Appl. Sci., Volume 13, Issue 14 (July-2 2023) – 566 articles

Synthetic fuels significantly reduce pollutant emissions and the carbon footprint of ICE applications. Among these fuels, oxymethylene dimethyl ethers (OME_X) are an excellent candidate to entirely or partially replace conventional fuels in compression ignition (CI) engines due to their attractive properties. The very low soot particle formation tendency allows the decoupling of the soot-NO_X trade-off in CI engines. In addition, innovative piston geometries have the potential to reduce soot formation inside the cylinder in the late combustion stage. This work aims to analyze the potential of combining OME_X with an innovative piston geometry to reduce soot formation inside the cylinder. In this way, several blends of OME_X-Diesel were tested using a radial-lips bowl geometry and a conventional reentrant bowl. Tests were conducted in an optically accessible engine under simulated EGR conditions, reducing the in-cylinder oxygen content. For this purpose, 2-colour pyrometry and high-speed excited state hydroxyl chemiluminescence techniques were applied to trace the in-cylinder soot formation and oxidation processes. The results confirm that increasing OME_X in Diesel improves the in-cylinder soot reduction under low oxygen conditions for both piston geometries. Moreover, using radial lips bowl geometry significantly improves the soot reduction, from 17% using neat Diesel to 70% less at the highest OME_X quantity studied in this paper. Full article

This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents. Full article

Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification. Full article

At present, the induction motor (IM), synchronous reluctance motor (SynRM), ferrite-assisted synchronous reluctance motor (ferrite-assisted SynRM) and interior permanent magnet motor (IPM) are research hotspots, but comprehensive comparative research on the four motors is still rare. This paper mainly compares the four motors from the aspects of electromagnetic performance, material cost and temperature distribution. Firstly, the volume of the four motors is ensured to be the same. The influence of the rotor design parameters of the SynRM, ferrite-assisted SynRM and IPM on the electromagnetic properties of the machine is analyzed. Secondly, based on the effects of each parameter, the overall design parameters of the four motors are determined. The electromagnetic performance, material cost and temperature of the four motors are compared and discussed. Finally, the comparison results are summarized, and the advantages of the four motors are analyzed. In different applications, the electromagnetic performance, heat dissipation and cost requirements of the four motors are different. Therefore, this paper makes a comprehensive comparison of the four motors to provide a reference for the selection of motors for different applications. Full article

As a novel biological computing device, the Spiking Neural P system (SNPS) has powerful computing potential. The application of SNPS in the field of arithmetic operation has been a hot research topic in recent years. Researchers have proposed methods and systems for implementing basic arithmetic operations using SNPS. This paper studies four basic arithmetic operations, improves the parallelization of addition and multiplication methods, and designs more effective natural number addition and multiplication SNPS, as well as SNPS for subtraction and for division of natural numbers based on multiple subtractions. The effectiveness of the proposed SNPS is verified by example. Compared with the same kind of SNPS, for the addition operation the number of neurons used in our system is reduced by 50% and the time overhead is reduced by 33%, while for the multiplication operation the number of neurons is reduced by 40%. Full article

Stargardt disease is the most common form of juvenile-onset macular dystrophy. Spectral-domain optical coherence tomography (SD-OCT) imaging provides an opportunity to directly measure changes to retinal layers due to Stargardt atrophy. Generally, atrophy segmentation and prediction can be conducted using mean intensity feature maps generated from the relevant retinal layers. In this paper, we report an approach using advanced OCT-derived features to augment and enhance data beyond the commonly used mean intensity features for enhanced prediction of Stargardt atrophy with an ensemble deep learning neural network. With all the relevant retinal layers, this neural network architecture achieves a median Dice coefficient of 0.830 for six-month predictions and 0.828 for twelve-month predictions, showing a significant improvement over a neural network using only mean intensity, which achieved Dice coefficients of 0.744 and 0.762 for six-month and twelve-month predictions, respectively. When using feature maps generated from different layers of the retina, significant differences in performance were observed. This study shows promising results for using multiple OCT-derived features beyond intensity for assessing the prognosis of Stargardt disease and quantifying the rate of progression. Full article

A scientifically accurate assessment of tunnel health is the prerequisite for the safe maintenance and sustainability of the in-service road tunnel. The changing trend of tunnel health is not considered in existing research. Most evaluation indicators are static and the ambiguities or randomness at the boundary of the health level intervals is neglected in most evaluation methods. In this paper, the evaluation index system combined with dynamic, and static is set to solve these problems. The changing trend of the health state of tunnels is analyzed through the cubic b-spline function. The weights of evaluation indicators are calculated based on the AHP-improved entropy method. The health evaluation method is proposed based on combing the extension theory and the cloud model improved by the cloud entropy optimization algorithm. Finally, the evaluation results of the proposed method are compared with the detection data of the Beilongmen Tunnel of Zhangzhuo Expressway. The results demonstrate that 80% of the evaluation results in the sample tunnel data are consistent with the standard results, and the remaining 20% show a lower level of health than the standard results. This reflects the evaluation results are impacted by the trend of tunnel health status changes. The health state can be more accurately evaluated by dynamic indicators. The improved extension cloud model is feasible and applicable in the health assessment of tunnels. This work provides innovative ideas for the evaluation of the health state of tunnels and theoretical support for the formulation of reasonable maintenance measures. Full article

In this study, a comprehensive and realistic model of laser light interaction with skin and hair was constructed. The model was applied to study the characteristics of laser-tissue interaction for the deeply penetrating Nd:YAG laser. Three types of finite element method (FEM) models were developed. In the first model, the hair shaft grew straight out of the follicle; in the second model, it grew at a variable angle; and in the third model, an array of hair was considered. The transport equation and heat diffusion equation were solved with the mesh-based Monte Carlo method and partial differential equations, respectively. The results of the simulations indicated that the area of necrosis increased with increasing fluence; cooling had a limited effect on the extent of necrosis, particularly at a fluence of 80 J/cm². The thermal damage to hair follicles on the periphery of an irradiated array of hair may be insufficient for achieving necrosis. The pulse itself and the short cooling-down period after the pulse contributed the most to the final thermal damage to the hair follicle. The FEM modeling of laser-tissue interaction has proven to be a useful tool for studying the influence of different therapeutic parameters on the resulting hair and skin damage. Full article

Modeling the D and T fluxes in Fusion Neutron Source based on a tokamak fuel cycle systems was performed consistently with the core and divertor plasma. An indirect integration of ASTRA, SOLPS4.3, and FC-FNS codes is used. The feedback coupling is realized between the pumping and puffing systems in the form of changes in the isotopic composition of the core and edge plasma. In the ASTRA code, instead of electrons, ions were used in the particle transport equations. This allows better estimates of the flows of the D/T components of the fuel that have to be provided by the gas puffing and processing systems. The particle flows into the plasma from pellets, required to maintain the target plasma density <n_e> = (6–8) × 10¹⁹ m⁻³ are 10²² particles/s. In the majority of the working range of parameters, additional ELM stimulation is necessary (by ~1-mm³-size pellets from the low magnetic field side) in order to maintain the controlled energy losses at the level δW_ELM~0.5 MJ. For the starting load of the FC and steady-state operation of the facility, up to 500 g of tritium are required taking into account the radioactive decay losses. Full article

Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly imbalanced and have regularization to use real-time patient data due to privacy concerns. Traditional models do not generalize on unseen cases and are also unable to preserve data privacy. Which incentivizes performance degradation in existing models with privacy limitations. To tackle generalization and privacy issues together, we introduce the framework SF-ECG, a source-free domain adaptation approach for patient-specific ECG classification. This framework does not require source data during adaptation, which solves the privacy issue during adaptation. We adopt a generative model (GAN) that learns to synthesize patient-specific ECG data in data-inefficient classes to make additional source data for imbalanced classes. Then, we use the local structure clustering method to strongly align target ECG features with similar neighbors. After seizing clustered target features, we use a classifier that is trained on source data with generated source samples, which makes the model generalizable in classifying unseen data. Empirical results under different experimental conditions in various interdomain datasets prove that the proposed framework achieves 0.8% improvements in UDA settings, along with preserving privacy and generalizability. Full article

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature fusion text classification model called CAC, based on the Combination of CNN and Attention. The model adopts the idea of first extracting local features and then calculating global attention, while drawing inspiration from the interaction process between membranes in membrane computing to improve the performance of text classification. Specifically, the CAC model utilizes the local feature extraction capability of CNN to transform the original semantics into a multi-dimensional feature space. Then, global attention is computed in each respective feature space to capture global contextual information within the text. Finally, the locally extracted features and globally extracted features are fused for classification. Experimental results on various public datasets demonstrate that the CAC model, which combines CNN and Attention, outperforms models that solely rely on the Attention mechanism. In terms of accuracy and performance, the CAC model also exhibits significant improvements over other models based on CNN, RNN, and Attention. Full article

The inelastic response of reinforced concrete (RC) frames under seismic loading is influenced by mechanical and geometrical properties and by the reinforcement arrangement of the beam–column members. In this paper, the seismic response and recentering behavior of RC frames is investigated numerically via cyclic pushover analysis and described by means of three synthetic behavioral indexes, namely a recentering index, a hardening index, and a ductility index. A fiber–hinge formulation is used to describe the inelastic behavior of the RC elements, and the versatile pivot hysteresis model is implemented at the material level to capture the possible pinching effects ascribed to the weak transverse reinforcement and to poor construction details that might be observed in the existing RC structures. This model is first validated against the experimental results from the literature and then applied, within a wide parametric study, to a set of 80 RC frame scenarios featured by various combinations of axial load levels and reinforcing details. As the output of this parametric study, practical design abacuses are constructed to describe the trends of the above-mentioned behavioral indexes, which are usefully related to specific mechanical and loading features of the analyzed RC frames. The reliability of the obtained results and the usefulness of the constructed abacuses in anticipating the overall cyclic behavior of a generic RC building, depending on the actual mechanical parameters of the RC sections at each story level, is finally demonstrated through a nonlinear time history analysis of an eight-story RC frame, representative of the substandard RC frames built in the 1970s in Italy. Full article

The complexity of modern power systems is increasing because of the development of various intermittent generators. In practical reliability evaluations, it is essential to include both the failure of conventional generators and the output characteristics of renewable energy; the use of the latter has increased rapidly. The weather-dependent nature of renewable energy output, which is inexplicable in the load duration curve method, highlights the need for further study of the methods of a reliability evaluation that can consider temporal characteristics. This paper proposes a deterministic reliability evaluation method based on the Booth–Baleriaux method, chronologically extended to address the preventative maintenance schedule of a generator and the characteristics of renewable energy. The proposed method was applied to an IEEE reliability test system for performance verification, and a reliability evaluation was performed considering various chronological patterns. The proposed method was also applied to determine the adequate capacity reserve that should be installed in a Korean power system. The proposed method is stable, and it produced robust results. Full article

Industry 4.0 will not only change what we are and what we do, but also who we are. As a result of the rapid introduction of new technologies, which is characteristic for Industry 4.0, there will be a change in the labour market. It allows people, things and machines to be connected in real time, thus ensuring that the necessary information is exchanged between them. There are advantages, but also negatives: one of the impacts of Industry 4.0 is the gradual transformation of the labour market, leading to a demand for new professional skills and the digitalisation of work. Thus, it brings with it the need for employees to adapt to the changing sub-conditions of the labour market. The aim of this article is to identify and highlight the need for changes in the field of competences in connection with the application of Industry 4.0 methods and techniques. For the purpose of this research, a valid data collection instrument (questionnaire for the research) was developed and distributed to enterprises in the field of selected industry sectors in the Slovak Republic. In total, the research sample consisted of n = 556 respondents. On the basis of the obtained results, we conclude that early identification of future needs in terms of competences gives the possibility of setting up training activities aimed at acquiring new, missing and needed-for-the-future competences of employees. Full article

The main treatment approaches for sudden sensorineural hearing loss (SSNHL) involve oral and intratympanic corticosteroids, but their efficacy remains controversial. The study objective was to evaluate the benefit of oral corticosteroids followed by intratympanic salvage treatment. This was conducted by comparing the hearing results of post-treatment patients arriving early and pretreatment patients arriving late over the same time points after the onset of HL. A cohort of 776 patients with SSNHL was classified into four groups by time from onset of symptoms to the initiation of treatment (weeks). The post-treatment audiometry of those patients presenting during the first and second week post-HL was compared to the pretreatment audiometry of those presenting in weeks three and four. The post-treatment audiometry of week one and pretreatment audiometry of week three was conducted 17.2 ± 4 and 19.4 ± 3 (p = 0.13) days post-HL onset, respectively. The post-treatment audiometry of week two and pretreatment audiometry of week four was conducted on days 24.6 ± 4 and 25.2 ± 3 (p = 0.32). The pure-tune average for week one and three groups was 36.7 ± 28 and 37.5 ± 19 dB (p = 0.55), and for weeks 2 and 4, it was 31.7 ± 22 and 36.6 ± 23 dB (p = 0.1). Similarly, no significant differences in speech recognition threshold and speech discrimination were found. These results question the benefit of corticosteroid treatment for SSNHL and suggest that improvements may be due to the natural healing process. Full article

The exterior location of a user can be accurately determined using a global positioning system (GPS). However, accurately locating objects indoors poses challenges due to signal penetration limitations within buildings. In this study, an MLP with stochastic gradient descent (SGD) among artificial neural networks (ANNs) and signal strength indicator (RSSI) data received from a Zigbee sensor are used to estimate the indoor location of an object. Four fixed nodes (FNs) were placed at the corners of an unobstructed area measuring 3 m in both length and width. Within this designated space, mobile nodes (MNs) captured position data and received RSSI values from the nodes to establish a comprehensive database. To enhance the precision of our results, we used a data augmentation approach which effectively expanded the pool of selected cells. We also divided the area into sectors using an ANN to increase the estimation accuracy, focusing on selecting sectors that had measurements. To enhance both accuracy and computational speed in selecting coordinates, we used B-spline surface equations. This method, which is similar to using a lookup table, brought noticeable benefits: for indoor locations, the error margin decreased below the threshold of sensor hardware tolerance as the number of segmentation steps increased. By comparing our proposed deep learning methodology with the traditional fingerprinting technique that utilizes a progressive segmentation algorithm, we verified the accuracy and cost-effectiveness of our method. It is expected that this research will facilitate the development of practical indoor location-based services that can estimate accurate indoor locations with minimal data. Full article

In recent years, there has been a surge of Metaverse applications and tools striving to capture the attention of both the general public and businesses, with a particularly strong potential within the tourism sector. However, there has been significant criticism towards major corporations for marketing a concept of the Metaverse that fails to align with reality. On the other hand, smaller entities such as Spatial-io, which is an innovative metaverse platform, are introducing a different style of the Metaverse, one that is highly accessible from contemporary devices like smartphones, tablets, VR headsets, and traditional PCs via WebXR platforms. This article delves into and scrutinizes various methodologies of a tourism-oriented Metaverse, considering its prospective utility as a vehicle to attract more visitors. A virtual tourist information center was established on the Spatial-io Metaverse platform to promote Valencia, Spain. This research scrutinizes the navigation, accessibility, and usability of the service from a conventional PC browser, contrasting it with the experience offered by the Meta Quest 2 virtual reality headset. The study’s quantitative and qualitative data analysis indicates that these innovative services are highly regarded, particularly when a real person (not a bot) provides information, fostering trust and offering details about various tourist attractions within the promoted city. The comparison of user inquiries’ time and depth aligns with the immersion level, demonstrating more positive feedback when the service is accessed through the VR system rather than a standard PC browser. Full article

Based on the analysis of the waste heat distribution characteristics of a typical ship two-stroke low-speed main engine (model: MAN 8S65ME-C8.6HL, the specified maximum continuous rating SMCR: 21,840 kW) under different loads, two different types of organic Rankine cycle (ORC) systems, namely the basic system (BORC) and the preheated system(PORC), were constructed to recover the ship main engine’s exhaust gas waste heat and jacket cooling water waste heat. Using the thermodynamic simulation model of the system, the main performance indexes, including net output power of the two ORC systems were studied with the variation of seawater temperature and main engine load, and the annual ship fuel saving and annual carbon emission reduction generated by the two systems were compared and analyzed. It was found that the maximum net output power of the BORC system and PORC system were 445.3 kW and 491.3 kW, respectively, when the ship’s main engine load was 100%, and the outboard seawater temperature was 20 °C; the maximum thermal efficiency was 12.84% and 12.71%, respectively; under the annual operation, the fuel saving of BORC system and PORC system can be 456 tons and 510 tons, respectively, and the carbon emission reduction was 1416 tons and 1581 tons, respectively. The analysis found that the net output power of the PORC system is always greater than that of the BORC system. When the outboard seawater is lower, and the main engine load is more than 80%, the net output power difference between the PORC system and BORC system gradually expands, and the improvement of ORC system performance is more evident by adding a preheater. It can be concluded that when the ship was mainly operated in the sea area with low seawater temperature and the main engine was running under high load most of the time, selecting the PORC system to recover the waste heat of the main engine was more advantageous. Full article

The influence of hafnium metal (Hf) and sulfate ions (${\mathrm{S}\mathrm{O}}_4^{2-}$) on the acidic properties of SiO₂ mesopores synthesized by a non-hydrothermal method was studied using the following characterization techniques; TG-DTG, XRD, BET, SEM, TEM, EDS, FTIR, n-butylamine titration, FTIR-pyridine, and alcohol dehydration. The incorporation of 3.6% mol of Hf during the silicate synthesis step caused the characteristic structural arrangement of MCM-41 to collapse. However, an increase in the acid strength of the catalyst of up to 315 mV was observed, with Brönsted and Lewis-type acid sites being mostly present therein. Furthermore, the acidity of Hf- and (${\mathrm{S}\mathrm{O}}_4^{2-}$) -modified SiO₂ in the dehydration of ethanol and methanol was evaluated, resulting in a selectivity towards ethylene and dimethyl ether, respectively. Acid solids have enormous potential to produce important compounds for the chemical industry using alternative routes other than petrochemical processes. They also represent a significant advance for biorefineries. Full article

Among the amazing properties of graphene, superlubricity is one of the most promising properties. This property can be used in industrial field components to reduce friction without using liquid lubricants, and therefore, improve machines’ efficiency and reliability with low environmental impact thanks to the elimination of oil or grease lubricants. In this paper, copper alloy samples for electrical purposes were coated with graphene by four different deposition processes. The investigated synthesis processes are direct grown graphene on bulk Cu, transferred graphene, and self-assembled graphene from graphene flakes. Ball-on-disk tests were performed to evaluate the tribological performance of samples. The aim was to compare the effect on the tribological performance given by different types of coatings, taking also into consideration industrial scalability. Interestingly, not all graphene nano-coatings being compared proved effective in reducing friction and wear in gross sliding conditions. The results show that the cost-effective self-assembled graphene is the longer-lasting nano-coating among those investigated in this work, and can reduce both friction and wear. Tests revealed that graphene coatings can be applied as a solid lubricant, reducing friction up to 78%, and reducing the average wear volume up to 40%. Full article

This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data. Full article

The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM model for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R² by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model. Full article

Multi-objective task graph scheduling is a well-known NP-hard problem that plays a significant role in heterogeneous distributed systems. The solution to the problem is expected to optimize all scheduling objectives. Pretty large state-of-the-art algorithms exist in the literature that mostly apply different metaheuristics for solving the problem. This study proposes a new hybrid algorithm comprising an improved multi-objective differential evolution algorithm (DE) and Pareto-front neighborhood search to solve the problem. The novelty of the proposed hybrid method is achieved by improving DE and hybridizing it with the neighborhood search method. The proposed method improves the performance of differential evolution by applying appropriate solution representation as well as effective selection, crossover, and mutation operators. Likewise, the neighborhood search algorithm is applied to improve the extracted Pareto-front and speed up the evolution process. The effectiveness and performance of the developed method are assessed over well-known test problems collected from the related literature. Meanwhile, the values of spacing and hyper-volume metrics are calculated. Moreover, the Wilcoxon signed method is applied to carry out pairwise statistical tests over the obtained results. The obtained results for the makespan, reliability, and flow-time of 50, 18, and 41, respectively, by the proposed hybrid algorithm in the study confirmed that the developed algorithm outperforms all proposed methods considering the performance and quality of objective values. Full article

The agricultural policy of the European Union is currently focused on increasing the area of organic farming. Medicinal plants, including milk thistle (Silybum marianum [L.] Gaertn.), are particularly suitable for this type of cultivation. The aim of this study was to compare milk thistle fruits from organic and conventional farming in terms of the content of silymarin and individual flavonolignans, oil content, microbiological purity, as well as antimicrobial activity of the silymarin extract, mainly in relation to microorganisms responsible for skin infections. The raw material of Silybi mariani fructus obtained from organic farming did not differ in terms of silymarin and oil content compared to the raw material from conventional cultivation. However, it differed in the composition of silymarin and the level of microbiological contamination. Raw material from organic farming was mostly characterized by a higher proportion of the sum of silydianin and silychristin in the silymarin complex than the sum of silybinin A and silybinin B. In the samples from conventional cultivation, only genotypes with a predominance of silybinins were present. Although the total number of microorganisms (TAMC) and yeasts and molds (TYMC) on fruit from organic farming were several times higher than on fruit from conventional farming, it was still within the standards set for food products. All raw materials were free of Escherichia coli, Salmonella spp. and Listeria monocytogenes. In addition, it was shown that the silymarin extract from organic farming was generally characterized by greater antimicrobial activity, especially in relation to Staphylococcus aureus, which is resistant and troublesome in skin infections. Full article

Green manufacturing has become a new production mode for the development and operation of modern and future manufacturing industries. The flexible job shop scheduling problem (FJSP), as one of the key core problems in the field of green manufacturing process planning, has become a hot topic and a difficult issue in manufacturing production research. In this paper, an improved multi-objective wolf pack algorithm (MOWPA) is proposed for solving a multi-objective flexible job shop scheduling problem with transportation constraints. Firstly, a multi-objective flexible job shop scheduling model with transportation constraints is established, which takes the maximum completion time and total energy consumption as the optimization objectives. Secondly, an improved wolf pack algorithm is proposed, which designs individual codes from two levels of process and machine. The precedence operation crossover (POX) operation is used to improve the intelligent behavior of wolves, and the optimal Pareto solution set is obtained by introducing non-dominated congestion ranking. Thirdly, the Pareto solution set is selected using the gray relational decision analysis method and analytic hierarchy process to obtain the optimal scheduling scheme. Finally, the proposed algorithm is compared with other algorithms through a variety of standard examples. The analysis results show that the improved multi-objective wolf pack algorithm is superior to other algorithms in terms of solving speed and convergence performance of the Pareto solution, which shows that the proposed algorithm has advantages when solving FJSPs. Full article

As an essential enabling technology to realize advanced concepts such as digitization, intelligence, and service, information technology plays a critical role in shaping modern society and driving innovation across various industries and domains. The concept of the digital twin is attracting attention from academics and industry, and how to apply it in various fields. In this paper, the performance of the magnetic bearing system may be simulated in real-time using a digital twin, especially the resulting vibration from the unbalanced rotor mass, which caused a drop in performance and a high risk of system instability and potential safety accidents. It is suggested to use a model-data combination driven digital twin model to examine its dynamic characteristics and vibration mechanism. The vibration data of the magnetic bearing was collected through experiments and compared with the data derived from the simulation results. The efficiency of the suggested strategy is demonstrated by confirming that digitally anticipated vibration signals are consistent with physical space measurements. The result shows that the fine digital twin geometric model of magnetic bearing is more consistent with the actual operation. By allowing the identification of problems before they become critical, using a digital twin may increase the dependability of magnetic bearings while reducing the possibility of unexpected downtime or failures. Full article

The nature of the fracture and fragmentation processes in concrete medium under blast loading is the transformation of the medium from continuum to discontinuity. Coupled with the significant rate correlation of concrete medium, its mechanical behavior presents a high degree of complexity. When tackling this problem, the finite element method (FEM) frequently encounters problems such as grid distortion and even negative volume, whereas the material point method (MPM) can efficiently avoid these problems. Furthermore, the original Holmquist-Johnson-Cook (HJC) model does not take the segmented characteristics of the calculation function for the dynamic increasing factor into consideration. As a result, in this article, first, the calculation function for the dynamic increasing factor in the HJC model was modified by the Split-Hopkinson Pressure Bar (SHPB) experiment, and an improved HJC model was proposed; second, an MPM simulation program was developed, and the improved HJC concrete model was embedded into the simulation program; and finally, the simulation program was verified by numerical examples, and the results show that the developed simulation program can better simulate the fracture and fragmentation process of the concrete medium under blast loading, especially the pulverization characteristics of the medium in the near zone of the load. Full article

Texas has abundant natural resources, making it a good place for renewable energy facilities to build. Unfortunately, property taxes are the highest tax on an incoming renewable energy facility in the state. In order to increase renewable energy in the state, Texas tax code Chapter 313 was introduced. Chapter 313 allows school districts the opportunity to offer a 10-year limit, ranging from USD 10 million to USD 100 million, on the taxable value of a new green energy project. With Chapter 313 ending in 2022, the following question is raised: how do tax incentives that increase the number of applications for producing renewable energy in Texas impact the wholesale, real-time pricing of electricity in the state? Skew-t regression models were implemented on a large dataset, focusing on the designated North, Houston, and West regions of the Electricity Reliability Council of Texas (ERCOT), since these regions account for 80% of the state’s energy consumption. Analysis focused on the hours ending at 3 AM, 11 AM, and 4 PM, due to the ERCOT’s time-of-day pricing. Three key findings related to the above question resulted. First, tax incentives that increase the number of active wind and solar facilities lead to a statistically significant (p < 0.0001) reduction in wholesale electricity price (USD/MWh), ranging between 2.31% and 6.6% across the ERCOT during different hours of the day. Second, for a 10% increase in tax-incentivized green energy generation, during a 24-hour period, there is a statistically significant (p < 0.0001) reduction in the generation cost (USD/MWh), ranging between 0.82% and 1.96%. Finally, electricity price reductions from solar energy are much lower than those from wind generation and/or are not statistically significant. Full article

Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) and Support Vector Regression (SVR) to predict the internal temperature of a greenhouse. A meteorological station is installed in the greenhouse to collect internal data (temperature, humidity, and dew point) and external data (temperature, humidity, and solar radiation). The data comprises a one year, and is divided into seasons for better analysis and modeling of the internal temperature. The study involves sixteen experiments corresponding to the four models and the four seasons and evaluating the models’ performance using R${}^2$, RMSE, MAE, and MAPE metrics, considering an acceptability interval of ±2 ${}^{°}$C. The results show that LR models had difficulty maintaining the acceptability interval, while the SVR models adapted to temperature outliers, presenting the highest forecast accuracy among the proposed algorithms. Full article