use of emerging technologies (e.g., artificial intelligence) to transform the delivery of public services and improve governance. The study objective is to explore the potential of e-government 3.0 to enhance citizen participation, improve public service delivery, and increase responsiveness and compliance of administrative systems in relation to citizens by integrating emerging technologies into government operations using as a background the evolution of e-government over time. The paper analyzes the challenges faced by municipalities in responding to citizen petitions, which are a core application of local democracies. The author starts by presenting an example of an e-petition system (as in use today) and analyses anonymized data of a text corpus of petitions directed to one of the Romania municipalities. He will propose an AI model able to deal faster and more accurately with the increased number of inputs, trying to promote it to municipalities who, for some reason, are still reluctant to implement AI in their operations. The conclusions will suggest that it may be more effective to focus on improving new algorithms rather than solely on ‘old’ technologies.