bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Epidemiology of Pseudomonas aeruginosa in Brazil: A Systematic Review and Meta-Analysis
Authors: Rodrigues Yan Corrêa , Silva Marcos Jessé Abrahão , dos Reis Herald Souza , dos Santos Pabllo Antonny Silva , Sardinha Daniele Melo , Gouveia Maria Isabel Montoril , dos Santos Carolynne Silva , Marcon Davi Josué , Aires Caio Augusto Martins , Souza Cintya de Oliveira , Quaresma Ana Judith Pires Garcia , Lima Luana Nepomuceno Gondim Costa , Brasiliense Danielle Murici , Lima Karla Valéria Batista
Published: October 2024

Background: Globally, Pseudomonas aeruginosa is a high-priority opportunistic pathogen which displays several intrinsic and acquired antimicrobial resistance (AMR) mechanisms, leading to challenging treatments and mortality of patients. Moreover, its wide virulence arsenal, particularly the type III secretion system (T3SS) exoU+ virulotype, plays a crucial role in pathogenicity and poor outcome of infections. In depth insights into the molecular epidemiology of P. aeruginosa, especially the prevalence of high-risk clones (HRCs), are crucial for the comprehension of virulence and AMR features and their dissemination among distinct strains. This study aims to evaluate the prevalence and distribution of HRCs and non-HRCs among Brazilian isolates of P. aeruginosa. Methods: A systematic review and meta-analysis were conducted on studies published between 2011 and 2023, focusing on the prevalence of P. aeruginosa clones determined by multilocus sequence typing (MLST) in Brazil. Data were extracted from retrospective cross-sectional and case-control studies, encompassing clinical and non-clinical samples. The analysis included calculating the prevalence rates of various sequence types (STs) and assessing the regional variability in the distribution of HRCs and non-HRCs. Results: A total of 872 samples were analyzed within all studies, of which 298 (34.17%) were MLST typed, identifying 78 unique STs. HRCs accounted for 48.90% of the MLST-typed isolates, with ST277 being the most prevalent (100/298—33.55%), followed by ST244 (29/298—9.73%), ST235 (13/298—4.36%), ST111 (2/298—0.67%), and ST357 (2/298—0.67%). Significant regional variability was observed, with the Southeast region showing a high prevalence of ST277, while the North region shows a high prevalence of MLST-typed samples and HRCs. Conclusions: Finally, this systematic review and meta-analysis highlight the role of P. aeruginosa clones in critical issue of AMR in P. aeruginosa in Brazil and the need of integration of comprehensive data from individual studies.
Phenotypic, Genetic, and Metabolite Variability among Genotypes of Vicia sativa L.
Authors: Avramidou Eleni , Sarri Efi , Papadopoulou Evgenia-Anna , Petsoulas Christos , Tigka Evangelia , Tourvas Nikolaos , Pratsinakis Emmanouil , Ganopoulos Ioannis , Tani Eleni , Aliferis Konstantinos A. , Abraham Eleni M. , Madesis Panagiotis , Vlachostergios Dimitrios
Published: October 2024

Vicia sativa L., commonly known as the common vetch, is an annual, self-pollinating legume used primarily as fodder both by livestock and wildlife. Additionally, it contributes to environmental balance through nitrogen fixation and the improvement of soil properties. The phenotypic, genetic, and metabolite variability among four advanced lines (BK45, BK29, BK23, BK27) and two commercial varieties (M-6900, BI-65) of V. sativa were evaluated in order to be used for future breeding programs aimed at producing genetically improved varieties. BK45 was the most promising line due to its high genetic polymorphism, but also because it exhibited a significant amount of seed production and high seed quality based on its metabolomics profile. A stepwise multiple regression analysis (MRA) revealed a relationship between SCoT alleles, seed, and biomass yield. Additionally, several statistically significant marker bands linked to metabolites were found using the SCoT marker analysis. Hence, data assessed via MRA may be helpful in marker-assisted breeding programs. Finally, the two commercial varieties can be further exploited in breeding programs due to their high genetic diversity.
Advanced Structural Monitoring Technologies in Assessing the Performance of Retrofitted Reinforced Concrete Elements
Authors: Naoum Maria C. , Papadopoulos Nikos A. , Sapidis George M. , Chalioris Constantin E.
Published: October 2024

Climate change induces extreme effects with lower-than-designed restoration periods, imposing the necessity of strengthening the structural integrity of existing and mainly older RC structures, which are often demonstrated to be under-reinforced in terms of the shear capacity, mainly due to outdated and old design codes/standards. Thus, finding cost-effective and feasible methods to strengthen RC elements is becoming increasingly important. Thin RC layers for jacketing represent a modern advancement in repairing and retrofitting RC members. In this context, U-shaped mortar jackets were employed to strengthen three shear-critical beams. In addition, a critical aspect in the success of any jacketing method is the degree of bonding and interaction between the original member and the new jacket. Additionally, the performance of these U-shaped jackets was assessed using an Electro-Mechanical-Impedance-based (EMI-based) method using a Piezoelectric-Transducer-enabled (PZT-enabled) technique. The integration of advanced monitoring technologies in retrofitting applications offers valuable insights into the performance and longevity of the retrofit system. Therefore, this study aims to experimentally investigate the cohesion between construction materials and assess the effectiveness of U-shaped jackets. Through the proposed Structural Health Monitoring (SHM) technique, any degradation at the interface or slippage of the retrofitting jacket can be promptly detected, restraining further damage development and potential failure of the structure.
Design of a Multi-Vision System for a Three-Dimensional Mug Shot Model to Improve Forensic Facial Identification
Authors: Giuliani Samuele , Tosti Francesco , Lopes Pierpaolo , Ciampini Claudio , Nardinocchi Carla
Published: October 2024

A traditional mug shot is a front and side view of a person from the shoulder up, taken by law enforcement. Forensic science is exploring the benefit of working with 3D data offered by new technologies, and there is an increasing need to work with 3D mug shots. Among the various available techniques, a multi-view photogrammetric approach achieves the highest accuracy in the shortest acquisition time. In this work, a multi-view photogrammetric system for facial reconstruction based on low-cost cameras is developed with the aims of verifying the performance of such cameras for the production of a 3D mug shot with submillimetre accuracy and assessing the improvement of facial matching using a 3D mug shot over traditional 2D mug shots. The tests were carried out in both a virtual and a real-world environment, using either a virtual or a 3D-printed 3D model. The outcome is a point cloud, which describes the face. The quantitative analysis of the errors was realized through the distances between the mesh of the acquired 3D model and the point cloud. A total of 80% of the points with a distance of less than ±1 mm was obtained. Finally, the performance on facial recognition of the 3D mug shot is evaluated against the traditional 2D mug shot using the NeoFace Watching software (NeoFACE) with a score increment of up to 0.42 points, especially in scenarios where the suspect is not captured from a frontal view.
Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems
Authors: Tanyıldız Hayriye , Batur Şahin Canan , Batur Dinler Özlem
Published: October 2024

Industrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities. With increased internet connectivity and integration with the Internet of Things (IoT), ICSs become more vulnerable to cyberattacks, which can have serious consequences, such as service interruption, financial losses, and security hazards. Threat actors target these systems with sophisticated attacks that can cause devastating damage. Cybersecurity vulnerabilities in ICSs have recently led to increasing cyberattacks and malware exploits. Hence, this paper proposes to develop a security solution with dynamic and adaptive deceptive patching strategies based on studies on the use of deceptive patches against attackers in industrial control systems. Within the present study’s scope, brief information on the adversarial training method and window size manipulation will be presented. It will emphasize how these methods can be integrated into industrial control systems and how they can increase cybersecurity by combining them with deceptive patch solutions. The discussed techniques represent an approach to improving the network and system security by making it more challenging for attackers to predict their targets and attack methods. The acquired results demonstrate that the suggested hybrid method improves the application of deception to software patching prediction, reflecting enhanced patch security.
A Model for a Green, Lean Sustainable Improvement with Performance Measurement
Authors: Berlec Tomaž , Peperko Aljoša
Published: October 2024

With a turbulent market and a desire to withstand force, many companies lower the production price of their products using different approaches. On the one hand, they often use lean principles to reduce production costs, while they are compelled by the government to adopt green practices on the other. The proposed approach (model) is a combination of green, lean sustainable improvement, leading a company through the green, lean process with measurable results and optimization functions. In addition to its return on investment (ROI), a company can calculate its output (from material to emission flow) based on its input. Based on the radar chart we have produced, they can also know what they have achieved and in which direction they need to optimize to develop in as green and sustainable a way as possible. One example of a Slovenian automotive company demonstrates how the implementation of the proposed measures can effectively reduce environmental impact, while also ensuring a quick return on investment.
Model-Driven Approach to Cloud-Portability Issue
Authors: Moravcik Marek , Segec Pavel , Kontsek Martin , Zidekova Lubica
Published: October 2024

This paper focuses on the portability of Cloud Computing (CC) services, specifically on the problems with the portability of Infrastructure as a Service (IaaS). We analyze the current state of CC with the intention of standardizing the portability of CC solutions. CC IaaS providers often use proprietary solutions, which leads to a problem known as “vendor lock-in”. Another problem might appear during migration between two providers if huge scripts are written in a proprietary language. To solve the portability problem, we applied the Model-Driven Architecture (MDA) approach to propose the general IaaS reference architecture. Using a generic IaaS model, we are able to describe entities of the IaaS environment and then design necessary transformation rules for specific IaaS environments in a simplified but flexible way. Using this model, we continue designing transformation rules that define the transcript of IaaS services. The CC-portability problem is thus solved by transforming a specific IaaS service description from one description to another through the generic model. This approach is extensible and can be adopted for the evolution of CC services. Therefore, it can be used as a generic solution to IaaS-portability issues. Using this flexible approach, the introduction of a new CC environment requires only the design of a single transformation rule that prevents proprietary peer-to-peer full-mesh mappings. Thanks to the proposed model and the transformation rules described, we were able to experimentally confirm the functionality of the transfer of the environment description between three cloud providers.
Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
Authors: Park Yesol , Son Gyujin , Rho Mina
Published: October 2024

Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information from textual sources. Considering the structure of the entities it addresses, BioNER tasks are divided into two categories: flat NER, where entities are non-overlapping, and nested NER, which identifies entities embedded within another. While early studies primarily addressed flat NER, recent advances in neural models have enabled more sophisticated approaches to nested NER, gaining increasing relevance in the biomedical field, where entity relationships are often complex and hierarchically structured. This review, thus, focuses on the latest progress in large-scale pre-trained language model-based approaches, which have shown the significantly improved performance of NER. The state-of-the-art flat NER models have achieved average F1-scores of 84% on BC2GM, 89% on NCBI Disease, and 92% on BC4CHEM, while nested NER models have reached 80% on the GENIA dataset, indicating room for enhancement. In addition, we discuss persistent challenges, including inconsistencies of named entities annotated across different corpora and the limited availability of named entities of various entity types, particularly for multi-type or nested NER. To the best of our knowledge, this paper is the first comprehensive review of pre-trained language model-based flat and nested BioNER models, providing a categorical analysis among the methods and related challenges for future research and development in the field.
Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
Authors: Trezza Alfonso , Visibelli Anna , Roncaglia Bianca , Spiga Ottavia , Santucci Annalisa
Published: October 2024

Integrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorithms enable the discovery of new insights into disease mechanisms and patient variability, advancing our understanding of individual responses to treatment. However, the integration of AI into PM presents some challenges, including concerns about data privacy and the rigorous validation of AI models in clinical practice. Despite these challenges, AI holds immense potential to revolutionize PM, offering a more personalized, efficient, and effective approach to healthcare. Collaboration among AI developers and clinicians is essential to fully realize this potential and ensure ethical and reliable implementation in medical practice. This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being.
Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes
Authors: Cho Mingoo , Gim Jinsu , Kim Ji Hoon , Kang Sungwook
Published: October 2024

The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges posed by differences in material properties and the complex nature of FSW, where traditional experimental methods are time-consuming and costly. FSW experiments were conducted under a variety of conditions, and the resulting temperature data were utilized as input for a heat transfer analysis. The maximum temperature and temperature gradient obtained from the analysis were employed as input variables for training the ANN. The ANN was optimized using the Hyperband tuner and validated against experimental results. The model successfully predicted tensile strength with an average error of 5.4%, demonstrating its potential for predicting mechanical properties under different welding conditions. This approach offers a more efficient and accurate method for optimizing FSW processes.
Occlusion Removal in Light-Field Images Using CSPDarknet53 and Bidirectional Feature Pyramid Network: A Multi-Scale Fusion-Based Approach
Authors: Senussi Mostafa Farouk , Kang Hyun-Soo
Published: October 2024

Occlusion removal in light-field images remains a significant challenge, particularly when dealing with large occlusions. An architecture based on end-to-end learning is proposed to address this challenge that interactively combines CSPDarknet53 and the bidirectional feature pyramid network for efficient light-field occlusion removal. CSPDarknet53 acts as the backbone, providing robust and rich feature extraction across multiple scales, while the bidirectional feature pyramid network enhances comprehensive feature integration through an advanced multi-scale fusion mechanism. To preserve efficiency without sacrificing the quality of the extracted feature, our model uses separable convolutional blocks. A simple refinement module based on half-instance initialization blocks is integrated to explore the local details and global structures. The network’s multi-perspective approach guarantees almost total occlusion removal, enabling it to handle occlusions of varying sizes or complexity. Numerous experiments were run on sparse and dense datasets with varying degrees of occlusion severity in order to assess the performance. Significant advancements over the current cutting-edge techniques are shown in the findings for the sparse dataset, while competitive results are obtained for the dense dataset.
Application of Computer Vision and Lego Mindstorms in Measuring Brewster’s Angle and Refractive Index
Authors: Rivera-Ortega Uriel
Published: October 2024

In this implementation, an innovative and automated measurement and educational-demonstrative resource is proposed that estimates Brewster’s angle and the index of refraction of semitransparent materials. The apparatus, built and programmed with LEGO Mindstorms model Ev3, utilizes a micro:bit board and a machine vision camera (Huskylens cam). This camera detects the intensity extinction of a reflected laser beam at the polarization angle. The Ev3 connection cables and general input/output electronic interfaces have been modified to establish communication with the intelligent brick. Upon completion, the Brewster’s angle and the index of refraction are displayed on the intelligent brick screen. It will also be shown that the percentage error corresponding to the measurement of the refractive index of a glass and acrylic sample is 1.92% and 0.47%, respectively. According to its characteristics, this resource is suitable for use in undergraduate or graduate physics and engineering laboratories/courses.
Construction of Robotics and Application of the Optical-Flow Algorithm in Determining Robot Motions
Authors: Nguyen Anh Van , Hoang Van Tien , Tran The Hung
Published: October 2024

This article presents the research results in the application of image processing in determining the position, direction, and moving speed of different objects when they move in the free space in the field of view of measuring cameras. The research includes developing an algorithm to detect, identify, and locate objects and an algorithm to calculate the movement direction and instantaneous velocity of the object. Two robots with measurement systems were designed for testing, one carries the investigating object, and the other the camera. These robots can communicate with a computer system using a tele-wireless system. A program was also built for capturing images and analyzing the state of the model automatically. Experimental results show that the position, angle, and velocity of different objects can be captured well. The average error in determining the direction of movement is an average of 1.25°, and the error of the moving speed is less than 0.5 m/s. The research results provide a potential tool for designing robots for highly effective detection. The algorithm and the measurement system are simple and inexpensive but highly effective, and they can be used in the initial process of determining an object. A detailed description of the algorithm, robot system, and testing will be presented in this study.
A Loose Integration of High-Rate GNSS and Strong-Motion Records with Variance Compensation Adaptive Kalman Filter for Broadband Co-Seismic Displacements
Authors: Wang Runjie , Wu Haiqian , Shen Rui , Kang Junyv
Published: October 2024

The loose integration system of high-rate GNSS and strong-motion records based on Kalman filtering technology is currently a research focus for capturing broadband co-seismic displacements. To address the problem of time-varying system noise variance in the standard Kalman filter (SKF), a variance compensation adaptive Kalman filter (VC-AKF) was adopted in this study to obtain more accurate high-precision broadband co-seismic displacement and provide reliable data support for seismic scientific research and practical applications. The algorithm continuously updates the system noise variance and calculates the state vector by collecting prediction residuals in real time. To verify the effectiveness and superiority of this method, a numerical simulation and a seismic experiment from the 2017 Ms 7.0 Jiuzhaigou earthquake were carried out for comparative analysis. Based on the simulation results, the precision of the proposed algorithm was 46% higher than that of the SKF. The seismic experiment results indicate that the proposed VC-AKF approach can eliminate the baseline shift of accelerometers and weaken the influence of time-varying system noise variance towards more robust displacement information.
Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
Authors: Chen Rui , Zhu Yanping , Chen Qi , Zhu Chenyang
Published: October 2024

To address the limitations of traditional footstep vibration signal localization algorithms, such as limited accuracy, single feature extraction, and cumbersome parameter adjustment, a motion target localization method for step vibration signals based on deep learning is proposed. Velocity vectors are used to describe human motion and adapt it to the nonlinear motion and complex interactions of moving targets. In the feature extraction stage, a one-dimensional residual convolutional neural network is constructed to extract the time–frequency domain features of the signals, and a channel attention mechanism is introduced to enhance the model’s focus on different vibration sensor signal features. Furthermore, a bidirectional long short-term memory network is built to learn the temporal relationships between the extracted signal features of the convolution operation. Finally, regression operations are performed through fully connected layers to estimate the position and velocity vectors of the moving target. The dataset consists of footstep vibration signal data from six experimental subjects walking on four different paths and the actual motion trajectories of the moving targets obtained using a visual tracking system. Experimental results show that compared to WT-TDOA and SAE-BPNN, the positioning accuracy of our method has been improved by 37.9% and 24.8%, respectively, with a system average positioning error reduced to 0.376 m.
Non-Destructive Testing of Joints Used in Refrigerated Vehicle Bodies
Authors: Kowalczyk Jakub , Tyczewski Przemysław
Published: October 2024

This paper focuses on the non-destructive evaluation of adhesive joints used in vehicles designed for transporting food products. The research and analysis were limited to the joints used in connecting elements of the cargo space. Two non-destructive methods were employed in the study: ultrasonic and thermographic techniques. Both methods confirmed the feasibility of evaluating adhesive joints in the construction of food transport vehicles, with the thermographic method proving to be much faster in identifying large areas of deadhesion in the plating. The ultrasonic method, on the other hand, allows for the inspection of sheathing and aluminum profiles. The predefined decibel drop in the height of the first two pulses on the ultrasonic defectoscope screen for areas with high-quality joints was less than 3.5 dB. In contrast, for areas with adhesion-related damage, the decibel drop in the first two pulses exceeded 4.5 dB.
Biofilm Production Capability of Clinical Aeromonas salmonicida Subspecies salmonicida Strains under Stress Conditions
Authors: Aksentijević Ksenija , Rajewska Aleksandra Daria , Wojnarowski Konrad , Cholewińska Paulina , Korzeniowska Malgorzata , Steinbauer Peter , Palić Dušan , Misic Dusan
Published: October 2024

Biofilm formation of clinical isolates of Aeromonas salmonicida subspecies salmonicida was compared using scarce (minimal M9 and ABTG w/o amino acids) and enriched nutrient media (Tryptone Soya broth) at 8 °C, 16 °C, and 25 °C using direct enumeration of viable cells in biofilm (log CFU), crystal violet staining (ODc) of the formed biofilm biomass, and liquid–air border biofilm formation (pellicle test). Whole-genome sequencing (WGS) was performed with the usage of an Oxford nanopore system by Genomics and Transcriptomics Labor Düsseldorf (Heinrich-Heine-Universität Düsseldorf, Germany). A bioinformatic analysis was performed with the usage of Geneious Prime® 2023.0.4 (Biomatters, Inc., Boston, MA, USA). All data were analyzed using Statistica software version 13.0, and changes in biofilm production in correlation to changes in the type of nutritional medium and temperature were compared between groups using a one-way ANOVA analysis and Tukey’s test. All isolates formed biofilms in minimal M9 at 8 °C and 16 °C, and nine isolates failed to form biofilms in minimal M9 at 25 °C. In an ABTG medium, all isolates produced biofilms at 8 °C; however, three isolates at 16 °C and seven isolates at 25 °C failed to form any biofilms. Significant biofilm formation was observed in TSB at all temperatures. Some strains that formed a good biofilm in solid–liquid interface did not have the ability to form a pellicle (liquid–air border biofilm), and vice versa. In all cases of nutritional medium and temperature changes, there were statistically significant differences in the intensity of biofilm production, especially in the detected number of viable cells inside biofilms (log CFU, p < 0.005). Multiple biofilm-production genes, including polar flagella (filM) LuxR family (transcriptional regulators) and VapA family of histidine-kinase-associated genes, were sequenced from all studied isolates. Genetic differences based on geographical origin were not observed among the isolates. Significant variations in isolates’ ability to form biofilms were observed, possibly due to epigenetic factors. The optimal temperature for biofilm formation of A. salmonicida subspecies salmonicida in scarce media was 8 °C, and the majority of isolates were not capable of biofilm formation at 25 °C without enriched nutrient media.
Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
Authors: Janssens Robin , Mannens Erik , Berkvens Rafael , Denis Stijn
Published: October 2024

Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment’s state.
Evaluation of Petrographic and Geomechanical Properties of Inzari Formation Rocks for Their Suitability as Building Materials in the Nizampur Basin, Pakistan
Authors: Ayaz Hassan , Xu Jiancong , Aslam Muhammad Usama , Ahmad Sohail
Published: October 2024

This study focuses on the petrographic and geomechanical properties of the Inzari Formation rocks in Tar Khel Village, District Swabi, Khyber Pakhtunkhwa, Pakistan, on their suitability as building materials for structures such as bridges, buildings, and roads. Five fresh samples from various areas of this formation were analyzed and identified as siliciclastic mudstone through petrographic analysis. The results of physical properties (water absorption, specific gravity, and porosity) and mechanical properties (unconfined compression strength, unconfined tensile strength, Schmidt hammer test, and shear strength) indicate the Inzari Formation rocks have unconfined compressive strengths ranging from 23 to 94 MPa, specific gravity values ≥2.78, and low porosity and water absorption values (<1%), suggesting their excellent potential as building materials. Comparatively, the unconfined compressive strength values of the Inzari Formation rocks are similar to those of the NikanaiGhar limestone, which averaged 93.348 MPa, further confirming their suitability for construction. Additionally, the low porosity values of the Inzari Formation rocks align with the porosity range of 0.31% to 0.41% reported in the NikanaiGhar limestone study. Based on these findings, the Inzari Formation rocks are appropriate for use as fine aggregate in concrete, asphalt, and other construction materials.
AI-Aided Proximity Detection and Location-Dependent Authentication on Mobile-Based Digital Twin Networks: A Case Study of Door Materials
Authors: Park Woojin , An Hyeyoung , Yim Yongbin , Park Soochang
Published: October 2024

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition between a service provider and a client in mobile–mobile interaction is not trivial. This is because of not only the avoidance of face-to-face communication due to safety and health concerns but also the difficulty of matching up the online user using mobiles with the real person in the physical world. So, a novel mutual recognition scheme for mobile–mobile interaction is highly necessary. This paper comes up with a novel cyber-physical secure communication scheme relying on the digital twin paradigm. The proposed scheme designs the digital twin networking architecture on which real-world users form digital twins as their own online abstraction, and the digital twins authenticate each other for a smart service interaction. Thus, inter-twin communication (ITC) could support secure mutual recognition in mobile–mobile interaction. Such cyber-physical authentication (CPA) with the ITC is built on the dynamic BLE beaconing scheme with accurate proximity detection and dynamic identifier (ID) allocation. To achieve high accuracy in proximity detection, the proposed scheme is conducted using a wide variety of data pre-processing algorithms, machine learning technologies, and ensemble techniques. A location-dependent ID exploited in the CPA is dynamically generated by the physical user for their own digital twin per each mobile service.
Using Laser Profilometry to Investigation FDM Printing Parameters for Outer-Perimeter Analysis and Surface Quality Improvement
Authors: Kalman Olha , Husár Jozef , Lazorík Peter
Published: October 2024

This study explores the optimization of fused deposition modeling (FDM), a prominent 3D printing technology known for its accessibility and cost-effectiveness. The research aimed to identify and reduce errors associated with key printing parameters, specifically the layer height, printing temperature, and printing speed. Advanced tools such as a Keyence laser scanner and microscope were used to evaluate the dimensional accuracy and surface quality of various samples. The results indicate that the optimal settings for the layer height (0.16 mm), printing temperature (250 °C), and printing speed (350 mm/s) significantly minimize variation, resulting in more consistent and accurate prints. The results also showed that the samples printed with these optimized parameters had the lowest variability, underscoring the critical importance of precisely managing these factors. The findings highlight the critical role of fine-tuned FDM parameters in improving the quality and reliability of printed objects and provide valuable insights for further advances in 3D printing processes.
Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
Authors: Lan Qing , Zhu Ye , Lin Baojun , Zuo Yizheng , Lai Yi
Published: October 2024

The rotational mechanism, which plays a critical role in energy supply, payload antenna pointing, and attitude stabilization in satellites is essential for the overall functionality and performance stability of the satellite. This paper takes the space turntable of a specific satellite model as an example, utilizing high-frequency and high-dimensional telemetry data. An improved informer model is used to predict and diagnose features related to the turntable’s operational health, including temperature, rotational speed, and current. In this paper, we present a forecasting method for turntable temperature data using a hybrid model that combines singular spectrum analysis with an enhanced informer model (SSA-Informer), comparing the results with threshold limits to determine if faults occur in the satellite’s rotational mechanism. First, during telemetry data processing, singular spectrum analysis (SSA) is proposed to retain the long-term and oscillatory trends in the original data while filtering out noise from interference. Next, the improved informer model predicts the turntable temperature based on the mapping relationship between the turntable subsystem’s motor current and temperature, with multiple experiments conducted to obtain optimal parameters. Finally, temperature thresholds generated from the prediction results are used to forecast faults in the rotational mechanism over different time periods. The proposed method is compared with current popular time-series prediction models. The experimental results show that the model achieves high prediction accuracy, with reductions of at least 10% in both the MAE and MSE than CNN-LSTM, DA-RNN, TCN-SE and informer, demonstrating the outstanding advantages of the SSA and improved informer-based method in predicting temperature faults in satellite rotational mechanisms.
A Systematic Analysis of Nutritional and Mineral Composition and Toxicity in Acacia Species Leaves
Authors: Pedro Soraia I. , Gonçalves Joana , Horta Carmo , Gonçalves José C. , Gominho Jorge , Gallardo Eugenia , Anjos Ofélia
Published: October 2024

In the present study, the nutritive composition and mineral content of the leaves of eight species of Acacia trees were evaluated to assess their potential for different purposes, such as food resources for ruminants and mineral sources in soils. Caco-2 cells were employed to assess cytotoxicity, revealing that the extracts exhibited no cytotoxic effects after cellular incubation, suggesting their suitability as an alternative animal feed. The leaves proved to be a promising source of protein and fiber, offering an alternative to meet the needs of ruminants. The protein content differed among species, ranging from 18.96% in A. cyclops to 14.04% in A. melanoxylon. Similarly, fiber content varied from 35.52% in A. melanoxylon to 16.43% in A. cyclops. The species displayed moderate to high levels of minerals, particularly concentrations of Ca, P, and K. These values varied among species, ranging from 8452.90 to 28,465.31 mg/kg DM for Ca, 309.61 to 1293.82 mg/kg DM for P, and 5557.24 to 11,475.85 mg/kg DM for K, respectively. Each species displayed a distinct profile regarding the analyzed parameters, suggesting varied potential for their respective use. Additionally, vibrational spectroscopy analysis proved to be a highly valuable and dependable method for distinguishing between species.
AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability
Authors: Simion Dragos , Postolache Florin , Fleacă Bogdan , Fleacă Elena
Published: October 2024

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector.
A Simulation Study of FRP-PCM Reinforcement for Tunnel Linings with Void Defects
Authors: Lin Qiwei , Jiang Yujing , Wang Jing , Sugimoto Satoshi
Published: October 2024

Voids behind tunnel linings can be formed either during or after the construction phase, occurring due to inadequate backfilling, substandard workmanship, water erosion, or gravitational forces. Investigations into numerous tunnels in which collapses occurred while in operation have indicated that voids behind the liner constitute the primary contributors to these failures. Consequently, it is imperative to devise lining reinforcement strategies tailored to the specific conditions encountered in the field. Fiber-reinforced plastic (FRP) represents a viable alternative construction material that has been widely utilized in the reinforcement of concrete structures. It is essential to quantitatively assess the reinforcing effect of FRP grids when they are employed in the restoration of deteriorated tunnel linings, thereby facilitating the development of effective maintenance designs. In this study, we aimed to enhance the sensitivity analysis of the reinforcement method by evaluating the impact of voids through the analysis of bending moments and axial forces within the tunnel lining. The effects of voids based on the different locations in which they occur were explored numerically through an Elastoplast finite element analysis. The study involved simulating tunnel linings that had been reinforced with FRP grids and assessing the effects of such reinforcement in tunnels afflicted with various structural problems. Based on the outcomes of these simulations, the internal forces within the lining are scrutinized, and the efficacy of the reinforcement is appraised.
Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa
Authors: Wu Yuezhong , Xie Huan , Gu Lin , Chen Rongrong , Chen Shanshan , Wang Fanglan , Liu Yiwen , Chen Lingjiao , Tang Jinsong
Published: October 2024

As mental health issues become increasingly prominent, we are now facing challenges such as the severe unequal distribution of medical resources and low diagnostic efficiency. This paper integrates finite state machines, retrieval algorithms, semantic-matching models, and medical-knowledge graphs to design an innovative intelligent auxiliary evaluation tool and a personalized medical-advice generation application, aiming to improve the efficiency of mental health assessments and the provision of personalized medical advice. The main contributions include the folowing: (1) Developing an auxiliary diagnostic tool that combines the Mini-International Neuropsychiatric Interview (M.I.N.I.) with finite state machines to systematically collect patient information for preliminary assessments; (2) Enhancing data processing by optimizing retrieval algorithms for efficient filtering and employing a fine-tuned RoBERTa model for deep semantic matching and analysis, ensuring accurate and personalized medical-advice generation; (3) Generating intelligent suggestions using NLP techniques; when semantic matching falls below a specific threshold, integrating medical-knowledge graphs to produce general medical advice. Experimental results show that this application achieves a semantic-matching degree of 0.9 and an accuracy of 0.87, significantly improving assessment accuracy and the ability to generate personalized medical advice. This optimizes the allocation of medical resources, enhances diagnostic efficiency, and provides a reference for advancing mental health care through artificial-intelligence technology.
Structural Design and Study of an Integrated Cutter System Based on Machine Operation
Authors: Liu Sijin , Han Kaixuan , Wang Huawei , Chen Hao , Ma Yuyang , Huo Junzhou
Published: October 2024

In the process of shield tunneling, the cutter will inevitably be worn and damaged and will need to be replaced frequently. The low efficiency and high safety risk of traditional manual cutter change have prompted robotic cutter change to become the mainstream trend of current research. However, it is difficult to realize mechanical automation disassembly due to the existence of many fragmented parts and complex disassembly steps in the traditional cutter system. Therefore, this paper proposes an eccentric integrated cutter system, which greatly simplifies the disassembly process while retaining the excellent fastening structure of the traditional cutter system. The evaluation system of the cutter system was established through the analytic hierarchy process, and it was verified that the eccentric integrated cutter system has obvious superiority in realizing automated disassembly and assembly and, at the same time, that it has good structural strength. Finally, vibration experiments were carried out based on shield construction conditions. The experimental results show that after 14 h of continuous vibration, the residual preload ratio of the integrated cutter sample stabilized at more than 90%, which indicates good anti-loosening performance.
Comparative Analysis of Piezoelectric Transducers for Low-Power Systems: A Focus on Vibration Energy Harvesting
Authors: Lacerda Iusley S. , Silva Antonio A. , Fernandes Eisenhawer M. , Senko Richard , Oliveira Andersson G. , Delgado João M. P. Q. , Diniz Diego D. S. , Figueiredo Maria J. , Lima Antonio G. B.
Published: October 2024

With advances in technology, the generation of electrical energy through the harvesting of energies dissipated in the form of mechanical vibration, known as power harvesting, has received increasing attention in recent decades. It is undoubtedly an interesting means to power systems with low energy consumption. This research aims to evaluate an energy generation system based on the piezoelectric effect activated by mechanical excitation and develop a system capable of powering devices and sensors for temperature monitoring in a practical situation, such as in an engine room, aiming to ensure its safe operation. Two transducers subjected to vibrational excitation were evaluated, and then an energy generation system using a buck DC-DC converter circuit was assessed. The transducer was connected to the input of the board, the microcontroller to the output, and the LM35 temperature sensor along with the battery was used to ensure the circuit’s autonomy. Additionally, the Attiny85 microcontroller was programmed to perform temperature monitoring tasks in a continuous low-energy-consumption mode. The obtained spectral analysis results showed a maximum generation power of 8.88 mW for the PZT-5H transducer and 3.3 mW for the P5-13B transducer. The use of LTC3588-1 increased the autonomy of the monitoring system by 64.3% and reduced the system’s usage time in cases of temperature anomalies by 50%.
Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature
Authors: Iammarino Erica , Marcantoni Ilaria , Sbrollini Agnese , Morettini Micaela , Burattini Laura
Published: October 2024

Changes in cardiac function and morphology are reflected in variations in the electrocardiogram (ECG) and, in turn, in the cardiac risk indices derived from it. These variations have led to the introduction of normalization as a step to compensate for possible biasing factors responsible for inter- and intra-subject differences, which can affect the accuracy of ECG-derived risk indices in assessing cardiac risk. The aim of this work is to perform a scoping review to provide a comprehensive collection of open-access published research that examines normalized ECG-derived parameters used as markers of cardiac anomalies or instabilities. The literature search was conducted from February to July 2024 in the major global electronic bibliographic repositories. Overall, 39 studies were selected. Results suggest extensive use of normalization on heart rate variability-related indices (49% of included studies), QT-related indices (18% of included studies), and T-wave alternans (5% of included studies), underscoring their recognized importance and suggesting that normalization may enhance their role as clinically useful risk markers. However, the primary objective of the included studies was not to evaluate the effect of normalization itself; thus, further research is needed to definitively assess the impact and advantages of normalization across various ECG-derived parameters.