bluuebunny/crossref_metadata_2025 huggingface dataset:
An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering
Authors: Wang Lin , Liu Xiyu , Qu Jianhua , Zhao Yuzhen , Gao Liang , Ren Qianqian
Published: July 2023

In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators and a distributed parallel computing model of monodirectional tissue-like P systems (MTP), is constructed and proposed, which is simply named ECPSO-MTP. In the proposed ECPSO-MTP, two kinds of evolution rules for objects are defined and introduced to rewrite and modify the velocity of objects in different elementary membranes. The velocity updating model uses environmental factors based on partitioning information and randomly replaces global best to improve the clustering performance of ECPSO-MTP. The crossover operator for the position of objects is based on given objects and other objects with crossover probability and is accomplished through the hybridization of the global best of elementary membranes to reject randomness. The membrane structure of ECPSO-MTP is abstracted as a network structure, and the information exchange and resource sharing between different elementary membranes are accomplished by evolutional symport rules with promoters for objects of MTP, including forward and backward communication rules. The evolution and communication mechanisms in ECPSO-MTP are executed repeatedly through iteration. At last, comparison experiments, which are conducted on eight benchmark clustering datasets from artificial datasets and the UCI Machine Learning Repository and eight image segmentation datasets from BSDS500, demonstrate the effectiveness of the proposed ECPSO-MTP.
Application of Simulation in the Optimization of the Blood Plasma Storage Process
Authors: Trebuna Peter , Kliment Marek , Pekarcikova Miriam
Published: July 2023

The paper deals with the process of optimizing the creation of the output product, which is the Cobas HIV-1 blood plasma separation card. The capabilities of the Tecnomatix Plant Simulation software will be used in this process. The application of simulation software will help to reveal the possibilities of improvement during the entire production process of the said product and to debug all potential improvement errors on the digital model without physically interfering with the current production process. The paper contains a basic theoretical clarification of some basic terms. The following content describes individual parts of the production process in question. An analysis will be carried out, on the basis of which the most significant bottlenecks will be revealed. The data used to create the simulation were collected based on the recording of working time and its use on the given production line. This kind of observation also revealed initial bottlenecks. For these bottlenecks, a methodology for their effectiveness and criteria for their optimization is subsequently proposed. Subsequently, an optimization solution is proposed, which is verified and evaluated by simulation. The collected data were then transferred to a digital model in the form of a simulation. Based on the results of the simulation, the given optimization proposals will be evaluated at the end. Production on the line has been optimized gradually and thanks to this we can see several variants that can be compared, and the best one can be chosen. The development of the entire optimization process is the possibility to increase the production output from the original 350 produced pieces up to 699 pieces in the same time horizon. In the process of optimization, methodologies were created that followed it and gave instructions for the optimization of productions of a similar nature. In addition to the mentioned optimization simulation variants, the paper also contains a design solution proposal in the form of 3D models, when several workstations are united into one workstation.
Evolution Laws of Stress–Energy and Progressive Damage Mechanisms of Surrounding Rock Induced by Mining Disturbance
Authors: Bai Jinzheng , Dou Linming , Li Xuwei , Ma Xiaotao , Lu Fangzhou , Han Zepeng
Published: July 2023

The rock burst induced by the mutual disturbance of mining and excavation is significantly influenced by high static load stress and external dynamic load disturbance. In this paper, the evolution characteristics and progressive damage mechanism of surrounding rock in the process of mutual disturbance of mining and excavation are systematically studied. The results show that the evolution of surrounding rock stress can be roughly divided into three stages: rapid rise in the early stage, continuous rise and step-like decline in the middle stage, and slow rise in the late stage. In the process of parallel mining, the overlying rock movement above the goaf shows the sequence of horizontal penetration of tiny fissures—fracture intensification transition to stratification—non-coordinated caving of middle–low overlying rock—obvious horizontal cracks in the upper key layer. Only under the quasi-static loading mining action does the upper key layer not reach the breaking condition. The wave side of the heading face which is close to the focal point is affected by the dynamic load disturbance, the acceleration duration is short, and the attenuation is relatively fast, so it is the area prone to the earliest impact failure in the face of mining disturbance. The conclusion is helpful to deepen the understanding of the coal burst mechanism of mutual disturbance of mining and excavation.
Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language
Authors: Jahić Sead , Vičič Jernej
Published: July 2023

In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4, with a batch size of 10, achieved an accuracy of over 92%. Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.
Theory and Data-Driven Competence Evaluation with Multimodal Machine Learning—A Chinese Competence Evaluation Multimodal Dataset
Authors: Xian Teli , Du Peiyuan , Liao Chengcheng
Published: July 2023

In social interactions, people who are perceived as competent win more chances, tend to have more opportunities, and perform better in both personal and professional aspects of their lives. However, the process of evaluating competence is still poorly understood. To fill this gap, we developed a two-step empirical study to propose a competence evaluation framework and a predictor of individual competence based on multimodal data using machine learning and computer vision methods. In study 1, from a knowledge-driven perspective, we first proposed a competence evaluation framework composed of 4 inner traits (skill, expression efficiency, intelligence, and capability) and 6 outer traits (age, eye gaze variation, glasses, length-to-width ratio, vocal energy, and vocal variation). Then, eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) were utilized to predict and interpret individual competence, respectively. The results indicate that 8 (4 inner and 4 outer) traits (in descending order: vocal energy, age, length-to-width ratio, glasses, expression efficiency, capability, intelligence, and skill) contribute positively to competence evaluation, while 2 outer traits (vocal variation and eye gaze variation) contribute negatively. In study 2, from a data-driven perspective, we accurately predicted competence with a cutting-edge multimodal machine learning algorithm, low-rank multimodal fusion (LMF), which exploits the intra- and intermodal interactions among all the visual, vocal, and textual features of an individual’s competence behavior. The results indicate that vocal and visual features contribute most to competence evaluation. In addition, we provided a Chinese Competence Evaluation Multimodal Dataset (CH-CMD) for individual competence analysis. This paper provides a systemic competence framework with empirical consolidation and an effective multimodal machine learning method for competence evaluation, offering novel insights into the study of individual affective traits, quality, personality, etc.
Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example
Authors: Chen Ming-Huang , Chen Shang-Liang , Lin Yu-Sheng , Chen Yu-Jen
Published: July 2023

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.
A Method of Assessing the Effectiveness of the Use of Available Resources When Implementing Production Processes
Authors: Kochańska Joanna , Burduk Anna
Published: July 2023

This paper deals with an assessment of the effectiveness of resource management in production processes. A comprehensive and standardized approach to the issue of effectiveness contributes to the maximization of the potential of production resources. In this paper, the concept of effectiveness was used in the sense of the praxeological approach, and its components were distinguished by availability, performance and quality, which were assessed through the prism of losses occurring in production processes. The main purpose of this work was to develop a method for assessing the effectiveness of the use of available resources when implementing production processes. The method is based on a set of quantitative indicators and takes into account the categorization of production losses. Losses in availability, performance and quality in terms of areas of production, logistics, and maintenance, as well as organization and management, were identified. It was developed in response to the research gap identified in the literature review, which showed that there is a lack of a quantitative method for assessing the effectiveness of production processes. The method also takes into account the categorization of production losses, and at the same time allows the contribution of the main areas (production and production-related) with regard to the generation of these losses. The method is primarily intended to identify the causes of problems occurring in any production process, to determine their importance in a measurable way, and to monitor the course of processes after introducing changes of a different nature. The method was verified on a real example in a branch of an automotive industry company located in Lower Silesia.
Lattices-Inspired CP-ABE from LWE Scheme for Data Access and Sharing Based on Blockchain
Authors: Chen Taowei , Ren Zhixin , Yu Yimin , Zhu Jie , Zhao Jinyi
Published: July 2023

To address the quantum attacks on number theory-based ciphertext policy attribute-based encryption (CP-ABE), and to avoid private key leakage problems by relying on a trustworthy central authority, we propose a lattice-inspired CP-ABE scheme for data access and sharing based on blockchain in this paper. Firstly, a CP-ABE-based algorithm using learning with errors (LWE) assumption is constructed, which is selective security under linear independence restriction in the random oracle model. Secondly, the blockchain nodes can act as a distributed key management server to offer control over master keys used to generate private keys for different data users that reflect their attributes through launching transactions on the blockchain system. Finally, we develop smart contracts for proving the correctness of proxy re-encryption (PRE) and provide auditability for the whole data-sharing process. Compared with the traditional CP-ABE algorithm, the post-quantum CP-ABE algorithm can significantly improve the computation speed according to the result of the functional and experimental analysis. Moreover, the proposed blockchain-based CP-ABE scheme provides not only multi-cryptography collaboration to enhance the security of data access and sharing but also reduces average transaction response time and throughput.
Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection
Authors: Alwasel Bader , Aldribi Abdulaziz , Alreshoodi Mohammed , Alsukayti Ibrahim S. , Alsuhaibani Mohammed
Published: July 2023

In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.
Predicting Commercial Building Energy Consumption Using a Multivariate Multilayered Long-Short Term Memory Time-Series Model
Authors: Dinh Tan Ngoc , Thirunavukkarasu Gokul Sidarth , Seyedmahmoudian Mehdi , Mekhilef Saad , Stojcevski Alex
Published: July 2023

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most commonly used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate, multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE of 30%, RMSE of 25%, and MAPE of 20% compared with the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE of 10%, RMSE of 20%, and MAPE of 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%.These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.
Convergence Check Phase-Field Scheme for Modelling of Brittle and Ductile Fractures
Authors: Lesičar Tomislav , Polančec Tomislav , Tonković Zdenko
Published: July 2023

The paper proposes a novel staggered phase-field framework for modelling brittle and ductile fractures in monotonic and cyclic loading regimes. The algorithm consists of two mesh layers (displacement and phase field) and a single special-purpose, user-defined finite element, which controls global convergence of the coupled problem and passing of the solution variables between mesh layers. The proposed algorithm is implemented into FE software ABAQUS. For the problem of high cyclic fatigue, a cycle-skipping scheme is also introduced. The proposed methodology is verified on the usual benchmark examples. Small-strain theory is applied, but it has been demonstrated that extension to large strains is straightforward using only the ABAQUS built-in option. The efficiency and stability of the proposed framework was proven by comparison of computational time and the number of iterations per increment in the RCTRL scheme.
Technical-Economic Opportunities of Mixture Optimization and Recycled Aptitude at a Pre-Casting Concrete Plant
Authors: Montero Jesús , Cervera-Gascó Jorge
Published: July 2023

Cost optimization is a key factor in the competitiveness of construction companies. Hence, in the case of a concrete manufacturing plant, the technical-economic optimization of the process involved in producing concrete is a determining factor in its activity being more competitive than that of other companies. Thus, the main objective of this experimentation is the optimization of the different concrete dosages used in a pre-casting plant. The cement volume, the water/cement (W/C) ratio, the influence of an accelerator additive and replacing coarse aggregate with recycled aggregate were analysed. As an application of the economic results, three different hypotheses were analysed for different mix combinations in a real concrete structure model. The results show that it is essential to monitor the actual initial moisture state of aggregates and the perfect definition of the effective water/cement ratio of the dosage (Wef/C) in manufacturing in order to obtain more homogeneous production. It is proposed to simplify the number of different mix proportions and reduce cement by 10–20% without decreasing resistance and functional capabilities. Regarding additive optimization, the results show that using low rates of accelerator additive significantly improves compressive strength. Replacing natural aggregates with recycled aggregates obtained from the concrete waste of non-conforming elements is suited to manufacturing recycled concrete using replacement rates of 50% and 100%. The cost analysis for a real industrial building applying the different dosing options studied shows that a cost reduction of up to 17.8% can be achieved.
Identification of Subsurface Mesoscale Crack in Full Ceramic Ball Bearings Based on Strain Energy Theory
Authors: Bai Xiaotian , Zhang Zhaonan , Shi Huaitao , Luo Zhong , Li Tao
Published: July 2023

Subsurface mesoscale cracks exist widely in the outer ring of full ceramic ball bearings (FCBBs), which is a potential threat for the stable operation of related devices such as aero engines, food processing machinery, and artificial replacement hip joints. This paper establishes a dynamic model of subsurface mesoscale cracks in the outer ring of FCBBs based on strain energy theory, and the influence of different crack lengths on the running state is analyzed. The existence of mesoscale cracks is regarded as weakening on the stiffness coefficient, and the deterioration degree of outer ring stiffness of subsurface cracks is thereby quantified. It is found that a small wave peak appears in the vibration time-domain signal when there is a mesoscale crack on the outer ring subsurface, and the crack evolution is evaluated by the amplitude of the corresponding feature frequency. Finally, the accuracy of the model is verified by experiments. The model realizes the identification and degree evaluation of subsurface mesoscale cracks in FCBBs, and provides theoretical references for the diagnosis and status monitoring for FCBB rotor systems.
Innovative Implantable Left Ventricular Assist Device—Performance under Various Resistances and Operating Frequency Conditions
Authors: Jasinski Ryszard , Tesch Krzysztof , Dabrowski Leszek , Rogowski Jan
Published: July 2023

This paper presents the operation of an innovative left ventricular assist device under various resistances and operating frequencies. The operating principle of the device is based on pulsatile blood flow, which is forced by a suction–discharge device pumping helium into a set of intra-cardiac balloons. In this way, the ejection fraction of the left ventricle is increased, and the mitral valve is additionally occluded. What is more, the suction–discharge device is part of a portable pumping system that is synchronized with the heart cycle by monitoring the ECG signal. The device is implanted in a minimally invasive manner and is suitable for patients with stage D heart failure accompanied with residual mitral regurgitation. A model of the heart was built on the basis of a realistically reconstructed heart geometry and is part of an overall test stand that allows for realistic conditions in the heart of patients with end-stage heart failure to be reproduced. In the following sections, example measurements of the pressures in the heart chambers and balloons are shown, demonstrating that the device works correctly at least on a laboratory scale. The entire device, including the pumping system, is portable and powered by a set of lithium-ion batteries. From the measurements, it was observed, for example, that the flow rate varies with the frequency of the portable external balloon pumping system, up to 2.5 kg/min for 100 cycles/min at low flow resistance. As the flow resistance of the hydraulic system increases, the pressure in the heart chamber and aorta increases while the flow rate decreases.
Morphological Design of a Bicycle Propulsion Component Using the Hierarchical Analysis Process (AHP)
Authors: Villanueva Pedro , Bona Sergio , Lostado-Lorza Rubén , Veiga Fernando
Published: July 2023

There are many mechanical and/or electrical energy storage devices nowadays which can be mounted on standard bicycles. The current trend regarding bicycle energy storage devices is to develop and improve electrical and electronic systems that can ease transportation. However, this paper shows the design process of a purely mechanical energy storage device, with no electrical components, which instead aims to entertain the user, producing a stimulus related to speed and physical exertion. The mechanical device has been designed according to an aspect or fashion known as steampunk, so that the mechanical elements forming the device (springs and spur gears) are visible to the user. The storage and discharge of energy are only produced by the user. In order to charge the device, after reaching an appropriate speed, the user uses the pedals in reverse motion. Alternatively, the mechanism can also be charged with a controlled braking system by actuating on a crank. The design process was based on the total design of Pugh and the AHP and QFD techniques.
Efficacy of Blended Learning Techniques in Medical and Dental Education: Students’ Opinions in Relation to Their Habits as Internet Consumers
Authors: Dascalu Cristina Gena , Antohe Magda Ecaterina , Topoliceanu Claudiu , David Cristina , Purcarea Victor Lorin
Published: July 2023

The success of blended learning programmes, based on digital tools, depends on several factors that significantly influence students’ performance, of which demographics (gender and age group) are the most common. Other factors play an important role too. Among these, we focused on the students’ general attitude towards the digital environment and the importance they attach to it in their daily activities. We investigated a sample of five hundred fifty-one students from four Medical Universities in Romania, mostly females (76.2%), from years 1 and 2 of study (63.7%) and aged between 18 and 20 years (53.9%), and we recorded their general opinion about the effectiveness of four types of teaching tools and the intensity of using Internet services in their daily activities. We conducted univariate (hypothesis testing) and multivariate (two-step clustering) analyses of the recorded responses, and we found that the students’ general behaviour as the users of Internet services significantly influences their choices for certain teaching tools over others. Students who prefer classic oral presentations and PowerPoint presentations are predominantly females, aged between 18 and 20, who use Internet services mainly for communication. Students who prefer educational videos are also mainly females, evenly distributed by age groups; they enjoy using multimedia resources during learning and use Internet services for communication, information or entertainment. Students who prefer the use of online documentary sources are evenly distributed by gender and are over 20 years of age; they also enjoy using multimedia resources during learning and use Internet services mainly for communication and information. The obtained results show that, to optimally combine the teaching tools used in blended learning, it is useful to assess in advance the attitude towards the digital environment of the students to whom it is addressed.
Experimental Study on the Bearing Capacity of Reinforced Concrete Pipes with Corrosion-Thinning Defects Repaired by UHP-ECC Mortar Spraying
Authors: Zhang Peng , Gong Chenkun , Wu Qingqing , Zeng Cong
Published: July 2023

The in situ spraying method is widely used because of its advantages as a trenchless pipeline repair technology, including a fast construction speed and close bonding between the repair lining layer and the reinforced concrete pipe. However, current research on high-performance spray repair materials, the bearing capacity of pipelines before and after repair, and the failure modes between the two interfaces after repair is insufficient. Through laboratory tests designed with multiple sets of control tests, this paper outlines the bearing capacity of reinforced concrete pipelines with corrosion thinning defects repaired with ultra-high-performance concrete. The variation law of the residual bearing capacity of reinforced concrete pipes and the influence of different corrosion degrees, repair thicknesses, and interface forms on the bearing capacity of reinforced concrete pipes were studied following UHP-ECC for pipe repair. The results showed that the bearing capacity of the structure decreased with an increase in the corrosion thickness of the pipeline. After repair with ultra-high-performance concrete, the bearing capacity of corroded pipelines greatly improved. When the corrosion and repair thicknesses were the same, the bearing capacity of the repaired pipeline with different interface forms was very different. After the interface was implanted with nails, spray repair was carried out and the bearing capacity of the pipeline improved the most, followed by the naturally bonded interface. When plastic film was pasted on the repair interface, the bearing capacity of the pipeline improved the least.
Climate Change Impacts on Frost and Thaw Considerations: Case Study of Airport Pavement Design in Canada
Authors: Sutherland Rolim Barbi Paula , Tavassoti Pejoohan , Tighe Susan L.
Published: July 2023

Rising temperatures due to climate change can significantly impact the freeze–thaw condition of airport pavements in cold regions. This case study investigates the implications of warming temperatures on the freeze–thaw penetration and frost heave of pavements in critical airports across Canada. To this end, different methods were used in the quantification process through climate change simulations considering emission scenario RCP8.5 in 20 and 40 year time horizons. The results show that climate change would have different design implications for airport pavements, depending on their location. The predictions suggest a shallower frost penetration depth, and possibly less frost heave, for the airports not underlain by permafrost, while airports over permafrost areas might experience an increase in thickness of the active layer, ranging from 41 to 57 percent, by 2061. Among the different methods used in this study, it was observed that some methods performed better in predicting the frost depth of fine soils, while others worked better in the frost depth prediction of coarse soils. The results indicate the need for more mechanistic models to provide a more realistic prediction of freeze–thaw penetration, as compared to existing empirical models.
Application of Biogas from Quinoa, Wheat, and Andean Guinea Pig Residuals as Biofuels for Gas Turbines
Authors: González Álvarez José Francisco , Velázquez-Martí Borja , Gaibor-Chávez Juan , Franco Rodríguez John Eloy , Rico Carlos
Published: July 2023

This article shows the effect that biogases obtained from crop residuals from the Andean region have on the performance of a whole medium-sized electrical-generating gas turbine. This technology could be used to supply electricity in energy-depressed areas where biogas is the only accessible resource. The gas turbine worked with higher efficiencies when the obtained biogases were used compared to natural gas. The biofuel that presented the highest efficiencies was the one obtained from wheat residuals alone. Despite this fact, this biofuel would be the most prone to create aerodynamic problems in the stages of the gas turbine. In this work, it was found that the addition of guinea pig manure to different crop residuals created biofuels less prone to create aerodynamic problems in the compression and expansion stages. In particular, the studied biofuel that had the most similar aerodynamic behavior to the design natural gas case was the one obtained from guinea pig manure and quinoa residuals. On the other hand, this biogas presented the lowest efficiencies of the studied biofuels. Despite this fact, this biofuel showed higher efficiencies than the natural gas case. In the gas turbine combustion chamber, all the studied biofuels operated at lower temperatures than the ones with natural gas, even in the high-power range. This would be an important feature for the running of the combustion chamber and the high-pressure turbine superalloys.
Effect of the Shape of Ultrasonic Vessels on the Chemical Properties of Extracts from the Fruit of Sorbus aucuparia
Authors: Krzywicka Monika , Kobus Zbigniew
Published: July 2023

The goal of this study was to analyse the effect of sonoreactor dimensions on the effectiveness of the ultrasound-assisted extraction (UAE) of bioactive substances from rowan (Sorbus aucuparia L.). Sonication was carried out with a VC750 Sonics processor at the following amplitudes of ultrasound: 12, 24, and 36 µm. The frequency of the ultrasound was 20 kHz. Extraction was conducted in a 2 s on–4 s off pulse system. The total phenolic content and antioxidant activity were determined using a spectrophotometric method. The pH value of the extracts was measured using a combined pH metric electrode, type EPS-1 (Elmetron). Response surface methodology (RSM) was used to optimise the investigated variables. On the basis of the developed model, the following variable values were obtained: TPC—12.48 gallic acid equivalent (GAE)/g, 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) values—126.54 μmol Trolox (TE)/g, 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) values—79.58 μmol TE/g, ferric reducing antioxidant power assay (FRAP) values—120.65 μmol TE/g for an extraction vessel with a diameter of 35 mm and TPC—11.68 mg GAE/g, ABTS values—120.49 μmol TE/g, DPPH values—75.90 μmol TE/g, FRAP values—155.54 μmol TE/g for an extraction vessel with a diameter of 25 mm. Significant influences of time, ultrasound amplitude, and alcohol concentration on the course of the extraction process in the tested extraction vessels were found. The optimum extraction conditions for an extraction vessel with a diameter of 35 mm were obtained for an ethyl alcohol concentration of about 60%, and for an extraction vessel with a diameter of 25 mm the optimum conditions were for an alcohol concentration in the range of 30–50%. A very strong positive linear correlation was found between the concentration of ethyl alcohol and the pH value of the obtained extracts for both extraction reactors. The developed models of pulsed ultrasound-assisted extraction were characterised by a good predictive capacity (77.49–91.39%) and can be used for obtaining polyphenols from rowan.
Research on the Vehicle Steering and Braking Stability Region
Authors: Wang Xianbin , Li Weifeng , Zhang Fugang , Li Zexuan , Bao Wenlong
Published: July 2023

Solving the stability region in the plane motion of vehicles has become a hot research topic in vehicle handling stability under extreme conditions, but there is still a lack of research on the stability region under steering and braking conditions. In this paper, a five-degree-of-freedom (5DOF) nonlinear dynamic model of a vehicle with braking torque introduced is established, and the model is transformed into an equivalent system by using the D’Alembert principle. Then, the equilibrium points of the equivalent system are solved by using an improved hybrid algorithm combining the genetic algorithm (GA) and sequential quadratic programming (SQP) method. According to the bifurcation characteristics of the equilibrium points, the boundary of the stability region at the given initial longitudinal velocity is determined, and the three-dimensional stability region is fitted. Finally, the stability region of the equivalent system and the original system are analyzed by the energy dissipation method, and the stability region determined by the equilibrium point bifurcation method is verified by simulation. The results show that as the braking torque increases, the number of equilibrium points increase to three from one, the equilibrium bifurcation method proposed in this paper can effectively solve the stability region of the equivalent system, and the solution results are consistent with the original system stability region. When the limited braking torque is 500 N·m and the initial longitudinal velocity increases from 30 m/s to 50 m/s, the absolute value of the front wheel steering angle at the boundary point changes from less than 0.02 rad to more than 0.02 rad.
Test–Retest Repeatability of Human Gestures in Manipulation Tasks
Authors: Digo Elisa , Caselli Elena , Polito Michele , Antonelli Mattia , Gastaldi Laura , Pastorelli Stefano
Published: July 2023

The importance of performance excellence and operator’s safety is fundamental not only when operators perform repetitive and controlled industrial tasks, but also in case of abrupt gestures due to inattention and unexpected circumstances. Since optical systems work at frequencies that are too low and they are not able to detect gestures as early as possible, combining the use of wearable magneto-inertial measurement units (MIMUs) with the adoption of deep learning techniques can be useful to instruct the machine about human motion. To improve the initial training phase of neural networks for high classification performance, gesture repeatability over time has to be verified. Since the test–retest approach has been poorly applied based on MIMUs signals in a context of human–machine interaction, the aim of this work was to evaluate the repeatability of pick-and-place gestures composed of both normal and abrupt movements. Overall, results demonstrated an excellent test–retest repeatability for normal movements and a fair-to-good test–retest repeatability for abrupt movements. In addition, results suggested important information about the application of deep learning to identify the types of movements: the test showed how to improve reinforcement learning for the identification of onset gestures, whereas the retest allowed for defining the time necessary to retrain the network.
The Pectoral of Tovsta Mohyla: Understanding the Gold Insignia of Ancient Scythia
Authors: Babenko Leonid
Published: July 2023

The gold pectoral from Tovsta Mohyla is a masterpiece of Greco-Scythian metalwork, the most prominent and esteemed of all the finds uncovered in 260 years of excavating the Scythian kurgans. After the pectoral was discovered on 21 June 1971 by B. Mozolevsky, dozens of other scientists joined him in studying it. The researchers have raised a wide range of interesting questions revolving around the origin of the pectoral, the technology involved in its production, its stylistic features, and the interpretation of its depictions. However, so far, none of these questions has been answered definitively. This article provides an overview of the author’s recent interdisciplinary research on the Tovsta Mohyla pectoral.
Hybrid Prediction Model of Air Pollutant Concentration for PM2.5 and PM10
Authors: Ma Yanrong , Ma Jun , Wang Yifan
Published: July 2023

To alleviate the negative effects of air pollution, this paper explores a mixed prediction model of pollutant concentration based on the machine learning method. Firstly, in order to improve the prediction performance of the sparrow search algorithm least square support vector machine (SSA-LSSVM), a reverse learning strategy-lens principle is introduced, and a better solution is obtained by optimizing the current solution and reverse solution at the same time. Secondly, according to the nonlinear and non-stationary characteristics of the time series data of PM2.5 and PM10, the variational mode decomposition (VMD) method is used to decompose the original data to obtain the appropriate K value. Finally, experimental verification and an empirical analysis are carried out. In experiment 1, we verified the good performance of the model on University of California Irvine Machine Learning Repository (UCI) datasets. In experiment 2, we predicted the pollutant data of different cities in the Beijing–Tianjin–Hebei region in different time periods, and obtained five error results and compared them with six other algorithms. The results show that the prediction method in this paper has good robustness and the expected results can be obtained under different prediction conditions.
Analysis of Water Infiltration under Impermeable Dams by Analytical and Boundary Element Methods in Complex
Authors: Cipu Elena Corina
Published: July 2023

The boundary element method (BEM) is used by applying Cauchy’s formula to the boundary of the water movement domain under a dam. By approximating the border with a polygon through linear interpolation, the relationships between the complex velocities on each edge of the polygon are analytically deduced. For the case of the flow domain described by a semi- circular closed contours, the numerical values of the velocity are computed and compared with those obtained only analytically. Conclusions on the analytical and numerical context are drawn.
New Conditions for Testing the Asymptotic Behavior of Solutions of Odd-Order Neutral Differential Equations with Multiple Delays
Authors: Masood Fahd , Moaaz Osama , Askar Sameh S. , Alshamrani Ahmad
Published: July 2023

The purpose of this research is to investigate the asymptotic and oscillatory characteristics of odd-order neutral differential equation solutions with multiple delays. The relationship between the solution and its derivatives of different orders, as well as their related functions, must be understood in order to determine the oscillation terms of the studied equation. In order to contribute to this subject, we create new and significant relationships and inequalities. We use these relationships to create conditions in which positive and N-Kneser solutions of the considered equation are excluded. To obtain these terms, we employ the comparison method and the Riccati technique. Furthermore, we use the relationships obtained to create new criteria, so expanding the existing literature on the field. Finally, we provide an example from the general case to demonstrate the results’ significance. The findings given in this work provide light on the behavior of odd-order neutral differential equation solutions with multiple delays.
Gait Analysis in Neurorehabilitation: From Research to Clinical Practice
Authors: Bonanno Mirjam , De Nunzio Alessandro Marco , Quartarone Angelo , Militi Annalisa , Petralito Francesco , Calabrò Rocco Salvatore
Published: July 2023

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients’ quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.
Extracellular Matrix-Based and Electrospun Scaffolding Systems for Vaginal Reconstruction
Authors: Farzamfar Saeed , Elia Elissa , Richer Megan , Chabaud Stéphane , Naji Mohammad , Bolduc Stéphane
Published: July 2023

Congenital vaginal anomalies and pelvic organ prolapse affect different age groups of women and both have significant negative impacts on patients’ psychological well-being and quality of life. While surgical and non-surgical treatments are available for vaginal defects, their efficacy is limited, and they often result in long-term complications. Therefore, alternative treatment options are urgently needed. Fortunately, tissue-engineered scaffolds are promising new treatment modalities that provide an extracellular matrix (ECM)-like environment for vaginal cells to adhere, secrete ECM, and be remodeled by host cells. To this end, ECM-based scaffolds or the constructs that resemble ECM, generated by self-assembly, decellularization, or electrospinning techniques, have gained attention from both clinicians and researchers. These biomimetic scaffolds are highly similar to the native vaginal ECM and have great potential for clinical translation. This review article aims to discuss recent applications, challenges, and future perspectives of these scaffolds in vaginal reconstruction or repair strategies.
Comparison of Biological and Genetic Characteristics between Two Most Common Broad-Leaved Weeds in Paddy Fields: Ammannia arenaria and A. multiflora (Lythraceae)
Authors: Gao Yuan , Li Shenghui , Yuan Guohui , Fang Jiapeng , Shen Guohui , Tian Zhihui
Published: July 2023

Ammannia arenaria and A. multifloras, morphologically similar at the seedling stage, are the most common broad-leaved weeds in paddy fields. Our study showed that A. arenaria occupied more space than A. multifloras when competing with rice. However, A. multifloras germination has lower temperature adaptability. No difference in sensitivity to common herbicides between two Ammannia species was observed. Chloroplast (cp) genomes could be conducive to clarify their genetic relationship. The complete cp genome sequences of A. arenaria (158,401 bp) and A. multiflora (157,900 bp) were assembled for the first time. In A. arenaria, there were 91 simple sequence repeats, 115 long repeats, and 86 protein-encoding genes, one, sixteen, and thirty more than those in A. multiflora. Inverted repeats regions expansion and contraction and the phylogenetic tree based on cp genomes demonstrated the closely relationship between the two species. However, in A. arenaria, 20 single nucleotide polymorphisms in the CDS region were detected compared to A. multiflora, which can be used to distinguish the two species. Moreover, there was one unique gene, infA, only in A. arenaria. This study provides reliable molecular resources for future research focusing on the infrageneric taxa identification, phylogenetic resolution, population structure, and biodiversity of Ammannia species.
Interactive Effects of Flooding Duration and Sediment Texture on the Growth and Adaptation of Three Plant Species in the Poyang Lake Wetland
Authors: Liu Ying , Li Jie , Liu Yizhen , He Liang , Yang Shanshan , Gong Huiying , Xu Ruixin , Yao Xingzi , Ge Gang
Published: July 2023

Flooding duration and sediment texture play vital roles in the growth and adaptation of wetland plants. However, there is a lack of research on the interactive effects of flooding duration and sediments on wetland plants. A two-factor experiment with flooding duration and sediment texture was designed in the study, involving three plant species commonly found in the Poyang Lake wetland (i.e., Carex cinerascens, Phalaris arundinacea, and Polygonum criopolitanum). Our findings were as follows: (i) Sediments play a crucial role in the growth and adaptation of hygrophilous plants, but they exhibited a weaker effect than flooding. (ii) Sediment texture mediates flooding to affect the stressing responses of wetland plant functional traits, including the leaf chlorophyll content, the plant height, and the number of leaves and ramets. (iii) Sediment texture forms interactive effects with flooding duration and directly influences hygrophilous plants. The results of this study help provide theoretical insights from a more scientific perspective for the prediction of hygrophilous plant dynamics and to facilitate the formulation of wetland management.