bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Optimized Ternary PLA/PHB/Organoclay Composites Processed through Fused Filament Fabrication and Injection Molding
Authors: Buyuksoy-Fekraoui Kubra , Lacoste Clément , Pucci Monica Francesca , Lopez-Cuesta José-Marie , Perrin Didier
Published: May 2022

The aim of this study was to investigate the structure–properties relationship of ternary blends of polylactide/polyhydroxybutyrate (PLA/PHB)/organo-modified layered silicate (OMLS). Morphological, thermal, rheological, and mechanical characterizations were performed to understand the influence of OMLS on PLA/PHB (70/30 wt%) formulations optimized through modifications with an epoxy-based chain extender, the use of a plasticizer, as well as the influence of the type of processing route: injection molding or fused filament fabrication. The addition of OMLS allowed the blend compatibility to be improved, with the appearance of a single melting peak on DSC thermograms at 146 °C, as well as the reduction in the size of the nodules for the injected molded specimens. Concerning the printed samples, AFM analysis revealed a coalescence of the PHB minor phase due to its degradation. This phenomenon was dramatically enhanced in the presence of OMLS and has been ascribed to the degradation of both the organo-modifier and the PHB minor phase in the blend. Rheological and mechanical tests (17% decrease in Young’s modulus and 13% decrease in elongation at break) confirmed this degradation that would have occurred during the manufacturing of the filaments and the printing of specimens due to additional thermal and cooling steps.
Preparation and Characterization of Magnetite Talc (Fe3O4@Talc) Nanocomposite as an Effective Adsorbent for Cr(VI) and Alizarin Red S Dye
Authors: Nayl AbdElAziz A. , Abd-Elhamid Ahmed I. , Ahmed Ismail M. , Bräse Stefan
Published: May 2022

In this work, the adsorption of Cr(VI) ions and the organic dye Alizarin Red S (ARS) was investigated using magnetite talc (Fe3O4@Talc) nanocomposite. Different characterization techniques such as scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, X-ray Diffraction (XRD), and thermogravimetric analysis (TGA) were used to demonstrate the physical and chemical properties of the fabricated Fe3O4@Talc nanocomposite. In addition, the adsorption isothermic, kinetic, and thermodynamic properties were illustrated. The results demonstrate that the investigated adsorption processes obeyed the Langmuir isotherm model for Cr(VI) and the Freundlich isotherm model for ARS dye, with a maximum adsorption capacity of 13.5 and 11.76 mg·g−1, respectively, controlled by pseudo second-order kinetics. Regeneration and reusability studies demonstrated that the prepared Fe3O4@Talc nanocomposite is a promising and stable adsorbent with considerable reusability potential.
Structure Formation in Antifriction Composites with a Nickel Matrix and Its Effect on Properties
Authors: Olaleye Kayode , Roik Tetiana , Kurzawa Adam , Gavrysh Oleg , Vitsiuk Iulia , Jamroziak Krzysztof
Published: May 2022

The paper is devoted to studying the chemical elements distribution in the material’s structure depending on the manufacturing technological parameters and their effect on properties of a new self-lubricating antifriction composite based on powder nickel alloy EP975 with CaF2 solid lubricant for operation at temperature 800 °C and loads up to 5.0 MPa, in air. The study is focused on the features of alloying elements distribution in the composite matrix, which depends on the manufacturing technology. A uniform distribution of all alloying elements in the studied composite was shown. The chemical elements’ uniform distribution in the material is associated with one of the most important preparatory technological operations in the general manufacturing technology used. This is a technological operation of mixing powders with subsequent analysis of the finished mixture. The uniform distribution of chemical elements determines the uniform arrangement of carbides and intermetallics in the composite. General manufacturing technology, which includes the main operations, such as hot isostatic pressing technology and hardening heat treatment, contributed to the obtainment of a practically isotropic composite with almost the same properties in the longitudinal and transverse directions. Because of the composite’s structural homogeneity, without texturing, characteristics are isotropic. Improving the material’s structural homogeneity helps to keep its mechanical and anti-friction qualities stable at high temperatures and stresses in the air. The performed studies demonstrated the correctness of the developed manufacturing technology that was confirmed by the electron microscopy method, micro-X-ray spectral analysis, mechanical and tribological tests. The developed high-temperature antifriction composite can be recommended for severe operating conditions, such as friction units of turbines, gas pumping stations, and high-temperature units of foundry metallurgical equipment.
Substituting Resistance Spot Welding with Flexible Laser Spot Welding to Join Ultra-Thin Foil of Inconel 718 to Thick 410 Steel
Authors: Kumar Nikhil , Dhara Sisir , Masters Iain , Das Abhishek
Published: May 2022

This paper investigated various aspects of replacing existing micro-resistance spot welding (micro-RSW) with micro-laser spot welding for joining Inconel 718 thin foils to thick 410 steel stack-up to allow faster, non-contact joining together with flexibility in spot positioning and removal of tip dressing required for RSW electrodes. The joint quality was evaluated based on the mechanical strength, microstructural characteristics and joint strength at elevated temperature as these joints are often used for high-temperature applications. Experimental investigations were performed using micro-RSW and micro-laser spot welding to obtain the 90° peel and lap shear specimens, each comprising four spots. The obtained strength from laser joints was significantly higher than that of micro-RSW joints due to larger weld nugget formation and interface width. The process map for obtaining good quality welds was also identified, and about a 17% reduction in joint strength was obtained when welded specimens were subjected to elevated temperature (i.e., 500 °C) in comparison with room temperature. This reduction was compensated for using the flexibility of laser welding to add two extra spots. The overall performance of the micro-laser spot welds was found to be better than the micro-RSW considering joint strength, flexibility in placing the spots and time to produce the welds.
Investigation of Concrete Shrinkage Reducing Additives
Authors: Statkauskas Martynas , GRINYS Audrius , Vaičiukynienė Danutė
Published: May 2022

This paper analyzes the efficiency of shrinkage reducing additives for the shrinkage deformations of ordinary Portland cement (OPC) concrete and its mechanical properties. OPC concrete was modified with an organic compound-based shrinkage reducing additive (SRA), quicklime, polypropylene fiber, and hemp fiber. It was found that a combination of 2.5% quicklime and 1.5% SRA led to the highest reduction in shrinkage deformations in concrete, and the values of shrinkage reached up to 40.0%. On the contrary, compositions with 1.5% SRA were found to have a significant reduction in compressive strength after 100 freeze-thaw cycles. Hemp fiber did not show a significant shrinkage reduction, but it is an environmentally friendly additive, which can improve OPC concrete flexural strength. Polypropylene fiber can be used in conjunction with shrinkage reducing additives to improve other mechanical properties of concrete. It was observed that 3.0 kg/m3 of polypropylene fiber in concrete could increase flexural strength by 11.7%. Moreover, before degradation, concrete with polypropylene fiber shows high fracture energy and decent residual strength of 1.9 MPa when a 3.5 mm crack appears. The tests showed a compressive strength decrease in all compositions with shrinkage reducing additives and its combinations after 28 days of hardening.
Atomic Structure Evaluation of Solution-Processed a-IZO Films and Electrical Behavior of a-IZO TFTs
Authors: Kim Dongwook , Lee Hyeonju , Kim Bokyung , Zhang Xue , Bae Jin-Hyuk , Choi Jong-Sun , Baang Sungkeun
Published: May 2022

Understanding the chemical reaction pathway of the metal–salt precursor is essential for modifying the properties of solution-processed metal-oxide thin films and further improving their electrical performance. In this study, we focused on the structural growth of solution-processed amorphous indium-zinc-oxide (a-IZO) films and the electrical behavior of a-IZO thin-film transistors (TFT). To this end, solution-processed a-IZO films were prepared with respect to the Zn molar ratio, and their structural characteristics were analyzed. For the structural characteristic analysis of the a-IZO film, the cross-section, morphology, crystallinity, and atomic composition characteristics were used as the measurement results. Furthermore, the chemical reaction pathway of the nitrate precursor-based IZO solution was evaluated for the growth process of the a-IZO film structure. These interpretations of the growth process and chemical reaction pathway of the a-IZO film were assumed to be due to the thermal decomposition of the IZO solution and the structural rearrangement after annealing. Finally, based on the structural/chemical results, the electrical performance of the fabricated a-IZO TFT depending on the Zn concentration was evaluated, and the electrical behavior was discussed in relation to the structural characteristics.
The Utility of Recycled Rice Husk-Reinforced PVC Composite Profiles for Façade Cladding
Authors: Sudoł Ewa , Kozikowska Ewelina , Choińska Emilia
Published: May 2022

The production process of construction profiles from natural fibre-reinforced polymer composites, as well as their assembly, generates considerable amounts of waste. The study analysed the possibility of utilising the said waste to produce profiles with the same intended use as products made from the primary material. The analysis involved the recycling of rice husk-reinforced PVC profiles. As a result of the applied post-processing, a composite of higher homogeneity and better filler dispersion than the product made of primary material was obtained. A slight improvement in thermal properties was observed. From the DSC test, Tg values of 78 °C and nearly 80 °C were obtained, while from the TGA test, mass loss values of 0.6% and 0.4% and the decomposition temperatures of 211 °C and 217 °C were noted for profiles of primary and secondary material, respectively. A softening temperature of 75 °C was obtained for primary material profiles, while nearly 77 °C was obtained for secondary. The more favourable mechanical properties of recycled profiles were also maintained. The higher value of flexural strength, flexural modulus, impact strength and hardness by 31%, 24%, 48% and 40% were obtained, respectively. After hydrothermal cycling, the same properties were higher for secondary material profiles by 35%, 20%, 68%, and 67%, respectively. The recorded level of performance properties of recycled products, better than those of primary material standard construction products’, allows us to conclude that profiles made of waste are useful for façade claddings.
Analytical and Numerical Modeling of Stress Field and Fracture in Aluminum/Epoxy Interface Subjected to Laser Shock Wave: Application to Paint Stripping
Authors: Papadopoulos Kosmas , Tserpes Konstantinos
Published: May 2022

In this paper, analytical and numerical models have been developed to compute the stress field and predict fracture of the aluminum/epoxy interface subjected to laser shock loading, in the frame of the investigation of the paint stripping process. An explicit finite element (FE) model combined with the cohesive zone modeling (CZM) method, an analytical stress analysis model, and a spall fracture model have been developed. The numerical model has been calibrated and validated against tests in terms of the stripping pattern, while the analytical models have been compared with the numerical model. The models were combined to generate computational tools for decreasing computational effort. The FE model with the CZM is the most accurate tool although it is the most computationally expensive. The spall fracture model gives trusted estimations of the spall strength of the interface which are very sensitive to the interface thickness and when incorporated into a continuum FE-based damage model can predict the stripping initiation faster than the FE model with the CZM. The analytical stress analysis model can be used to efficiently describe the shock wave propagation into the material system, but it can give only a rough estimation of the tensile stress at the epoxy, which when combined with the spall strength does not give reliable predictions of the stripping initiation. The three models require as input different material properties, some of which are very difficult to determine. Nevertheless, the availability of accurate material parameters and properties of the aluminum, the epoxy, and, especially, their interface can significantly improve the efficiency of the developed models.
Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete
Authors: Khan Kaffayatullah , Ahmad Waqas , Amin Muhammad Nasir , Aslam Fahid , Ahmad Ayaz , Al-Faiad Majdi Adel
Published: May 2022

Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variables, such as the water-cement ratio, the strength of the parent concrete, recycled aggregate replacement ratio, density, and water absorption of recycled aggregate, all impact the RAC’s CS. Many studies have been carried out to ascertain the influence of each of these elements separately. However, it is difficult to investigate their combined effect on the CS of RAC experimentally. Experimental investigations entail casting, curing, and testing samples, which require considerable work, expense, and time. It is vital to adopt novel methods to the stated aim in order to conduct research quickly and efficiently. The CS of RAC was predicted in this research utilizing machine learning techniques like decision tree, gradient boosting, and bagging regressor. The data set included eight input variables, and their effect on the CS of RAC was evaluated. Coefficient correlation (R2), the variance between predicted and experimental outcomes, statistical checks, and k-fold evaluations, were carried out to validate and compare the models. With an R2 of 0.92, the bagging regressor technique surpassed the decision tree and gradient boosting in predicting the strength of RAC. The statistical assessments also validated the superior accuracy of the bagging regressor model, yielding lower error values like mean absolute error (MAE) and root mean square error (RMSE). MAE and RMSE values for the bagging model were 4.258 and 5.693, respectively, which were lower than the other techniques employed, i.e., gradient boosting (MAE = 4.956 and RMSE = 7.046) and decision tree (MAE = 6.389 and RMSE = 8.952). Hence, the bagging regressor is the best suitable technique to predict the CS of RAC.
Mapping Research Knowledge on Rice Husk Ash Application in Concrete: A Scientometric Review
Authors: Amin Muhammad Nasir , Ahmad Waqas , Khan Kaffayatullah , Sayed Mohamed Mahmoud
Published: May 2022

This study aimed to carry out a scientometric review of rice husk ash (RHA) concrete to assess the various aspects of the literature. Conventional review studies have limitations in terms of their capacity to connect disparate portions of the literature in a comprehensive and accurate manner. Science mapping, co-occurrence, and co-citation are a few of the most difficult phases of advanced research. The sources with the most articles, co-occurrences of keywords, the most prolific authors in terms of publications and citations, and areas actively involved in RHA concrete research are identified during the analysis. The Scopus database was used to extract bibliometric data for 917 publications that were then analyzed using the VOSviewer (version: 1.6.17) application. This study will benefit academics in establishing joint ventures and sharing innovative ideas and strategies because of the statistical and graphical representation of contributing authors and countries.
Study on the Material Properties of Microconcrete by Dynamic Model Test
Authors: Zhang Chunyu , Zhang Jinpeng , Ren Qichao , Xu Jianguo , Wang Bo
Published: May 2022

As an important water conveying structure, the seismic safety of the hydraulic aqueduct has attracted considerable interest. Different from the general bridge structure, the seismic analysis of the aqueduct structure needs to consider its fluid–structure interaction. The existing numerical simulation methods cannot truly reflect the fluid–solid coupling mechanism. Therefore, scholars began to use shaking table tests to study the fluid–structure interaction mechanism. However, the research is immature, and it is mostly focused on the seismic response analysis, and there are few studies on the model test similarity ratio and model material properties. Based on this, in this paper, according to the requirements of the test similarity ratio, the orthogonal experiment was used to explore the influence of barite sand content, water–cement ratio, fine sand ratio, and lime ratio on the mechanical properties of microconcrete. The performance indicators of microconcrete under different mix ratios vary widely, with a minimum variation of 19% and a maximum of 102%. Barite sand has the most significant control effect on the density, and the water–cement ratio has the most significant control effect on the compressive strength and elastic modulus. The density variation range is 2.37–2.81 g/cm3, the cube compressive strength variation range is 18.37–36.94 MPa, and the elastic modulus variation range is 2.11 × 104–3.28 × 104 MPa. This study will provide certain evidence for the similarity ratio design and material selection of the scaled model test of the fluid–solid coupling structure.
Europium Clustering and Glassy Magnetic Behavior in Inorganic Clathrate-VIII Eu8Ga16Ge30
Authors: Pérez Nicolás , Sahoo Manaswini , Schierning Gabi , Nielsch Kornelius , Nolas George S.
Published: May 2022

The temperature- and field-dependent, electrical and thermal properties of inorganic clathrate-VIII Eu8Ga16Ge30 were investigated. The type VIII clathrates were obtained from the melt of elements as reported previously. Specifically, the electrical resistivity data show hysteretic magnetoresistance at low temperatures, and the Seebeck coefficient and Hall data indicate magnetic interactions that affect the electronic structure in this material. Heat capacity and thermal conductivity data corroborate these findings and reveal the complex behavior due to Eu2+ magnetic ordering and clustering from approximately 13 to 4 K. Moreover, the low-frequency dynamic response indicates Eu8Ga16Ge30 to be a glassy magnetic system. In addition to advancing our fundamental understanding of the physical properties of this material, our results can be used to further the research for potential applications of interest in the fields of magnetocalorics or thermoelectrics.
COVID-19 Pandemic Response Robot
Authors: Lee Min-Fan Ricky , Chen Yi-Ching Christine
Published: May 2022

Due to an arising COVID-19 positive confirmed case in Taiwan, the screening of body temperature, mask wearing and quarantined violation is enhanced. A mobile robot that conducts this task is demanded to reduce the human labor. However, conventional robots suffer from several limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), different viewpoints, the scale of objects, low mobility, less functionality, and some environmental limitations. As for the thermal imager, it displays the current heat spectrum colors, and needs manual monitoring. This paper proposes applying Simultaneous Localization and Mapping in an unknown environment and using deep learning for detection of temperature, mask wearing, and human face on the Raspberry Pi to overcome these problems. It also uses the A* algorithm to do path planning and obstacle avoidance via 3D Light Detection and Ranging to make the robot move more smoothly. Evaluating and implementing different Simultaneous Localization and Mapping algorithms and deep learning models, then selecting the most suitable method. Root Mean Square Error of three Simultaneous Localization and Mapping algorithms are compared. The predictions of deep learning models are evaluated via the metrics (model speed, accuracy, complexity, precision, recall, precision–recall curve, F1 score). In conclusion, Google Cartographer for building a map, Convolutional Neural Network for mask wearing detection, and only looking once for human face detection achieve the best result among all algorithms.
An Innovative Pose Control Mechanism for a Small Rudderless Underwater Vehicle
Authors: Lee Min-Fan Ricky , Chen Yen-Chun
Published: May 2022

Current and conventional pose (position and orientation) control of a small underwater vehicle is achieved by using rudders (yaw motion control) and elevators (pitch motion control), but these suffer from non-linear, indirect and complex control issues. This paper proposes an innovative pose control mechanism for small underwater vehicles. The mass shifter mechanism is designed and fabricated to control pitch and yaw motion with a single propeller only. The center of mass of the underwater vehicle is altered by moving a pair of counterweights on fixed tracks. The pitch and yaw are achieved by controlling the position of the counterweight pairs. The proposed system is designed, fabricated and tested in a real underwater environment for proof-of-concept. The result shows a simpler, more efficient and more effective pose control mechanism than conventional technology.
On-Line Detection of Demagnetization for Permanent Magnet Synchronous Motor via Flux Observer
Authors: Cao Liqian , Wu Zhong
Published: May 2022

Demagnetization in permanent magnet synchronous motor (PMSM), caused by high temperature or inverse magnetic field, may increase loss and torque ripple, and even degrade the system stability in severe cases. On-line detection can identify the incipient demagnetization of permanent magnets (PMs), as well as providing reference for subsequent fault-tolerant control, so as to avoid further demagnetization. Therefore, an on-line demagnetization detection method is proposed in this paper by using flux observer. First, an observer is established in the three-phase stationary reference frame by taking the stator currents and the amplitudes of the fundamental and harmonic components of flux as state variables. Then, three demagnetization indexes are presented to evaluate the properties of PMs based on the observed flux information. The proposed method can directly track the amplitude of harmonic flux and evaluate the severity of the demagnetization more comprehensively. Simulation and experimental results demonstrate the effectiveness of the proposed method.
Numerical and Experimental Analysis for the Dynamics of Flawed–Machining Rod–Disk Rotor with Inner Misalignment
Authors: Jin Xin , Liu Yi
Published: May 2022

The nonlinear dynamic effects of the misalignment between inner disks in a flawed–machining rod–disk rotor is studied. Non–uniform stress distribution due to inner misalignment is obtained based on 3D static solution. The concomitant unbalances, including constant mass eccentricity and speed–variant rotor bending, are taken into account in the nonlinear dynamics. The dynamic results show that rotor bending leads to stability reduction and vibration growth. There is a distinctive feature in that the rotor’s vibration goes up again after critical speed. The maximum allowable inner misalignment is obtained according to its stability boundaries. An uneven tightening method is also presented to reduce adverse effects when the inner misalignment exists. Moreover, an experiment is designed to measure the vibration characteristics for the rod–disk rotor bearing system with inner misalignment. The results show that the theoretical result of vibration amplitude of the flawed rod–rotor bearing system is basically consistent with the experimental value. It is also found that the precise rotor performs the periodic motion, but the flawed rod–disk rotor exhibits the period–doubling orbit. This phenomenon proves that the flawed rod–disk rotor loses stability more easily than the precise rotor due to inner misalignment. However, the amplitude of harmonic frequency components for the precise rod–disk rotor system is obviously larger than the flawed rod–disk rotor system with inner misalignment. The peak value of the vibration amplitude increases when the inner misalignment becomes larger. On the whole, this work presents numerical and experimental analysis to study the dynamics of flawed-machining rod–disk rotor with inner misalignment. It also establishes the relationship between centration precision and dynamic features.
Effect of Inlet Condition on the Performance Curve of a 10 MW Supercritical Carbon Dioxide Centrifugal Compressor
Authors: Tong Zhiting , Yang Xiaomin , Shang Pengxu , Ma Jingyuan , Zhang Yue , Zhang Chao
Published: May 2022

The influence of inlet condition upon the performance and stability for a 10 MW supercritical carbon dioxide centrifugal compressor is investigated using the computational fluid dynamics method. The inlet conditions which are considered are as follows, a constant inlet pressure of 8.0 MPa with varying inlet temperatures of 308.15 K, 308 K, 306 K, and 304 K, and a fixed inlet temperature of 308.15 K with different inlet pressures of 7.5 MPa, 8.0 MPa and 8.5 MPa. The numerical method with the k-omega based shear-stress-transport turbulence model is validated compared to the published experimental data. The numerical result shows that a small variation of temperature or pressure significantly has huge impact on the compressor performance operating near the critical point of supercritical carbon dioxide. As the compressor inlet pressure increases, the compression factor of the working fluid becomes lower, resulting in an enlargement of the pressure ratio. However, the decrease in inlet temperature leads to a higher compression factor of the working fluid and a reduced pressure ratio. The variation of the isentropic efficiency curve is mainly attributed to the change in the compressor inlet volumetric flow rate. The locations of the stall point and choke point are dependent on the values of inlet pressure and temperature.
A Novel Electronic Chip Detection Method Using Deep Neural Networks
Authors: Zhang Huiyan , Sun Hao , Shi Peng , Minchala Luis Ismael
Published: May 2022

Electronic chip detection is widely used in electronic industries. However, most existing detection methods cannot handle chip images with multiple classes of chips or complex backgrounds, which are common in real applications. To address these problems, a novel chip detection method that combines attentional feature fusion (AFF) and cosine nonlocal attention (CNLA), is proposed, and it consists of three parts: a feature extraction module, a region proposal module, and a detection module. The feature extraction module combines an AFF-embedded CNLA module and a pyramid feature module to extract features from chip images. The detection module enhances feature maps with a region intermediate feature map by spatial attentional block, fuses multiple feature maps with a multiscale region of the fusion block of interest, and classifies and regresses objects in images with two branches of fully connected layers. Experimental results on a medium-scale dataset comprising 367 images show that our proposed method achieved mAP0.5=0.98745 and outperformed the benchmark method.
Development of a Two-Dimensional Ultrasonic Device for Vibration-Assisted Milling
Authors: Rashed Engy Osama , Nofal Adel , Abd EI-Malek Ahmed H. , Hossam Mohab
Published: May 2022

Two-dimensional vibration-assisted machining (VAM) is a technique used to improve the cutting performance of machining processes by reducing the cutting forces and the temperature, extending the tool life, and improving the surface quality. Among the developed 2D vibratory devices for VAM, some limitations affect the process stability. This paper aims to present a 2D vibratory device to be applied during the milling process with a setup structure overcoming the limitations of the other developed devices. The device operates at high vibration frequency, prevents the coupling effect problem, provides precise vibration motion, and is easily manufactured, assembled, installed, and maintained. Finite element analysis (FEA) was applied to verify and modify the proposed design. The components of the final design were manufactured, and the generated vibration in each direction was evaluated. A detailed control system was presented to maintain the stability for the applied vibration. Finally, slot-milling experiments were conducted to validate and test the device’s performance. The cutting experiments results showed some benefits of applying the ultrasonic vibration and validated that the 2D vibratory device is functioning properly.
High Drug Capacity Doxorubicin-Loaded Iron Oxide Nanocomposites for Cancer Therapy
Authors: Kovrigina Ekaterina , Chubarov Alexey , Dmitrienko Elena
Published: May 2022

Magnetic nanoparticles (MNPs) have great potential in the drug delivery area. Iron oxide (Fe3O4) MNPs have demonstrated a promising effect due to their ferrimagnetic properties, large surface area, stability, low cost, easy synthesis, and functionalization. Some coating procedures are required to improve stability, biocompatibility, and decrease toxicity for medical applications. Herein, the co-precipitation synthesis of iron oxide MNPs coated with four types of primary surfactants, polyethylene glycol 2000 (PEG 2000), oleic acid (OA), Tween 20 (Tw20), and Tween 80 (Tw80), were investigated. Dynamic light scattering (DLS), ζ-potential, and transmission electron microscopy (TEM) techniques were used for morphology, size, charge, and stability analysis. Methylene blue reactive oxygen species (ROS) detection assay and the toxicity experiment on the lung adenocarcinoma A549 cell line were conducted. Two loading conditions for anticancer drug doxorubicin (DOX) on MNPs were proposed. The first one provides high loading efficiency (~90%) with up to 870 μg/mg (DOX/MNPs) drug capacity. The second is perspective for extremely high capacity 1757 μg/mg with drug wasting (DOX loading efficiency ~24%). For the most perspective MNP_OA and MNP_OA_DOX in cell media, pH 7.4, 5, and 3, the stability experiments are also presented. MNP_OA_DOX shows DOX pH-dependent release in the acidic pH and effective inhibition of A549 cancer cell growth. The IC50 values were calculated as 1.13 ± 0.02 mM in terms of doxorubicin and 0.4 ± 0.03 µg/mL in terms of the amount of the nanoparticles. Considering this, the MNP_OA_DOX nano theranostics agent is a highly potential candidate for cancer treatment.
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation
Authors: Ribeiro Jorge , Andrade Pedro , Carvalho Manuel , Silva Catarina , Ribeiro Bernardete , Roque Licínio
Published: May 2022

Aircraft maintenance is a complex domain where designing new systems that include Machine Learning (ML) algorithms can become a challenge. In the context of designing a tool for Condition-Based Maintenance (CBM) in aircraft maintenance planning, this case study addresses (1) the use of Playful Probing approach to obtain insights that allow understanding of how to design for interaction with ML algorithms, (2) the integration of a Reinforcement Learning (RL) agent for Human–AI collaboration in maintenance planning and (3) the visualisation of CBM indicators. Using a design science research approach, we designed a Playful Probe protocol and materials, and evaluated results by running a participatory design workshop. Our main contribution is to show how to elicit ideas for integration of maintenance planning practices with ML estimation tools and the RL agent. Through a participatory design workshop with participants’ observation, in which they played with CBM artefacts, Playful Probes favour the elicitation of user interaction requirements with the RL planning agent to aid the planner to obtain a reliable maintenance plan and turn possible to understand how to represent CBM indicators and visualise them through a trajectory prediction.
On the Use of Morpho-Syntactic Description Tags in Neural Machine Translation with Small and Large Training Corpora
Authors: Donaj Gregor , Sepesy Maučec Mirjam
Published: May 2022

With the transition to neural architectures, machine translation achieves very good quality for several resource-rich languages. However, the results are still much worse for languages with complex morphology, especially if they are low-resource languages. This paper reports the results of a systematic analysis of adding morphological information into neural machine translation system training. Translation systems presented and compared in this research exploit morphological information from corpora in different formats. Some formats join semantic and grammatical information and others separate these two types of information. Semantic information is modeled using lemmas and grammatical information using Morpho-Syntactic Description (MSD) tags. Experiments were performed on corpora of different sizes for the English–Slovene language pair. The conclusions were drawn for a domain-specific translation system and for a translation system for the general domain. With MSD tags, we improved the performance by up to 1.40 and 1.68 BLEU points in the two translation directions. We found that systems with training corpora in different formats improve the performance differently depending on the translation direction and corpora size.
Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring
Authors: Zhang Wenjie , Gui Wenhao
Published: May 2022

This paper discusses statistical inference and optimal design of constant-stress accelerated life testing for the Chen distribution under progressive Type-II censoring. The scale parameter of the life distribution is assumed to be a logarithmic linear function of the stress level. The maximum likelihood estimates of the parameters are obtained. Then, the observed Fisher information matrix is derived and utilized to construct asymptotic confidence intervals. Meanwhile, the parametric bootstrap methods are provided for the interval estimation. In addition, the Bayes estimates under the squared error loss function are obtained by applying the Tierney and Kadane technique and Lindley’s approximation. As for the optimal design, D- and A-optimality criteria are considered to determine the optimal transformed stress level. Finally, the simulation is carried out to demonstrate the proposed estimation techniques and the optimal criteria, and a real data set is discussed.
Research on Multicriteria Decision-Making Scheme of High-Speed Railway Express Product Pricing and Slot Allocation under Competitive Conditions
Authors: Fang Yuxuan , Zhang Xiaodong , Li Yueyi
Published: May 2022

Scientifically and reasonably pricing products and allocating slots are key to improving the profitability and competitiveness of high-speed railway expresses. In this research, we focus on the freight transportation pricing and slot allocation of high-speed railway express companies in a competitive environment. The goals of this research are to make decisions on the pricing and slot allocation schemes of the high-speed railway express between sections through the method of multicriteria decision making, and then to test these changes in reality. This research innovatively takes the high-speed freight Electric Multiple Unit (EMU) train as its research object and innovatively applies the revenue management theory to high-speed railway express research in a competitive environment, proposing a comprehensive decision-making model based on the sharing rate model. The results show that, by adopting the scheme proposed in this research, the income of high-speed railway express companies can be increased by 13.6%. In addition, the method proposed in this research also enriches the current theory on high-speed railway freight transportation, providing strategies for companies to expand their market and increase profit.
A Comprehensive Comparison of the Performance of Metaheuristic Algorithms in Neural Network Training for Nonlinear System Identification
Authors: Kaya Ebubekir
Published: May 2022

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems.
Chatterjea and C`iriC`
-Type Fixed-Point Theorems Using (α − ψ) Contraction on C*-Algebra-Valued Metric Space
Authors: Masmali Ibtisam , Omran Saleh
Published: May 2022

In the present paper, we provide and verify several results obtained by using the Chatterjea and C`iric` fixed-point theorems by using (α−ψ)-contractive mapping in C*-algebra-valued metric space. We provide some examples and an application to illustrate our results. Our study extends and generalizes the results of several studies in the literature.
Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization
Authors: Yang Qiang , Jing Yufei , Gao Xudong , Xu Dongdong , Lu Zhenyu , Jeon Sang-Woon , Zhang Jun
Published: May 2022

Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.
On the Probability of Finding Extremes in a Random Set
Authors: Răducan Anișoara Maria , Rădulescu Constanța Zoie , Rădulescu Marius , Zbăganu Gheorghiță
Published: May 2022

We consider a sequence Zjj≥1 of i.i.d. d-dimensional random vectors and for every n≥1 consider the sample Sn={Z1,Z2,…,Zn}. We say that Zj is a “leader” in the sample Sn if Zj≥Zk,∀k∈{1,2,…,n}and Zj is an “anti-leader” if Zj≤Zk,∀k∈{1,2,…,n}. After all, the leader and the anti-leader are the naive extremes. Let an be the probability that Sn has a leader, bn be the probability that Sn has an anti-leader and cn be the probability that Sn has both a leader and an anti-leader. One of the aims of the paper is to compute, or, at least to estimate, or if even that is not possible, to estimate the limits of this quantities. Another goal is to find conditions on the distribution F of Zjj≥1 so that the inferior limits of an,bn,cn are positive. We give examples of distributions for which we can compute these probabilities and also examples when we are not able to do that. Then we establish conditions, unfortunately only sufficient when the limits are positive. Doing that we discovered a lot of open questions and we make two annoying conjectures—annoying because they seemed to be obvious but at a second thought we were not able to prove them. It seems that these problems have never been approached in the literature.
Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm
Authors: Qais Mohammed H. , Hasanien Hany M. , Turky Rania A. , Alghuwainem Saad , Tostado-Véliz Marcos , Jurado Francisco
Published: May 2022

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA’s robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems.
A Distributed Formation Joint Network Navigation and Positioning Algorithm
Authors: Ye Lvyang , Yang Yikang , Ma Jiangang , Deng Lingyu , Li Hengnian
Published: May 2022

In view of the problem that the leader-follower joint navigation scheme relies too much on the absolute navigation and positioning accuracy of the leader node, under the conditions of distributed network-centric warfare (NCW) and to meet the location service accuracy, reliability, and synergy efficiency of the future integrated communication, navigation (ICN), we built a joint navigation and positioning system with low Earth orbit (LEO), airborne data link, and inertial navigation system (INS) as the core; designed a ranging and time-synchronization scheme of the joint navigation and positioning system; and established a joint navigation and positioning method for formation and networking based on mutual ranging and velocity measurement information between aircrafts. Finally, based on the designed LEO constellation, the universality, effectiveness, superiority, and potential superiority of algorithm are verified, respectively. The simulation results show that the scheme can meet the requirements of joint location services in challenging environments, and could be used as a reference scheme for future ICN integration.
The Effect of Cerebrolysin on Anxiety, Depression, and Cognition in Moderate and Severe Traumatic Brain Injury Patients: A CAPTAIN II Retrospective Trial Analysis
Authors: Mureșanu Ioana Anamaria , Grad Diana Alecsandra , Mureșanu Dafin Fior , Hapca Elian , Benedek Irina , Jemna Nicoleta , Strilciuc Ștefan , Popescu Bogdan Ovidiu , Perju-Dumbravă Lăcrămioara , Cherecheș Răzvan Mircea
Published: May 2022

Background and Objectives: Traumatic brain injuries represent an important source of disease burden requiring emergency inpatient care and continuous outpatient tailored rehabilitation. Although most TBIs are mild, patients are still developing post-TBI depression, anxiety, and cognitive impairments. Our secondary retrospective trial analysis aimed to (1) analyze correlations between HADS-Anxiety/HADS-Depression and scales that measure cognitive and motor processes in patients treated with Cerebrolysin compared to the placebo group and (2) compare anxiety and depression scores among the two treatment groups. Materials and Methods: Our secondary retrospective analysis focused on TBI patients with moderate and severe disability divided into two groups: Cerebrolysin (treatment) and saline solution (procedural placebo). We analyzed data from 125 patients. We computed descriptive statistics for nominal and continuous variables. We used Spearman’s correlation to find associations between HADS and other neuropsychological scales and the Mann–Whitney U test to compare HADS-Anxiety and HADS-Depression scores among the two study arms. Results: Our sample consisted of patients with a mean age of 45.3, primarily men, and with a 24 h GCS (Glasgow Coma Scale) mean of 12.67. We obtained statistically significant differences for HADS-Anxiety during the second and third visits for patients treated with Cerebrolysin. Our results show that Cerebrolysin has a large effect size (0.73) on anxiety levels. In addition, there are positive and negative correlations between HADS-Anxiety and Depression subscales and other neuropsychological scales. Conclusions: Our secondary database analysis supports the existing body of evidence on the positive effect of Cerebrolysin on post-TBI mental health status. Future confirmatory trials are necessary to clarify the link between the intervention and measured outcomes.