Appl. Sci., Volume 13, Issue 13 (July-1 2023) – 649 articles

The utilization of low-cost AGVs in the industry is increasing every day, but the efficiency of these systems is low due to the lack of a central management system. Low-cost AGVs’ main characteristic is navigation via magnetic sensors, which they follow via magnetic tape on the ground with a low-level automation system. The disadvantages of these systems are mainly due to only one circuit assignment and the lack of system intelligence. Therefore, in this study, AGV pools were employed to determine the required AGV number. This study begins by calculating the required AGV number for each AGV circuit combination assigned to every parking station by the time window approach. Mathematical-solution-based mixed integer programming was developed to find the optimum solution. Computational difficulties were handled with the development of a genetic-algorithm-based approach to find the solutions for complex cases. If production requirements change, system parameters can be changed to adapt to the production requirements and there is a need to determine the number of AGVs. It was demonstrated that AGVs and pool combinations did not lead to any loss in production due to the lack of available AGVs. It was shown that the proposed approach provides a fleet size which requires five fewer AGVs, with a 29% reduction in the number of AGVs. The effects of system parameter changes were also investigated with artificial neural networks (ANNs) to estimate the required AGVs in the case of production requirement changes. It is necessary to determine the effect of the change in system parameters on the number of AGVs without compromising on computational cost and time, especially for complex systems. Thus, in this study, an artificial neural network (ANN), the response surface method (RSM), and multiple linear regression (MLR) techniques were used to examine the effects of the system parameter changes on the AGV number. In the present case, the ANN obtained the solution at a good rate with reduced computational costs, time, and correction errors compared to the GA, at 0.4% (ANN), 7% (RSM), and 24% (MLR). The results show that the ANN provides solutions which can be used in workshops to determine the number of AGVs and also to predict the effect of changes in system parameters. Full article

Determining the ultimate bearing capacity of piled foundations has been one of the most important problems in geotechnical engineering. It has been observed that the pile loading test evaluation methods based on the mathematical model give values far away from the failure load, even in piles that have reached the failure state. For this reason, it is aimed at developing a new mathematical pile loading test evaluation method based on the load-settlement curve. It has been thought that the main problem with the pile loading test evaluation methods based on other mathematical models giving values far from the failure load is that the methods iterate excessively. The new method proposed in the study was developed by taking this situation into account. The performance of the proposed new method was investigated using eight pile loading tests conducted in various provinces of Turkey. In order to verify the reliability of the newly developed method, the study was completed by applying multiple comparison tests with other methods in the literature (theoretical methods, finite element analysis methods, and pile loading test evaluation methods). According to the applied analysis of variance, it was concluded that the proposed new method remained within the 95% confidence interval and was usable. Full article

Ground-penetrating radar (GPR) is a nondestructive testing technology that is widely applied in infrastructure maintenance, archaeological research, military operations, and other geological studies. A crucial step in GPR data processing is the detection and classification of underground structures and buried objects, including reinforcement bars, landmines, pipelines, bedrock, and underground cavities. With the development of machine learning algorithms, traditional methods such as SVM, K-NN, ANN, and HMM, as well as deep learning algorithms, have gradually been incorporated into A-scan, B-scan, and C-scan GPR image processing. This paper provides a summary of the typical machine learning and deep learning algorithms employed in the field of GPR and categorizes them based on the feature extraction method or classifier used. Additionally, this work discusses the sources and forms of data utilized in these studies. Finally, potential future development directions are presented. Full article

In the field of robotics, a lot of theoretical models have been settled to formalize multi-agent systems and design distributed algorithms for autonomous robots. Among the most investigated problems for such systems, the study of the Uniform Circle Formation (UCF) problem earned a lot of attention for the properties of such a convenient disposition. Such a problem asks robots to move on the plane to form a regular polygon, running a deterministic and distributed algorithm by executing a sequence of look–compute–move cycles. This work aims to solve the UCF problem for a very restrictive model of robots: they are punctiform, anonymous, and indistinguishable. They are completely disoriented, i.e., they share neither the coordinate system nor chirality. Additionally, they are opaque, so collinearities can hide important data for a proper computation. To tackle these system limitations, robots are equipped with a persistent light used to communicate and store a constant amount of information. For such a robot model, this paper presents a solution for UCF for each of the three scheduling modes usually studied in the literature: fully synchronous, semi-synchronous, and asynchronous. Regarding the time complexity, the proposed algorithms use a constant number of cycles (epochs) for fully synchronous (semi-synchronous) robots, and linearly, many epochs in the worst case for asynchronous robots. Full article

The traditional concept of the primary, secondary, tertiary and later quaternary economy is based on several structurally divided and related tasks and processes in processing raw materials and earth resources. Gradually, a new concept of the functioning of the economy was created, called “circular economy” or “circular economy”. Its basis is the transformation of linear economic processes managing the use of raw materials to create a sustainable economic growth model. The circular economy transforms economic activity associated with the consumption of limited resources into the more efficient reuse of resources. Based on the above, the presented article aims, based on theoretical and empirical analysis, to identify the potential of processing and using non-energy raw material—recycled aggregate—in the construction industry and to propose a concept for information modeling of the parameters of sustainable construction using this non-energy raw material per the principles of the circular economy. The solution to this research problem is realized through theoretical analysis and comparison of approaches to the circular economy, reuse of non-energy raw materials in the construction industry and analysis for the creation of a concept based on the use of information needed for sustainable construction planning through building information modeling (BIM). Based on my research, my results will be presented, the applicability of which is verified through a case study. The object of the case study is the construction of a new building, which will represent a set of five similar constructions interconnected by underground floors (garages, technical facilities of buildings) and communication spaces (corridor, hall). The priority of the construction of the centre is to build a sustainable building, i.e., to implement the work using sustainable methods with the greatest possible use of sustainable materials and procedures, which will reduce the impact on the ecosystem and support the goals of the circular economy. Traditional, natural raw materials will be replaced by recycled secondary raw materials within individual constructions and elements. When choosing suitable raw materials, the design of the BIM library of sustainable elements will help. The BIM library will act as a link between manufacturers and BIM digital replicas of real building products and components. Full article

Trajectory similarity search (TSS) is a common operation for spatiotemporal data analysis. However, the existing TSS methods are mainly focused on GPS trajectories produced by moving objects such as vehicles. Further, these corresponding optimization strategies cannot be directly applied in the metro scenario because the metro passenger trajectory is totally different from the GPS trajectory. To fill this gap, we systematically analyze the unique spatiotemporal characteristics of metro passenger trajectories and propose a similarity search solution named PMMTss for the metro system. The core idea of this solution has two key points: first, we design a multi-layer index based on the spatiotemporal feature of metro trajectories, and all points of a trajectory are stored in this index. Second, we design a parallel multi-way merging-based trajectory similar search method, in which the candidate trajectory segments are merged and filtered. We evaluate this solution on a large dataset (Shenzhen Metro data for 3 consecutive months, 6.976 million trajectories with 260 million records). When lengths of input trajectories are 16, 32, and 64, respectively, the corresponding search times are 0.004 s, 0.016 s, and 0.036 s, respectively. Compared to the baseline PPJion+, the query times are reduced by 99.7%, 98.8%, and 97.6%, respectively. Full article

Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises’ own technology. Due to the cost of patent research and development and the risk of infringement, this paper constructs a product innovation identification path that integrates the FOS and BERTopic model from the perspective of invalid patents. The path consists of three stages, including the identification of the problem to be solved by the product based on functional analysis, the extraction of the subject matter elements based on the core failed patent technology, and the generation and evaluation of innovative solutions based on TRIZ theory and the best- worst method (BWM). Finally, the feasibility of the path constructed in this paper is verified by taking a slurry pump as an example. The application results show that the product innovation identification path constructed in this paper can provide theoretical support for enterprises to carry out technological innovation activities efficiently. Full article

In this experimental work, the microbiological, physicochemical, organoleptic, and rheological properties of yoghurts produced by ultrafiltered goat’s milk using two volume-reduction ratios and three probiotic starters were studied. It was established that the dry matter, fats, proteins, count of lactic acid bacteria, titratable acidity, and dynamic viscosity increased and the pH decreased with the rise of the volume-reduction ratio during ultrafiltration. All yoghurts exhibited Bingham plastic flow behaviour. We recommend using a volume-reduction ratio of 3 and MZ₂f + Bifidobacterium bifidum BB-87 to produce probiotic Bulgarian yoghurts with the highest dry matter contents (23.02%), protein contents (10.20%), fat contents (9.80%), number of viable lactic acid cells (9.34 logN), viscosity (4.99 Pa·s at shear rate of 1.22 s⁻¹), and organoleptic properties and the highest score (15) in the range of this experiment. Full article

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level. Full article

Equinus and equinovarus foot deviations (EVFD) are the most frequent lower limb acquired deformities in stroke survivors. We analysed the contribution that the tibialis anterior (TA), extensor digitorum longus (EDL) and plantarflexor muscles play in EVFD via a biomechanical musculoskeletal model of the ankle–foot complex. Our model was composed of 28 bones (connected by either revolute joints or bone surface contacts), 15 ligaments (modelled as non-linear springs), and 10 muscles, modelled as force actuators. Different combinations of muscle contractions were also simulated. Our results demonstrate that, compared to the condition when the foot is suspended off the ground, the contraction of the TA alone produces dorsiflexion (from −18° to 0°) and a greater supination/inversion (from 12° to 30°). The EDL alone produces dorsiflexion (from −18° to −6°), forefoot pronation (25°) and calcaneal eversion (5.6°). Only TA and EDL synergistic action can lead the foot to dorsiflexion suitable for most daily life activities (≥20°) without any deviation in the frontal plane. When pathological contractures of the plantarflexor muscles were simulated, foot deformities reproducing EVFD were obtained. These results can be relevant for clinical applications, highlighting the importance of EDL assessment, which may help to design appropriate functional surgery and plan targeted rehabilitation treatments. Full article

The Madonna with Child by Andrea Mantegna owned by the Museo Poldi Pezzoli in Milan is painted on canvas with an unusual distemper technique. During the period of 1863–1865, the painting was restored by Giuseppe Molteni. The identification of potential retouchings by Molteni, possibly covering part of the original layer, was the object of this work carried at the Opificio delle Pietre Dure. To evaluate the extent of both Molteni’s intervention and Mantegna’s original layer, the MA-XRF spectrometer developed by CHNet-INFN was used to discriminate between the two paint layers and identify the materials and the extension of both “artists”. Indeed, the elemental maps showed that Molteni’s work entirely covered the mantle of the Virgin, even changing the fold of the draperies and enriching the red robe with shell gold highlights, giving a different appearance to the painting. Moreover, MA-XRF also revealed that the original Mantegna was still mostly intact underneath Molteni’s layer, thereby providing a decisive guide for conservation works. These results indeed formed the basis for the technical decision to remove the varnish and Molteni’s version, unveiling the original Mantegna. A second MA-XRF campaign was then carried out to fully characterise the materials of this unusual painting technique. Full article

Surface water quality assessment is an important component of environmental protection and sustainable development. In this study, 24 sampling sites were arranged in the Wanquan River area of Hainan Island, China, in 2021, and nine water quality indicators were measured. The water quality of the Wanquan River was assessed using the single factor pollution index method and the Nemerow pollution index method; the spatial distribution characteristics of pollutants were revealed, and the sources of pollution were further analyzed using factor analysis. The results show that the overall water quality of the Wanquan River basin is good, with the average values of all indicators meeting China’s Class III water quality standards. The results of the single factor pollution index method showed that 29% of the sampling sites were in the no pollution class, 38% in the slight pollution class, 25% in the light pollution class, and 8% in the moderate pollution class. The results of the Nemerow pollution index showed that 25% of the sampling sites were in the clean category, 17% in the cleaner category, 42% in the light category, and 17% in the moderate category. The results of the factor analysis show that agricultural activities and domestic sewage discharge are the main sources of pollution, with nitrogen and phosphorus being the most important factors affecting water quality. This paper proposes several measures to reduce water pollution in the Wanquan River, including improving agricultural activities, improving wastewater treatment, and strengthening environmental monitoring. The findings have practical implications for reducing water pollution in rivers and lakes and can provide a reference for policy decisions related to water resource management and environmental protection. Full article

The scoliosis report is a diagnosis made by the clinician looking at X-ray images of the spine. However, with numerous images, writing the report can be time-consuming and error-prone. Therefore, this paper proposes an automatic generation model of the end-to-end scoliosis Lenke classification report. The model automatically generates a short diagnostic text to explain the results of the classifiers’ Lenke classification diagnosis of scoliosis. Instead of reproducing the original diagnostic report, the original diagnostic report is described as a short sentence with diagnostic significance. In the model, the CBAM attention module is added to the residual’s path of ResNet-50 to extract key regional features of the image, and the improved Long Term and Short Term Memory Network (M-LSTM) fusion attention mechanism with additional gated operations is used as the decoder to generate more relevant description statements. The model was verified on the scoliosis dataset from Guizhou Orthopaedic Hospital, and the generated diagnostic text obtained good scores on BLEU and CIDEr evaluation indexes, and also satisfactory scores on the evaluation criteria of five professional clinicians. Therefore, the diagnostic text generated by this method had good performance in accuracy and semantic expression. Full article

White mustard plant (Sinapis alba L.) is an easy-to-grow species with low soil requirements and is often sown as a catch crop in Northern Europe to reduce nitrate leaching, especially during the winter. There are studies showing the high nutritional value of mustard seeds, which have a wide application, mainly in food production. Still little is known about the young shoots or plants of different cultivars of white mustard, although in Asian countries, eating them raw is quite common. The aim of the research was to determine the proximate composition, antioxidant activity and polyphenolic compound content in young green plants of the Polish cultivars of white mustard: Borowska (traditional, with a high content of erucic acid and glucosinolates), Bamberka (non-erucic with glucosinolates) and Warta (non-erucic with low glucosinolates content; double-improved). Young plants were harvested in three terms. The first harvest took place at the plover stadium and the next ones at 7-day intervals (31, 38 and 45 day after sowing). In freeze-dried plant material, proximate composition and antioxidant activity with the ABTS and FRAP methods, as well as phenolic compound content, were measured. The highest concentration of protein was measured in cultivars Warta and Borowska after 31 and 38 days of sowing. Harvest time and cultivar affected antioxidant activity and total polyphenol content in young mustard plants. Thirty-eight days after sowing, the examined cultivars of the young plants of mustard had the highest antioxidant activity and total polyphenolic compound content. Green young mustard plants have strong antioxidant properties at the basic level, they are classified as functional foods and are similar to other edible leafy plants such as celery, spinach and Brussels sprouts. Full article

Conventional ship mooring in ports has many shortcomings such as a high safety risk, low efficiency and high labor intensity. In order to explore and develop the theory and key technologies of intelligent automatic mooring systems, this paper takes an intelligent mooring system based on a parallel anti-rolling mechanism as the research and development object. A new mooring method integrating ship hydrodynamics, mechanism kinematics and intelligent algorithms is proposed. Through numerical simulation and comparative analysis of the model, the motion inhibition effect of mooring ships under different working conditions is obtained. The results show that the control strategy and intelligent algorithm of the system can realize the active control of the wharf mooring ships and achieve the goal of improving wharf stability conditions through an intelligent mooring system. Full article

Thermal discomfort body language has been shown to be a psychological representation of personnel’s particular thermal comfort. Individual thermal comfort differences are ignored in public building settings with random personnel flow. To solve this issue, we suggested a Bayesian group thermal dissatisfaction rate prediction model based on thermal discomfort body language expression and subsequently implemented intelligent indoor temperature and humidity control. The PMV-PPD model was utilized to represent the group’s overall thermal comfort and to create a prior distribution of thermal dissatisfaction rate. To acquire the dynamic distribution of temperature discomfort body language, data on thermal discomfort body language expression were collected in a real-world office setting experiment. Based on Bayesian theory, we used personalized thermal discomfort body language expressions to modify the group’s universal thermal comfort and realized the assessment of the thermal dissatisfaction rate by combining commonality and personalization. Finally, a deep reinforcement learning system was employed to achieve intelligent indoor temperature and humidity control. The results show that when commonality and personalized thermal comfort differences are combined, real-time prediction of thermal dissatisfaction rate has high prediction accuracy and good model performance, and the prediction model provides a reference basis for reasonable indoor temperature and humidity settings. Full article

Resolution and mapping bandwidth are the two most important image performance indicators that reflect satellite synthetic aperture radar (SAR) imaging reconnaissance capability. The PRI-staggered signal can simultaneously achieve high resolution in azimuth and wide swath during SAR imaging, and is an important signal form of SAR. It is important for anti-SAR reconnaissance to de-interleave the staggered PRI signal from the mixed signals. To address the problem that the existing staggered signal de-interleaving algorithms cannot accommodate PRI jitter and are computationally inefficient, this paper proposes an efficient algorithm for de-interleaving staggered PRI signals. A clustering-based square sine wave interpolation method and a threshold criterion are proposed, improving computational efficiency while suppressing interference between sub-PRIs and the frame period of the staggered PRI signal. In addition, a sequence retrieval algorithm incorporating matched filter theory is proposed to improve the separation accuracy of radar pulse sequences. The simulation shows that the novel algorithm can adapt to PRI jitter and de-interleave staggered PRI signals from mixed signals with high efficiency. Compared with the existing staggered signal de-interleaving algorithm, the computational efficiency is improved by an order of magnitude. Full article

The study of fractures in the subsurface is very important in unconventional reservoirs since they are the main conduits for hydrocarbon flow. For this reason, a variety of equivalent medium theories have been proposed for the estimation of fracture and fluid properties within reservoir rocks. Recently, the Galvin model has been put forward to model the frequency-dependent elastic moduli in fractured porous rocks and has been widely used to research seismic wave propagation in fractured rocks. We experimentally investigated the feasibility of applying the Galvin model in fractured tight stones. For this proposal, three artificial fractured tight sandstone samples with the same background porosity (11.7% ± 1.2%) but different fracture densities of 0.00, 0.0312, and 0.0624 were manufactured. The fracture thickness was 0.06 mm and the fracture diameter was 3 mm in all the fractured samples. Ultrasonic P- and S-wave velocities were measured at 0.5 MHz in a laboratory setting in dry and water-saturated conditions in directions at 0°, 45°, and 90° to the fracture normal. The results were compared with theoretical predictions of the Galvin model. The comparison showed that model predictions significantly underestimated P- and S- wave velocities as well as P-wave anisotropy in water-saturated conditions, but overestimated P-wave anisotropy in dry conditions. By analyzing the differences between the measured results and theoretical predictions, we modified the Galvin model by adding the squirt flow mechanism to it and used the Thomsen model to obtain the elastic moduli in high- and low-frequency limits. The modified model predictions showed good fits with the measured results. To the best of our knowledge, this is the first study to validate and calibrate the frequency-dependent equivalent medium theories in tight fractured rocks experimentally. Full article

In recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this issue, this paper proposes a Generative Adversarial Network (GAN) model comprising a filter-enhanced Multi-Layer Perceptron (MLP) generator and a linear discriminator to mitigate bias and improve the accuracy of the recommendation. The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. This adversarial process enables each component to optimize and improve itself while strengthening the correction effect. To enhance the accuracy of G generation, we evaluate the influence of three different input lists on the filter effect. Finally, we validate our model on two real-world datasets by comparing the effect of filter-augmented MLP and pure MLP generators. Our results demonstrate the effectiveness of filters, and our model achieves better recommendation accuracy than other baseline models. Full article

The sideslip angle is crucial for the lateral stability state and stability control of intelligent commercial vehicles. However, sensors that can be used for direct measurements are often complex, expensive, and difficult to install in commercial vehicles. To estimate the vehicle sideslip angle, a state observer derived from the extended Kalman filter (EKF) method is proposed, and the state observer is estimated based on steering torque rather than steering angle. The transfer functions between the sideslip angle–steering torque and sideslip angle–steering angle are established, respectively, and the analysis shows that the steering torque signal has a more rapid and more direct reaction due to the hydraulic pressure in the steering system. Finally, the proposed method is validated using Simulink/TruckSim simulation hardware-in-the-loop bench test, and the results show that the proposed method can accurately reflect the actual state of the sideslip angle with good reliability and effectiveness. Full article

It is of great practical importance to study the vibration response characteristics of super high-rise buildings under an earthquake action to provide a basis for seismic design and later maintenance of structures in coastal areas. During this study, the Shanghai World Financial Center (SWFC)’s health monitoring system was utilized to monitor earthquakes of magnitude 6.4 in Taiwan, 6.0 in Japan, 7.2 in the East China Sea, and 4.4 in Jiangsu, in real-time. Through the improved Envelope Random Decrement Technique (E-RDT), the dynamic properties of super high-rise buildings were examined under different earthquake effects in terms of the acceleration power spectrum, natural frequency, damping ratio, and mode shape. The results demonstrated that (1) the vibration responses of the structure in X (East–West) and Y (North–South) directions under four earthquakes were consistent, and with increasing floor height, the discreteness of the amplitude and acceleration signals of vibration responses increased. (2) The first two natural frequencies of the structure in X and Y directions decreased with the increase in amplitude, but the damping ratio increased with the increase in amplitude. The minimum values of the first two natural frequencies are 0.1498 Hz and 0.4312 Hz, respectively, and the maximum values of the first two damping ratios are 0.0086 and 0.0068, respectively. (3) Under different earthquake excitations, the SWFC’s mode shape’s estimates were similar, and their change trends in the X and Y directions were nonlinear as the number of floors increased. The structure was not seriously damaged by the four earthquakes. This study can provide helpful information for the seismic design of super high-rise buildings based on its findings. Full article

As an important research tool in neuroscience, event-related potential (ERP) technology enables in-depth analysis of the consumer’s product image cognition process and complements and verifies the product image cognition model at the ERP level. It provides an important theoretical basis for systematically capturing product image and improvement of the product image cognitive model. In this work, the correlation between ERP data, product image word pairs and the degree of semantic match with the product is investigated, and a support vector machine algorithm is selected to build a classification model with physiological data (behavioral data + ERP data) as the independent variable and the degree of semantic match with the product image as the dependent variable. By adjusting the model parameters, the final classification accuracy reaches 95.667%, which shows that the model has some reliability and is a viable research method for ERP-based product image researchers in the future. Full article

The COVID-19 infectious disease spread in the world represents, by far, one of the most significant moments in humankind’s recent history, affecting daily activities for a long period of time. The data available now allow important modelling developments for the simulation and prediction of the process of an infectious disease spread. The current work provides strong insight for estimation and prediction mathematical model development with emphasis on differentiation between three distinct methods, based on data gathering for Romanian territory. An essential aspect of the research is the quantification and filtering of the collected data. The current work identified five main categories considered as the model’s inputs: inside temperatures (°C), outside temperatures (°C), humidity (%), the number of tests and the quantified value of COVID-19 measures (%) and, as the model’s outputs: the number of new cases, the number of new deaths, the total number of cases or the total number of deaths. Three mathematical models were tested to find the optimal solution: transfer vector models using transfer functions as elements, autoregressive-exogenous (ARX) models, and autoregressive-moving-average (ARMAX) models. The optimal solution was selected by comparing the fit values obtained after the simulation of all proposed models. Moreover, the manuscript includes a study of the complexity of the proposed models. Based on the gathered information, the structure parameters of the proposed models are determined and the validity and the efficiency of the obtained models are proven through simulation. Full article

This study aims to evaluate the impact of the distributed generator (DG) connection to the grid. The simulated results present the parameters of the system required to install DG on the end of the main distribution feeder. Various parameters, such as voltage, current, and protective relay coordination are modelled after the actual provincial electricity authority (PEA) distribution system. Various case studies compared the coordination without and with DG connections to the grid by finding the difference of protective devices. The results indicate that the malfunction can be fixed in order of priority protective devices, which operate according to the parameter setting. Additionally, the coordinate functions between the recloser and fuse devices in both phase and ground configurations in the operating zone prevented the drop-out fuse melting or burning out. Based on the result, this problem is fixed by providing a directional recloser device and increasing the fuse-link rated with 40k installation for replacing the conventional sizing, which can improve the performance in case of fault occurrence to investigate the reliability and stability of the distribution system. Full article

Eccentric resistance training that focuses on the lengthening phase of muscle actions has gained attention for its potential to enhance muscle strength, power, and performance (among others). This review presents a methodological proposal for classifying eccentric exercises based on complexity, objectives, methods, and intensity. We discuss the rationale and physiological implications of eccentric training, considering its benefits and risks. The proposed classification system considers exercise complexity and categorizing exercises by technical requirements and joint involvement, accommodating various skill levels. Additionally, training objectives are addressed, including (i) Sports Rehabilitation and Return To Sport, (ii) Muscle Development, (iii) Injury Prevention, (iv) Special Populations, and (v) Sporting Performance, proposing exercise selection with desired outcomes. The review also highlights various eccentric training methods, such as tempo, isoinertial, plyometrics, and moderate eccentric load, each with different benefits. The classification system also integrates intensity levels, allowing for progressive overload and individualized adjustments. This methodological proposal provides a framework for organizing eccentric resistance training programs, facilitating exercise selection, program design, and progression. Furthermore, it assists trainers, coaches, and professionals in optimizing eccentric training’s benefits, promoting advancements in research and practical application. In conclusion, this methodological proposal offers a systematic approach for classifying eccentric exercises based on complexity, objectives, methods, and intensity. It enhances exercise selection, program design, and progression in eccentric resistance training according to training objectives and desired outcomes. Full article

Active debris removal (ADR) is positioned by space agencies as an in-orbit task of great importance for stabilizing the exponential growth of space debris. Most of the already developed capturing systems are designed for large specific cooperative satellites, which leads to expensive one-to-one solutions. This paper proposed a versatile hybrid-compliant mechanism to target a vast range of small uncooperative space debris in low Earth orbit (LEO), enabling a profitable one-to-many solution. The system is custom-built to fit into a CubeSat. It incorporates active (with linear actuators and impedance controller) and passive (with revolute joints) compliance to dissipate the impact energy, ensure sufficient contact time, and successfully help capture a broader range of space debris. A simulation study was conducted to evaluate and validate the necessity of integrating hybrid compliance into the ADR system. This study found the relationships among the debris mass, the system’s stiffness, and the contact time and provided the required data for tuning the impedance controller (IC) gains. This study also demonstrated the importance of hybrid compliance to guarantee the safe and reliable capture of a broader range of space debris. Full article

The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through blood vessels, facilitating the transportation of oxygen within the bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health conditions. In this study, we propose a novel approach to determine RBC elasticity by analyzing video recordings and using a convolutional neural network (CNN) for classification. Due to the scarcity of available blood flow recordings, computer simulations based on a numerical model are employed to generate a substantial amount of training data. The simulation model incorporates the representation of RBCs as elastic objects within a fluid flow, allowing for a detailed understanding of their behavior. We compare the performance of different CNN architectures, including ResNet and EfficientNet, for video classification of RBC elasticity. Our results demonstrate the potential of using CNNs and simulation-based data for the accurate classification of RBC elasticity. Full article

Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical environments may not always be impeccably accurate. In this paper, we investigate whether the presence of noise in ground truth data can be mitigated. We propose an innovative and efficient approach that addresses the challenge posed by noise in segmentation labels. Our method consists of four key components within a deep learning framework. First, we introduce a Vision Transformer-based modified encoder combined with a convolution-based decoder for the segmentation network, capitalizing on the recent success of self-attention mechanisms. Second, we consider a public CT spine segmentation dataset and devise a preprocessing step to generate (and even exaggerate) noisy labels, simulating real-world clinical situations. Third, to counteract the influence of noisy labels, we incorporate an adaptive denoising learning strategy (ADL) into the network training. Finally, we demonstrate through experimental results that the proposed method achieves noise-robust performance, outperforming existing baseline segmentation methods across multiple evaluation metrics. Full article

In recent years, polyethylene (PE) pipes have been widely utilized for urban natural gas transmission. However, as the use of high-density polyethylene (HDPE) pipes increases, their service life and long-term performance assessment have become one of the most significant issues to be addressed. There has been a ton of studies on PE pipe life prediction techniques both domestically and internationally, but very little has been carried out on PE pipe life prediction in actual gas working environments with varying acid and alkaline levels. This experiment accelerates the aging of PE pipes using acid and alkaline corrosion immersion experiments to determine the lifespan of PE pipes. This study aims to investigate the performance changes of HDPE under strong, weak, and neutral corrosion conditions using corrosion solutions with PH values of 1, 5, and 8, to propose the impact of corrosion caused by various acids and alkalies on the HDPE aging life for natural gas, and to develop a mathematical model between the aging life of polyethylene and the PH values of acid and alkali corrosion solutions. The studies involved soaking and corroding HDPE pipes with various acidity and alkalinity chemicals to speed up the aging process, and then the tensile test was used to determine the mechanical characteristics of the aged PE pipes. Based on our findings, the empirical equation between acidity and service life of PE pipes is obtained by the mathematical fitting method, and a life prediction model of buried city gas HDPE pipes is proposed. The actual life of the aged pipes is determined by the relationship between strain-hardening (SH) modulus and aging time. The findings demonstrate that the service life of PE pipes changes with different levels of acidity and alkalinity: 1.872 days, 1060.507 days, and 1128.58 days following corrosive solution-accelerated aging with solution acidities of PH1, PH5, and PH8, respectively. The life prediction method applies to various plastic pipes in comparable environments as well as HDPE city gas pipes that are subject to acid and alkali corrosion forces. Full article