bluuebunny/crossref_metadata_2025 huggingface dataset:
Perspectives for Generative AI-Assisted Art Therapy for Melanoma Patients
Authors: Jütte Lennart , Wang Ning , Steven Martin , Roth Bernhard
Published: September 2024

Digital technologies are making their mark in medicine, and especially also in art therapy, offering innovative therapeutic interventions for patients, including those with melanoma skin cancer. However, the integration of novel technologies, such as AI-generated art, brings along ethical, psychological, and technical challenges that are viewed differently among therapists. We aim to gauge art therapists’ views on the ethical, application, and challenge facets of utilizing AI-generated art from medical images in therapy. The focus is on assessing its applicability and limitations for melanoma patients. Art therapists were surveyed via a questionnaire focusing on their experience, digital tool familiarity, and views on AI in therapy, encompassing ethics, benefits, challenges, and applicability for melanoma. Art therapists have already implemented digital technologies and acknowledged potential therapeutic benefits of creating personalized artworks with generative artificial intelligence. Attention needs to be given to technological hurdles and the necessity for supplementary interventions. Views on the method’s adaptability varied, underscoring a need for tailored, patient-focused applications. Art therapists are welcoming AI-generated art as a promising creative therapeutic tool and acknowledge potential therapeutic benefits. There are ethical, technical, and psychological challenges that must be addressed for application in therapeutic sessions. Therapists should navigate AI integration with sensitivity, adhering to ethical norms around consent and privacy. Future studies should show the therapeutic benefit in practice with emphasis on equipping therapists to manage the technical complexities effectively. Furthermore, it is important to ensure that patients can influence the AI output, allowing for creative moments in the process.
Transcriptomic Characterization of Male Formosan Pangolin (Manis pentadactyla pentadactyla) Reproductive Tract and Evaluation of Domestic Cat (Felis catus) as a Potential Model Species
Authors: Méar Laura Orama , Tseng IShin , Lin Kuei-Shien , Hsu Chia-Lin , Chen Szu-Hua , Tsai Pei-Shiue
Published: September 2024

The Formosan pangolin (Manis pentadactyla pentadactyla) is an endemic animal of Taiwan. Due to their reduced population and behavior, very little is known about this enigmatic species. To unravel male pangolin reproduction, in the present study, we built a complete genomic database of the male Formosan pangolin reproductive tract and revealed highly expressing genes as well as critical signaling pathways and their associated biological processes in both the testis and the epididymis. Moreover, we evaluated the domestic cat (Felis catus) as a potential model species for male pangolin reproduction by comparing their testicular transcriptomes. We demonstrated a clear tissue-specific gene expression supporting the unique biological signature of each reproductive tissue and identified critical genes of the different reproductive organs. Pathway enrichment analysis revealed unique pathways in the testis as well as a clear epididymal transition. Furthermore, domestic cats, despite being the closest domestic species to pangolin, demonstrated their unfitness as a male reproduction model species as clear differences in spermatid differentiation and metabolism were observed. These results enable a better understanding of male pangolin reproduction characteristics and may inspire improvements in in Formosan pangolin conservation strategies.
Behavioral Transition Path and Pivotal Nodes Regulating Attack in Initial Encounters between Unfamiliar Pigs
Authors: Wang Zhen , Xiao Zhaowei , Li Hao , Shi Zhengxiang , Pu Shihua
Published: September 2024

This study leverages the inherent curiosity of pigs and their competitive nature among conspecifics to explore behavioral transition paths and critical nodes that govern aggression during initial encounters between unfamiliar individuals. Two consecutive experiments were designed to investigate these dynamics under controlled conditions. In Experiment 1, unfamiliar pigs engaged in one-on-one fights with quick retreats, displaying a simple behavioral sequence of looking followed by attacking. In Experiment 2, the addition of new pigs to resident groups resulted in a more complex and structured behavioral sequence. Resident pigs exhibited a ‘four-step’ exploratory behavior pattern: looking, sniffing, touching, and attacking. Further analysis revealed distinct exploratory pathways. In Experiment 1, only short behavioral paths were observed, while Experiment 2 revealed both long and short paths. Specifically, Experiment 2 uncovered seven types of behavioral transition paths, four of which were long and three short, highlighting different combinations of the basic behaviors. The transition paths involving aggression were more varied in Experiment 2 compared to Experiment 1. Overall, the 15 most frequent and obvious behavioral transition paths were identified across both experiments. Eight types of paths were categorized based on the transitions between the basic behaviors. These findings enhance our understanding of the behavioral dynamics in unfamiliar pig encounters, emphasizing the complexity of social interactions and the conditions under which aggression occurs.
An Updated Review of Emerging Sources of Selenium in Weaned Piglet Nutrition
Authors: Zhou Wenyue , Yang Zheng , Han Jiajun , Chen Xingping , Zou Tiande , You Jinming , Chen Jun
Published: September 2024

The antioxidant and immune systems of weaned piglets are not fully mature and are also subjected to serious stress challenges related to oxidative stress and inflammation. Selenium (Se) is an essential element for pigs, with documented roles encompassing antioxidative and anti-inflammatory properties via selenoproteins. Sodium selenite and Se-enriched yeast are commonly acknowledged as conventional sources of Se for piglets. In the past decade, several novel Se sources have emerged in the field of weaned piglet nutrition. In this review, we will initially outline the historical timeline of Se sources as reported in weaned piglet nutrition. Afterwards, our attention will turn towards the nutritional regulation of Se sources in relation to the antioxidant and anti-inflammatory aspects of healthy weaned piglets. Ultimately, we will provide a detailed review highlighting the potential of emerging Se sources in alleviating various adverse effects of stress challenges faced by weaned piglets. These challenges include oxidative stress, enterotoxigenic Escherichia coli infection, lipopolysaccharide-induced inflammation, heat stress, and exposure to feed mycotoxins. The output of this review will emphasize the fundamental importance of incorporating emerging Se sources in the diet of weaned piglets.
Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams
Authors: Gonçalves Pereira João , Fernandes Joana , Mendes Tânia , Gonzalez Filipe André , Fernandes Susana M.
Published: September 2024

Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host’s immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment.
Honeydew Honey as a Reservoir of Bacteria with Antibacterial and Probiotic Properties
Authors: Grabek-Lejko Dorota , Worek Mariusz
Published: September 2024

The purpose of this study was to isolate, identify, and evaluate the antibacterial and probiotic potential of bacteria from honeydew honey collected in Poland. Isolates (189 colonies from 10 honey samples) were evaluated for their antimicrobial activity against Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Yersinia enterocolitica, and then identified by MALDI-TOF-MS. The isolates with the greatest antimicrobial properties were screened for their probiotic potential. The total number of bacteria isolated from honey did not exceed the value of 2.5 × 102 CFU/mL. The Bacillus pumilus/altitudinis, B. licheniformis, and Bacillus cereus groups were the dominant identified bacteria. Almost 16% of the isolates expressed antibacterial potential against three pathogenic bacteria, over 20% against two, while almost 34% of the isolates did not inhibit any. The survival rate of the isolates under gastrointestinal tract conditions was higher after 4 h of exposure to bile salts (>60% survival rate for 66.66% of the isolates), while at pH 2.0, it was lower (>50% survival rate for 44% of the isolates). The most resistant isolate B. pumilus/altitudinis survived at a rate of 77% at low pH and 108% with bile salts. These results confirmed that honeydew honey is a promising reservoir of bacteria that produces metabolites with antimicrobial and probiotic potential.
Consumption Trends of Antifungal and Antiprotozoal Agents for Human Systemic Use in Kazakhstan from 2017 to 2023
Authors: Semenova Yuliya , Kussainova Assiya , Kassym Laura , Aimurziyeva Ainur , Semenov Daniil , Lim Lisa
Published: September 2024

Background/Objectives: While multiple studies have investigated antibiotic consumption rates, there are few studies on the consumption of systemic antifungals and antiprotozoals. This study aims to fill this gap by providing a comprehensive analysis of nationwide consumption trends in Kazakhstan over a seven-year period (2017–2023). Methods: Defined daily doses per 1000 inhabitants per day were calculated for systemic antifungals (J02 code of the Anatomical Therapeutic Chemical Classification System (ATC)) and antiprotozoals (P01 code of the ATC). Time series analyses were applied to examine historical trends, evaluate the impact of the COVID-19 pandemic, and make future projections until 2030. Results: The total consumption increased over the study period, with an average annual percent change of 1.11% for antifungals and 5.48% for antiprotozoals. Fluconazole was the most consumed antifungal agent, whereas metronidazole was the most consumed antiprotozoal agent. The COVID-19 pandemic had a positive but insignificant effect on the consumption of antifungals and a negative and also insignificant effect on the consumption of antiprotozoals. Forecast modeling indicates that the future trends in antifungal and antiprotozoal consumption until 2030 will largely remain stable, with the exception of antiprotozoal consumption in the hospital sector, which is projected to decline. Conclusions: These findings offer valuable insights into the development and implementation of targeted antimicrobial stewardship programs in Kazakhstan.
Oxidative Stress: The Hidden Catalyst Fueling Atherosclerosis and Cardiovascular Disease
Authors: Loffredo Lorenzo , Carnevale Roberto
Published: September 2024

Atherosclerosis is a pathological condition characterized by the inflammation of arterial vessels, leading to serious cardiovascular outcomes such as myocardial infarction, stroke, and death [...]
A Review of Recent Curcumin Analogues and Their Antioxidant, Anti-Inflammatory, and Anticancer Activities
Authors: Kaur Kirandeep , Al-Khazaleh Ahmad K. , Bhuyan Deep Jyoti , Li Feng , Li Chun Guang
Published: September 2024

Curcumin, as the main active component of turmeric (Curcuma longa), has been demonstrated with various bioactivities. However, its potential therapeutic applications are hindered by challenges such as poor solubility and bioavailability, rapid metabolism, and pan-assay interference properties. Recent advancements have aimed to overcome these limitations by developing novel curcumin analogues and modifications. This brief review critically assesses recent studies on synthesising different curcumin analogues, including metal complexes, nano particulates, and other curcumin derivatives, focused on the antioxidant, anti-inflammatory, and anticancer effects of curcumin and its modified analogues. Exploring innovative curcumin derivatives offers promising strategies to address the challenges associated with its bioavailability and efficacy and valuable insights for future research directions.
Experimental Study of the Influence of Occupants on Speech Intelligibility in an Automotive Cabin
Authors: Liang Linda , Ren Miao , Liao Linghui , Zhao Ye , Xiong Wei , Ou Liuying
Published: September 2024

Adding occupants to an enclosed space often leads to perceptible changes in the sound field and, therefore, speech intelligibility; however, this issue has not yet been examined in automotive cabins. This study investigated the effect of occupants in an automotive cabin on SI. Binaural room impulse responses (BRIRs) were measured in an automotive cabin with an artificial mouth and dummy head under different speaker–listener position configurations and occupancy modes. Based on the measured BRIRs, the speech transmission index (STI) was determined, and subjective speech–reception thresholds (SRTs) in Mandarin Chinese were assessed. The results indicate that speech intelligibility mostly decreased slightly after adding additional occupants. In most cases, the occupants did not significantly affect speech intelligibility, with STI variations of no more than the just-noticeable difference and SRT variation within 1 dB. When the listener was in the back-right seat, the effect of the occupants on speech intelligibility could not be ignored, with STI variations of up to 0.07 and an SRT variation of 2 dB under different occupancy modes. In addition, the influence of front-row passengers on the speech intelligibility of rear-row listeners was extremely small, and vice versa. Furthermore, altering the distribution of occupants had an effect comparable to changing the number of occupants.
Enhancing Performance and Sustainability of Engine Lubricants and Biolubricants by Dispersing SiO2 Nanoparticles Coated with KH570-Silane Coupling Agent
Authors: Piri Homeyra , Renzi Massimiliano , Bietresato Marco
Published: September 2024

One of the technical possibilities to enhance the properties of lubricants and biolubricants is dispersing nanoparticles in them. Although conceptually simple, this operation faces challenges related to: (1) obtaining an initial good dispersion of the nanoparticles in the liquid and (2) ensuring the stability of this dispersion to avoid coalescence. The objective of this study is to verify possible improvements of the stability and characteristics of conventional and bio-based lubricants by efficiently dispersing in them surface-modified SiO2 nanoparticles. The silane coupling agent KH570 was utilized to modify the surface properties of SiO2 nanoparticles, facilitating their dispersion within the lubricants. Nanolubricants and nanobiolubricants were prepared using a two-step technique. The dispersion stability of these lubricants was assessed using sedimentation photography, FTIR, and UV-Vis spectrophotometric analyses. The addition of SiO2 nanoparticles resulted in enhanced physicochemical properties of the resulting lubricant, including slight increases in density and viscosity, as well as a higher viscosity index. Chemical analyses, such as TAN and TBN measurements, confirmed that the nanoparticle addition at various concentrations (0.25%, 0.5%, 0.75%, and 1.0%) did not introduce critical acidity levels or compromise the alkaline reserve. ICP-OES analysis indicated minimal impact on essential additive concentrations, supporting the feasibility of SiO2 nanoparticles in enhancing lubricant properties without destabilizing additives. The stability of the nanolubricants was monitored over 77 days, with visible sedimentation beginning around the 30th day and becoming more pronounced by the 54th and 77th days. Bio-lubricants exhibited slightly higher sedimentation than conventional lubricants. Optimizing the sonication time proved to be crucial, with longer sonication times (2.5 h) significantly improving the stability of nanolubricants across various concentrations of added nanoparticles. FTIR analysis confirmed the presence of SiO2 and KH570, indicating no alteration to the basic functional structures of the lubricants and biolubricants. UV-Vis spectrophotometry further underscored the importance of optimizing sonication time for enhanced stability. Overall, this study demonstrates that incorporating surface-modified SiO2 nanoparticles enhances the properties and stability of conventional and biolubricants, offering potential for improved performance in industrial and engine applications.
Using Machine Learning to Calibrate Automated Performance Assessment in a Virtual Laboratory: Exploring the Trade-Off between Accuracy and Explainability
Authors: Zafeiropoulos Vasilis , Kalles Dimitris
Published: September 2024

Hellenic Open University has been developing Onlabs, a virtual biology laboratory simulating its on-site laboratory, for its students to be trained before the on-site learning activities. The evaluation of user performance in Onlabs is based on a scoring algorithm, which admits some optimization by means of Genetic Algorithms and Artificial Neural Networks. Moreover, for a particular experimental procedure (microscoping), we have experimented with incorporating into it some background knowledge about the procedure, which allows one to break it down in a series of conceptually linked steps in a hierarchical fashion. In this work, we review the flat and hierarchical modes used for the calibration of the automated assessment mechanism and offer an experimental comparison of both approaches with the aim of devising automated scoring schemes which are fit for training in an at-a-distance learning context. Overall, the genetic algorithm fails to deliver good convergence results in the non-hierarchical setting but performs better in the hierarchical one. On the other hand, the neural network most of the time converges, with the non-hierarchical network achieving a slightly better convergence than the hierarchical one, with the latter, however, delivering a smoother and more realistic assessment mechanism.
Study on Roof-Cutting and Support of a Retreating Roadway under the Double Influence of Large Mining Heights
Authors: Peng Linjun , Liu Weidong , Peng Chengyuan
Published: September 2024

When the coal mining face enters the final stage of mining, the roadway faces the superimposed influence of surrounding rock stress redistribution and roof rotary moment. As affected by the strong disturbance in the coal mining process, the roof plate of the roadway has undergone serious deformation, which seriously affects the stability of the roadway. Taking the 108 working face of the Jinjitan coal mine as the engineering background, a comprehensive study was conducted on the control of the perimeter rock in the retracement of a tunnel in a heavy coal seam with a large mining height. By analyzing the physical properties of the enclosing rock of the retreated roadway, and using theoretical analysis, numerical simulation, on-site monitoring, and other methods, the characteristics of the peripheral rock’s movement relationship and mineral pressure manifestation in the final mining stage of the large-height working face have been studied. The structural mechanics model was established, and in the case where the support cannot be solved just by strengthening the support, the design scheme of “blasting roof break + constant resistance anchor cable support” was innovatively tried. FLAC3D simulation results show that the stress release of the surrounding rock is more adequate when the height of roof cutting is 20 m. The stress of the surrounding rock near the roadway is reduced by 30~40%, and the stress state is reasonable. The constant resistance and large deformation anchors can absorb the deformation energy of the rock body, maintain constant working resistance and stable deformation, and have good rock stability control, which is conducive to the stability of the roadway.
A Model for Determining the Dependability of Continuous Subsystems in Coal Mines Using the Fuzzy Logic Approach
Authors: Stanic Nikola , Gomilanovic Miljan , Markovic Petar , Krzanovic Daniel , Doderovic Aleksandar , Stepanovic Sasa
Published: September 2024

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability, and low costs. These subsystems are usually part of the thermal power plant’s coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 are utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model’s effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system’s status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.
A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
Authors: Zhang Qirui , Xu Bing , Liu Hu , Zhang Yu , Yu Zhiqiang
Published: September 2024

The convolutional neural network (CNN) has been widely applied in medical image segmentation due to its outstanding nonlinear expression ability. However, applications of CNN are often limited by the receptive field, preventing it from modeling global dependencies. The recently proposed transformer architecture, which uses a self-attention mechanism to model global context relationships, has achieved promising results. Swin-Unet is a Unet-like simple transformer semantic segmentation network that combines the dominant feature of both the transformer and Unet. Even so, Swin-Unet has some limitations, such as only learning single-scale contextual features, and it lacks inductive bias and effective multi-scale feature selection for processing local information. To solve these problems, the Residual Local induction bias-Unet (RL-Unet) algorithm is proposed in this paper. First, the algorithm introduces a local induction bias module into the RLSwin-Transformer module and changes the multi-layer perceptron (MLP) into a residual multi-layer perceptron (Res-MLP) module to model local and remote dependencies more effectively and reduce feature loss. Second, a new densely connected double up-sampling module is designed, which can further integrate multi-scale features and improve the segmentation accuracy of the target region. Third, a novel loss function is proposed that can significantly enhance the performance of multiple scales segmentation and the segmentation results for small targets. Finally, experiments were conducted using four datasets: Synapse, BraTS2021, ACDC, and BUSI. The results show that the performance of RL-Unet is better than that of Unet, Swin-Unet, R2U-Net, Attention-Unet, and other algorithms. Compared with them, RL-Unet produces significantly a lower Hausdorff Distance at 95% threshold (HD95) and comparable Dice Similarity Coefficient (DSC) results. Additionally, it exhibits higher accuracy in segmenting small targets.
A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
Authors: Hu Lin , Zhao Baoqi , Wang Guangji
Published: September 2024

With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%.
Bridge Alignment Prediction Based on Combination of Grey Model and BP Neural Network
Authors: Li Qingfu , Xie Jinghui
Published: September 2024

The continuous improvement of bridge construction technology has resulted in an ongoing expansion of bridge spans, which has concomitantly increased the difficulty of controlling the alignment of long-span bridges during construction. In order to address the issue of the grey prediction model exhibiting a significant discrepancy in its alignment predictions for long-span continuous girder bridges, a pre-camber prediction method for bridges based on a combination of the grey model (GM) and BP neural network (GM-BP) is proposed. Firstly, the parameters are identified according to their influence on the pre-camber, and the appropriate parameters are selected as the original data to improve the efficiency of prediction. Subsequently, the original data are preliminarily fitted by the GM(1,1) model, and the predicted values are used as inputs for training the neural network. Finally, the new predicted values are output using the nonlinear fitting ability of the BP neural network. To assess the efficacy of the model, it is applied to the prediction of the pre-camber of the girder segments of a bridge under cantilever construction. The pre-camber prediction for 11#–13# girder sections was based on 10 sets of monitoring data from constructed girder sections. The results demonstrated that the average relative error of the GM-BP combined prediction model was 3.01%, which was 5.68% less than that of the GM(1,1) model, and the overall prediction exhibited a closer alignment with the original data. The GM-BP combined prediction model is an effective method for ensuring the alignment control of bridge construction and is able to achieve high accuracy and stability in its predictions in the case of limited and irregular data.
Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
Authors: Hong Tao , Lin Zhiyan , Li Yi , Liu Tong
Published: September 2024

The use of airships to launch space probes has been an effective means of conducting typhoon surveys in recent years, but the carrying area and weight of airships are very limited, so it is necessary to reduce the weight and volume of the release device as much as possible. This paper reports the first use of a probe with cylindrical and omnidirectional characteristics, as well as a four-arm spiral receiving antenna, which can also act as the downward release device of the inner wall and improve the space utilization rate. Through further analysis and improvement, it was determined that the four-armed helical antenna can be printed on the medium using a flexible material, which not only reduces the weight of the device but also avoids direct contact between the antenna and the sounding device and improves the stability of the antenna. The designed antenna was modeled and simulated using Ansys HFSS 2021 simulation software.The simulation results show that the antenna presented in this study achieves good performance at the center frequency of 403 MHz, the input voltage VSWR and return loss of the antenna are ideal, and the antenna has a good directional map.
SimMolCC: A Similarity of Automatically Detected Bio-Molecule Clusters between Fluorescent Cells
Authors: Hattori Shun , Miki Takafumi , Sanjo Akisada , Kobayashi Daiki , Takahara Madoka
Published: September 2024

In the field of studies on the “Neural Synapses” in the nervous system, its experts manually (or pseudo-automatically) detect the bio-molecule clusters (e.g., of proteins) in many TIRF (Total Internal Reflection Fluorescence) images of a fluorescent cell and analyze their static/dynamic behaviors. This paper proposes a novel method for the automatic detection of the bio-molecule clusters in a TIRF image of a fluorescent cell and conducts several experiments on its performance, e.g., mAP @ IoU (mean Average Precision @ Intersection over Union) and F1-score @ IoU, as an objective/quantitative means of evaluation. As a result, the best of the proposed methods achieved 0.695 as its mAP @ IoU = 0.5 and 0.250 as its F1-score @ IoU = 0.5 and would have to be improved, especially with respect to its recall @ IoU. But, the proposed method could automatically detect bio-molecule clusters that are not only circular and not always uniform in size, and it can output various histograms and heatmaps for novel deeper analyses of the automatically detected bio-molecule clusters, while the particles detected by the Mosaic Particle Tracker 2D/3D, which is one of the most conventional methods for experts, can be only circular and uniform in size. In addition, this paper defines and validates a novel similarity of automatically detected bio-molecule clusters between fluorescent cells, i.e., SimMolCC, and also shows some examples of SimMolCC-based applications.
An Ensemble Deep Neural Network-Based Method for Person Identification Using Electrocardiogram Signals Acquired on Different Days
Authors: Byeon Yeong-Hyeon , Kwak Keun-Chang
Published: September 2024

Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesanstalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods.
Deep Learning Model-Based Real-Time Inspection System for Foreign Particles inside Flexible Fluid Bags
Authors: Lim Chae Whan , Son Kwang Chul
Published: September 2024

Intravenous fluid bags are essential in hospitals, but foreign particles can contaminate them during mass production, posing significant risks. Although produced in sanitary environments, contamination can cause severe problems if products reach consumers. Traditional inspection methods struggle with the flexible nature of these bags, which deform easily, complicating particle detection. Recent deep learning advancements offer promising solutions in regard to quality inspection, but high-resolution image processing remains challenging. This paper introduces a real-time deep learning-based inspection system addressing bag deformation and memory constraints for high-resolution images. The system uses object-level background rejection, filtering out objects similar to the background to isolate moving foreign particles. To further enhance performance, the method aggregates object patches, reducing unnecessary data and preserving spatial resolution for accurate detection. During aggregation, candidate objects are tracked across frames, forming tracks re-identified as bubbles or particles by the deep learning model. Ensemble detection results provide robust final decisions. Experiments demonstrate that this system effectively detects particles in real-time with over 98% accuracy, leveraging deep learning advancements to tackle the complexities of inspecting flexible fluid bags.
Morphological and Three-Dimensional Analysis for the Clinical Reproduction of Orthodontic Attachments: A Preliminary Study
Authors: Nicita Fabiana , Salmeri Fabio , Runci Anastasi Michele , Aquilio Emidio , Lipari Frank , Centofanti Antonio , Favaloro Angelo
Published: September 2024

In invisible orthodontics, the role of composite attachments in facilitating complex tooth movements is crucial. This study, which evaluates the efficacy of a novel clinical attachment procedure, holds significant implications for the field. The technique used two templates (one pre-drilled and the other pre-loaded with high-viscosity composites) and was compared with the standard procedure. Fifty attachments were planned for four dental arch prototypes. Dental impressions were taken using digital scans for virtual planning and after tested techniques. The stereolithographic files (STL) obtained were aligned with those of the virtual planning, and a colorimetric map was used to evaluate the composite resin’s maximum excess and defect deviation. The enamel–resin interfaces were observed by scanning electron microscopy (SEM). The Fisher test for the distribution of detachments and morphological defects and the Mann–Whitney test for the maximum values of excess and defect were used. No significant results were found between groups for morphological defects and detachments, and the maximum values of defect and excess were reported. SEM images for the experimental technique showed integrated adhesion. This innovative procedure, which has proven reliable and operationally straightforward, holds promise, instilling confidence in its practicality and potential to advance the field of orthodontics.
Comparative Studies on Nanocellulose as a Bio-Based Consolidating Agent for Ancient Wood
Authors: Fornari Anastasia , Rocco Daniele , Mattiello Leonardo , Bortolami Martina , Rossi Marco , Bergamonti Laura , Graiff Claudia , Bani Stefania , Morresi Fabio , Pandolfi Fabiana
Published: September 2024

In this work, nanocellulose aqueous dispersions were studied as a bio-inspired consolidating agent for the recovery and conservation of ancient wood and compared with two of the most used traditional consolidants: the synthetic resins Paraloid B-72 and Regalrez 1126. The morphology of crystalline nanocellulose (CNC), determined using Scanning Electron Microscopy (SEM), presents with a rod-like shape, with a size ranging between 15 and 30 nm in width. Chemical characterization performed using the Fourier-Transform Infrared Spectroscopy (FT-IR) technique provides information on surface modifications, in this case, demonstrating the presence of only the characteristic peaks of nanocellulose. Moreover, conductometric, pH, and dry matter measurements were carried out, showing also in this case values perfectly conforming to what is found in the literature. The treated wood samples were observed under an optical microscope in reflected light and under a scanning electron microscope to determine, respectively, the damage caused by xylophages and the morphology of the treated surfaces. The images acquired show the greater similarity of the surfaces treated with nanocellulose to untreated wood, compared with other consolidating agents. Finally, a colorimetric analysis of these samples was also carried out before and after a first consolidation treatment, and after a second treatment carried out on the same samples three years later. The samples treated with CNC appeared very homogeneous and uniform, without alterations in their final color appearance, compared to other traditional synthetic products.
A Multidisciplinary Learning Model Using AGV and AMR for Industry 4.0/5.0 Laboratory Courses: A Study
Authors: Cservenák Ákos , Husár Jozef
Published: September 2024

This paper presents the development of a multidisciplinary learning model using automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) for laboratory courses, focusing on Industry 4.0 and 5.0 paradigms. Industry 4.0 and 5.0 emphasize advanced industrial automation and human–robot collaboration, which requires innovative educational strategies. Motivated by the need to align educational practices with these industry trends, the goal of this research is to design and implement an effective educational model integrating AGV and AMR. The methodology section details the complex development process, including technology selection, curriculum design, and laboratory exercise design. Data collection and analysis were conducted to assess the effectiveness of the model. The design phase outlines the structure of the educational model, integrating AGV and AMR into the laboratory modules and enriching them with industry collaboration and practical case studies. The results of a pilot implementation are presented, showing the impact of the model on students’ learning outcomes compared to traditional strategies. The evaluation reveals significant improvements in student engagement and understanding of industrial automation. The implications of these findings are discussed, challenges and potential improvements identified, and alignment with current educational trends discussed.
A High-Precision Active Vibration Isolation Control System: Experimental Study
Authors: Nguyen Tan-Ngoc , Lee Dong-Hun , Huynh Thinh , Kim Young-Bok
Published: September 2024

Nowadays, the demand for high-precision devices is becoming more intense, thanks to the growing complexity and sophistication of modern technology and applications, including microscopy, nanomeasurement and analysis instruments. However, challenges arise because environmental factors such as vibration negatively affect their performance. An active vibration isolation system (AVIS) is one of the most recent solutions for that, but the high degree-of-freedom (DoF) nature results in it strongly suffering from unwanted interactions. Hence, in this paper, a robust decoupling controller is designed to handle the mentioned disadvantages. The system model is first presented, followed by the proposed decoupling techniques, and then, a feedback controller is designed by applying the mixed-sensitivity H∞ control framework. Experimental studies are conducted to investigate the effectiveness of the proposed AVIS and compare it with other systems, namely, a passive vibration isolation system, a proportional–derivative (PD)-controlled AVIS, and a robust controlled AVIS. The robustness and decoupling performance of the proposed controller are guaranteed by suppressing external vibrations and isolating interactions, and, therefore, stabilizing the system.
Alternative Sources of Raw Materials for the Ceramic Industry through Granite Waste Recycling: A Case Study from Buddusò, Sardinia, Italy
Authors: Vaccaro Carmela , Aquilano Antonello , Marrocchino Elena
Published: September 2024

The sourcing of raw materials is becoming an increasingly concerning issue for the European Union, which is adopting a series of strategies aimed at mitigating the dependence on third countries for procurement. The supply of feldspars, for instance, which is significant within the ceramic industry, raises concerns due to heavy reliance on third countries. Therefore, it is imperative to identify potential alternative sources for procurement, including the recycling of waste from the extraction of ornamental granite rocks enriched in such minerals. The Sardinia region in Italy, once a global leader in granite extraction, has now witnessed a significant reduction in production due to intense competition worldwide, resulting in severe economic repercussions. Additionally, traces of this period remain in the territory in the form of enormous quantities of extraction waste, defacing the landscape and posing potential environmental issues. This study is part of a broader research initiative aimed at identifying a physical process to concentrate rare earth element (REE) minerals from granite waste in a quarry located in Buddusò, Sardinia. The specific objective of this study is to analyze the whole rock composition, crushing powder, and the diamagnetic by-products generated by gravity and magnetic separations to identify potential applications for their use.
Chemical and Sensory Properties of Corn Extrudates Enriched with Tomato Powder and Ascorbic Acid
Authors: Obradović Valentina , Babić Jurislav , Jozinović Antun , Ačkar Đurđica , Šubarić Drago
Published: September 2024

The chemical and sensory properties of corn extrudates enriched with spray-dried tomato powder (TP) in 4, 6 and 8% ratios were researched. Two extrusion temperature regimes were used: 135/170/170 °C (E1) and 100/150/150 °C (E2). Ascorbic acid (AA) at levels of 0.5 and 1% was also added to the raw mixtures in order to prevent the undesirable oxidation of the constituents, primarily carotenoids. AA was especially efficient in the case of the lutein content and 1% AA, but lutein originating from TP was more sensitive to the extrusion conditions than corn lutein, and zeaxanthin was more sensitive than lutein. Lycopene, α-carotene, 13-cis-β carotene and 9-cis-β carotene degraded completely in all the samples, at both extrusion regimes. The proposed models for the color of the extrudates showed the significant influence of TP and AA. Extrudates obtained at the E1 temperature regime containing 4% TP and pure corn extrudate with 1% AA were the best-rated samples by the sensory panel.
Comparative Analysis of Concrete Cracking and Reinforcement Corrosion in Concrete and Ultra-High-Performance Concrete Short Columns after Accelerated Corrosion
Authors: Wu Hengyu , Tong Teng , Wang Xu , Huang Jingzhi
Published: September 2024

In this study, nine concrete short columns and nine UHPC (ultra-high-performance concrete) short columns were produced, and an accelerated corrosion test method was used to compare and analyze the cracking phenomena and reinforcement corrosion after different durations of electrical current application. The analysis revealed that the corrosion rate of the UHPC specimens was approximately half of that of the concrete specimens, demonstrating excellent corrosion resistance. Although the corrosion rate of the UHPC specimen was lower, the length of the internal steel reinforcement decreased more significantly under prolonged electrification. For example, after 38 days of accelerated corrosion, the internal steel reinforcement in the ordinary concrete specimen measured 48 mm in length, while in the UHPC specimen, it measured only 43 mm. It was also found that the corrosion rates of both the concrete and UHPC specimens were significantly lower than the theoretical corrosion rate. This discrepancy is attributed to the fact that Faraday’s law, used to calculate the theoretical corrosion rate, does not fully account for factors such as the thickness of the protective layer and chloride ion concentration. The actual corrosion rate of the concrete specimens was generally only 70% of the theoretical value, while the UHPC specimens showed a corrosion rate which was only 40% of the theoretical value.
Domain-Aware Neural Network with a Novel Attention-Pooling Technology for Binary Sentiment Classification
Authors: Yue Chunyi , Li Ang , Chen Zhenjia , Luan Gan , Guo Siyao
Published: September 2024

Domain information plays a crucial role in sentiment analysis. Neural networks that treat domain information as attention can further extract domain-related sentiment features from a shared feature pool, significantly enhancing the accuracy of sentiment analysis. However, when the sentiment polarity within the input text is inconsistent, these methods are unable to further model the relative importance of sentiment information. To address this issue, we propose a novel attention neural network that fully utilizes domain information while also accounting for the relative importance of sentiment information. In our approach, firstly, dual long short-term memory (LSTM) is used to extract features from the input text for domain and sentiment classification, respectively. Following this, a novel attention mechanism is introduced to fuse features to generate the attention distribution. Subsequently, the input text vector obtained based on the weighted summation is fed into the classification layer for sentiment classification. The empirical results from our experiments demonstrate that our method can achieve superior classification accuracies on Amazon multi-domain sentiment analysis datasets.