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Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

The selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box–Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.

SRMs have high torque ripples. Modifying the stator and rotor shape of SRMs is one of their methods to remove torque ripples. In this study, new rotor and stator geometries have been proposed for SRM. Performance characteristics are obtained and dynamic analyses are carried out for both novel and classical 6/4 SRMs. FFT analyses of the torque characteristics are also performed for the both SRMs. The changes in performance and the FFT results of torque characteristics are determined for both the current-controlled and uncontrolled operations in both SRMs. 23.60% and 15.20% improvements in torque ripples are achieved for the uncontrolled and current-controlled operations in SRMs respectively. When the FFT analyses are applied to the torque characteristics, the THD values are obtained as 21.19% and 8.31% in the uncontrolled and controlled motor current operations respectively. A 122 W three-phase 6/4 SRM simulation based on the MATLAB–SIMULINK environment is performed to validate the improvement of the torque ripple of the proposed model.

Poly(ethylene oxide) (PEO)-based polymer electrolytes are a promising class of materials for use in lithium-ion batteries due to their high ionic conductivity and flexibility. In this study, the effects of polymer architecture including linear, star, and hyperbranched and salt (lithiumbis(trifluoromethanesulfonyl)imide (LiTFSI)) concentration on the glass transition (Tg), microstructure, phase diagram, free volume, and bulk viscosity, all of which play a significant role in determining the ionic conductivity of the electrolyte, have been systematically studied for PEO-based polymer electrolytes. The branching of PEO widens the liquid phase toward lower salt concentrations, suggesting decreased crystallization and improved ion coordination. At high salt loadings, ion clustering is common for all electrolytes, yet the cluster size and distribution appear to be strongly architecture-dependent. Also, the ionic conductivity is maximized at a salt concentration of [Li/EO ≈ 0.085] for all architectures, and the highly branched polymers displayed as much as three times higher ionic conductivity (with respect to the linear analogue) for the same total molar mass. The architecture-dependent ionic conductivity is attributed to the enhanced free volume measured by positron annihilation lifetime spectroscopy. Interestingly, despite the strong architecture dependence of ionic conductivity, the salt addition in the highly branched architectures results in accelerated yet similar monomeric friction coefficients for these polymers, offering significant potential toward decoupling of conductivity from segmental dynamics of polymer electrolytes, leading to outstanding battery performance.

In this study, new 4-chloro-5-(2-isopropyl-5-methylphenoxy)phthalonitrile compound, containing bioactive thymoxy group, and its metal-free phthalocyanine and metallo-phthalocyanine derivatives were synthesized for the first time. Their structures were determined by spectroscopic methods such as FTIR, UV–Vis, 1H-, and 13C-NMR (for phthalonitrile derivative), MALDI-TOF mass spectrometry (for phthalocyanine derivatives) and elemental analysis as well. The phthalocyanines showed excellent solubility in polar and nonpolar solvents without aggregation and absorb at long wavelengths with their high molar coefficient. In N,N-dimethylformamide, the effects of the type of central metal ions [metal-free, indium(III) acetate, lutetium(III) acetate, magnesium(II) or zinc(II)] in the phthalocyanine, containing bioactive thymoxy group, cavity on the spectroscopic, photophysical, and photochemical properties of the phthalocyanines were determined. These features are compared with each other. Lutetium(III) acetate phthalocyanine did not show any fluorescence, while metal-free phthalocyanine and indium(III) acetate phthalocyanine showed low fluorescence. It was determined that magnesium phthalocyanine significantly enriched the fluorescence, and zinc phthalocyanine had appropriate and sufficient fluorescence. Lutetium(III) acetate and zinc(II), especially indium(III) acetate phthalocyanines, could produce large amounts of singlet oxygen. Metal-free and magnesium phthalocyanines had the capacity to produce sufficient singlet oxygen (it means production of enough amount of singlet oxygen by a photosensitizer candidate during PDT applications). All phthalocyanines have sufficient and suitable photostability (it means an ideal photosensitizer should be stable under light irradiation until complete its PDT activation, and it should be decomposed after its PDT activation so that it does not accumulate in the body). With these determined properties, magnesium(II), especially indium(III) acetate and zinc(II) phthalocyanines, may be suitable candidates as type II photosensitizers for photodynamic therapy applications. Lutetium(III) acetate phthalocyanine may be a photosensitizer candidate in photocatalytic applications.

© 2022 The Author(s)Battery energy storage systems (BESS) are essential for flexible and reliable grid performance as the number of renewable energy sources in grids rises. The operational life of the batteries in BESS should be taken into account for maximum cost savings, despite the fact that they are beneficial for economical grid operation. In this context, this paper present a new battery cycle counting perspective for energy management of grid-connected BESS. For this purpose battery\"s one full charge–discharge cycle characteristic is compared with the operating battery charge–discharge cycle every time step. This comparison was explained mathematically and graphically in detail. The results are compared with the rain flow counting method which is the most popular cycle counting algorithm. Consequently, this cycle counting approach successfully counts the battery charge/discharge cycles and it has shown that has an advantage for BESSs due to being specifically developed just for batteries.

The cold filter plugging point (CFPP) value of biodiesel fuel blends need to be improved because of it has been reported that utilizing high concentrations of biodiesel blends in cold climates can cause major issues. Therefore, the main objective of the study is to improve CFPP value of biodiesel blends using ethyl proxitol additive. It is also to examine combustion and emission of the blends behavior. Biodiesel blends were prepared as; 5% ethyl proxitol-10% biodiesel-85% ultra-low sulfur diesel (ULSD) named as E5B10 and 5% ethyl proxitol-20% biodiesel-75% ULSD named as E5B20 fuel. These fuel blends were compared with ULSD fuel in a compression ignition engine at a constant engine speed of 1500 rev/min and four different loads (idle, 1, 2, and 3 bar BMEP) of eddy current dynamometer. The addition of ethyl proxitol led to a decrease in in-cylinder pressure and temperature due to an about 1˚ later SOC and 2˚ earlier EOC compared to ULSD. The pressure rise rate was slightly higher compared to ULSD at both low and high loads, with a difference of about 1 bar per degree. As a result, these blends with improved CFPP value can be used in cold climates.

Noise barriers are one of the common solutions to control road traffic noise. Many studies have also shown that noise barriers cause reductions in near-road air pollutant concentrations. In this study, the simultaneous effects of a specific noise barrier application on near-road noise and air pollution at a specific location were investigated. In this context, air pollution, noise, and meteorological parameters were measured simultaneously at two points, road and receptor sides of a 50 m long, 4 m high glass fiber reinforced concrete noise barrier on a highway section. Results indicated that the noise barrier has an average 23 % reduction effect on the NOx concentration in addition to the noise level reduction at the receptor side. Besides, bi-weekly average passive sampler measurement results for BTEX pollutants indicate lower values at the receptor side of the barrier compared to the free field measurement results. In addition to real-time and passive sampler measurements, NOx and noise dispersions were modeled using RLINE and SoundPLAN 8.2 software, respectively. Comparisons of the measurement results with the model results indicated strong correlations. Model-calculated NOx and noise values under the free field conditions are highly compatible with a correlation coefficient (r) of 0.78. Although the noise barrier has a reduction effect on both parameters, it has been observed that their dispersion mechanisms are different. This study showed that noise barriers considerably affect the dispersion of road-sourced air pollutants at the receptor side. Further studies are needed to optimize noise barrier designs with different physical and material properties and application scenarios considering noise and air pollutants together.

Electrochemical properties of MPcs were determined with electrochemical and in-situ spectroelectrochemical measurements to investigate the influence of the metal center and the number and positions of the substituents. While H2Pc only illustrated Pc based redox processes, the incorporation of TiO2+ and Cl−1-Mn3+ cations into the cavity of Pc ring considerably altered the redox richness of the complexes. Two metal-based reduction couples were observed in addition to the Pc-based processes for both of TiOPc and MnPc. Moreover, altering the number and position of 4-(2-phenylpropan-2-yl)phenoxy substituents slightly influenced the basic redox activity of Pc ring and metal centers. All of these results supported the proposed structure of the complexes. During the redox reactions, pronounced spectral changes were observed with the in situ spectroelectrochemical measurements which caused distinct color changes. Rich redox responses and pronounced spectral and color changes of the complexes indicated their worth as functional material in various electrochemical technologies.

Considering the increasing demand for cloud computing, and the financial and environmental impact of the increasing energy consumption trend of data centers, improving energy efficiency is vital for cloud service providers. In this study, an energy-aware virtual machine scheduling model is proposed which is based on the multi-unit nondiscriminatory combinatorial auction. The model includes a powerful bidding language that allows users to declare their complicated virtual machine requests using logical AND and OR relations along with the time constraints. The study also presents the formal definition of the model and the associated optimization problem for determining the optimum schedule and energy-efficient placement of VMs on physical servers. The optimization problem is formulated using integer linear programming and several heuristic solution methods including the Genetic Algorithm are proposed for this problem. The performances of the model and the proposed heuristics are assessed on a comprehensive test suite. The proposed model is estimated to provide approximately a 37% improvement in revenues, and the solution methods are estimated to provide high-quality solutions within only 5% of the optimum which enable the model to be deployed in large-scale clouds.

The most distinctive features of sound insulation structures are their flexibility and porosity. Therefore, the flexible epoxy matrix material was made cellular using a suitable foaming agent. In addition, hollow glass microspheres (HGMs) were added to the epoxy matrix. Thus, the sound wave refraction was increased by obtaining cavities in the cell walls. Structures with different densities and voids were created by changing the ratios of the filling material and foaming agents used in the sandwich. An aluminum (Al) honeycomb was used to protect the insulation materials\" structural integrity and ensure the homogeneous distribution of sound waves. The effect of density differences on sound insulation values was investigated. The mechanical properties of sandwich structures were determined using compression and three-point bending tests. The distribution of the filler in the matrix was visualized using SEM. TGA, DSC, thermal conductivity, dielectric, and flammability tests were also performed to determine their thermal, electrical, and flammability properties. During the formation of cells in the flexible epoxy, the HGMs were positioned in the cell wall by internal gas pressure. Low-density structures performed better at low frequencies, while high-density structures at high frequencies.

Engine researchers focused on alcohol fuels since the invention of diesel engines in the 1900s, and the rise in petrochemical costs in the 1970s triggered this concern. This study investigates the impacts of the injection start timing, pilot injection application, and boost air pressure increase, on combustion and exhaust emissions in a common rail diesel engine fueled with ethanol/butan-2-ol/diesel blends. The lowest combustion noise was obtained in the pilot injection application as 83.8 dB in E15B3. The results indicated that the peak point of cylinder gas pressure rose by more than 5 % in the application of pilot fuel injection and advanced injection timing, compared to conventional engine operating conditions. However, maximum CO2 and NOx were seen in the pilot injection application using FBDF as 5.7 % and 670 ppm, respectively. As alcohol rate increases in fuel blends, the average 1.3 ◦CA in the ignition delay period was increased, while the total combustion period was shortened more than 6 ◦CA. This result shows that the combustion reactions of alcohol/diesel fuels occur faster than pure diesel. However, the variation in the ignition delay and total combustion periods of the test fuels considerably reduced with pilot fuel injection application. These results indicates that pilot fuel injection may be applied very controlled according to changing engine conditions. It was calculated in statistical analysis that except for the coefficient variation of the maximum pressure increase rate, the other coefficient variation values were relatively stable and below 3 %. In addition, it was determined that the results of the finite element analysis are proportional to the pressure values obtained experimentally

© 2023 Wiley Periodicals LLC.In this study, we have synthesized a series of 3-(pyridin-2-yl)-2-(pyridin-2-ylimino)thiazolidin-4-ol derivatives regioselectively from 2-iminothiazolidin-4-ones using LiAlH4 at room temperature. Due to the presence of the restricted rotation around the N3-Caryl single bond, the formation of M/P isomers was observed. The OH group of the hemiaminal was found to orient itself on the same side with pyridyl nitrogen during this restricted rotation to form an intramolecular hydrogen bond, which was demonstrated by the computational DFT study. This orientation presumably inhibited the occurrence of dehydration and stabilized the molecule.

In recent experiments on the solidification of the binary eutectic alloy succinonitrile-(D)camphor carried out on board of the International Space Station (ISS), a transition from rod to lamellar patterns was observed for low growth velocities. The transition was interpreted in terms of a competition between a propagative instability of lamellae and a drift induced by a transverse temperature gradient. Phase-field simulations of a symmetric model alloy support this scenario: for a fixed transverse temperature gradient, the transition from rods to lamellae occurs for a critical composition at fixed velocity, and for a critical velocity at fixed composition. Since the alloy and control parameters used in experiments and simulations are different, our results strongly suggest that this morphological transition is generic for eutectic alloys.

The finite element method is the most common method used to solve engineering and mathematical model problems. Related solution areas include structural analysis, heat transfer, mass transport and electromagnetic potential. FEM is a specific numerical method used to solve partial differential equations in two or three space variables. To solve a problem, FEM breaks up a large system into smaller, simpler parts called finite elements. First, a three-dimensional model of the system to be analyzed is produced. While defining the model, the materials and connection types found in the model are also deter-mined. Afterwards, the model is divided into small parts and analyzed under specified conditions. A crash test will be conducted in this study. For this, the conditions of the vehicle parts will be examined by hitting a wall under the conditions of which the model design of the vehicle is determined. Crash tests were carried out for two different bumper thicknesses on the same vehicle and the test results were examined. The vehicle speed is modeled as 20 m/s in the simulation carried out from the moment of impact until 0.14 seconds later. The effect of buffer thickness on axial displacements at impact and energy changes are presented.

IEEEWhile the zeitgeist of today prescribes localization as a key to eliminate the excessive transportation for manufacturing firms, firm-level practical and workable guidelines that go beyond maxims are scant. Laden with analytical methods, this paper offers a phronetic, customizable framework based on Lean Six Sigma (LSS). The framework mimics Define-Measure-Analyze-Improve-Control (DMAIC) process. The early phases of the framework, ex-ante, prescribe the use of a variety tools and techniques from Lean Six sigma (LSS) and employ fuzzy analytic hierarchy process (FAHP) to extract the vital few root-causes that may impede localization. The latter phases suggest the use of design of experiments (DOE) to evaluate the root-causes, ex-post. The paper outlines a clear and established procedure and demonstrates each analytical phase using evidence from a two-year real-world application in steel industry.

© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Many prediction methods proposed in the literature can be concerned under two main headings: probabilistic and non-probabilistic methods. In particular, as a kind of non-probabilistic model, artificial neural networks (ANNs), having different properties, have been commonly and effectively used in the literature. Some ANNs operate the additive aggregation function in the structure of their neuron models, while others employ the multiplicative aggregation function. Recently proposed dendritic neural networks also have both additional and multiplicative neuron models. The prediction performance of such an artificial neural network will inevitably be negatively affected by the outliers that the time series of interest may contain due to the neuron model in its structure. This study, for the training of a dendritic neural network, presents a robust learning algorithm. The presented robust algorithm is the first for the training of DNM in the literature as far as is known and uses Huber\"s loss function as the fitness function. The iterative process of the robust learning algorithm is carried out by particle swarm optimization. The productivity and efficiency of the suggested learning algorithm were evaluated by analysing different real-life time series. All analyses were performed with original and contaminated data sets under different scenarios. The R-DNM has the best performance for the original data sets with a value of 2.95% in the ABC time series, while the FTSE showed the best performance in approximately 27% and the second best in 33% of all analyses. The proposed R-DNM has been the least affected by outliers in almost all scenarios for contaminated ABC data sets. Moreover, it has been the least affected model by outliers in approximately 71% of the 90 analyses performed for the contaminated FTSE time series. The obtained results show that the dendritic artificial neural network trained by the proposed robust learning algorithm produces the satisfactory predictive results in the analysis of time series with and without outliers.

While speaking anxiety is one of the most problematic aspects observed in English as a foreign language (EFL) context, a limited number of studies focused on the impact of collaborative learning on speaking anxiety in different learning environments. This study investigates the effects of collaborative tasks on foreign language learners' speaking anxiety in face-to-face and online learning contexts. In this experimental study, the data were collected from 34 foreign language students with a questionnaire consisting of a background part and a scale for measuring speaking anxiety. Even though the results revealed changes in speaking anxiety levels after the collaborative instruction in the face-to-face environments, no differences were found between face-to-face and online environments in terms of the impact of collaborative instruction on learners' speaking anxiety.

This study reports new data for 1028 non-fatal occupational accidents dated between January 2010and April 2015 by applying the analytical hierarchy process (AHP) technique. A comprehensive surveywas conducted at four shipyards in Tuzla, Istanbul and Yalova regions in Turkey and a workplacequestionnaire appropriate for the AHP technique was carried out. The obtained results indicated thatinadequate safety equipment and protective clothing, unsuitable usage of machines and tools, and disobeyingoccupational health and safety (OHS) procedures were the most common risk factors for theaccidents. Hence the preventive measures could be identified by analyzing non-fatal accident data.After the identification of the descriptive measures, the priority order of these measures was asked ofthe occupational safety professionals in the shipbuilding industry, and we used the AHP method toevaluate the results.

© 2023 Informa UK Limited, trading as Taylor & Francis Group.Deep learning methods have been successfully applied to remote-sensing problems for several years. Among these methods, CNN-based models have high accuracy in solving the land classification problem using satellite or aerial images. Although these models have high accuracy, this generally comes with large memory size requirements. However, it is desirable to have small-sized models for applications, such as the ones implemented on unmanned aerial vehicles, with low memory space. Unfortunately, small-sized CNN models do not provide as high accuracy as with their large-sized versions. In this study, we propose a novel method to improve the accuracy of CNN models, especially the ones with small size, by injecting traditional features into them. To test the effectiveness of the proposed method, we applied it to the CNN models SqueezeNet, MobileNetV2, ShuffleNetV2, VGG16 and ResNet50V2 having size 0.5 MB to 528 MB. We used the sample mean, grey-level co-occurrence matrix features, Hu moments, local binary patterns, histogram of oriented gradients and colour invariants as traditional features for injection. We tested the proposed method on the EuroSAT dataset to perform land classification. Our experimental results show that the proposed method significantly improves the land classification accuracy especially when applied to small-sized CNN models.

© 2023, The Minerals, Metals & Materials Society.It is becoming crucial to design/fabricate eco-friendly, sustainable electronic and photonic devices to minimize the carbon footprint for future systems. In this study, we have demonstrated a steady photoresponse enhancement of the self-powered GaN ultraviolet photodetector (GaN-UVPD) via sequentially deposited gold nanoparticles (Au NPs) under 254, 302, and 365 nm UV light exposure. The AuNP-deposited GaN-UVPD exhibited excellent responsivity of 0.65 A/W and detectivity of 6.51 × 1012 cm.Hz1/2 W−1 under 302 nm UV light without any external power. Moreover, the sensitivity of the device increased from 1.98 × 106% to 3.32 × 106% following Au nanoparticle deposition. Additionally, the plausible mechanisms for the self-powered and Au nanoparticle-induced photoresponse enhancement have been discussed. In brief, the high-performance photoresponsivity of our self-powered GaN-UVPD could find many useful applications in sustainable energy and eco-friendly optoelectronic devices.

In this study, the average power consumption of an electrode welding machine during the welding process was estimated using the features of the sound emitted during welding. First, the instantaneous values of electrode current and voltage and the sound emitted during the welding process were recorded simultaneously. The minimum, maximum, average, root mean square (RMS), and energy values of the sound data were found and feature extraction was performed, and the instantaneous power and average power values were calculated using the instantaneous current and voltage values. Three Adaptive Neuro-Fuzzy Inference Systems (ANFIS) using the sound features as inputs and average power values as outputs were created, and their results were compared. The average power values consumed during the welding process have been successfully estimated at a rate of 87-95%.

This paper presents a methodology for generating geometries for interior permanent magnet (IPM) motors in electric vehicles (EVs) through the application of artificial intelligence (AI) and image processing (IP) techniques. Due to the implementation of green agreements and policies aimed at reducing greenhouse gas emissions, EVs have become popularity. As a consequence, the improvement studies on the powertrain and battery system of EVs are focused. Especially for the powertrain, design optimization studies of e-motor have increased in the literature. One of the most widely used e-motor topologies is interior permanent magnet (IPM) motor. However, designing the IPM motor presents a challenge due to the dynamic considerations with geometric constraints. Therefore, e-motor designers encounter challenges related to determining initial geometry and the long time of the optimization process. To address these challenges, a novel approach is proposed that leverages machine learning (ML) techniques in combination with IP to generate initial geometries and design parameters for IPM motors. The proposed approach generates images of the motor geometry and extract dimensional features from the resulting images by using artificial neural networks (ANNs). The proposed method performs an analysis of the input vectors to reduce their size using techniques such as Histogram, 2D Maximum, 2D Mean, 2D Minimum, 2D Standard Deviation, and 2D Variance to enhance feature extraction. Additionally, FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) are used to improve the neural network process in generating the image geometry. Further, the generated image geometry is improved by applying digital filtering techniques such as Log, FFT, Log+FFT, Laplacian, Sobel, and Histogram Equalization. Finally, the trained ANNs are tested to validate the results by using Ansys RMXprt and Maxwell. Eventually, the proposed method represents an innovative solution to generating initial geometries for IPM motors in EVs that satisfies desired design requirements. This approach leverages the power of AI and image processing techniques, which could lead to significant improvements in the optimization process for IPM motors, accelerate the designer’s analysis process, and enhance the performance of EVs.

The ambiguity of a polysemous word, regardless of its language, is referred to as the ability to have more than one meaning. Many techniques from Word Sense Disambiguation (WSD) have been used to clarify the ambiguity. With these techniques, the correct sense of a word according to its particular context can be determined. In English, there exist many ambiguous words. For example, the word cell may have different meanings based on its context. For instance, it may mean the basic structural unit of all organisms in biology domains, or it may mean a device that delivers an electric current as the result of a chemical reaction in textual materials about technology or electricity, or it completely means another different thing: a room where a prisoner is kept in textual materials about prison. In this study, to enhance the performance of the WSD task, we develop a methodology with Graph Convolutional Neural Networks (GCN), including normalization, thresholding, and regularization modules. We attempt to improve the traditional Text GCN algorithm with a semantic diffusion process, which increases the classification performance of the WSD task. As far as we know, there is no work for such a comprehensive classifier for the WSD task in English. To show the effect of the suggested model, we performed experiments on the SensEval dataset, which is a very popular dataset and benchmark in the WSD domain. The experiment results show that the regularization effect and diffusion process in GCN and Text GCN architectures are powerful strategies for the WSD task.

© 2022, Akadémiai Kiadó, Budapest, Hungary.Diversity of editorial boards of academic journals has recently become one of the most frequently discussed topics in scientific communication. Diversity of editorial board is considered to be an important element affecting the journal, the field and the region in terms of the number and diversity of publications. In parallel with this increasing awareness in the academic circles, many studies on diversity in the literature at both the field and journal levels have been carried out. In this study, the editorial boards of academic journals in the field of Library and Information Sciences were analyzed in terms of accommodating geographical representation, and the effect of such diversity, if any, on the number of publications was investigated. The data set consisted of the journals in the mentioned field in the SCImago web page Journal Rankings. The analyzes were carried out based on 6126 persons registered to 212 journals, including chief editors. The findings of the study showed that 75.11% of all members of editorial boards were from North American and European countries. While Asian countries (Central and South Asia, and East and Southeast Asia) were represented by 13.16% in journals in the field of Library and Information Sciences. A linear correlation was found between the regions where the chief editors of the journals were registered and the assignments of the editorial boards. Moreover, a linear correlation was found between the editors of journals (chief editors and other editors assigned to boards) and the scientific outputs (citable documents, citations, and h-index) in terms of regional relationship. The consistency of the results with those of other studies in the literature clearly demonstrates a need for more transparent processes in academic publishing. A policy to ensure a fair and diverse representation in boards of journals features numerous multifaceted positive effects.

The assembly line balancing problem is an important issue for every manufacturing company. A balanced assembly line ensures that a product is produced in the optimum time, and as a result of this effect, less machinery, materials and labour are used during this production. In this article, theoretical information about assembly line balancing has been given, and then the data needed for assembly line balancing has been obtained by making a time study of the production of Downlight Luminaire. With these data obtained, assembly line balancing was done and compared by using Hoffman and Comsoal methods. The aim of this study is to investigate the applicability of Hoffman and Comsoal methods, which are one of the heuristic assembly line balancing methods, in the assembly lines of the companies producing in the lighting sector.

In the present study, the effect of different ratios of GelMA concentration has been exhibited for wound dressing implementation by the electrospinning method using a new polymer combination of Gelatin methacrylate (GelMA)/Polycaprolactone (PCL)/Chitosan (CS). The nanofiber composites were fabricated due to their biocompatible, biodegradable, improved mechanical strength, low degradation rate, and hydrophilic nature to develop cell-mimicking, cell adhesion, proliferation, and differentiation. Different concentrations of GelMA were added to the PCL/CS solution as 5, 10, and 20 wt%, respectively, in the formic acid/acetic acid (7:3) solution. A photoinitiator was added to the solution for photo-crosslinking of GelMA. The influence of different solution concentrations (5, 10, and 20 wt%) on the structure’s nanofiber production and fiber morphology was examined. SEM micrographs revealed that varied GelMA concentrations resulted in suitable and stable nanofiber composites. The average diameter of nanofiber composites grows as the GelMA concentration rises. FTIR, DSC, tensile test, degradation, and swelling test were evaluated. The results demonstrated that high mechanical strength, hydrophilic properties, and a slow degradation rate were observed with the presence and increment of GelMA concentration within the nanofiber composites. The antibacterial potential of GelMA/PCL/CS nanofiber composites was evaluated against P. aeruginosa and S. aureus using a disc diffusion assay. In vitro cell culture research was conducted by seeding NIH 3T3 fibroblast cells on nanofiber composites, proving these cells’ high cell proliferation rate, viability, and adhesion. 10 wt% GelMA-based nanofiber composites were found to have great potential for wound dressing applications.

Halophilic organisms are a novel attractive option as cell factories for the production of industrially valuable bioproducts. Halomonas elongata is the cell factory of choice for ectoine production, but its levan production has not been well researched. Based on this scientifc motivation, in this study, we evaluated the chemical and biological properties of levan produced by the halophilic extremophile Halomonas elongata 153B (HeL). First, the central composite design was used to determine the optimal process variables for maximum levan biosynthesis. Then, the levan produced from HeL was purifed, quantifed, and chemically characterized with FTIR, 1 H-NMR, and GPC analyses. This was followed by antioxidant, anti-infammatory, antibioflm, and antimicrobial activity tests to assess its biological activities as well as a cytotoxcity assay. Maximum levan yields of 5.13±0.38 g/L were achieved after dialysis at the optimum levels of process variables. The 1 H-NMR spectrum of HeL revealed characteristic signals. It showed a strong antioxidant activity of 67.88% and the best radical scavenger. At a concentration of 400 µg/mL, HeL showed the most anti-infammatory efcacy. Also, at all indicated concentrations (250, 500, 750, and 1000 μg/mL) HeL, acted against bioflms formed by Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 6538, Pseudomonas aeruginosa ATCC 11778, Candida albicans ATCC 10231. Furthermore, HeL displayed antimicrobial activities against all strains tested. Finally, HeL showed high Cell viability in all dosages and no cytotoxicity was observed. In light of these results, HeL may have high potential in the medical, pharmaceutical and dermo-cosmetics industries.

Cations with suitable sizes to occupy an interstitial site of perovskite crystals have been widely used to inhibit ion migration and promote the performance and stability of perovskite optoelectronics. However, such interstitial doping inevitably leads to lattice microstrain that impairs the long-range ordering and stability of the crystals, causing a sacrificial trade-off. Here, we unravel the evident influence of the valence states of the interstitial cations on their efficacy to suppress the ion migration. Incorporation of a trivalent neodymium cation (Nd3+) effectively mitigates the ion migration in the perovskite lattice with a reduced dosage (0.08%) compared to a widely used monovalent cation dopant (Na+, 0.45%). The photovoltaic performances and operational stability of the prototypical perovskite solar cells are enhanced with a trace amount of Nd3+ doping while minimizing the sacrificial trade-off.

Packaging must have a good commercial appearance and is generally obtained by ink transferred to its substrate. It is important that the ink used in packaging printing is produced from environmentally friendly and sustainable raw materials as well as being suitable for the printing system. The increasing demand in the field of printed packaging and the scarcity of resources to meet this demand have accelerated the search for new sources for inks. For this purpose, inks produced in the laboratory using a modified cellulose-based binder, a commercial acrylic resin and a commercial soybean protein were compared with a commercial ink. As a result of the study, it was determined that the printability properties of the ink obtained by using the modified cellulose-based binder were better than the ink obtained with commercial soybean protein. It was determined that it showed printability properties close to the ink produced with commercial acrylic binders. By using modified cellulose-based water-based flexographic ink instead of other commonly used binders, more environmentally friendly sustainable inks can be produced.

This study aims to provide a great deal of insight into how risk perception and purchase intention differ for the consumer with different online shopping motives. Data were collected through a questionnaire with 359 voluntary consumers aged 18–28 living in Istanbul. 14 motives leading them to online shopping through digital platforms for textile products were defined with the focus group technique. First, clustering analysis was conducted to define consumers according to the guiding motives in online shopping. Next, whether the consumer segments identified by these motives have different levels of risk perception and purchase intention was investigated via ANOVA analysis.

In this article, a kinematic continuous time swarm model is considered by extending the results in the paper \"A class of attractions/repulsion functions for stable swarm aggregations\" (as shown in the International Journal of Control, vol. 77, pages 1567-1579). The extension regards a more general class of attraction/repulsion functions that can be used to explain the formation of rotational patterns in a swarm of agents. The main characteristic of this model is the introduction of two coordinate-coupling matrices weighting both the attractive and the repulsive interactions among the agents. A stability analysis is presented to characterize the swarm in terms of size and cohesiveness. Moreover, a class of attraction/repulsion functions is presented to guarantee the cohesiveness of the swarm in a polytopic region and a steady-state motion of the agents in a rotational frame around the swarm centroid. Numerical simulations are provided to illustrate the obtained results.

The active SCR aftertreatment system is one of the most crucial technology for the NOx reduction of diesel engines. One of the essential parameters of this technology is the urea spray performance on the catalyst.This study presents an experimental and numerical investigation of urea spray behavior used in the heavy-duty diesel engine\"s selective catalytic reduction (SCR) aftertreatment systems. The custom test rig is designed and built to simulate exhaust aftertreatment systems of heavyduty diesel vehicles. Urea injector parameters were observed in this test rig with optic windows for spray and flow visualization. This test rig is available to simulate diesel engines in the sense of exhaust mass flow rate, temperature and spray control unit. The discussion is made about the effects of droplet size of spray and velocity distribution upon flow characterization.A detailed assessment of the numerical model was presented, and validation was carried out for different interest measurement locations. The predicted droplet size distributions, breakup performance, and velocities are numerically and correlated with the experimental data. The validated model is subsequently used to study the urea-flow mixing dynamics to develop a urea mixer numerically. Test results show that smaller droplets enhance the mixing and thus catalyst efficiency. Mixer design performance can be assessed numerically in the droplet size break up based on the developed criteria called mixer performance criteria. Upstream and downstream of the mixer, droplet size can be extracted from the simulation, and different mixer designs can be compared in terms of the breakup performance.(c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/

The use of bio-based building blocks for the synthesis of polymers is increasing day by day. Among the bio-based building blocks, eugenol is a highly promising monomer for the preparation of thermoset materials. In this study, we combined thiol-ene photopolymerization (TEP) and thermal azide-alkyne cycloaddition click reactions to prepare eugenol-based thermally stable, P-, N-, and Si-containing networks. To this end, we synthesized a P-containing, eugenol-based monomer bearing azide group and a siloxane compound containing an alkyne group. By mixing these monomers with multifunctional thiols and by utilizing a dual-curing strategy, we managed to obtain optically transparent and thermally stable coatings with excellent adhesion to glass substrates. Thermal stability, optical transmittance, pendulum hardness, solvent resistance, and the adhesion performance of the coatings were evaluated. The gel contents of the thermoset materials were found to be over 95%. At 600 °C, the char yields of the dual-cured coatings were found to be over 30%. Coatings were also found to be resistant to acidic and basic conditions as well as solvents.

Inadequate environmental resources and overpopulation reveal the need to protect and recover natural resources attentively. In this sense, the reverse logistics concept emerged as a key solution since it deals with product flow from the final user to the origin. There are various items that need to be considered in well-planned reverse logistics network designs and one of these items is batteries which include hazardous and precious materials in it. Hence, waste management of batteries via recycling becomes a very significant issue from both economic and environmental benefits. Accordingly, depending on the importance of the topic, a two-stage methodology is proposed in this study for providing a network design under multiple objectives. Within the first stage, the importance weights of objectives are obtained via Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and they are found as 0.248 for cost minimization, 0.3 for carbon emission minimization, 0.256 for employment rate maximization and 0.196 for development rate maximization. Afterward, in the second stage, a Multi-Objective Mixed Integer Linear Programming Model (MO-MILP) is developed to design the reverse logistics network and an application is performed in Turkey for validation. The model is solved for various scenarios including different quantities to be collected. Hence, it is obtained that the satisfaction degrees for employment and development objectives are 100% in all of the scenarios. However, the satisfaction degree of carbon emission minimization is around 96% and the less satisfied objective is the cost minimization having a satisfaction degree of 75% on average.

The city of Istanbul is constantly exposed to air pollution due to its high population, heavy traffic — sea and air transport — and urban industry. This study basically aims to determine the recent level of airborne heavy metals, using lichen biomonitoring method. The cosmopolitan foliose lichen Xanthoria parietina growing abundantly on trees was sampled from 16 urban green spaces in 8 districts on the Anatolian side of Istanbul. Multi-element analysis by ICP-MS was applied to measure the accumulation of 10 potentially toxic trace elements in lichen samples. Spatial distributions of element levels in the air in the sampling areas are shown by mapping. According to the analysis data, the sequence of element deposition levels in lichen samples was as follows; Al > Fe > Mn > Zn > Cr > Cu > Pb > V > Ni > As. Most of the measured atmospheric element amounts yielded results much higher than the reference material in all areas. It was detected that the highest pollution in terms of Al, Cu, Fe, Mn, and Ni elements was in Elmasburnu Nature Park area in Beykoz district, which is a touristic place by the sea. Changes in the city’s air quality over the years have been evaluated by comparing element levels in these locations in a previous biomonitoring study and some differences were found. The resulting data is valuable for periodic monitoring of toxic elements in the air, for determining causes of air pollution, and for taking precautions.

In this numerical and statistical study, the effect of mass flow rates for shell and fluid temperature for tube on temperature and pressure drops was analyzed in double pipe heat exchanger under counterflow. Numerical calculations were performed using computational fluid dynamics (CFD) in ANSYS Fluent. Calculations were carried out according to the L8 orthogonal array with two control factors in accordance with Taguchi method. Although the different mass flow rates of the fluid passing through the shell were chosen as the first control factor, different fluid temperatures of the fluid passing through the tube were selected for the second control factor. Signal-to-noise ratio analysis was used to decide the impacts and optimum levels of the mass flow rates and fluid temperatures on temperature and pressure drop. The significance levels and contribution percentages of each variable on the temperature and pressure change occurring in the heat exchanger were calculated using analysis of variance. The accuracy of the analyses was compared numerically and statistically by comparing the obtained optimum CFD results with the estimated Taguchi results. This study can be used as a guiding research in the project and cost calculations of heat exchangers to be produced experimentally

Elevated humidity levels in medical, food, and pharmaceutical products may reduce the products’ shelf life, trigger bacterial growth, and even lead to complete spoilage. In this study, we report a humidity indicator that mechanically bends and rolls itself irreversibly upon exposure to high humidity conditions. The indicator is made of two food-grade polymer films with distinct ratios of a milk protein, casein, and a plasticizer, glycerol, that are physically attached to each other. Based on the thermogravimetric analysis and microstructural characterization, we hypothesize that the bending mechanism is a result of hygroscopic swelling and consequent counter diffusion of water and glycerol. Guided by this mechanism, we demonstrate that the rolling behavior, including response time and final curvature, can be tuned by the geometric dimensions of the indicator. As the proposed indicator is made of food-grade ingredients, it can be placed directly in contact with perishable products to report exposure to undesirable humidity inside the package, without the risk of contaminating the product or causing oral toxicity in case of accidental digestion, features that commercial inedible electronic and chemo-chromatic sensors cannot provide presently.

In this study, a software has been developed for calculating static strength of bolted steel plates. This software has been developed by adding equations covering the bending moment that occurs during a tensile test as well as under real loading conditions. In order to test the accuracy of this program, 5 samples with the M6 bolt connection were prepared. In addition, the simulation result was compared with the experimental work performed with tensile tests and the finite element analysis made in Ansys

Bu çalışmada, farklı oranlarda %3yttria stabilize zirkonyum oksit (ZrO2) ilavesinin atık koyun femur kemiklerinden elde edilmiş olan hidroksiapatite (KHA) etkisi incelenmiştir. Bu amaçla 4 farklı kompozit hazırlandı ve preslendikten sonra 1100-1300oC arasında 4 saat süre ile sinterlendi. Saf koyun hidroksiapatit için artan sinterleme sıcaklıkları ile yoğunluk, kısmi yoğunluk, sertlik ve kırılganlık indeksi artarken, en yüksek yatay (8.23±0.28 MPa) ve dikey basma (82.48±5.50 MPa) mukavemeti ve kırılma tokluğu (0.70±0.11 MPam1/2) 1200oC’ de elde edildi. Saf KHA’ in 1300oC’ de sinterlenmesi beta trikalsiyum fosfat (β-TCP), alfa trikalsiyum fosfat (α-TCP) ve kalsiyum oksit (CaO) fazlarına dekompoze olmasına neden olmuştur. Bu sıcaklık için saf KHA’ daki dekompoze olma oranı %4.1 olarak hesaplandı. KHA-ZrO2 kompozitlerinde dekompoze olma oranı, artan sinterleme sıcaklığı ve ZrO2 oranı ile %48.1’ e kadar artmıştır. KHA-ZrO2 kompozitlerinde iki farklı kalsiyum zirkonat (CaZr4O9 ve CaZrO3) fazı dedekte edilmiş olup, bu kompozitlerde dekompozisazyonun artışının CaZrO3 fazından kaynaklandığı kanaatine varılmıştır. 1200oC’ de sinterlenen KHA-2.5ZrO2 kompozitinin KHA-ZrO2 kompozitleri içerisinde en iyi özelliklere sahip olduğu belirlendi. Bu kompozitin kırılma tokluğu 1.25±0.08 MPam1/2, dikey ve yatay basma mukavemeti değerleri ise 122.0±2.12 MPa ve 11.21±0.42 MPa’ dır. Ancak; KHA-ZrO2 kompozitinin yetersiz kırılma tokluğu özelliği nedeniyle insan vücudunda yüke dayanım gerektirmeyen uygulamalarda kullanılması önerilmektedir.

Yapılan çalışmada depolama sistemlerinde kullanılan otomatik depolama vincinin titreşiminin kontrol edilmesi ve bu sayede ürün alma bırakma zamanının azaltılması için sisteme girilen sinyalin ileri besleme kontrolü ile uygulanması incelenmiştir. Kontrol edilmesi amaçlanan otomatik depolama vinci için yapının indirgenmiş bir modeli belirlenmiş ve buna uygun bir matematik model ortaya koyulmuştur. Kontrol edilecek sistem tek eksende çalışmakta ve bu ekseni tek bir motor kontrol etmektedir. Sistemin aldığı komuta göre takip edeceği trapezoid biçimdeki yörünge planlaması tasarlanan bir algoritma ile belirlenmiştir. Algoritmaya girilen konum, hız, ivme ve jerk değerleriyle ve algoritmadan jerk türevi çıktısı alınmıştır. Sisteme giren jerk türevi komutu, asenkron motora tork komutu olarak girilmiş ve diğer hatalar PID geri-beslemeli kontrol ile azaltılmıştır. Sistemin Simulink ortamında modeli hazırlanmış, örnek bir 10 metrelik komut ile sistem simülasyonu yapılmıştır. In this paper we study the feed-forward control of vibrations in the ASRS stacker crane, hence reducing the pick & place time of the payload that it is carrying. The stacker crane structure in our study has been simplified and an expedient mathematical model has been presented. In accordance with the reference command signal, a trapezoidal trajectory to follow is constructed by an algorithm. With the position, velocity, acceleration and jerk inputs to the algorithm, the derivative of the jerk is taken as an output. The jerk derivative command translates as a torque command to the asynchronous motor and errors are reduced by PID feed-back control. The model of the system is constructed in the Simulink environment, and the system was simulated with a sample 10-meter displacement command.

Supply chains have rapidly developed and become more complex to increase productivity, reduce costs and meet demand. Moreover, globalization affected complexity and uncertainty, so many risks have arisen in surviving among rivals. Today, supply chain management became more difficult for recognizing the details and controlling the process. So, recognition and assessment of the risks in advance became vital to provide the continuity of the working processes of the company and not cause big losses. This study aims to minimize the damage that may occur by providing the risk assessment. For this purpose, a risk assessment methodology in which risks are determined via literature review and expert views is proposed. Afterwards, a new technique based on Failure Mode and Effects Analysis (FMEA) is utilized. Intuitionistic Fuzzy Analytic Hierarchy Process and Weighted Product are synergistically used within FMEA. Moreover, an application in a widely known fast-moving consumer goods company is performed to validate the proposed methodology. Keywords: Risk Assessment; Supply Chain Management; Failure Mode and Effects Analysis; Intuitionistic Fuzzy Analytic Hierarchy Process; Weighted Product

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world in social, economic, and scientific fields. Urgent needs in public health have brought with them innovative approaches, including diagnosis, prevention, and treatment. To exceed the coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put forward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the world, is not only viral infections but also has been the pulsion in the development of novel approaches in many fields such as diagnosis, treatment, translational medicine, virology, microbiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biology, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies. In this review, the effects of the COVID-19 pandemic on the development of various scientific areas of health biotechnology are discussed.

Recently, there has been an increasing interest in environmentally friendly methods for the removal of toxic dyes to enable sustainable textile dyeing processes. In this study, a highly efficient, non-toxic, low-cost MgO particles were prepared by sol-gel technique and utilized for the removal of Reactive Red 21 azo dye by adsorption process. The prepared MgO particles were characterized by Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, X-Ray Diffraction, and Particle Size Analysis. The batch adsorption studies were performed for optimizing the parameters affecting adsorption. The adsorption behavior of Reactive Red 21 was accurately characterized by the Langmuir model. The adsorption process was found to be thermodynamically spontaneous at room temperatures as indicated by the negative Gibbs free energy change (∆G) value of −30.65 kj/mol. The kinetic studies indicate that the pseudo-second-order model provides a good fit to the adsorption of Reactive Red 21. The adsorption capacity of the prepared MgO particles for Reactive Red 21 was determined to be 355 mg/g at room temperature over a wide pH range of 5–9, with a contact time of 20 min. The regeneration of dye-adsorbed MgO particles was conducted at 500 °C for 2 h. The regenerated MgO particles were then utilized for adsorbing Reactive Red 21 five times with a sufficiently high dye removal efficiency. The prepared MgO particles provided a 98 % dye removal in real textile wastewater containing Reactive Red 21 dye.

In recent years, higher penetrations of renewable energy generations helped persuade sustainable energy and environmental targets, although their intermittent and fluctuated nature raised technical challenges. However, hybrid power system can minimize these technical issues by integrating flexible generation capable renewable energy sources like biomass. This study deals with optimizing gridintegrated and stand-alone biomass-based hybrid power system for the energy demand of a rural region containing poultry farms. Besides solar and wind energy, energy storage integration is evaluated regarding overall technical-environmental-economic performance, considering the actual manure potential using HOMER Pro. In addition, sensitivity analyses are performed, considering the estimated load and inflations using an artificial neural network method. Using biomass and solar hybrid options offers more autonomous, environmentally friendly, and economic advantages. Biomass-based hybrid power system with solar energy reduced net present cost by around 12% and increased renewable fraction by 7%, and grid-connected options can provide 88.9% renewable fraction. In addition, the energy storage integration increased renewable fraction by around 10% and reduced excess energy by 16%. The proposed biomass-based hybrid power system achieves cost-effective sizing of solar or wind-based hybrid systems, empowers the reliability of renewable energy and presents good consistency of decarbonization targets.

Basically, in the working principle, the transition from high temperature to low temperature is achieved, while the temperature value of the one with the lower temperature rises, this process can continue until the temperature value of the other decreases and reaches equilibrium. Therefore, heat exchangers are highly preferred in the industry where heat transfer is possible, in mass production facilities where nonstop production takes place such as the pharmaceutical and paper industry, and in sectors where energy efficiency is of utmost importance. In the analyses, it is aimed to investigate the changes in the fluid behavior of the conventional one-piece type baffle plate shell and tube heat exchanger at different baffle plate intervals by keeping it constant at different flow rates. Here, water was used as the working fluid to examine the changes in fluid behavior, the direction in which the heat transfer rate per pressure drop changes, the pressure drop and the effects on the body side heat transfer coefficient. As a result, it has been determined that the distance between the baffle plates used in the conventional one-piece type shell and tube heat exchanger varies and the values compared at different flow ranges differ.

The main outdoor factors that reduce the efficiency of the photovoltaic (PV) panel are the reflection and refraction of light, dirt, dust, and organic waste accumulating on the panel surface. In this article, an antireflection, self-cleaning coating was applied on the PV panel cover glass with a new method. With the coating, the surface has been given a hydrophobic feature. As a coating method, a 3-D printer has not been seen in the literature and used as a new method. The electrospinning method has also been tried as an alternative method. Solutions in different combinations were developed using polylactic acid or polymethylmethacrylate polymer, chloroform ($\text{CHCl}_3$) as a solvent, and silicon dioxide ($\text{SiO}_2$) and titanium dioxide ($\text{TiO}_2$) nanoparticles as primary materials in a modified 3-D printer for bioprinting. Five PV panels were obtained by applying different 3-D parameters from three solutions, which have the best results. Coating thicknesses are in the range of 3.12-8.47 mu m. Coated and uncoated PV panels were tested in outdoor conditions for ten-day periods. The power outputs of the PV panels were measured, and their ten-day average efficiency was presented. According to the results, the highest efficiency increase is 8.7%. The highest light transmittance is 88.2% at 550 nm. In addition, hydrophobic properties were observed on all surfaces and the water contact angle was measured as 96.18 degrees.