Applied Sciences doi: 10.3390/app14062567
Authors: J. D. Tamayo-Quintero J. B. Gómez-Mendoza S. V. Guevara-Pérez
Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community.
]]>Applied Sciences doi: 10.3390/app14062564
Authors: Daniel Soto-Guerrero José Gabriel Ramírez-Torres Eduardo Rodriguez-Tello
Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.
]]>Applied Sciences doi: 10.3390/app14062568
Authors: Nader Vahdati Aamna Alteneiji Fook Fah Yap Oleg Shiryayev
Engine mounts serve three primary purposes: (1) to support the weight of the engine, (2) to lessen the transmitted engine disturbance forces to the vehicle structure/chassis or airplane fuselage, and (3) to limit the engine motion brought on by shock excitations. The engine mount’s stiffness must be high to control large engine motions and low to control chassis or vehicle body vibration. When hydraulic engine mounts are used, a device called a decoupler creates the dual stiffness requirement. However, numerous investigations have shown that the decoupler has the potential to rotate within its cage bound and become stuck or sink and obstruct fluid flow between the fluid chambers due to a density mismatch between the decoupler and the working fluid. In addition, most hydraulic engine mounts with a decoupler no longer act as vibration isolators but as hydraulic dampers. This study suggests a new amplitude-sensitive hydraulic engine mount design without a decoupler, where the vibration isolation of the engine mount is retained and there is a 75% reduction in the peak frequency, which further enhances the engine mount’s capabilities in comparison to the current hydraulic engine mounts with a decoupler. The new design concept and its mathematical model and simulation results will be presented.
]]>Applied Sciences doi: 10.3390/app14062566
Authors: Isehaq Al-Huseini Maryam Al-Ismaili Ammar Boudaka Srinivasa Rao Sirasanagandla
Vascular calcification is calcium deposition occurring in the wall of blood vessels, leading to mechanical stress and rupture due to a loss of elasticity and the hardening of the vessel wall. The role of the Transient Receptor Channel Vanilloid 4 (TRPV4), a Ca2+-permeable cation channel, in the progression of vascular calcification is poorly explored. In this study, we investigated the role of TRPV4 in vascular calcification and the development of abdominal aortic aneurysm (AAA). Experimental mice were randomly divided into four groups: wild-type (WT) sham operated group, WT CaCl2-induced aortic injury, TRPV4-KO sham operated group, and TRPV4-KO CaCl2-induced aortic injury. The TRPV4-knockout (TRPV4-KO) mice and wild-type (WT) mice were subjected to the CaCl2-induced abdominal aortic injury. In histopathological analysis, the aorta of the TRPV4-KO mice showed extensive calcification in the tunica media with a significant increase in the outer diameter (p < 0.0001), luminal area (p < 0.05), and internal circumference (p < 0.05) after CaCl2 injury when compared to WT mice. Additionally, the tunica media of the TRPV4-KO mice aorta showed extensive damage with apparent elongation and disruption of the elastic lamella. These results indicate a protective function of TRPV4 against vascular calcification and the progression of AAA after CaCl2 injury.
]]>Applied Sciences doi: 10.3390/app14062565
Authors: Hongfeng Gao Tiexin Xu Renlong Li Chaozhi Cai
Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is −4 dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods.
]]>Applied Sciences doi: 10.3390/app14062563
Authors: Busra Besevli Erhan Kayabasi Abdulrazzak Akroot Wadah Talal Ali Alfaris Younus Hamoudi Assaf Mohammed Y. Nawaf Mothana Bdaiwi Jawad Khudhur
In this study, it is proposed to generate electrical energy by recovering the waste heat of an annealing furnace (AF) in an iron and steel plant using combined cycles such as steam Rankine cycle (SRC), organic Rankine cycle (ORC), Kalina cycle (KC) and transcritical CO2 cycle (t-CO2). Instead of releasing the waste heat into the atmosphere, the waste heat recovery system (WHRS) discharges the waste heat into the plant’s low-temperature oxygen line for the first time, achieving a lower temperature and pressure in the condenser than conventional systems. The waste heat of the flue gas (FG) with a temperature of 1093.15 K from the reheat furnace was evaluated using four different cycles. To maximize power generation, the SRC input temperature of the proposed system was studied parametrically. The cycles were analyzed based on thermal efficiency and net output power. The difference in SRC inlet temperature is 221.6 K for maximum power output. The proposed system currently has a thermal efficiency and total power output of 0.19 and 596.6 kW, respectively. As an environmental impact, an emission reduction potential of 23.16 tons/day was achieved. In addition, the minimum power generation cost of the proposed system is $0.1972 per kWh.
]]>Applied Sciences doi: 10.3390/app14062561
Authors: Chunling Wang Tianyi Hang Changke Zhu Qi Zhang
The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and the Czech Republic. This study aims to develop a practical approach for land cover classification in the Czech Republic, with the goal of efficiently acquiring spatial distribution information regarding its forest resources. This approach is based on multi-level feature extraction and selection, integrated with advanced machine learning or deep learning models. To accomplish this goal, the study concentrated on two typical experimental regions in the Czech Republic and conducted a series of classification experiments, using Sentinel-2 and DEM data in 2018 as the main data sources. Initially, this study extracted various features, including spectral, vegetation, and terrain features, from the study area, then assessed and selected key features based on their importance. Additionally, this study also explored multi-level spatial contextual features to improve classification performance. The extracted features include texture and morphological features, as well as deep semantic information learned by utilizing a deep learning model, 3D CNN. Finally, an AdaBoost ensemble learning model with the random forest as the base classifier is designed to produce land cover classification maps, thus obtaining the spatial distribution of forest resources. The experimental results demonstrate that feature optimization significantly enhances the extraction of high-quality features of surface objects, thereby improving classification performance. Specifically, morphological and texture features can effectively enhance the discriminability between different features of surface objects, thereby improving classification accuracy. Utilizing deep learning networks enables more efficient extraction of deep feature information, further enhancing classification accuracy. Moreover, employing an ensemble learning model effectively boosts the accuracy of the original classification results from different individual classifiers. Ultimately, the classification accuracy of the two experimental areas reaches 92.84% and 93.83%, respectively. The user accuracies for forests are 92.24% and 93.14%, while the producer accuracies are 97.71% and 97.02%. This study applies the proposed approach for nationwide classification in the Czech Republic, resulting in an overall classification accuracy of 90.98%, with forest user accuracy at 91.97% and producer accuracy at 96.2%. The results in this study demonstrate the feasibility of combining feature optimization with the 3D Convolutional Neural Network (3DCNN) model for land cover classification. This study can serve as a reference for research methods in deep learning for land cover classification, utilizing optimized features.
]]>Applied Sciences doi: 10.3390/app14062562
Authors: Yongyong Zhao Jinghua Wang Guohua Cao Xu Yao
This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly, model predictive control (MPC) and sliding mode control (SMC) schemes are developed using a simplified model, and their stability is analyzed using the Lyapunov method. Numerical simulations under two disturbances validate the superior tracking performance of the SMC scheme. Secondly, an Nth-order linear active disturbance rejection control (LADRC) is designed based on a simplified model and optimization problems. The second-order LADRC-SMC scheme reduces the contact-force control error in the SMC scheme by ten times. Finally, a fourth-order LADRC-SMC with a Smith Predictor (LADRC-SMC-SP) scheme is formulated, employing each loop controller independently. This scheme simplifies the design and enhances performance. Compared to numerical simulations of the above and existing schemes, the LADRC-SMC-SP scheme eliminates delay oscillations, shortens convergence time, and demonstrates fast force-position tracking responses, minimal overshoot, and strong disturbance rejection. The peak contact-force error in the LADRC-SMC-SP scheme was ten times smaller than that in the LADRC-SMC scheme. The integral square error (ISE) values for the tracking errors of joint angles θ1 and θ2, and contact force f, are 1.6636×10−28 rad2⋅s, 1.7983×10−28 rad2⋅s, and 1.8062×10−30 N2⋅s, respectively. These significant improvements in control performance address the challenges in single-leg dynamic systems, effectively handling disturbances, delays, and motor saturation.
]]>Applied Sciences doi: 10.3390/app14062560
Authors: Adina Pop Moldovan Simona Dumitra Cristina Popescu Radu Lala Nicoleta Zurbau Anghel Daniel Nisulescu Ariana Nicoras Coralia Cotoraci Monica Puticiu Anca Hermenean Daniela Teodora Marti
Anthracyclines, including epirubicin (Epi), are effective chemotherapeutics but are known for their cardiotoxic side effects, primarily inducing cardiomyocyte apoptosis. This study investigates the protective role of hesperetin (HSP) against cardiomyopathy triggered by Epi in a murine model. Male CD1 mice were divided into four groups, with the Epi group receiving a cumulative dose of 12 mg/kg intraperitoneally, reflecting a clinically relevant dosage. The co-treatment group received 100 mg/kg of HSP daily for 13 days. After the treatment period, mice were euthanized, and heart tissues were collected for histopathological, immunofluorescence/immunohistochemistry, and transmission electron microscopy (TEM) analyses. Histologically, Epi treatment led to cytoplasmic vacuolization, myofibril loss, and fiber disarray, while co-treatment with HSP preserved cardiac structure. Immunofluorescent analysis of Bcl-2 family proteins revealed Epi-induced upregulation of the pro-apoptotic protein Bax and a decrease in anti-apoptotic Bcl-2, which HSP treatment reversed. TEM observations confirmed the preservation of mitochondrial ultrastructure with HSP treatment. Moreover, in situ detection of DNA fragmentation highlighted a decrease in apoptotic nuclei with HSP treatment. In conclusion, HSP demonstrates a protective effect against Epi-induced cardiac injury and apoptosis, suggesting its potential as an adjunctive therapy in anthracycline-induced cardiomyopathy. Further studies, including chronic cardiotoxicity models and clinical trials, are warranted to optimize its therapeutic application in Epi-related cardiac dysfunction.
]]>Applied Sciences doi: 10.3390/app14062559
Authors: Jing Kai Sim Kaichao William Xu Yuyang Jin Zhi Yu Lee Yi Jie Teo Pallavi Mohan Lihui Huang Yuan Xie Siyi Li Nanying Liang Qi Cao Simon See Ingrid Winkler Yiyu Cai
An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual environment is created that duplicates real-world situations and enables immersive and interactive learning in the educational metaverse. For this purpose, we built a digital twin of the Nanyang Technological University (NTU) campus as a foundation, called NTUniverse. It is designed as an educational metaverse in which various academic and analytical applications are digitized as 3D content embedded within this virtual campus. The approach to digitally twinning educational systems and embedding them within virtual campuses enables remote and collaborative learning as well as professional technical skills training. It also makes feasible the analysis of abstract concepts, complicated structures, dynamic processes, and sensitive industrial procedures virtually, which is otherwise challenging if not impossible to perform in the real world. The work offers important insights into the behaviors and interactions of systems in the metaverse by evaluating design choices and user interests. NTUniverse is an attempt to explore a novel approach that addresses remote education and training challenges. Three efforts with NTUniverse will be discussed in this work, including (1) digitalization of the NTU campus; (2) campus train modelling and simulation; and (3) science, technology, engineering and mathematics education.
]]>Applied Sciences doi: 10.3390/app14062557
Authors: Benedikt Tappauf Karoline Alten Marianne Legenstein Marlene Ofner Rainer Flesch
The planning application and approval process of railway tracks is generally accompanied by a vibration immission assessment. Starting with the source spectrum, which is ideally obtained through measurements, the German guideline VDI 3837 recommends a series of multiplications using transfer spectra which account for the various subdomains of the wave propagation path, such as the effect of the superstructure, the free field propagation, the soil-structure coupling and the transmission inside buildings. Typically, these one-third octave spectra are an average over empirical reference values. While simplified empirical relations are prone to a large variance, the use of artificial vibration sources allows the actual vibration transmission behavior from the tracks to the immission points to be quantified. Using so-called transfer admittances, also known as transfer mobilities, which account for all dynamic interactions along the transmission path (track, tunnel structures, foundations, structural properties), together with force density spectra for relevant rail vehicles, the authors investigate the practical application of the method presented in Report No. 0123 of the Federal Transit Administration (2018) for the frequency range 5–200 Hz. The article demonstrates how such force density spectra were obtained for the most common train types in the Austrian rail network at two different track sections using artificial vibration sources. Furthermore, practical aspects are discussed and a recently developed approximation method for estimating line transfer admittances from point transfer admittances using simplified models is introduced.
]]>Applied Sciences doi: 10.3390/app14062558
Authors: Hongye Liu Xiai Chen
Person re-identification aims to identify the same pedestrians captured by various cameras from different viewpoints in multiple scenarios. Occlusion is the toughest problem for practical applications. In video-based ReID tasks, motion information can be easily obtained from sampled frames, and provide discriminative human part representations. However, most motion-based methodologies are designed for video frames which are not suitable for processing single static image input. In this paper, we propose a Motion-Aware Fusion (MAF) network, aiming to acquire motion information from static images in order to improve the performance of ReID tasks. Specifically, a visual adapter is introduced to enable visual feature extraction, either from image or video data. We design a motion consistency task to guide the motion-aware transformer to learn representative human-part motion information and greatly improve the learning quality of features of occluded pedestrians. Extensive experiments on popular holistic, occluded, and video datasets demonstrate the effectiveness of our proposed method. This method outperforms state-of-the-art approaches by improving the mean average precision (mAP) by 1.5% and rank-1 accuracy by 1.2% on the challenging Occluded-REID dataset. At the same time, it surpasses other methods on the MARS dataset with an improvement of 0.2% in mAP and 0.1% in rank-1 accuracy.
]]>Applied Sciences doi: 10.3390/app14062556
Authors: Yaohan Wang Zeyang Song Lidong Zhang
Safety signs serve as an important information carrier for safety standards and rule constraints. Detecting safety signs in mines is essential for automatically early warning of unsafe behaviors and the wearing of protective equipment while using computer vision techniques to realize advanced safety in the AI and IoT era. This work aims to propose an improved YOLOV4-tiny safety signs detection model applying deep learning to detect safety signs in mines. The dataset employed in this study was derived from coal mines and analogous environments, comprising a total of ten types of safety signs. It was partitioned into training, validation, and test sets following a distribution ratio of (training set + validation set) to test set = 9:1, with the training set to validation set ratio also set at 9:1. Then the attention mechanism ECANet was introduced into the model, which strengthened the network’s learning of places that need attention. Moreover, the Soft-NMS algorithm was used to retain more correct prediction frames and optimize the detection model to further improve the detection accuracy. The Focal Loss function was introduced to alleviate the problem of category imbalance in one-stage safety signs detection. Experimental results indicate that the proposed model achieved a detection precision of 97.76%, which is 7.55% and 9.23% higher than the YOLOV4-tiny and Faster RCNN algorithms, respectively. Besides, the model performed better in the generalization because it avoided the over-fitting phenomenon that occurred in the YOLOV4-tiny and the Faster RCNN. Moreover, the advantages of the improved model were more prominent when detecting small target areas and targets under dim conditions in coal mines. This work is beneficial for the intelligent early warning system with surveillance cameras in coal mines.
]]>Applied Sciences doi: 10.3390/app14062555
Authors: Ayman Noor Ziad Algrafi Basil Alharbi Talal H. Noor Abdullah Alsaeedi Reyadh Alluhaibi Majed Alwateer
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.
]]>Applied Sciences doi: 10.3390/app14062554
Authors: Wendimu Fanta Gemechu Wojciech Sitek Gilmar Ferreira Batalha
This study investigates the application of regression neural networks, particularly the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics, such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the models’ effectiveness and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R2 of 0.9900. The model, which was trained with different datasets for nearly 40 steel grades, enables the prediction of hardenability curves, but is limited to the range of the training dataset. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.
]]>Applied Sciences doi: 10.3390/app14062553
Authors: Paolo Caccianiga Saverio Ceraulo Gérard Rey Dario Monai Marco Baldoni Gianluigi Caccianiga
Introduction: Within the field of periodontology, there has been a proposal for the utilization of noninvasive laser therapy as a potential treatment for persistent periodontitis. The Laser-Assisted New Attachment Procedure (LANAP) employs an Nd:YAG laser as a specific technique. Through its interaction with endogenous chromophores, the Nd: YAG laser exhibits a selective effect on the evaporation of granulation tissue, therefore establishing a correlation with reduced bleeding. The study also examined Oxygen High-Level Laser Therapy (OHLLT). The OHLLT technique employs a high-power diode laser in combination with hydrogen peroxide solutions to facilitate the liberation of singlet oxygen, which possesses antibacterial attributes, within the periodontal pockets. The existing literature indicates their potential to promote the regeneration of tooth support tissues. Objective: The aim of this study is to assess the subjective pain levels reported by patients who have undergone surgery using the OHLLT protocol versus those who have undergone surgery using the LANAP technique. Methods: A total of 20 individuals with a stage III–IV periodontitis diagnosis were recruited for the study. The participants were randomly divided into two groups, each consisting of 10 individuals: Group 1, comprising patients treated according to the LANAP protocol, and Group 2, comprising patients treated according to the OHLLT protocol. After their initial session of nonsurgical periodontal therapy, individuals provided feedback regarding their level of pain, utilizing a Numerical Rating Scale (NRS) comprising time intervals of 0 h (T0), 6 h (T1), 12 h (T2), 24 h (T3), 48 h (T4), and 7 days (T5). The Wilcoxon–Mann–Whitney statistical test was employed to assess the variations in NRS scores between Group 1 and Group 2 at each recording period. (p ≤ 0.05). In addition, a microbiological assessment of the bacterial load in the periodontal region was conducted on all subjects using real-time PCR testing at two time points: prior to treatment (T0) and seven days post-treatment (T5). Results: The findings of this study indicate that the OHLLT group exhibited significantly lower pain levels compared to the LANAP group at all time intervals, except for the preoperative period, where no significant difference was observed (p < 0.05). Group 2 exhibited a more rapid decrease in pain, as demonstrated by a score test approaching zero within 24 h. The quantity of periodontal bacteria seen seven days post-treatment was similar between the two groups and was found to be decreased compared to the pre-treatment levels. Conclusions: The OHLLT and LANAP regimens have demonstrated efficacy in the nonsurgical management of periodontal disease. Nevertheless, it should be noted that the OHLLT approach does not subject the patient to any thermal hazards, unlike the LANAP method. The postoperative discomfort experienced following the OHLLT procedure is indeed reduced, as this technique is characterized by lower invasiveness and reduced dependence on the operator.
]]>Applied Sciences doi: 10.3390/app14062552
Authors: Ujwal Sharma Uma Shankar Medasetti Taher Deemyad Mustafa Mashal Vaibhav Yadav
This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots’ potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots’ performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors.
]]>Applied Sciences doi: 10.3390/app14062551
Authors: Grzegorz Ilewicz Edyta Ładyżyńska-Kozdraś
The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test.
]]>Applied Sciences doi: 10.3390/app14062550
Authors: Antreas Kantaros Florian Ion Tiberiu Petrescu Hamed Abdoli Olaf Diegel Simon Chan Mihaiela Iliescu Theodore Ganetsos Iulian Sorin Munteanu Liviu Marian Ungureanu
Additive manufacturing has been widely used in various industries, including the healthcare sector. Over the last few decades, AM has been playing an important role in the medical field in different areas, including surgical planning, implants, and educational activities. For surgical applications, AM can help surgeons practice and plan an operation until they are confident with the process. This can help to reduce operational risk and time. In addition, it can help to demonstrate the problem to other colleagues. AM has also been used to produce 3D models to teach students and doctors about human anatomy. This paper aims to comprehensively review the diverse applications of additive manufacturing within the domains of surgical planning and medical education. By focusing on the multifaceted roles played by AM in these critical areas, a contribution to the growing body of knowledge that underscores the transformative potential of this technology in shaping the future of healthcare practices is sought to be made.
]]>Applied Sciences doi: 10.3390/app14062547
Authors: Abeer Alabbas Khalid Alomar
The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored the role of Arabic chatbots in education. This study introduces Tayseer, an Arabic AI-powered web chatbot that enables instant access to college information and communication between students and colleges. This study aims to improve the abilities of chatbots by integrating features into one model, including responding with audiovisuals, various interaction modes (menu, text, or both), and collecting survey responses. Tayseer uses deep learning models within the RASA framework, incorporating a customized Arabic natural language processing pipeline for intent classification, entity extraction, and response retrieval. Tayseer was deployed at the Technical College for Girls in Najran (TCGN). Over 200 students used Tayseer during the first semester, demonstrating its efficiency in streamlining the advisory process. It identified over 50 question types from inputs with a 90% precision in intent and entity predictions. A comprehensive evaluation illuminated Tayseer’s proficiency as well as areas requiring improvement. This study developed an advanced CA to enhance student experiences and satisfaction while establishing best practices for education chatbot interfaces by outlining steps to build an AI-powered chatbot from scratch using techniques adaptable to any language.
]]>Applied Sciences doi: 10.3390/app14062549
Authors: Shengyang Li Zhen Wang Wanfeng Zhang
Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms.
]]>Applied Sciences doi: 10.3390/app14062548
Authors: Shanshan Song Hongfa Yu Haiyan Ma
This study employed a rapid freezing method to investigate the impact of individual additions of expansion agent, steel fibers, and High Elasticity Module Polyethylene Fiber (HEMPF), as well as their combinations, on the freeze-thaw resistance of High-Strength Concrete (HSC). The findings reveal that the non-air-entrained HSC of C80 exhibits excellent freeze-thaw resistance. However, this resistance is sensitive to the curing environment’s humidity. The expansion agent has a negative impact on the freeze-thaw resistance of HSC, while steel fibers and HEMPF fibers have positive effects. Combining HSC with an expansion agent and high elasticity modulus fibers ensures not only high freeze-thaw resistance but also a total alteration of humidity sensitivity, leading to an extended freeze-thaw life of HSC under dry curing conditions.
]]>Applied Sciences doi: 10.3390/app14062546
Authors: Rou-Han Li Mao-Yuan Li Xiang-Yang Zhu Xiang-Wei Zeng
In this paper, an innovative method is put forward for estimating the dynamic mechanical behaviors of reinforced concrete (RC) column members by applying the random forest algorithm. Firstly, the development of dynamic modified coefficient (DMC) predictive models and the realization of the proposed method were elaborated. Then, due to the lack of dynamic loading tests on RC column members, a numerical model of RC columns considering the dynamic modification on flexural, shear and bond-slip behaviors was developed on the OpenSees platform, and the model accuracy and the effectiveness were verified with the available test results. Moreover, by comparing the simulated results of the hysteretic curve using numerical models with different complexities, the influences of dynamic modification and the deformation sub-element were investigated. Furthermore, a numerical experiment database was established to obtain the training data for developing the DMC predictive models of critical mechanical behavior parameters, including the yielding bearing capacity, ultimate bearing capacity and displacement ductility. Finally, the results of feature importance for different input parameters were studied, and the model accuracy was evaluated using the test set and available experimental data. It was revealed that the predictive models developed using the random forest algorithm can be employed to reliably estimate the dynamic mechanical behaviors of RC column members.
]]>Applied Sciences doi: 10.3390/app14062545
Authors: Cosmina Elena Jercălău Cătălina Liliana Andrei Lavinia Nicoleta Brezeanu Roxana Oana Darabont Suzana Guberna Gabriela Postolea Octavian Ceban Crina Julieta Sinescu
Background: Non-ST-elevation myocardial infarction (NSTEMI), a disease of mounting interest, continues to pose challenges and cast shadows of doubt on determining the optimal timing for revascularization. The current guidelines on NSTEMI recommend coronary angiography based on the GRACE score, emphasizing the critical need for early invasive assessment (within 24 h); very-high-risk patients have to undergo this intervention even sooner, within 2 h. We believe that a reality check of these assumptions is needed and that we should endeavor to update these strategies using new predictive markers. Materials and methods: Our study included patients hospitalized for NSTEMI over the course of 16 months. Simple blood parameters, namely MCV (mean corpuscular volume), MPV (mean platelet volume), RDW (red blood cell distribution width), and PDW (platelet distribution width), were analyzed in correlation with the extent of the myocardial infarction area and with complications during hospitalization and at 30-day follow-up. Results: The parameters mentioned above have been identified as statistically relevant indicators of prognosis in patients with NSTEMI. Conclusions: In the present day, living in the world of the blue sky concept allows us to search for new diagnostic algorithms. Therefore, the combination of these parameters can constitute the DNA strands of a new and up-to-date score stratification.
]]>Applied Sciences doi: 10.3390/app14062544
Authors: Luisa F. Cabeza Mercè Teixidó Francesco Guarino Roberta Rincione Manel Díaz Rosa M. Gil Maurizio Cellura Carles Mateu
The concept of a smart city is becoming increasingly popular to improve citizens’ quality of life. Institutions are also committed to enhancing the sustainability of cities by implementing the Sustainable Development Goals (SDGs). This paper presents a Balaguer case study investigating energy demand monitoring, decreasing energy demand, and citizen acceptance in a municipality district. The study collected data from three sources: (1) quantitative data coming from on-site sensors; (2) quantitative data from a simulation of the area; and (3) qualitative data from questionnaires developed with a totem located in the city center. This study shows the importance of citizen science in contributing towards the increased awareness of energy demand, renewable energy, and climate change. But it also shows how citizen science can improve research quality involving the municipality authorities. This study also was instrumental in contributing to the increase in awareness among municipality authorities and capacity building on the topic. This activity may also contribute towards the implementation of actions to reduce the energy demand in public buildings and helping them in deploying policies to decrease energy demand in buildings, increase the use of renewable energy, and increase awareness among citizens. The government will use the information gathered to develop policies for citizen improvement.
]]>Applied Sciences doi: 10.3390/app14062543
Authors: Dejiang Wang Jiangming Huang
Box culverts, as a commonly employed structural form for culverts, play a critical role in traversing topographic barriers, ensuring the safety and smooth operation of transportation means such as roads and railways. However, traditional design methodologies are often time-consuming and prone to inaccuracies, failing to achieve the efficiency and precision required by modern engineering construction. To address these challenges, using the Revit 2021 and Midas Civil 2021 software platforms, we developed a Building Information Modeling (BIM) parametric modeling method for box culverts using Dynamo’s visual programming capabilities. This method enables the rapid and accurate automated generation of box culvert BIM models. Furthermore, this study proposes an effective strategy for conversion between box culvert BIM models and Midas Civil finite element models, as well as internal force visualization within a BIM project. A case study involving a box culvert underpass beneath an expressway in an urban setting was modeled parametrically and structurally validated, demonstrating that the approach not only significantly enhances modeling efficiency but also strengthens computational capabilities through bidirectional data exchange between BIM and Finite Element Analysis (FEA) software. This research has effectively advanced the application and practical implementation of BIM technology in box culvert engineering.
]]>Applied Sciences doi: 10.3390/app14062542
Authors: Vincent A. Cicirello
Evolutionary computation is now nearly 50 years old, originating with the seminal work of John Holland at the University of Michigan in 1975 which introduced the genetic algorithm [...]
]]>Applied Sciences doi: 10.3390/app14062541
Authors: Liming Jiang Xingfa Shen Qingbiao Zhao Jian Yao
Chinese spelling errors are commonplace in our daily lives, which might be caused by input methods, optical character recognition, or speech recognition. Due to Chinese characters’ phonetic and visual similarities, the Chinese spelling check (CSC) is a very challenging task. However, the existing CSC solutions cannot achieve good spelling check performance since they often fail to fully extract the contextual information and Pinyin information. In this paper, we propose a novel CSC framework based on multi-label annotation (MLSL-Spell), consisting of two basic phases: spelling detection and correction. In the spelling detection phase, MLSL-Spell uses the fusion vectors of both character-based pre-trained context vectors and Pinyin vectors and adopts the sequence labeling method to explicitly label the type of misspelled characters. In the spelling correction phase, MLSL-Spell uses Masked Language Mode (MLM) model to generate candidate characters, then performs corresponding screenings according to the error types, and finally screens out the correct characters through the XGBoost classifier. Experiments show that the MLSL-Spell model outperforms the benchmark model. On SIGHAN 2013 dataset, the spelling detection F1 score of MLSL-Spell is 18.3% higher than that of the pointer network (PN) model, and the spelling correction F1 score is 10.9% higher. On SIGHAN 2015 dataset, the spelling detection F1 score of MLSL-Spell is 11% higher than that of Bert and 15.7% higher than that of the PN model. And the spelling correction F1 of MLSL-Spell score is 6.8% higher than that of PN model.
]]>Applied Sciences doi: 10.3390/app14062540
Authors: Xueming Du Zhihui Li Hongyuan Fang Bin Li Xiaohua Zhao Kejie Zhai Binghan Xue Shanyong Wang
China is renowned for its extensive underground engineering projects and the complex geological and hydrological conditions it faces. Grouting treatment technology is widely employed in deep-buried mines and tunnels, where grouting parameters such as materials, pressure, volume, and hole arrangement significantly impact the effectiveness of grouting. This review paper comprehensively examines current research on grouting materials, theories, experiments, and numerical simulations. It summarizes the various factors that must be considered during the grouting process of fissures and explores the diffusion mechanisms of grout under their influence. Furthermore, further research is needed on the mechanisms and treatment methods for poor grouting in rock masses, the distribution patterns of fissures, optimization methods for grouting parameters, and grout quality assessment techniques. Future research should focus on developing more efficient experimental methods with higher accuracy levels while advancing grouting technologies. Establishing comprehensive and accurate rock mass models along with improving monitoring capabilities are also crucial aspects to consider. Therefore, studying the diffusion mechanisms of grout in fissured rock masses is of significant importance for the practical operation of underground engineering projects.
]]>Applied Sciences doi: 10.3390/app14062539
Authors: Yanjun Feng Guangzhi Pan Chuan Wu
Downhole drilling tool vibration measurement is crucial for drilling exploration safety, so real-time monitoring of vibration data is required. In this research, a honeycomb triboelectric nanogenerator (H-TENG) capable of adapting to various downhole environments is proposed. It can measure the frequency of downhole drilling equipment’s vibrations and transfer mechanical energy to electrical energy for use in powering other low power downhole meters. In order to preliminarily verify the possibility of sensors used for vibration measurement of downhole drilling tools, we built a simulated vibration platform to test the sensing performance and vibration energy collection performance of H-TENG. According to the testing results, the measurement range of vibration frequency and amplitude are 0 to 11 Hz and 5 to 25 mm, respectively, and the corresponding measurement errors are less than 5% and 6%, respectively. For vibrational energy harvesting, when four sensors are wired in series with a 107 resistance, the maximum power is approximately 1.57 μW. Compared to typical methods for measuring downhole vibration, the honeycomb triboelectric nanogenerator does not need an external power source, it has greater reliability and output power, and it can vary its shape to adapt to the complicated downhole environment. In addition, the H-TENG can be combined freely according to the diameter of the drill string, and even if one sensor unit is damaged, the other units can still be used normally.
]]>Applied Sciences doi: 10.3390/app14062538
Authors: Yaya Jia Jiachen Huang Qingkuan Liu Zonghan Zhao Menghui Dong
With the widespread adoption of 5G telecommunication networks, to reduce construction costs, it has become necessary to add new equipment or antennas to existing 4G and 3G telecommunication towers. This significantly changes the original aerodynamic shape of the towers, leading to a substantial increase in the wind load, which may exceed the original structure’s bearing capacity and pose a threat to the structure’s safety. This study employed three-dimensional numerical simulation methods to systematically investigate the impact of various antenna arrangement parameters, such as the arrangement number, arrangement form, and arrangement layers, on the wind load characteristics of telecommunication towers. The findings revealed that the antenna arrangement form significantly affects the sensitivity of the telecommunication tower’s wind load to the wind direction, with more uniform antenna arrangements resulting in less sensitivity. Compared to the drag coefficient and the windward base overturning moment coefficient, the tower’s lateral force coefficient and the crosswind base overturning moment coefficient are more sensitive to changes in the wind direction. The change patterns in the tower’s overturning force coefficient and overturning moment coefficient with the antenna arrangement number are essentially the same. However, as the antenna arrangement number increases, the growth rate of the tower’s overturning moment coefficient is about twice that of the overturning force coefficient. The tower’s overturning force coefficient increases approximately linearly with the increase in antenna arrangement layers, while the tower’s overturning moment coefficient exhibits a nonlinear increase with the increase in antenna arrangement layers. The rate of increase in the wind load with the antenna arrangement layers is significantly greater than that with the antenna arrangement number. Thus, to reduce wind load, it is advisable in practical engineering applications to increase the antenna arrangement number per layer, thereby reducing the antenna arrangement layers. The study also summarized a calculation method for the structural wind load of telecommunication towers, taking into account the influence of antenna arrangement parameters, providing a reliable basis for the structural design and safety assessment of telecommunication towers in practical engineering.
]]>Applied Sciences doi: 10.3390/app14062534
Authors: Patrycja Bazan Michał Gajda Przemysław Nosal Agnieszka Bąk Kinga Setlak Michał Łach
This study examines the relationship between the size of copper particles and the properties of epoxy resin. Epoxy resin is a type of thermosetting resin commonly used as a matrix in polymer matrix composite materials reinforced with glass or carbon fibers. As part of this study, three microscale and two nanoscale composite samples modified with copper oxide particles of varying sizes were produced. This study included mechanical property tests such as static tensile tests, static bending tests, and impact tests. The results of the strength tests were compared to modeling results. Additionally, an accelerated thermal aging process was conducted to determine the impact of external conditions on the behavior of the produced composites. This study concluded with an analysis of thermal conductivity. The test results revealed that the size of the copper particles significantly impacted the tested properties. The composites with copper oxide particles on the nanoscale demonstrated the best results. These composites have promising applications in the automotive and aviation industries due to their strength, resistance to external factors, and increased thermal conductivity, suggesting their potential for producing materials that effectively dissipate heat.
]]>Applied Sciences doi: 10.3390/app14062533
Authors: Huadong Zhou Xiangyang Mu
Time-varying nonlinear external disturbances, as well as uncertainties in model and hydrodynamic parameters, make remotely operated vehicles (ROVs) trajectory tracking control complex and difficult. To solve this problem, this paper proposes a fast sliding mode controller with a fixed-time disturbance observer (FSMC-FDO), which consists of a sliding mode controller based on a fast reaching law and a novel fixed-time disturbance observer. The FSMC can solve the contradiction between system response time and chatter amplitude in sliding mode control. The FDO can compensate for time-varying external disturbances. The Lyapunov theory is used to prove the stability of the entire control scheme. Simulation results show that FSMC-FDO exhibits a good trajectory tracking performance with a better robustness than the conventional sliding mode control (CSMC) on the basis of exponential reaching law (ERL), while significantly reducing chatter.
]]>Applied Sciences doi: 10.3390/app14062537
Authors: Mingkun Liu Chuang Wang Yanpeng Li Yuchen Li Lixin Liu Ziwen Xing
Due to the lack of oil injection cooling, it is usually necessary for dry twin-screw compressors to design cooling jackets to carry away the heat generated during operation. In order to investigate to what extent a cooling jacket can improve the performance of screw compressors, this study set up an experimental platform for a dry twin-screw compressor applied in fuel cell vehicles and used water as the working liquid in the cooling jacket. Then, the performance parameters of the screw compressor under different rotating speeds, discharge pressures, and cooling water flow rates were measured. It can be considered that the existence of a water cooling jacket is of great significance for improving the performance of dry screw compressors and improving extreme operating conditions. The research results may provide a reference for the development and improvement of dry twin-screw compressors in the future.
]]>Applied Sciences doi: 10.3390/app14062536
Authors: Guoxu Xin Bo Wang Haozhang Zheng Linfeng Zeng Xinxin Yang
Fault water inflow is one of the most severe disasters that can occur during the construction of hard and brittle rock tunnels. These tunnels traverse brittle fault breccia zones comprising two key components: a damage zone dominated by low-strain fractures and an internally nested high-strain zone known as the fault core. Structural heterogeneity influences the mechanical and hydraulic properties within fault breccia zones, thereby affecting the evolving characteristics of water inflow in hard rock faulting. Based on the hydraulic characteristics within hard rock fault zones, this paper presents a generalized dual-porosity fluid-solid coupling water inflow model. The model is utilized to investigate the spatiotemporal evolution patterns of water pressure, inflow velocity, and water volume during tunneling through heterogeneous fault zones in hard rock. Research findings indicate that when tunnels pass through the damage zones, water inrush velocity is high, yet the water volume is low, and both decrease rapidly over time. Conversely, within the core regions of faults, water inflow velocity is low, yet the water volume is high, and both remain relatively stable over time. Simulation results closely align with the water inflow data from China’s largest cross-section tunnel, the Tiantai Mountain Tunnel, thus validating the accuracy of the evolutionary model proposed in this paper. These findings offer a new perspective for devising effective prevention strategies for water inflow from heterogeneous faults.
]]>Applied Sciences doi: 10.3390/app14062535
Authors: Antonio Raimondo
This paper presents a numerical investigation of the R-curve effect in delamination propagation in composite materials. The R-curve effect refers to the phenomenon whereby resistance to crack propagation increases with the advancement of the delamination, due to toughening mechanisms, such as fiber bridging. Numerical models often neglect this effect assuming a constant value of the fracture toughness. A numerical approach based on cohesive elements and on the superposition of two bilinear traction-separation laws is adopted here to accurately predict the R-curve effect in skin-doubler composite specimens subjected to three-point bending tests. The carbon-epoxy material presents two different sensitivities to the fiber bridging phenomenon resulting in two different R-curves. Comparisons with literature experimental data, in terms of load and delaminated area vs. applied displacement, and ultrasonic C-scan images show the effectiveness of the adopted approach in simulating the R-curve effect. The predicted numerical stiffness aligns with the experimental scatter, although the maximum load is slightly underestimated by approximately 15% compared with the average experimental results. The numerical model accurately predict the R-curve effect observed in the experimental data, demonstrating a 31% increase in the maximum load for the material configuration exhibiting greater sensitivity to fiber bridging.
]]>Applied Sciences doi: 10.3390/app14062532
Authors: Yinyin Dai Fei Wang Jiliang Luo
To ensure opacity, it is optimal to retain as many as possible occurring event sequences. Contrary to this problem, the other optimal goal is to preserve the minimal occurring event sequences. Based on the choosing cost, an optimal opacity-enforcing problem with minimal discount choosing cost is presented under two constraints in this paper. The first constraint is the opacity of the controlled system. The second is the retention of the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. Then, the solutions produced by the algorithms are proved to be correct by theoretical proof. Finally, some illustrations and an application example on location privacy protection for the algorithms are given.
]]>Applied Sciences doi: 10.3390/app14062531
Authors: Robert Ciobanu Ciprian Ion Rizescu Dana Rizescu Bogdan Gramescu
This paper proposes a series of experimental determinations carried out with the aim of generating new conclusions regarding the ability of 3D-printed gears to be integrated into mechanisms without lubrication. The main factors that influence the appearance of wear in non-lubricated contact are sliding speed, material hardness, surface finish, surface geometry, and material microstructure. The tests considered the type of material from which they were made and the 3D printing technology type. For testing the gear wheels, a mechatronic experimental setup was made consisting of two shafts with adjustable axial distances, a wheel loading system gears, an electric motor, and a command-and-control system. In terms of materials, four types of materials were monitored: PA (polyamide), PLA (polylactic acid), ABS (acrylonitrile butadiene styrene) and PP (photopolymer). The evaluation of the gear wear was carried out by checking the gearing on two flanks (Frenco ZWP 06) and by scanning with the ATOS CORE 135 3D scanner. The PA and PP gears failed to meet the structural integrity standards after the tests. The PLA gears exhibited superior resistance to abrasive wear compared to the ABS gears, whereas the ABS gears generally demonstrated stronger structural integrity.
]]>Applied Sciences doi: 10.3390/app14062529
Authors: Marcin Łukaszewicz Przemysław Leszczyński Sławomir Jan Jabłoński Joanna Kawa-Rygielska
Yeast biomass, a brewery by-product of the world’s substantial alcohol beverage industry, finds successful applications in the fodder industry and food additive production. This is attributed to its rich nutritional profile that comprises high protein and vitamin content. Nonetheless, in small-scale breweries, yeast slurries present a significant challenge, as the quantities obtained are insufficient to attract the attention of the food industry. The disposal of yeast contributes substantially to the organic load of wastewater (approximately 40%) and elevates water consumption (3–6 hL/hL of beer), consequently escalating production costs and environmental impact. In recent years, diverse potential applications of products derived from yeast biomass have emerged, encompassing the substitution of sera in cell culture media, the fortification of animal feed with vitamins and selenium, the utilization of beta-glucan in low-fat food products, and the development of functional foods incorporating yeast-derived peptides. These peptides exhibit the potential to safeguard the gastric mucosa, prevent hypertension, and address neurodegenerative disorders. The rising demand for value-added products derived from yeast underscores the potential profitability of processing yeast from small breweries. Due to the high equipment costs associated with yeast biomass fractionation, the establishment of specialized facilities in collaboration with multiple small breweries appears to be the most optimal solution.
]]>Applied Sciences doi: 10.3390/app14062528
Authors: Rui Li Yuanyuan Zhang Xiaodong Chu Lin Gan Jia Li Baohua Li Hongda Du
Induction-heating graphitization furnaces are widely used to produce high-purity graphite products due to their high heating rate, high-limit temperatures, safety, cleanliness, and precise control. However, the existing induction-heating systems based on copper coils have limited energy efficiency. This paper proposes a new induction-heating graphitization furnace based on graphene coils. Due to the excellent high-temperature resistance of the macroscopic graphene material, the coil can be placed closer to the graphite heater, which improves the electromagnetic efficiency; the coil itself does not need to pass cooling water, which reduces the heat loss of the furnace and ultimately results in a higher energy efficiency of the induction furnace. In this paper, a numerical model of the induction-heating process is established and verified, the temperature-field and electromagnetic-field distributions of the heating process are analyzed by using the model, and the energy balance calculations are performed for the original furnace and the new furnace. Through a comparison, it was found that the new furnace possesses an electromagnetic efficiency of 84.87% and a thermal efficiency of 20.82%, and it can reduce the energy consumption by 33.34%, compared with the original furnace. In addition, the influence of the coil parameters on the performance of the induction furnace is discussed. By changing the coil conductivity, the induction furnace can achieve an energy efficiency of 17.76%–18.11%. This study provides new ideas for the application of macroscopic graphene materials in high-temperature induction heating.
]]>Applied Sciences doi: 10.3390/app14062530
Authors: Daniel Silva Jasaui Ana Martí-Testón Adolfo Muñoz Flavio Moriniello J. Ernesto Solanes Luis Gracia
This work aims to identify and propose a functional pipeline for indie live-action films using Virtual Production with photorealistic real-time rendering game engines. The new production landscape is radically changing how movies and shows are made. Those were made in a linear pipeline, and now filmmakers can execute multiple tasks in a parallel mode using real-time renderers with high potential for different types of productions. Four interviews of professionals in the Spanish film and television market were conducted to obtain the whole perspective of the new paradigm. Following those examples, a virtual production set was implemented with an Antilatency tracking system, Unreal Engine (version 5.3), and Aximmetry (version 2023.3.2) as the leading software applications. Results are commented on, presenting how all the work is currently closely connected between pre-production, shooting, and post-production and analyzing its potential in different fields.
]]>Applied Sciences doi: 10.3390/app14062527
Authors: Huaiqiang Kang Fengjun Zhou Shen Gao Qizhi Xu
Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.
]]>Applied Sciences doi: 10.3390/app14062526
Authors: Yelu Wang Yongjun Zhou Xin Jiang Yu Zhao Huantao Zhang
The deflection dynamic load allowance (DLA) of stiff bridges with high piers requires sub-millimeter accuracy. New technologies such as the vision-based optical method and GNSS are not yet recognized for use in DLA measurements due to their smaller SNR. Presently, the scaffolding method is widely utilized for dynamic deflection measurements in dynamic load tests owing to the reliability of employing rigid contact. When scaffolding is not available, engineers have to resort to a suspension hammer system. However, the mass eccentricity of the hammer, stretched-wire length, and wind will decrease the measurement accuracy. To overcome these drawbacks of the suspension hammer method (SHM), a preloaded spring method (PSM) and the related stretched-wire-spring system (SWSS) are proposed in this paper. The dynamic deflection of the coupled vehicle-bridge-SWSS was obtained by vehicle-bridge interaction (VBI) analysis. The sensitivity parameters of the PSM were analyzed and optimized to minimize the measurement error. Indoor experiments and field dynamic load tests were conducted to validate the feasibility and accuracy of the PSM. Additionally, the differences in dynamic deflection measurements between the PSM and SHM in windy environments were compared. The results show that, in a windless environment, the DLAs of the PSM are affected by the spring stiffness, stretched-wire length, and stretched-wire section stiffness, independently of the preload force. When the wind speed is less than or equal to 8 m/s and the pier height is less than 30 m, the maximum deflection measurement error of the PSM is −2.53%, while that of the SHM is −15.87%. Due to its low cost and high accuracy, the proposed method has broad application prospects in the dynamic deflection measurement of stiff bridges with high piers.
]]>Applied Sciences doi: 10.3390/app14062525
Authors: Sifan Li Lantian Hu Jing Cao
Polarization sensitive optical coherence tomography (PSOCT) makes use of the birefringence information of the sample to compensate for the lack of internal tissue-specific contrast in conventional optical coherence tomography (OCT). Circularly polarized light is always used as an incident beam in PSOCT, but it is difficult to have perfect in practice. The manual calibration method of circularly polarized light suffers from the problems of complicated calibration operation and lack of stability. This study proposes a simple method to enhance the imaging of PSOCT without altering the system. A numerical calibration of circularly polarized light can be implemented using the original system setup, ensuring that the system’s complexity remains unchanged. Enhancements in imaging quality can be achieved through an algorithmic analysis of the captured interference fringe data. This calibration is applied in the field map of interference data before being quadrature-assembled. Notably, the proposed approach achieves high sensitivity in PSOCT. The birefringence image shows a more obvious layered structure. Improvements in the signal-to-noise ratio (SNR) were demonstrated for chicken breast, pork, and beef imaging at about 20 dB.
]]>Applied Sciences doi: 10.3390/app14062524
Authors: Pablo Fernández-Arias Álvaro Antón-Sancho Georgios Lampropoulos Diego Vergara
Green hydrogen, produced by water electrolysis with renewable energy, plays a crucial role in the revolution towards energy sustainability, and it is considered a key source of clean energy and efficient storage. Its ability to address the intermittency of renewable sources and its potential to decarbonize sectors that are difficult to electrify make it a strategic component in climate change mitigation. By using a method based on a bibliometric review of scientific publications, this paper represents a significant contribution to the emerging field of research on green hydrogen and provides a detailed review of electrolyzer technologies, identifying key areas for future research and technology development. The results reflect the immaturity of a technology which advances with different technical advancements, waiting to find the optimal technical solution that allows for its massive implementation as a source of green hydrogen generation. According to the results found in this article, alkaline (ALK) and proton exchange membrane (PEM) electrolyzers seem to be the ones that interest the scientific community the most. Similarly, in terms of regional analysis, Europe is clearly committed to green hydrogen, in view of the analysis of its scientific results on materials and electrolyzer capacity forecasts for 2030.
]]>Applied Sciences doi: 10.3390/app14062523
Authors: Natasha Rouse Kathryn Daltorio
Dynamic systems which underlie controlled systems are expected to increase in complexity as robots, devices, and connected networks become more intelligent. While classical stable systems converge to a stable point (a sink), another type of stability is to consider a stable path rather than a single point. Such stable paths can be made of saddle points that draw in trajectories from certain regions, and then push the trajectory toward the next saddle point. These chains of saddles are called stable heteroclinic channels (SHCs) and can be used in robotic control to represent time sequences. While we have previously shown that each saddle is visualizable as a trajectory waypoint in phase space, how to increase the fidelity of the trajectory was unclear. In this paper, we hypothesized that the waypoints can be individually modified to locally vary fidelity. Specifically, we expected that increasing the saddle value (ratio of saddle eigenvalues) causes the trajectory to slow to more closely approach a particular saddle. Combined with other parameters that control speed and magnitude, a system expressed with an SHC can be modified locally, point by point, without disrupting the rest of the path, supporting their use in motion primitives. While some combinations can enable a trajectory to better reach into corners, other combinations can rotate, distort, and round the trajectory surrounding the modified saddle. Of the system parameters, the saddle value provides the most predictable tunability across 3 orders of magnitude.
]]>Applied Sciences doi: 10.3390/app14062522
Authors: Zhaoyu Shou Mingquan Xie Jianwen Mo Huibing Zhang
As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance.
]]>Applied Sciences doi: 10.3390/app14062520
Authors: Yesenia Pacheco-Hernández Edmundo Lozoya-Gloria Clemente Mosso-González Jenaro Leocadio Varela-Caselis Nemesio Villa-Ruano
Herein, we present an integrative investigation of the nutritional and nutraceutical potential of Lactarius indigo, Clitocybe nuda, Clitocybe subclavipes, Russula delica, Russula brevipes, Clitocybe squamulosa, and Amanita jacksonii, which are edible mushrooms consumed in the northeastern highlands of Puebla, Mexico. The content of protein oscillated from 4.8 to 10.9 g 100 g−1 fresh weight (FW) whereas that of fiber ranged from 8.8 to 19.7 g 100 g−1 FW. The edible species presented low amounts of fat (1.5–3.4 g 100 g−1 FW) and reducing sugars (0.8–2.9 g 100 g−1 FW), whereas the content of vitamin C oscillated from 6.5 to 84.8 mg 100 g−1 dry weight (DW). In addition, four vitamins of B complex (thiamine, riboflavin, vitamin B6, and folate) were determined in different concentrations. A high abundance of potassium (92.3–294.3 mg 100 g−1 DW), calcium (139.1–446.9 mg 100 g−1 DW), and magnesium (81.3–339.1 mg 100 g−1 DW) was determined in most of the edible mushrooms, as well as detectable levels of p-hydroxybenzoic acid (2.2–48.7 mg 100 g−1 DW), protocatechuic acid (0.5–50.8 mg 100 g−1 DW), oleic acid (14.2–98.3 mg 100 g−1 DW), linoleic acid (748–1549.6 mg 100 g−1 DW), and linolenic acid (from 9.1 to 83.6 mg 100 g−1 DW). The total phenol content and antioxidant capacity significantly (p < 0.05) varied among the studied species, and their capacity to inhibit enzymes involved in glucose, lipid, and polyamine metabolism. Nevertheless, the hydroalcoholic extracts from A. jacksonii and L. indigo efficiently inhibited alpha-glucosidase and ornithine decarboxylase (IC50 < 50 µg mL−1), respectively. The evaluation of the same extracts on microorganisms associated with the gastrointestinal tract showed negligible toxicity on probiotics (MIC > 500 µg mL−1) and moderate toxicity against pathogenic bacteria (MIC < 400 µg mL−1). Based on the studied parameters, principal component analysis and orthogonal partial least squares discriminant analysis clustered these edible mushrooms into two main groups with similar biological or chemical properties.
]]>Applied Sciences doi: 10.3390/app14062519
Authors: Chao Xing Mingqun Liu Junzhen Peng Xueke Wang Yuhong Wang Zongsheng Zheng Shilin Gao Jianquan Liao
Ultra-low-frequency oscillation (ULFO) is a new problem of frequency stability in asynchronous network back-end power networks. The negative damping provided by hydropower units is the direct cause of ultra-low-frequency oscillation, and DC modulation is an effective means of suppressing system frequency oscillation. Based on the transient energy function and the limitation of DC modulation, this paper analyzes the conditions of suppressing system oscillation by DC modulation from the perspective of energy. The weight of unit energy participation is defined, and the calculation formula of transient oscillation energy in a certain oscillation mode is derived. DC modulation sensitivity is then used to create a DC modulation sequence table based on the Prony identification approach. Ultimately, the DC involved in the modulation is identified using the DC modulation sequence table and the transient oscillation energy, and the Deep Deterministic Policy Gradient (DDPG) algorithm is used to optimize the chosen DC modulation parameters.
]]>Applied Sciences doi: 10.3390/app14062521
Authors: Marek Pavlík Matej Bereš Ľubomír Beňa
People spend two-thirds of their time in buildings. Building materials are, therefore, natural shielding for us. Many studies describe the shielding effect of non-building materials. This study evaluates the shielding effectiveness (SE) of electromagnetic fields for various building materials over a frequency range of 1 GHz to 9 GHz. Measurements of SE, reflection (R), and calculated absorption (A) were conducted to determine the shielding properties of mineral wool (MW), hardened polystyrene (PT), extruded polystyrene (PE), polyurethane board (PUR), brick wall (BW), brick wall filled with mineral wool (BW-MW), and concrete wall. The results demonstrate that MW, PT, PE, and PUR exhibit low SE and R, indicating minimal shielding capabilities, with absorption values that do not significantly deviate from the level of measurement uncertainty. Conversely, BW, BW-MW, and concrete wall materials exhibit high SE, with notably increased absorption at higher frequencies, highlighting their potential for effective EMI shielding. Particularly, the concrete wall presents the highest absorption values, making it a superior choice for shielding applications. Reflection trends revealed a plateau for BW in the 6 GHz to 9 GHz range, indicating a frequency-dependent behavior of shielding mechanisms. This study underscores the importance of balancing reflective and absorptive properties in shielding materials and suggests that composite materials may offer enhanced performance. The findings of this research provide guidance for the selection and design of shielding materials in environments with a frequency spectrum of electromagnetic frequencies from 1 GHz to 9 GHz.
]]>Applied Sciences doi: 10.3390/app14062517
Authors: Jincai Yu Cheng Cheng Jintao Yang
Gas hydrate has gradually become a new potential energy resource. However, some engineering and environmental problems related to the mechanical properties of gas hydrate-bearing sediments (GHBS) during gas recovery may occur. Many studies have been carried out on the basic mechanical properties of GHBS samples based on laboratory tests, but their evolution characteristics and suitable models require further research. Based on a series of data analyses of published laboratory experimental results on GHBS samples with different hydrate saturations under various confining pressures, the evolution characteristics of strength and dilation parameters were investigated. It was found that cohesion (c) increases quickly to a peak value and then decreases gradually to a residual value with an increasing plastic shear strain, and the samples with higher hydrate saturations have higher initial values, peak values, and residual values of cohesion (c). The internal friction angle (φ) increases quickly with increasing plastic shear strain and then becomes stable at a residual value for all the samples with different hydrate saturations. The dilation angle (ψ) increases from negative to positive values with increasing plastic shear strain and then becomes stable at a residual value. These characteristics are likely to be related to the compaction occurring at the early stage of compression before expansion due to dilation. In this paper, a non-linearly fitted model is proposed considering the evolution of the mechanical parameters, and the verification tests show that the proposed model can simulate the stress–strain behaviors of the GHBS samples well. This model is also adopted in the stability analysis of submarine slopes containing hydrate reservoirs. The analytical approach is developed, accompanied by the strength reduction method.
]]>Applied Sciences doi: 10.3390/app14062518
Authors: Qing Li Xuelian You Yu He Yuan Zhou Renzhi Tang Jiangshan Li
Due to the influence of multiple tectonic movements in rift basins, the sequence and sedimentary filling modes of continental petroleum reservoirs are complex, which makes it difficult to establish isochronous stratigraphic frameworks and thus affects the accuracy of subsequent predictions of effective sand bodies. Taking the Guantao Formation of the Binxian uplift and the surrounding areas as an example, this study established the sequence stratigraphic framework of the Guantao Formation and discussed the controlling effect of sequence stratigraphy on sedimentary filling. According to a combination method of seismic data, well log data, the wavelet transform technique (WTT), and Integrated Prediction Error Filter Analysis (INPEFA) methods, the Guantao Formation in the study area can be divided into 1 long-term cycle (LNG), 4 mid-term cycles (MNG1–MNG4, from bottom to top), and 11 short-term cycles (SNG1–SNG11, from bottom to top). Based on comprehensive analysis of geological and seismic data, three sedimentary facies can be classified: alluvial fan facies, braided fluvial facies, and meandering fluvial facies. The sequence stratigraphic style of the study area has a significant controlling effect on sedimentation and sand body distribution. Different levels of cycles have different impacts on sedimentary facies/microfacies types, the development degree of each sedimentary microfacies, and sand body distribution. The long-term cycle controls the distribution of sedimentary facies, while the mid-term and short-term cycles control the distribution of sedimentary microfacies. The bottom interface of the Guantao Formation (T1) served as the dominant migration channel in the study area, connecting the reservoir and source rocks. When the base-level was in the low stage (MNG1), a large amount of sand bodies developed, forming favorable reservoirs for petroleum. The interlayers at the top of the long- and mid-term cycles served as seal layers to prevent oil and gas from escaping. The MNG1 cycle has a good combination of reservoir and seal, resulting in the accumulation of oil and gas in the MNG1 strata, which became the main oil- and gas-producing layer in the area. These study results can provide effective guidance for future prediction of the distribution of sand bodies and high-quality reservoirs.
]]>Applied Sciences doi: 10.3390/app14062515
Authors: Liguo Jin Liting Du Zhenghua Zhou Xin Bao
This paper adopts the fluid–structure coupling algorithm based on the acoustic fluid element, the fluid dynamic artificial boundary, and the consistent viscoelastic artificial boundary of solid media to establish a finite element model of the dynamic interaction of the reef-island–seawater system. Then, a numerical simulation of the seismic response of the reef-island site is carried out to study the seismic ground motion distribution patterns of the reef–seawater site and the reef-island–lagoon site. The innovation of this article is that the influence of reef–island topography and fluid–structure coupling is considered in the analysis when vertical ground motion is input. The results show that the slope angle of the bottom layer has a significant influence on the peak ground acceleration distribution and peak size on the island slope surface and the reef platform. For high-frequency input motion, a smaller reef platform width will induce a larger peak acceleration response on the reef platform. Seawater has a significant suppressive effect on vertical ground acceleration. The more high-frequency components of the input bedrock motion, the more obvious this suppression effect will be. The existence of the lagoon will amplify the maximum peak acceleration on the reef platform. According to the calculation results, lagoon terrain can amplify the maximum horizontal and vertical peak accelerations on the reef platform by about 19 and 6 times relative to the free-field results, respectively.
]]>Applied Sciences doi: 10.3390/app14062514
Authors: Diego Alonso Candia Pablo Palacios Játiva Cesar Azurdia Meza Iván Sánchez Muhammad Ijaz
Localization in hospitals can be valuable in improving different services in medical environments. In this sense, an accurate location system in this environment requires adequately enabling communication technology. However, widely adopted technologies such as Wireless Fidelity (WiFi), Bluetooth, and Radio Frequency Identification (RFID) are considered poorly suited to enable hospital localization due to their inherent drawbacks, including high implementation costs, poor signal strength, imprecise estimates, and potential interference with medical devices. The increasing expenses associated with the implementation and maintenance of these technologies, along with their limited accuracy in dynamic hospital environments, underscore the pressing need for alternative solutions. In this context, it becomes imperative to explore and present novel approaches that not only avoid these challenges but also offer more cost effective, accurate, and interference-resistant connectivity to achieve precise localization within the complex and sensitive hospital environment. In the quest to achieve adequate localization accuracy, this article strategically focuses on leveraging Visible Light Communication (VLC) as a fundamental technology to address the specific demands of hospital environments to achieve the precise localization and tracking of life-saving equipment. The proposed system leverages existing lighting infrastructure and utilizes three transmitting LEDs with different wavelengths. The Received Signal Strength (RSS) is used at the receiver, and a trilateration algorithm is employed to determine the distances between the receiver and each LED to achieve precise localization. The accuracy of the localization is further enhanced by integrating a trilateration algorithm with the sophisticated Particle Swarm Optimization (PSO) algorithm. The proposed method improves the localization accuracy, for example, at a height of 1 m, from a 11.7 cm error without PSO to 0.5 cm with the PSO algorithm. This enhanced accuracy is very important to meet the need for precise equipment location in dynamic and challenging hospital environments to meet the demand for life-saving equipment. Furthermore, the performance of the proposed localization algorithm is compared with conventional positioning methods, which denotes improvements in terms of the localization error and position estimation.
]]>Applied Sciences doi: 10.3390/app14062516
Authors: Aleš Marjetič Tomaž Ambrožič Simona Savšek
This article discusses the method of computing the values of the unknowns under the condition of the minimum sum of the squares of the residuals of the observations, also known as the least squares method, with the additional condition of taking into account the errors in the unknowns. The problem has already been treated by many authors, especially in the field of regression analysis and the computation of transformation parameters. We give an overview of the theoretical foundations of the least squares method and extensions of this method by considering the errors in the unknowns in the model matrix. So, the total least squares method is presented in this paper, fitting the regression line to a set of points and computing transformation parameters for the transition between the old and the new Slovenian national coordinate systems. Furthermore, for the first time, the method is also presented and tested in the S-transformation between different geodetic datum-dependent solutions. Also, for the first time, we systematically compare the results of the approach with conventional approaches in all three considered tasks. With the results based on relevant statistics, we confirm the suitability of the described method for dealing with the considered computational tasks.
]]>Applied Sciences doi: 10.3390/app14062510
Authors: Qiang Liu Yan Hui Shangdong Liu Yimu Ji
Keyphrase extraction is a critical task in text information retrieval, which traditionally employs both supervised and unsupervised approaches. Supervised methods generally rely on large corpora, which introduce the problems of availability, while unsupervised methods are independent of out-sources but also lead to defects like imperfect statistical features or low accuracy. Particularly in short-text scenarios, limited text features often result in low-quality candidate ranking. To address this issue, this paper proposes Y-Rank, a lightweight unsupervised keyphrase extraction method that extracts the average information content of candidate sentences as the key statistical features from a single document, and follows a graph construction approach based on similarity to obtain the semantic features of keyphrase with high-quality and ranking accuracy. Finally, the top-ranked keyphrases are acquired by the fusion of these features. The experimental results on five datasets illustrate that Y-Rank outperforms the other nine unsupervised methods, achieves enhancements on six accuracy metrics, including Precision, Recall, F-Measure, MRR, MAP, and Bpref, and performs the highest improvement in short text scenarios.
]]>Applied Sciences doi: 10.3390/app14062513
Authors: Mainur Kurmanbekova Jiangtao Du Stephen Sharples
Kazakhstan is in Central Asia and is the ninth-largest country in the world. Some socially vulnerable segments of the Kazakh population residing in subsidised social housing have experienced a range of problems due to the low quality of housing construction and its planning. Poor indoor environmental conditions in social housing contribute to occupants’ comfort, health, and general well-being. This study assessed social housing residents’ health and quality of life, focusing on their perceived indoor air quality and thermal comfort satisfaction. A cross-sectional survey in Kazakhstan was conducted to test the effects of environmental factors on social housing residents’ health and satisfaction. Four hundred thirty-one responses were analysed, and the SF12v2 questionnaire was used to measure the health-related quality of life. Multiple regression analysis showed that air quality negatively predicted the respondents’ physical (PCS) and mental (MCS) health. In addition, age, smoking, and employment status had a significantly negative effect on PCS, while education level had a predictive positive effect. Thermal conditions negatively predicted only MCS, as well as alcohol consumption. Next, the air-conditioning control factor had a negative effect. In contrast, low air circulation, low humidity, high solar gain, temperature imbalance, duration of the residence and alcohol consumption had a significantly positive effect on overall satisfaction with the temperature. The odour sources from tobacco, furniture and external sources were predictors of respondents’ overall air quality satisfaction, along with the duration of the residence, alcohol consumption and smoking status.
]]>Applied Sciences doi: 10.3390/app14062512
Authors: Mattia Pedrocco Alberto Pasetto Giulio Fanti Alberto Benato Silvio Cocuzza
The precise control of an aerial manipulator presents a formidable challenge due to the inherent mobility of its base, which is subject to both external disturbances and dynamic disturbances due to manipulator motions. In this paper, we introduce two Closed-Loop Inverse Kinematics (CLIK) control algorithms tailored to aerial manipulators. The first algorithm operates at the velocity level and uses the Generalized Jacobian for inverse kinematics, while the second one operates at the acceleration level. We evaluate their performance in a simulated environment, replicating real-world challenges such as the wind effect, sensors noise, uncertainty of the system inertial parameters, and impulsive forces at the end-effector. Trajectory tracking simulated experiments are carried out for a two- and three-degree-of-freedom (DOF) aerial manipulator tracking a circular trajectory with its end-effector. Both algorithms demonstrate promising results in coping with external disturbances and variations in the inertial parameters, enhancing the precision of the trajectory tracking control. The acceleration-level algorithm shows overall better performance compared to the velocity-level one in the face of greater implementation complexity and computational burden.
]]>Applied Sciences doi: 10.3390/app14062511
Authors: Chang Fang Chao Wang Haoran Zheng Peng Liu Wen Guo Yajing Chen Houfeng He Pengcheng Liu
In situ combustion (ISC), an efficient and economical method for enhancing heavy oil recovery in high-pressure, high-viscosity, and thermally challenged reservoirs, relies on the kinetics of crude oil oxidation. Despite an increased focus on kinetic models, there is a gap in understanding how oxidation kinetic parameters impact ISC effectiveness in heavy oil reservoirs. This study addresses this by selecting heavy oil samples from the G Block in the Liaohe oilfield and the M Block in the Huabei oilfield and conducting ramped temperature oxidation (RTO), pressure differential scanning calorimetry (PDSC), and thermogravimetric analysis (TGA) experiments. RTO detailed the thermal conversion process, categorizing oxidation into low-temperature oxidation (LTO), fuel deposition (FD), and high-temperature oxidation (HTO) stages. PDSC and TGA provided thermal characteristics and kinetic parameters. The feasibility of fire flooding was evaluated. Using CMG-STARS, an ISC model was established to analyze the impact of kinetic parameter changes. Activation energy significantly affected coke combustion, while the pre-exponential factor had a notable impact on cracking reactions. The recommended values for activation energy and the pre-exponential factor are provided. This study not only guides fire flooding experiments but also supports field engineering practices, particularly for in situ combustion in heavy oil reservoirs.
]]>Applied Sciences doi: 10.3390/app14062509
Authors: Minh Hoa Nguyen Anh Thi Le Van Duong Pham Hong Minh Pham Hoang Tung Do Duc Toan Le Thi Bich Vu Thanh Binh Nguyen
Carbon quantum dots (CQDs) are known for their intriguing optical properties, low toxicity, and high biocompatibility, which make them promising for biomedical applications. In this study, CQDs were synthesized by subjecting orange juice to microplasma as a carbon source at atmospheric pressure and low temperatures. The resulting CQDs exhibited a narrow size distribution, with an average diameter of approximately 4.5 nm and a pH value of 5.67. These CQDs exhibited strong blue emission characteristics. The antibacterial properties of the CQDs against Escherichia coli (E. coli) strains were evaluated using minimum inhibitory concentration assays. The study revealed that an effective inhibition of E. coli was achieved at a minimum inhibitory concentration of 0.1 ppm, while the minimum bactericidal concentration for this bacterial strain was 1 ppm, resulting in an average antibacterial efficacy of 57%. Notably, the antibacterial effects of the CQDs were observed without the need for additional light or oxidants, demonstrating the applicability of CQDs in combating bacterial strains.
]]>Applied Sciences doi: 10.3390/app14062508
Authors: Qing Xu Guowei Lin Haowei Li Yaoxun Feng
Regeneratively cooled scramjets are successfully used as propulsion devices in hypersonic vehicles. During operation, scramjets experience acceleration. This special process causes a dynamic flow process, and heat transfer in the cooling channel commonly occurs, which may cause hazards and control difficulties for scramjets. A dynamic numerical model with a modified heat transfer coefficient calculation method was established to study the transient flow and heat transfer processes in a cooling channel. The dynamic characteristics of the flow and heat transfer under different conditions were calculated and are discussed, including the changes in the inlet fuel mass flow, heat flux, and pressure working conditions. The results indicate that the stable time of the cooling channel outlet fuel temperature is related to the rate of change in the inlet mass flow and heat flux. The stable time of the outlet fuel temperature under decreasing heat flux working conditions was approximately 12.5 s. These results summarize the dynamic flow and heat transfer characteristics, which are significant for designing cooling channels in scramjets.
]]>Applied Sciences doi: 10.3390/app14062507
Authors: Sung Yong An Guy Ngayo Seng-Phil Hong
This study pioneers the enhancement of 5G antenna manufacturing efficiency and reliability by integrating blockchain and smart contract technologies, supported by an in-depth Analytic Hierarchy Process (AHP) analysis. At the heart of our innovation lies the blockchain-based SER-M (B-SER-M) model, which delineates ‘Subject’, ‘Environment’, and ‘Resources’ as crucial factors in the manufacturing process. Our refined AHP analysis reveals ‘Subject’ as the paramount factor, with a pivotal influence weight of 0.465, underscoring its significance in elevating production efficiency and reliability. The integration of blockchain technology facilitates impeccable record-keeping and tracking at each production stage, thereby bolstering data integrity and enhancing traceability. Furthermore, the incorporation of smart contracts streamlines operations by automating processes, enabling the rapid identification and resolution of issues. These technological advancements not only significantly elevate manufacturing efficiency but also markedly improve reliability and quality control across antenna production. The enhanced results of our study demonstrate the formidable potential of integrating cutting-edge technologies in manufacturing, presenting a solid model for sustaining industry competitiveness in an increasingly digital and interconnected realm. Our contributions lay the groundwork for transformative advancements in manufacturing practices, setting a new benchmark for the integration of blockchain and smart contract technologies in enhancing 5G antenna production efficiency and reliability.
]]>Applied Sciences doi: 10.3390/app14062506
Authors: Alberto Berenguer Adriana Morejón David Tomás Jose-Norberto Mazón
The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering and retrieval. The proposal leverages large language models to convert sensor data into FAIR-compliant formats and to provide word embedding representations of tabular data for subsequent exploration, enabling semantic comparison. The proposed system comprises two primary components. The first focuses on gathering data from sensors and converting it into a reusable structured format, while the second component aims to identify the most relevant sensor data to augment a given user-provided dataset. The evaluation of the proposed approach involved comparing the performance of various large language models in generating representative word embeddings for each table to retrieve related sensor data. The results show promising performance in terms of precision and MRR (0.90 and 0.94 for the best-performing model, respectively), indicating the system’s ability to retrieve pertinent sensor data that fulfil user requirements.
]]>Applied Sciences doi: 10.3390/app14062505
Authors: Sophort Siet Sony Peng Sadriddinov Ilkhomjon Misun Kang Doo-Soon Park
A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.
]]>Applied Sciences doi: 10.3390/app14062504
Authors: César A. Rodríguez Ángel Mariano Rodríguez Pérez Raúl López Julio José Caparrós Mancera
This study presents a detailed comparative analysis of different methods for evaluating seismic response in structures, focusing on maximum displacements and collapse assessment. The results obtained through modal spectral analysis, non-linear dynamic analysis, and the incremental pushover analysis applied to a specific structure are compared. It has been found that the choice of time step and the consideration of ductility are critical for obtaining accurate predictions. The results of the non-linear dynamic analysis of the building’s response indicate that an earthquake equivalent to the one that affected the city of Lorca (southeast Iberian Peninsula) in 2011 would have a devastating impact on the studied structure, highlighting the importance of the finite element method modelling in predicting the formation of plastic hinges and assessing structural safety. These findings highlight the importance of utilising multiple analysis approaches and detailed modelling to fully understand the seismic behaviour of structures and ensure adequate resistance and stability to extreme events.
]]>Applied Sciences doi: 10.3390/app14062503
Authors: Wei-Chih Su Liane-Jye Chen Chiung-Shiann Huang
This paper introduces a novel wavelet-based methodology for identifying the modal parameters of a structure in the aftermath of an earthquake. Our proposed approach seamlessly combines a subspace method with a stationary wavelet packet transform. By relocating the subspace method into the wavelet domain and introducing a weighting function, complemented by a moving window technique, the efficiency of our approach is significantly augmented. This enhancement ensures the precise identification of the time-varying modal parameters of a structure. The capacity of the stationary wavelet packet transform for rich signal decomposition and exceptional time-frequency localization is harnessed in our approach. Different subspaces within the stationary wavelet packet transform encapsulate signals with distinct frequency sub-bands, leveraging the fine filtering property to not only discern modes with pronounced modal interference, but also identify numerous modes from the responses of a limited number of measured degrees of freedom. To validate our methodology, we processed numerically simulated responses of both time-invariant and time-varying six-floor shear buildings, accounting for noise and incomplete measurements. Additionally, our approach was applied to the seismic responses of a cable-stayed bridge and the nonlinear responses of a five-story steel frame during a shaking table test. The identified modal parameters were meticulously compared with published results, underscoring the applicability and reliability of our approach for processing real measured data.
]]>Applied Sciences doi: 10.3390/app14062502
Authors: Dexin Yang Afang Jin Yun Li
A physics-guided neural network (PGNN) is proposed to predict the fatigue life of materials. In order to reduce the complexity of fatigue life prediction and reduce the data required for network training, the PGNN only predicts the fatigue performance parameters under a specific loading environment, and calculates the fatigue life by substituting the load into the fatigue performance parameters. The advantage of this is that the network does not need to evaluate the effect of numerical changes in the load on fatigue life. The load only needs to participate in the error verification, which reduces the dimension of the function that the neural network needs to approximate. The performance of the PGNN is verified using published data. Due to the reduction in the complexity of the problem, the PGNN can use fewer training samples to obtain more accurate fatigue life prediction results and has a certain extrapolation ability for the changes in trained loading environment parameters. The prediction process of the PGNN for fatigue life is not completely a black box, and the prediction results are helpful for scholars to further study the fatigue phenomenon.
]]>Applied Sciences doi: 10.3390/app14062501
Authors: Ireneusz Mrozek Vyacheslav N. Yarmolik
This paper addresses the problem of describing the complex linked coupling faults of memory devices and formulating the necessary and sufficient conditions for their detection. The main contribution of the proposed approach is based on using a new formal model of such faults, the critical element of which is the introduction of roles and scenarios performed by the cells involved in the fault. Three roles are defined such that the cells of the complex linked coupling faults perform, namely, the roles of the aggressor (A), the victim (V), and both (B), performed by two cells simultaneously in relation to each other. The memory march test and applied address sequence and background determine the scenario for implementing the roles of memory faulty cells. The necessary and sufficient conditions for detecting linked coupling faults are given based on a new formal model. Formally, the undetectable linked coupling faults are defined, and the conditions for their detection are formulated using multirun memory march tests. The experimental investigation confirmed the validity of the proposed formulated statements. Based on the example of a linked coupling fault, this study demonstrates the fulfillment of the necessary and sufficient conditions for its detection using a single march test. As demonstrated in this article, employing the approach proposed by the authors, a two-pass march C test, for instance, enables the attainment of 55.42% fault coverage for linked coupling faults, inclusive of undetectable faults identified by the single-pass march test.
]]>Applied Sciences doi: 10.3390/app14062500
Authors: Aatif Hussain Shazia Arshad Awais Hassan
Sports analytics utilizes data analysis techniques and computational methods to gain insights, make informed decisions, and facilitate improvements in the performance of individuals and teams. Cricket is one of the most popular games and continues to evolve worldwide. The availability of ball-by-ball data demands in-depth investigation of player strategies, team dynamics, and the impact of contextual variables. Existing studies explored various aspects of cricket analytics, including detecting key events, predicting outcomes, and ranking teams. However, the literature lacks a comprehensive integrated framework that processes unstructured sports commentary, extracts actionable insights, conducts a thorough player analysis, and develops strategic plans while considering contextual factors. This work aims to propose a bowling and fielding strategy to contain a batsman. For this purpose, we developed a comprehensive context-aware framework that collects data, extracts insights from commentary, identifies player strengths and weaknesses, and proposes cricket bowling and fielding strategies according to the given context. To evaluate this work, we implemented a case study that simulated different scenarios, and our framework suggested bowling and fielding strategies. In these simulations, the proposed strategies consistently demonstrated a substantial reduction in the number of runs that were scored. On average, the framework reduces the batsman’s score rate by 33%. These findings underscore the practical effectiveness of research in optimizing field placement and effectively reducing scoring opportunities. Finally, by bridging the gap between data analytics and cricket game strategy, this methodology provides a competitive advantage to coaches, captains, and players. In the future, we aim to involve temporal patterns to understand the evolving behavior of players.
]]>Applied Sciences doi: 10.3390/app14062494
Authors: Yalin Wen Wei Ke Hao Sheng
In nature, objects that use camouflage have features like colors and textures that closely resemble their background. This creates visual illusions that help them hide and protect themselves from predators. This similarity also makes the task of detecting camouflaged objects very challenging. Methods for camouflaged object detection (COD), which rely on deep neural networks, are increasingly gaining attention. These methods focus on improving model performance and computational efficiency by extracting edge information and using multi-layer feature fusion. Our improvement is based on researching ways to enhance efficiency in the encode–decode process. We have developed a variant model that combines Swin Transformer (Swin-T) and EfficientNet-B7. This model integrates the strengths of both Swin-T and EfficientNet-B7, and it employs an attention-guided tracking module to efficiently extract edge information and identify objects in camouflaged environments. Additionally, we have incorporated dense skip links to enhance the aggregation of deep-level feature information. A boundary-aware attention module has been incorporated into the final layer of the initial shallow information recognition phase. This module utilizes the Fourier transform to quickly relay specific edge information from the initially obtained shallow semantics to subsequent stages, thereby more effectively achieving feature recognition and edge extraction. In the latter phase, which is focused on deep semantic extraction, we employ a dense skip joint attention module to enhance the decoder’s performance and efficiency, ensuring accurate capture of deep-level information, feature recognition, and edge extraction. In the later stage of deep semantic extraction, we use a dense skip joint attention module to improve the decoder’s performance and efficiency in capturing precise deep information. This module efficiently identifies the specifics and edge information of undetected camouflaged objects across channels and spaces. Differing from previous methods, we introduce an adaptive pixel strength loss function for handling key captured information. Our proposed method shows strong competitive performance on three current benchmark datasets (CHAMELEON, CAMO, COD10K). Compared to 26 previously proposed methods using 4 measurement metrics, our approach exhibits favorable competitiveness.
]]>Applied Sciences doi: 10.3390/app14062499
Authors: Franz Landauer Klemens Trieb
Back pain in the presence of LSTVs (lumbosacral transitional vertebrae) was originally noted by Mario Bertolotti in 1917. The Lenke classification for scoliosis forms the current international standard. However, the connection between LSTVs and scoliosis is still poorly understood. The aim of this study is to show the connection between scoliosis and LSTVs in terms of their frequency and impact on further development. Our scoliosis outpatient clinic has an examination period that covers the years from 2014 to 2021. If LSTVs are radiologically suspected (AP and lateral X-ray of the spine) according to the Castellvi classification (II–IV), a further MRI investigation is initiated. Scoliosis shape is assigned according to the Lenke classification. Sagittal segmental differences are observed according to the vertical mean vertebral angle (VMVA) and its difference (Diff-VMVA). Assignment to the lumbar pain group is made in cases of a history of chronic or recurrent pain of more than 6 months. Differentiation is made at the initial presentation according to gender (male–female) and age (children and adults). Other associated diagnoses such as family correlations and the question of brace fitting are cited. Finally, the literature is reviewed from a scoliosis perspective and compared with the findings of the authors of this paper. A total of 1332 patients were evaluated, and LSTV Castellvi II–IV was confirmed in 72 of them (58 female and 14 male). The curvature extent of scoliosis in children and adults had a mean Cobb angle of 24.3° with a range from 11° to 55° (n-42) and 32.4° with a range from 12° to 66° (n-30), respectively. This indicates that 75% of n-54 patients were mostly classified as Castellvi II (pseudarthrosis) (IIA, 54.2%; IIB, 20.8%) in the total data. There were few patients classified with Castellvi III (22.2%) and IV (2.8%). A proportion of 87.5% of 72 patients were mostly classified as Lenke 1 (25.0% n-18) and Lenke 5 (62.5% n-45). According to the literature, the Diff-VMVA shows Cobb angles of 9.3° for Castellvi III and 5.3° for Castellvi IV. Half of the patients complain of chronic low back pain for at least 6 months or have recurrent complaints (48.6%). The complaints are mostly classified as Castellvi IIA (27.8%) and B (9.7%). The association of scoliosis and LSTVs yields only 13 studies (PubMed 04/18/2022) that focus on spine surgery issues and not on diagnostics. Attention should be given to the lumbosacral junction in Lenke 1 and Lenke 5. Nearly half of the patients in each category complain of chronic lumbar symptoms. This is notable in individual cases due to its rounding and thus its increased Diff-VMVA. Since only Castellvi II–IV is considered, a comparison of the frequency with other studies is not permissible. In conclusion, for scoliosis Lenke 5 and Lenke 1, the lumbosacral junction should be examined.
]]>Applied Sciences doi: 10.3390/app14062498
Authors: Dezhi Li Yunjun Lu Jianping Wu Wenlu Zhou Guangjun Zeng
Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strategy in the reinforcement model. The proposed method mainly includes the steps of relationship importance identification, reinforcement learning framework design, policy network design, and the training and testing of the causal reinforcement learning model. Specifically, a prior knowledge table is first constructed to indicate which relationship is more important for the problem to be queried; secondly, designing state space, optimization, action space, state transition and reward, respectively, is undertaken; then, the standard value is set and compared with the weight value of each candidate edge, and an action strategy is selected according to the comparison result through prior knowledge or neural network; finally, the parameters of the reinforcement learning model are determined through training and testing. We used four datasets to compare our method to the baseline method and conducted ablation experiments. On dataset NELL-995 and FB15k-237, the experimental results show that the MAP scores of our method are 87.8 and 45.2, and the optimal performance is achieved.
]]>Applied Sciences doi: 10.3390/app14062497
Authors: Xiaoyu Wu Zhibin He Zhenghao Wei Qi Zhang Zhibo Fan
This study is dedicated to the development of an advanced ship piping network programming tool to address the challenges faced by traditional text-based design and computation methods when dealing with complex and large-data-volume piping systems, such as burdensome programming tasks, high error rates, and difficulty in troubleshooting faults. Leveraging Microsoft’s WPF technology and the C# language, combined with Excel as a data input platform, this tool provides an intuitive graphical user interface, allowing users to intuitively build and analyze ship piping network models by dragging and dropping controls. The tool not only simplifies the design process of complex piping systems but also significantly improves efficiency and accuracy through automated data processing and calculations. It supports user customization of key pipeline characteristics, such as maximum flow and direction, further enhancing the applicability and accuracy of the piping network model. In addition, with optimized interaction design and data management methods, the tool significantly reduces the learning difficulty for users, while improving the reliability of design and efficiency of troubleshooting. The results of this study show the tool not only technically outperforms traditional methods but also provides a new efficient, intuitive, and user-friendly tool for the teaching and engineering applications of ship piping networks, paving a new path for the design and optimization of ship piping network systems, with significant practical application value and theoretical significance. Looking forward, this tool is expected to play a broader role in the instruction and industrial practices associated with ship piping networks, moving the field toward more efficient and intelligent development.
]]>Applied Sciences doi: 10.3390/app14062495
Authors: Richie Ranaisa Daru Monjur Morshed Rabby Tina Ko Yukti Shinglot Rassel Raihan Ashfaq Adnan
With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and MEG capabilities. However, to achieve translational results, such phantom heads should have matching geometric features and electrical properties. Here, we have designed and fabricated multilayer chopped carbon fiber–PDMS reinforced composites to represent phantom head tissues. Two types of phantom layers are fabricated, namely seven-layer and four-layer systems with a goal to achieve matching electrical conductivities in each layer. Desired electrical conductivities are obtained by varying the weight fraction of the carbon fibers in PDMS. Then, the prototype system was calibrated and tested with a 32-electrode EEG cap. The test results demonstrated that the phantom effectively generates a variety of scalp potential patterns, achieved through a finite number of internal dipole generators within the phantom sample. This innovative design holds potential as a valuable test platform for assessing wearable EEG technology as well as developing an EEG analysis process.
]]>Applied Sciences doi: 10.3390/app14062496
Authors: Paola D’Antonio Francesco Toscano Nicola Moretti Nicolino De Iorio Costanza Fiorentino
In Italy, the use of chainsaws for field operations such as Felling (FE), Delimbing (DE), and Bucking (BU) is widespread due to the topography, the medium–small size of farms, and the predominant presence of broad-leaved forests managed through coppicing. However, this has led to an increase in injuries and illnesses due to exposure to physical factors (e.g., noise, dust, and vibrations) and chemical agents (e.g., various volatile compounds). Occupational health and safety legislation in Italy has undergone several phases, including the approval of U.T. 81/2008. The present study aims to evaluate the noise generated by chainsaws and the concentration of pollutants (CO, VOC, and C6H6) present in chainsaw exhaust gases during interventions in a chestnut coppice in relation to the limits set by current legislation. The analysis of the noise generated by chainsaws during chestnut cutting operations showed that it exceeded the legal noise limits during all chainsaw activities, with peak levels of about 110 dB. The detected noise could cause important critical issues in relation to the health and safety of specialized operators. Furthermore, the correlation between the specific work (FE, DE, and BU) and the ratio between maximum and average values of CO and VOC emissions was evaluated. Notably, comparable levels of maximum VOC emissions were observed during the FE and BU phases. However, the average emission values during these phases exhibited significant differences, suggesting higher VOC production when the engine was running but not actively engaged in cutting. The highest emissions were recorded during the FE phase (CO = 135 ppm, VOC = 17.28 ppm, and C6H6 = 2.13 ppm).
]]>Applied Sciences doi: 10.3390/app14062493
Authors: Chang Chang Xiaotian Han Guangying Li Peng Li Wenchao Nie Peixuan Liao Cong Li Wei Wang Xiaoping Xie
Underwater wireless optical communication (UWOC) in harbor waters can facilitate real-time monitoring underwater instruments for environmental monitoring, underwater inspection, and maintenance tasks. This study delves into the complex dynamics of UWOC in four distinct harbor waters. The research employs Monte Carlo method incorporated with Fournier–Forand scattering phase function for simulating photon transmission. Key parameters such as the Transmitted full divergence angle, received aperture, and Field of View (FOV) are meticulously evaluated for their impact on power loss and time delay spread. Notably, the normalized power loss and time delay spread are found to be more significantly affected by communication distance than water quality, and the traditional Beer–Lambert law is ineffective in harbor water. The power loss of Harbor II, III, and IV are found to be 14.00 dB, 31.59 dB, and 41.59 dB lower than that of Harbor I, and the time delay spread of Harbor II, III, and IV is 30.56%, 9.67%, and 0.49% times that of the Harbor I under certain conditions. In addition, increasing the received aperture and FOV, particularly over longer distance, make little contribution to reduce the power loss and mitigate the time delay spread. Based on the fixed transmitted full divergence angle, the most applicable received FOV range is 1–3.2 rad, and the most ideal received aperture is 0.15–0.4 m. Under these conditions, the variation in normalized power loss is less than 2 dB. Additionally, the time delay spread remains within the same order of magnitude with the attenuation length (AL) held constant. These conclusions hold substantial technical relevance for the engineering design of UWOC systems in harbor waters.
]]>Applied Sciences doi: 10.3390/app14062492
Authors: Cheng Li Hao Wu Yunlong Lu Lei Xiong Chen Dong Jing Li
The Internet of Vehicles (IoV) has a significant impact on improving traffic efficiency and driving safety. In this paper, we propose an intelligent transportation credit system based on blockchain, and design a crossroad passing smart contract that allows time-sensitive convoys to pass a crossroad earlier by paying traffic tokens for right-of-way trade. Second, this paper formulates the time–cost optimization problem under the premise of protecting the privacy of preceding convoys. Based on game theory with incomplete information, two right-of-way optimal bid strategies are given. Both theoretical analysis and simulation prove that the strategy proposed in this paper effectively reduces the waiting time of time-sensitive vehicles at a crossroad and increases the trade success probability, which, in turn, improves the expected total profit of convoys and achieves Pareto improvement.
]]>Applied Sciences doi: 10.3390/app14062491
Authors: Stella Karatzetzou Dimitrios Parisis Serafeim Ioannidis Theodora Afrantou Panagiotis Ioannidis
Parkinsonism may be a clinical manifestation of a wide range of disease entities, and still poses a great diagnostic challenge. In an attempt to provide further insight into the differential diagnosis of PD versus progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), and Lewy body dementia (LBD), several biomarkers have been investigated, yielding inconclusive results, OCT being among them. The present review aims to explore the potential diagnostic value of evaluating retinal parameters through OCT implementation among patients presenting with a Parkinsonian syndrome, with an emphasis on effective differentiation between distinct syndromes. Having reviewed all the available literature published within the last decade, neurodegeneration seems to be paralleled with degeneration and alterations of the retina that may be quantified by OCT. Specific patterns of structural changes within the retina may provide valuable information on the underlying pathology, thus highlighting the role of OCT as a diagnostic tool within this group of patients. Although still not utilized in clinical practice, OCT, if further explored and validated, may significantly enhance overall Parkinsonism care.
]]>Applied Sciences doi: 10.3390/app14062490
Authors: Muhammad Hafizh Asma Mecheter Faris Tarlochan Pankaj B. Pathare
Mechanical damage and bruising of fruit is a critical problem in the food industry. Minimizing brusing and damage can be achieved by designing energy-absorbing structures and packaging systems in order to ensure the long-term quality of fresh produce. The aim of this study is to investigate the response and bruise susceptibility of pears under impact loading conditions through finite element analysis (FEA) methods. In this paper, three impact heights (0.25 m, 0.5 m, and 1.0 m), four impact material surfaces (poplar wood, rubber, cardboard, and acrylonitrile butadiene styrene (ABS) plastic), two packaging sizes (standard 0.22″ and sandwich lattice 2.1″), and three impact design structures (rigid, corrugated, and honeycomb) are considered. Based on mesh sensitivity analysis, a mesh element of 1.5 mm was adopted for all simulations, assuring the accuracy of results and considering the trade-off between mesh size and computational time. The response surface analysis approach was utilized in order to develop predictive empirical models related to pear bruising. Results revealed that the rubber-based impact platform yielded minimal bruise susceptibility at all heights, while standard-sized corrugated cardboard performed best at a height of 0.25 m. Furthermore, single, double, and triple layers of packaging cardboard were tested. We observed that adding a second soft layer of corrugated cardboard reduced the stress on the pear by around 33%. However, adding a third layer only reduced stress by 5%. The 3D-printed honeycomb ABS has potential as protective packaging but would require further investigations and parameter optimization. Stacking multiple layers of cardboard on top of each other is a cost-effective solution that could improve damping and, therefore, ensure good quality and increase the shelf life of the fresh produce. This study will help decision-makers select the optimal energy-absorbing material for cushioning and packaging designs in order to improve the handling and post-harvesting logistics of fresh produce.
]]>Applied Sciences doi: 10.3390/app14062488
Authors: Wojciech Janczukowicz Joanna Rodziewicz
The growing world population requires highly efficient food production methods [...]
]]>Applied Sciences doi: 10.3390/app14062489
Authors: Xingan Fu Youhua Wei Yun Su Haixia Hu
Shear wave velocity (VS) is a vital prerequisite for rock geophysics. However, due to historical, cost, and technical reasons, the shear wave velocity of some wells is missing. To reduce the deviation of the description of underground oil and gas distribution, it is urgent to develop a high-precision neural network prediction method. In this paper, an attention module is designed to automatically calculate the weight of each part of the input value. Then, the weighted data are fed into the long short-term memory network to predict shear wave velocities. Numerical simulations demonstrate the efficacy of the proposed method, which achieves a significantly lower MAE of 38.89 compared to the LSTM network’s 45.35 in Well B. In addition, the relationship between network input length and prediction accuracy is further analyzed.
]]>Applied Sciences doi: 10.3390/app14062486
Authors: Vito Burgio Janira Bei Mariana Rodriguez Reinoso Marco Civera Oliver Grimaldo Ruiz Cecilia Surace Nicola M. Pugno
Stapling devices have emerged as a widespread and effective option for soft tissue surgery, offering promising outcomes for patients by reducing complication rates and surgery time. This review aims to provide an exhaustive analysis of commercially available alternatives in the market, incorporating insights from market analysis, patent landscape, and the existing literature. The main focus lies in identifying and evaluating the most widely adopted and innovative stapling devices, including linear, linear cutting, circular, and powered staplers. In addition, this review delves into the realm of bioabsorbable staples, exploring the materials utilized and the surgical fields where these advanced staples find applications. To facilitate easy comprehension, the gathered information is presented in tables, highlighting the essential parameters for each stapling device. This comprehensive research about stapling devices is intended to aid healthcare practitioners and researchers in making informed decisions when choosing the most appropriate instrument for specific surgical procedures.
]]>Applied Sciences doi: 10.3390/app14062487
Authors: Ahmed Roshdy Abdullah Karar Samer Al Kork Taha Beyrouthy Amine Nait-ali
This paper addresses the limitations of relying solely on facial expressions for emotion recognition by proposing an advanced approach that emphasizes continuous monitoring of electroencephalography (EEG) signals. Recognizing the potential for deception in facial expressions, our study leverages the growing interest in EEG signals, tapping into advancements in deep learning and machine learning. By optimizing the configuration of EEG electrodes, our approach enhances the accuracy of emotion classification systems, offering a streamlined solution. The proposed multi-input system refines EEG-based emotion recognition efficiency and integrates facial expression analysis to enhance overall system effectiveness. Through the application of brain heat map topographies and facial expression recognition, our system, employing just nine electrodes, outperforms basic emotion recognition setups. Experimental results validate that combining facial expression analysis with EEG signals provides a more comprehensive and accurate understanding of human emotions. This innovative approach holds significance across various sectors, including healthcare, psychology, and human–computer interaction. The paper introduces a novel multi-input system approach, collaboratively fusing two powerful deep learning algorithms: two Convolutional Neural Networks (CNNs). The proposed EEG-based CNN algorithm achieves an efficiency of 87.43%, rising to 91.21% when integrated with the DeepFace CNN. The seamless integration of facial expressions and brain topographies enables the system to efficiently harness abundant information from both modalities, ensuring a thorough comprehension of human emotions. By capitalizing on the combined advantages of analyzing facial expressions and EEG-derived brain topography, this avant-garde technique substantially improves both precision and efficiency in emotion recognition systems. This enhancement establishes a foundation for the introduction of innovative applications across a spectrum of fields.
]]>Applied Sciences doi: 10.3390/app14062472
Authors: Marcin Chodnicki Mirosław Nowakowski Paweł Pietruszewski Mariusz Wesołowski Sławomir Stępień
The paper presents a study of the performance and development of unmanned ground vehicles (UGVs), establishing mathematical and numerical models of the chassis system. The model analysis is performed by 3D software package SolidWorks 2018 with finite element discretization. The mesh modelling and analysis are focused on studying the strength and stiffness of the robotic platform chassis and the distribution of stress and deformation in the extremal condition. The paper also presents an autopilot design with a new cascade control system for the autonomous motion of an unmanned ground vehicle based on proportional–integral–derivative (PID) and feedforward (FF) control. The PID-FF controller is part of a UGV used in a hybrid control system for precise control and stabilization, which is necessary to increase the vehicle motion stability and maneuver precision. The hybrid PID-FF control system proposed for the ground vehicle model gives satisfactory control quality while maintaining the simplicity of the control system. The presented tests performed in mechanical design and control analysis give good results and prove the usefulness of the designed unmanned device.
]]>Applied Sciences doi: 10.3390/app14062485
Authors: Meimei Wang Jianwei Zheng Shanshan Xue
Rock and soil masses in geotechnical engineering projects, such as tunnels, mines and slopes, undergo relative motion, exhibiting mechanical characteristics of solid–fluid transition under critical conditions. This work analyzes the characteristics of the solid–fluid transition interface and the mode of load transfer through biaxial compression particle flow photoelastic experiments on granular materials. The study documents that this interface forms an arch shape, marked by a force chain arch. The granular material exhibits two distinct states depending on its position: below the arch, the granular material is in a solid–fluid transitional state, with bearing capacity reduced, while above the arch, it is in a stable solid state, capable of bearing the overlying rock layer’s load. The presence of the force chain arch alters the direction of the originally downward-transferring load, redirecting it along the trajectory of the arch. Analysis of the force and stability of the force chain arch revealed that the arch shape parameters and boundary loads control the instability of the arch. Changes in the overlying and lateral loads lead to different types of instability of the force chain arch. The findings of the study are crucial for underground engineering construction and for the prevention of geological disasters related to granular material.
]]>Applied Sciences doi: 10.3390/app14062484
Authors: Paulina Pakosz Rafał Wołosiak Beata Drużyńska Ewa Majewska
During kopi luwak production, coffee fruit is subjected to enzymatic and microbial treatment. While microbial modification of coffee fruit or beans is often investigated, there is little information regarding the influence of the enzymatic part of the process. In this study, green Arabica and Robusta beans were modified using basic animal digestive enzymes (pepsin or trypsin with α-amylase) and various treatment times (3, 6 or 12 h) to determine their effect on bioactive and other quality-affecting compounds. Analyses of coffee composition were performed using spectrophotometric and HPLC methods. Modified and control samples were significantly affected by the treatment. Among enzymes used, only proteases exhibited noticeable impact on target compounds by increasing soluble protein content in green beans. The most advantageous modification time was 3 h. The composition of beans was altered by the roasting step, with the effect not quite corresponding to the previous stage. In conclusion, enzymatic treatment of green coffee beans provides a way to alter coffee composition, which can further influence its quality.
]]>Applied Sciences doi: 10.3390/app14062483
Authors: Nancy Mahmoud Joana Ferreira Anabela Raymundo Maria Cristiana Nunes
At present, the incorporation of microalgae into bread and related cereal products has attracted attention due to their potential for enhancing nutritional profiles and their impact on health. In this study, 4% of Chlorella vulgaris, Phaeodactylum tricornutum, and Tetraselmis chuii were added into wheat flour to produce bread and assesses their impact on the dough rheology behaviour, quality performance, nutritive value, and bioactive profile of bread. The results showed that T. chuii strengthened the dough network, whereas P. tricornutum exerted minimal influence. Notably, the incorporation of C. vulgaris induced a pronounced weakening of the protein network within the dough matrix, leading to disruptions in dough structure and subsequent alterations in starch gelatinisation and retrogradation. These changes lead to a reduction in the bread volume (22.7%) and a corresponding increase in its firmness when C. vulgaris was added. In contrast, T. chuii and P. tricornutum had no significant effect on bread volume. All microalgae species caused the dark green colour of the bread and enhanced the bread nutritional composition, namely in terms of protein content (14.7% increase in C. vulgaris bread) and mineral profile. The breads containing T. chuii exhibited a noticeable increase in both total phenolic content (from 7.22 in the control to 38.52 (µg GAE/g)) and antioxidant capacity (from 117.29 to 591.96 (µg TEAC/g) measured by FRAP).
]]>Applied Sciences doi: 10.3390/app14062482
Authors: Liu-Ying Yang Shu-Lin Jiao Lei Wang Yin-Jiu Li Mei Yang Ye-Lin Feng Juan Li Zong-Xiao Wei
With the continuous improvement of surface water environmental quality in China, sediment has gradually become the focus of research on internal pollution in reservoirs. To investigate the forms, distribution, and migration patterns of phosphorus in karst canyon reservoir sediments, we employed an improved sequential graded extraction method to determine phosphorus content in the sediments of the Wanfeng Reservoir. We studied the spatial distribution characteristics and release risk of phosphorus form in the sediments. The results showed that the total phosphorus (TP) content ranged from 79.37 to 438.04 mg·kg−1, while inorganic phosphorus (IP) content ranged from 77.32 to 424.64 mg·kg−1. Iron–aluminum-bound inorganic phosphorus (Fe/Al-Pi) accounted for 36.41% and was found to be the dominant form of IP. Organic phosphorus (OP) content ranged from 1.84 to 13.59 mg·kg−1, with weakly adsorbed organic phosphorus (H2O-Po) being the dominant form of OP. Potentially active inorganic phosphorus (NaHCO3-Pi) showed a highly significant positive correlation (p < 0.01) with Fe/Al-Pi, residual phosphorus (Res-P), and TP. This indicates that potentially active phosphorus (NaHCO3-P) is a significant potential source of phosphorus (P) in the reservoir. Biologically active phosphorus (BAP) content ranged from 66.97 to 201.46 mg·kg−1, with BAP/TP ratios ranging from 55.6% to 59.6%. The risk of phosphorus release from Wanfeng Reservoir sediments is high. The release of various forms of phosphorus from sediments is one of the important factors leading to the deterioration of water quality in the reservoir area in the future. To effectively manage water ecology in karst canyon reservoirs, it is essential to reduce the risk of endogenous phosphorus release.
]]>Applied Sciences doi: 10.3390/app14062481
Authors: Marc Kilian Klankers Adrian Rudloff Pouya Mohammadi Niclas Hoffmann Seyed Milad Mir Latifi Ramazan Gökay Rajal Nagwekar Robert Weidner Jochen J. Steil
Assistive devices like exoskeletons undergo extensive testing not least because of their close interaction with humans. Conducting user studies is a time-consuming process that demands expert knowledge, and it is accompanied by challenges such as low repeatability and a potential lack of comparability between studies. Obtaining objective feedback on the exoskeleton’s performance is crucial for developers and manufacturers to iteratively improve the design and development process. This paper contributes to the concept of using robots for objective exoskeleton testing by presenting various approaches to a robotic-based testing platform for upper-body exoskeletons. We outline the necessary requirements for realistically simulating use cases and evaluate different approaches using standard manipulators as robotic motion generators. Three approaches are investigated: (i) Exploiting the anthropomorphic structure of the robotic arm and directly placing it into the exoskeleton. (ii) Utilizing a customized, direct attachment between the robot and exoskeleton. (iii) Attaching a human arm dummy to the robot end effector to simulate a more realistic interface with the exoskeleton. Subsequently, we discuss and compare the results against the aforementioned requirements of a systematic testing platform. Our conclusion emphasizes that achieving objective and realistic testing necessitates highly specialized hardware, algorithms, and further research to address challenging requirements.
]]>Applied Sciences doi: 10.3390/app14062480
Authors: Juan José Parajó Antía Santiago-Alonso Pablo Vallet Tamara Teijeira Raquel San Emeterio María Villanueva Josefa Salgado
The ecotoxicity of a set of 30 ionic liquids, namely 23 aprotic compounds (APILs) and 7 protic compounds (PILs), was analyzed in this work by monitoring the inhibition of the bioluminescence of the bacteria Aliivibrio fischeri with varying concentrations of ILs utilizing the Microtox® standard toxicity test. The study covered ILs that have various synthetic natures, PILs and APILs, with a common anion or cation, and different alkyl chain lengths. The results indicate that both moieties, anion and cation, have an influence on toxicity, these being the ILs with the bis((trifluoromethyl)sulfonyl)imide (TFSI) anion and imidazolium cation, which are the most harmful, while those less toxic correspond to ammonium-based ILs. The alkyl chain length seems to have the most significant impact on toxicity, except for tris(pentafluoroethyl) trifluorophosphate (FAP) anion-based ILs, which, interestingly, showed the opposite behavior. A critical alkyl size (CAS) at C = 6 was observed for the rest of the families, resulting in a significant reduction in the effective concentration (EC) values: the connection between this CAS and toxicity has never been observed before, and it indicates a threshold that marks the end of harmlessness (C < 6) and the start of toxicity (C > 6).
]]>Applied Sciences doi: 10.3390/app14062479
Authors: Pere Marti-Puig Jose Ángel Hernández Jordi Solé-Casals Moises Serra-Serra
Accurate power curve modeling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 min in a wind farm (WF) consisting of five WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.
]]>Applied Sciences doi: 10.3390/app14062478
Authors: Chengyu Sun Ruiqian Cai Zhen’an Yao
The seismic image produced by pre-stack depth migration is more accurate and has clearer geological significance than the time image. However, the waveform of the depth-domain seismic image is affected not only by depth-dependent velocity variation but also by media attenuation, resulting in strong spectral variation of depth-domain seismic data. Therefore, depth-domain seismic inversion is still challenging. We propose a wavelet extraction method of attenuation media based on the generalized seismic wavelet, to address this issue. Then, the estimated depth-domain wavelets were applied to the direct acoustic impedance inversion. First, we investigated the effect of attenuation media on depth-domain source wavelets and derived an analytical formula for the depth-domain wavelets of attenuation media. Next, the time-domain generalized seismic wavelet was extended to the depth domain, which was utilized to study the feasibility of using the generalized seismic wavelet to characterize the seismic wavelet of the depth-domain attenuation media. Based on the orthogonal matching pursuit, we propose a method to extract the depth-domain generalized seismic wavelet directly from depth-domain seismic data. Finally, we applied this method to the depth-domain direct acoustic impedance inversion of a 3D field data example. Tests on the synthetic and 3D field datasets show that the proposed method can correctly extract the depth-domain seismic wavelet of attenuation media and attain direct inversion of the depth-domain acoustic impedance with high accuracy. Therefore, our method is effective and has robust potential in reservoir characterization, fluid prediction, and attribute extraction in the depth domain.
]]>Applied Sciences doi: 10.3390/app14062475
Authors: Marian Körber Roland Glück
This paper introduces, as a proof of concept, a tool chain for automated control and simulation of a robot team in the domain of production of carbon-fiber-reinforced polymers. The starting point is a CAD construction of a simple aviation component from which single cut pieces of carbon fiber, together withtheir properties, are extracted. Using this information and the layout of a given robot cell, various possibilities of assignments of cut pieces to grippers and robots or robot teams are determined. Subsequently, two approaches using an PDDL solver are introduced, with the goal of finding a scheduling for the lay-up process. Finally, the resulting process is simulated using a physics and rendering engine. The main purpose of this paper is to show the feasibility of such an approach; we do not concentrate on the optimization of single process steps and other details. Due to the modular structure of our approach, extensions and optimizations of the single blocks are easy to integrate. At the moment, digitization and automated control are little explored areas in the domain of production technology using pick and place processes in the aerospace industry. We think that our work will lead to further research in this direction.
]]>Applied Sciences doi: 10.3390/app14062477
Authors: Alessandro Vittorio Bergami
This paper presents an innovative approach for improving the seismic protection of existing structures by introducing an additional dissipative structure (ADS). The seismic energy impacting the building can be dissipated through the contribution provided by the ADS, thereby reducing the need for the existing building to ensure its seismic capacity. This retrofitting technique is well-suited for structures facing architectural restrictions or challenging-to-update elements. It can help to address foundational issues by applying loads to new external components. This paper describes the design of the ADS and proposes a displacement-based design procedure. The design process involves a nonlinear static analysis and a simple procedure that must be iteratively repeated until the retrofitting target is achieved. This approach is simple and computationally efficient and can also be used for complex and irregular structures. Such structures are frequently encountered, and existing structures often exhibit unusual geometries and materials requiring extensive numerical modeling. The efficacy of this technique was evaluated using the case study of a school building located in central Italy. The results of numerical analyses indicated that owing to the ADS’s contribution, the seismic capacity of both buildings was enhanced, addressing the challenges associated with complex foundational interventions.
]]>Applied Sciences doi: 10.3390/app14062476
Authors: Qi Mu Zuohui He Xueqian Wang Zhanli Li
The traditional Siamese object tracking algorithm uses a convolutional neural network as the backbone and has achieved good results in improving tracking precision. However, due to the lack of global information and the use of spatial and scale information, the accuracy and speed of such tracking algorithms still need to be improved in complex environments such as rapid motion and illumination variation. In response to the above problems, we propose SSTrack, an object tracking algorithm based on spatial scale attention. We use dilated convolution branch and covariance pooling to build a spatial scale attention module, which can extract the spatial and scale information of the target object. By embedding the spatial scale attention module into Swin Transformer as the backbone, the ability to extract local detailed information has been enhanced, and the success rate and precision of tracking have been improved. At the same time, to reduce the computational complexity of self-attention, Exemplar Transformer is applied to the encoder structure. SSTrack achieved 71.5% average overlap (AO), 86.7% normalized precision (NP), and 68.4% area under curve (AUC) scores on the GOT-10k, TrackingNet, and LaSOT. The tracking speed reached 28fps, which can meet the need for real-time object tracking.
]]>Applied Sciences doi: 10.3390/app14062473
Authors: Nathalia Hammes Claver Pinheiro Iran Rocha Segundo Natália Cândido Homem M. M. Silva Helena P. Felgueiras Graça M. B. Soares Elisabete Freitas Manuel F. M. Costa Joaquim Alexandre O. Carneiro
Nowadays, the growing concern about improving thermal comfort in different structures (textiles, buildings, and pavements, among others) has stimulated research into phase change materials (PCMs). The direct incorporation of PCMs into composite materials can cause mechanical impacts. Therefore, this study focuses on the design of phase change coaxial fibres (PCFs), using commercial cellulose acetate (CA) or recycled CA obtained from cotton fabrics (CAt) as the sheath and polyethylene glycol (PEG) 2000 as the core, via the wet spinning method; the fibres vary in molecular weight, concentration and ejection velocity. The fibres were assessed for their optical, chemical, thermal, and mechanical properties. The presence of PEG2000 is confirmed in the core of the fibres. Thermal analyses revealed a mass loss at high temperatures, attributable to the presence of PEG2000. Notably, the fibres with CA (Mn 30,000) showed superior thermal and mechanical performance. The melting point of PEG2000 incorporated into these PCFs coincided with the melting point of pure PEG2000 (about 55 °C), with a slight deviation, indicating that PCFs were obtained. Finally, the results point to the application of the fibres in civil engineering materials requiring a phase change between 50 and 60 °C, providing promising prospects for their use in applications requiring thermoregulatory properties.
]]>Applied Sciences doi: 10.3390/app14062474
Authors: Bojan V. Stimec Dejan Ignjatovic
This communication study integrates composite multimodal research on postmortem human submandibular glands, based on macromorphometry. The normal ductographic sialograms were pairwise analyzed using linear morphometry, whole-gland planimetry and fractal properties, such as main duct length, caliber and tortuosities, side branches and accessory ducts/lobes. All the examined parameters presented a significant correlation, i.e., symmetry between the left and the right submandibular glands. The morphometric data presented can serve as a valuable reference in clinical practice.
]]>Applied Sciences doi: 10.3390/app14062470
Authors: Hygo Sousa De Oliveira Rafael Albuquerque Pinto Eduardo James Pereira Souto Rafael Giusti
Continuous monitoring plays a crucial role in diagnosing hypertension, characterized by the increase in Arterial Blood Pressure (ABP). The gold-standard method for obtaining ABP involves the uncomfortable and invasive technique of cannulation. Conversely, ABP can be acquired non-invasively by using Photoplethysmography (PPG). This non-invasive approach offers the advantage of continuous BP monitoring outside a hospital setting and can be implemented in cost-effective wearable devices. PPG and ABP signals differ in scale values, which creates a non-linear relationship, opening avenues for the utilization of algorithms capable of detecting non-linear associations. In this study, we introduce Neural Model of Blood Pressure (NeuBP), which estimates systolic and diastolic values from PPG signals. The problem is treated as a binary classification task, distinguishing between Normotensive and Hypertensive categories. Furthermore, our research investigates NeuBP’s performance in classifying different BP categories, including Normotensive, Prehypertensive, Grade 1 Hypertensive, and Grade 2 Hypertensive cases. We evaluate our proposed method by using data from the publicly available MIMIC-III database. The experimental results demonstrate that NeuBP achieves results comparable to more complex models with fewer parameters. The mean absolute errors for systolic and diastolic values are 5.02 mmHg and 3.11 mmHg, respectively.
]]>Applied Sciences doi: 10.3390/app14062471
Authors: Zhiwei Sun Changjiang Kou Yu Lu Zhengguang Wu Aihong Kang Peng Xiao
The bond strength between basalt fibers and asphalt binders is an important parameter that can be used to evaluate the influence of basalt fibers on the mechanical properties of asphalt binders and asphalt mixtures. To date, however, there remains a lack of methods that can be used to assess the bond strength between basalt fibers and asphalt binders. This study employed a fiber-asphalt pull-out tester (POT). Significant upward, peak, and downward stages were observed from the relationship curves between the pull-out force (POF) and displacement, corresponding to the holding stage and reaching the maximum POF stage and the sliding or failure stage between fibers and asphalt binders. Maximum POF is recommended to calculate the bond strength between basalt fibers and asphalt binders. The types of asphalt binders suitable for basalt fibers and the appropriate fiber embedding depths for different types of asphalt binders guiding the selection of fiber length are recommended based on the influence of fiber embedding depth and asphalt binders on the fiber–asphalt bond strength. In addition, surface energy was used to calculate the bond strength as well. Surface energy was determined from contact angle measurements using the sessile drop method. Furthermore, a scanning electron microscope (SEM) was employed to examine the bond mechanism between asphalt binders and basalt fibers. These experiments showed how basalt fibers serve to reinforce asphalt mixtures by bonding with asphalt binders.
]]>Applied Sciences doi: 10.3390/app14062469
Authors: Shengrui Jiang Li Cheng Haiwen Yuan Xuan Li
In existing highlight removal methods, research on highlights on metal surfaces is relatively limited. Therefore, this paper proposes a new, simple, effective method for removing highlights from metal surfaces, which can better restore image details. Additionally, the approach presented in this paper is highly effective for highlight removal in everyday real-world highlight scenarios. Specifically, we first separate the image’s illumination space based on the Retinex model and generate a highlight mask using the mean plus standard deviation method. Then, based on the mask, we transform the original image and the image at the corresponding mask position to the V channel of the HSV space, achieving the effective elimination of highlights. To enhance the details of the restored image, this paper introduces a method involving adaptive Laplacian sharpening operators and gradient fusion for detail enhancement at highlight removal positions. Finally, a highlight-free image with well-preserved details is obtained. In the experimental phase, we validate the proposed method using real welding seam highlight datasets and real-world highlight datasets. Compared with the existing methods, the proposed method achieves high-quality qualitative and quantitative evaluation.
]]>