Use Of Different Numbers Research Articles

A modified neural network method for computing the Lyapunov exponent spectrum in the nonlinear analysis of dynamical systems

This study presents a modified neural network method for computing the Lyapunov exponent spectrum in non-linear dynamical systems. Its mathematical description is an introduction. The proposed modified neural network method allows the addition of bias and constant neurons to the neural network topology and the use of different numbers of activation functions to suit different cases. Various algorithms for computing Lyapunov exponents, such as the Benettin method, the Wolf method, the Rosenstein method, the Kantz method, the Synchronisation method, the Sano-Sawada algorithm and the proposed modification of the neural network method, are used for classical problems in nonlinear dynamics. These problems include the generalised Hénon map, the chaotic attractor of the Baier-Klein map, and the vibrations of mechanical systems such as the flexible Bernoulli-Euler beam and the flexible functionally graded porous closed cylindrical shell under alternating load. The comparative analyses presented in this study are aimed at validating the accuracy and effectiveness of the methods, and at identifying the most relevant approaches for different types of systems and classes of problems. The proposed method is demonstrated to be superior to existing methods based on time-series evaluation in terms of sample size and accuracy. Furthermore, it does not require the initial system equations.

Evolutionary swarm formation: From simulations to real world robots

Swarms of autonomous robots have become an interesting alternative for space and aerospace applications due to their versatility, robustness, and self-organising capability. Some of those applications, such as asteroid observation, convoy escort, and counter-drone systems, rely on stable formations achieved around a central point of interest. However, the use of different numbers of robots and the existence of a wide range of initial conditions contribute to make it a challenging problem. We propose in this research work a novel approach for self-organising a swarm of autonomous robots where the members’ movements depend only on their relative position (range and bearing) obtained from their respective radio beacons. An optimisation approach based on an evolutionary algorithm is proposed to calculate the optimal swarm’s parameters, e.g. speed and attracting/repelling forces, to achieve robust formations under different initial conditions and failure rates. Experiments are conducted using realistic simulations of six case studies featuring three, five, ten, fifteen, twenty, and thirty robots. The best valued configurations were tested on 420 scenarios showing that our proposal is robust since it has always achieved the desired circular formation. Finally, we have used real E-Puck2 robots to validate the swarm’s capability of self-organising around a central point of interest as well as its resilience to robot failure, obtaining successful circular formations in all the experiments.

A review of wearable sensors based fall-related recognition systems

Falls are an important factor in significantly deteriorating quality of life of older adults, consequently leading to both physical and psychological harm. A wearable-based fall-related recognition system (WFRS) indeed facilitates the prediction, detection, and classification of fall events in helping fallers. Previous studies have provided a relatively comprehensive introduction to WFRSs from the perspective of sensor types and recognition algorithms. However, while these studies provide a clear technical direction, how to choose the appropriate technology for each phase of the experiment is a stumbling block for newly interested researchers. Accordingly, a comprehensive review article covering the mainstream technologies of WFRSs is imperative and meaningful. This review analyzes 48 state-of-the-art researches in WFRSs from three databases (i.e., IEEE Explorer, ScienceDirect, and MDPI) and introduces the pipeline techniques that consist of data acquisition, preprocessing, feature extraction, model training, and evaluation. Specifically, we first analyze the pros and cons of the use of different number of sensors for data collection. We then introduce the widely used preprocessing techniques including filtering and data augmentation. Afterwards, we detail the extraction of various features and illustrate methods for the selection, training, and evaluation of fall recognition models. We finally discuss factors affecting the overall performance of a model and offer suggestions for future research.

Segmentation-based Decision Networks for Steel Surface Defect Detection

<p>With the advent of the Industrial 4.0 era, deep learning has been continuously applied to the task of surface defect detection, and effective progress has been made. However, the limited number of training samples and high labelling costs are considerable obstacles to the vigorous development of this task. Thus, we explore the use of different numbers of labels with various accuracies during training to achieve the maximum detection accuracy with the lowest cost. Our proposed method includes improved segmentation and decision networks. An attention mechanism is integrated into the segmentation subnetwork. Moreover, atrous convolutions are used in the segmentation and decision subnetworks. In addition, the original loss function is improved. Several experiments are carried out on the Severstal Steel Defect dataset collected in Germany, and the results show that each component improves the detection accuracy by 1% to 2%. Finally, when we add an appropriate number of pixel-level labels in the weakly supervised learning mode, the detection accuracy reaches that of the fully supervised mode with a significantly reduced annotation cost.</p> <p> </p>

Дослідження умов доцільності застосування подвоєних або потроєних шин колісних енергетичних засобів

Goal. To determine and justify the conditions under which the use of double/triple tires of wheeled energy vehicles instead of single ones will be appropriate. Methods. Theoretical studies were carried out using the basics of the theory of tractors and agricultural machines. The object of research was the MTZ1221 multipurpose-weed tractor, the front axle of which was studied in variants with single, double, and triple tires. Soil compaction in the passage zones of these tires was taken as an argument for the yield function of an agricultural crop (spring barley). Results. Calculations established that the intensity of reducing the depth of the wheel track, compacting the soil, and increasing the yield of the cultivated crop (spring barley) was higher when using a double tire compared to a single one, and lower in the case of replacing a double tire with a triple one. Due to the use of a triple tire instead of a single one, the depth of the created track was reduced. Conclusions. A new analytical dependence was obtained, which could be used when evaluating the feasibility of using doubled or tripled tires of wheeled energy vehicles to reduce their compacting effect on the soil and correspondingly increase the yield of cultivated crops. By evaluating the use of different numbers of front tires on the example of the MTZ1221 tractor, it was established that the intensity of reducing the depth of the wheel track, compacting the soil, and increasing the yield of the cultivated crop (spring barley) was higher for a double tire compared to a single tire, and lower in the case of replacing a double tire with a triple one. The use of a three-wheel tire instead of a single one led to a decrease in the depth of the created track by at least 30%, a decrease in soil compaction by 42%, and an increase in the yield of the cultivated crop by 2.2 times.

STUDI PERBANDINGAN SHELTER TERHADAP RESPON PASCA PRODUKSI LARVA LOBSTER PASIR Panulirus homarus PADA KONTAINER SISTEM RESIRKULASI

Sand lobster (Panulirus homarus) is a commodity with high economic value. The demand for sea crayfish consumption continues to increase from year to year. This is due to the decreasing availability of lobster and its effect on activities in catching lobster in the wild. Many lobster cultivation efforts have been carried out in an effort to meet the demand for lobster. This study aims to analyze the effect of the use of individual square shelters and PVC pipes on the post-production response of sand lobster Panulirus homarus in a recirculation system container. This research had been carried out from December 2020 to July 2021 at the Aquaculture Laboratory, Faculty of Fisheries and Marine Sciences, Universitas Satya Negara Indonesia. This experiment used a completely randomized design (CRD) consisting of three and each treatment had four replications. The treatments consist of: Treatment A: PVC pipe shelter; Treatment B: Individual Shelter; Treatment C: Control (without shelter). The use of different number of shelters has an effect on survival, specific growth rate, carapace length and feed conversion ratio of sand lobster post larvae. Treatment A gave the best production performance with survival, carapace length and feed conversion ratio of 88.89%±19.24a, 95.76±2.43 mm, 104.02±11.49 mm, and 6 ,7 ± 0.1 g/day. Keywords: Sand Lobster (Panulirus homarus), Shelter, Recirculation System, Growth

Remote characterization of chloride content in oven-dried concrete specimens by using synthetic aperture radar image models

Synthetic aperture radar (SAR) imaging is a remote sensing technique capable of performing noncontact subsurface inspection of dielectric materials like Portland cement concrete. Such techniques can be applied to field inspection of concrete structures and laboratory material characterization. The objective of this paper is to demonstrate the application of SAR imaging on characterizing chloride content inside oven-dried concrete specimens, using a 10-GHz central frequency SAR imaging sensor. Twelve concrete panel specimens (0.3x0.3x0.05 m3) with a 0.45 water-to-cement ratio were manufactured in six groups of different chloride contents (0%, 2%, 4%, 6%, 8% and 10% of cement weight). Five SAR image-based parameters were developed from each SAR image of concrete specimens for chloride, including integrated SAR amplitude of back reflection Iintb, integrated amplitude Iint, average maximum amplitude I-max, critical contour area Ac, and average Gaussian curvature of critical contour Kavg. A parametric analysis on the combined use of different numbers of SAR image parameters was carried out to determine the optimal application of SAR image parameters on the chloride detection problem in this paper. From our result, it is found that, for the purpose of chloride detection inside concrete specimens using single SAR parameter, integrated amplitude Iint shows the best performance. When using two SAR image parameters, combination of integrated amplitude Iint and critical contour area Ac shows the best performance among other two-parameter combinations. When using three SAR image parameters, combination of integrated amplitude Iint, critical contour area Ac, and average maximum amplitude I-max shows the best performance among other three-parameter combinations. Furthermore, it is interesting to report that combination of three SAR image parameters (Iint, Ac, and I-max) provides the overall best performance among all other combinations for the chloride detection problem in oven-dried concrete specimens.

Advanced technology for fabrication of reaction-bonded SiC with controlled composition and properties

An advanced fabrication technology of reaction-bonded SiC is developed, which includes the preparation of a C/SiC preform by repeated cycles of phenolic resin impregnation and pyrolysis, followed by infiltration with silicon melt. The use of different number of impregnation stages provides control of carbon content in the preform and the corresponding SiC content in final ceramics. The effect of the impregnation number on the preform characteristics and ceramics composition, thermal and mechanical properties are investigated comprehensively. With an increase of impregnation number up to four, SiC fraction in the ceramics enlarges to 93 vol%, thermal conductivity and Young’s modulus increase to 186 W/(m∙K) and 427 GPa respectively, which are superior to most reaction-bonded SiC. Flexural strength (225 MPa) and thermal expansion coefficient (2∙10−6 K-1) are not dependent on the impregnation number. The obtained results provide an opportunity to design and fabricate reaction-bonded SiC ceramics with a given set of properties.

Optimisation of deep neural networks for identification of epileptic abnormalities from electroencephalogram signals

An electroencephalogram (EEG) measures and records the electrical activity of the brain. It provides valuable information that can be used to identify epileptic abnormalities. However, the visual identification of such abnormalities from EEG signals by expert neurologists is time consuming. Therefore, several researchers have proposed using deep neural networks (DNNs) to automate the identification of these abnormalities. Their studies have examined the use of different numbers of layers, different numbers of parameters, and various operation types arranged in different architectures. This paper presents the shallowest 11-layer DNN architecture capable of classifying three classes of EEG signals: normal, preictal, and seizure. When the proposed architecture was applied to the standard University of Bonn EEG signal dataset, it achieved accuracy, specificity, and sensitivity values of 99.43%, 99.57%, and 99.10%, respectively. It not only had a better performance than the state of the art DNN architectures, but also had shallower layers with fewer parameters. This allowed it to more quickly identify epileptic abnormalities. Experiments were also conducted where the length of the EEG signals was reduced to 65% (2,662 samples with a period of 15.26 s), which in turn minimised the total parameters of the proposed architecture so that it was comparable to the smallest state-of-the-art architecture and decreased the lag time for identification. Even in these experiments, it was capable of producing equal performance measures, with the execution time reduced to only 69% of that when employing the full length of EEG signals.

On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

Automatic detection of multiple sclerosis lesions using Mask R‐CNN on magnetic resonance scans

Multiple Sclerosis (MS) causes the central nervous system to malfunction due to inflammation surrounding nerve cells. Detection of MS at an early stage is very important to prevent progressive MS attacks. Clinical findings, cerebrospinal fluid examinations, the evoked potentials, magnetic resonance imaging (MRI) findings have an important role in the diagnosis and follow-up of MS. However, many of the findings on MRI may indicate brain disorders other than MS. In addition, the clinical practices accepted by physicians for MS detection are very limited. In this study, a Mask R-CNN based method in two dataset is proposed for the automatic detection of MS lesions on magnetic resonance scans. We also improved the ROI detection stage with RPN in the Mask R-CNN to easily adapt for different lesion sizes. MS lesions in different sizes in the dataset are successfully detected with 84.90% Dice similarity rate and 87.03% precision rates using the proposed method. In addition, volumetric overlap error and lesion-wise true positive rate are obtained as 12.97% and 73.75%, respectively. Moreover, performance tests of the use of different numbers of GPU hardware structures are also performed and the evaluation of its effects on processing speed is performed on experimental studies..

A New Inverse Modeling Approach for Hydraulic Conductivity Estimation Based on Gaussian Mixtures

AbstractThis study proposes a new inverse algorithm to estimate the hydraulic conductivity (K) distribution based on a Gaussian Mixture Model that significantly reduces the number of parameters to be estimated during the inversion process. Moreover, a new objective function that increases the sensitivity of parameters using the spatial derivatives of hydraulic heads is introduced, and the algorithm is further improved by including a Bayes estimator that takes advantage of different possible solutions. The developed approach is tested through multiple synthetic experiments consisting of 250 randomly generated K fields resulting in different levels of heterogeneity and the use of different number of pumping tests, with a total of 1,000 cases of two‐dimensional configuration. A large number of cases are considered to ensure that our findings and conclusions are not based on a single realization. Results revealed significant improvements to K estimates, computational time, and predictions of independently conducted tests not used in the calibration effort when compared to a geostatistical inverse approach. Overall, our results reveal that the Gaussian Mixture inversion approach is able to achieve similar or higher levels of accuracy using half of the pumping tests and 20% of the computational time compared to a geostatistical inversion approach.

The impact of irrigation distribution uniformity and mulching soil on vegetative growth and yield of sweet fennel and squash plants

Water shortage under climate change impacts performed the driving forces to enhance the agricultural practices to maximize the food production. Improve the distribution of irrigation water with mulching soil to satisfy the plants needs and provide optimum conditions for production consider the main objective of the current study. Two experiments of the study were conducted out at Central Laboratory for Agriculture Climate (CLAC), Agriculture Research Center, Egypt during winter and spring seasons of 2016/2017 and 2017/2018 under open field conditions. The study objected to investigating the use of different numbers of PE lateral per growing bed (1 (OIL) and 2 (TIL)) with different distance between the inline drippers (30 and 50 cm) on sweet fennel and squash to get the harmony distribution of irrigation in clay soil combined with mulching or bare soil (control). The trails of the current study presented in split plot design. The vegetative growth characteristics, yield parameters and N, P and K (%) contents of sweet fennel and squash leaves were measured. The obtained results indicated that increasing no. of laterals from 1 to 2 laterals /bed while decrease the distance between drippers from 50 to 30 cm led to increase the vegetative characteristics, yield parameters and N, P, K (%) contents of sweet fennel and squash plants as a results of enhancing the soil moisture availability and nutrients uptake. The treatment TIL combined with 30 cm distance between emitters recorded the highest results of sweet fennel and squash followed by TIL combined by 50 cm distance between emitters. Applying black soil mulch led to increase the soil temperature during winter and spring seasons and also enhance the vegetative characteristics, yield parameters as well as N, P, K (%) contents of sweet fennel and squash plants compared to bare soil. Mulching soil led to increase the yield of sweet fennel and squash 35 % more than bare soil. Include applying two laterals per growing bed and black mulch within the agriculture practices to increase the yield of vegetable crops had a general benefits on food security and mitigation climate change impacts.

Preoperative Diagnostic Rates and Clinical Outcomes After Arthroscopic Stabilization Procedures for Panlabral Tear of the Glenohumeral Joint

To evaluate preoperative diagnostic rates for panlabral tear using imaging studies or physical examinations and to evaluate clinical outcomes after arthroscopic stabilization procedures with 2 different patient surgical positions. Patients who underwent arthroscopic stabilization for recurrent anterior shoulder instability with panlabral tear and were followed up for at least 2 years were included. A panlabral tear was defined as labral tear involving at least 270° of the glenoid surface on arthroscopic examination. All patients underwent preoperative magnetic resonance (MR) imaging or MR arthrography and physical examinations including anterior apprehension, posterior jerk, and compressive rotation tests. The clinical outcomes were assessed by the American Shoulder and Elbow Surgeons, Rowe, and visual analog scale for pain scores, and recurrence rate. According to patient position during surgery, patients were divided into group I (beach chair position) and group II (lateral decubitus position). Forty-eight patients (24 in group I and 24 in group II) were enrolled. Preoperative MR imaging or MR arthrography detected only 18.8% of panlabral tears. No patient had positive findings on all 3 physical examination tests for panlabral tear. Clinical outcomes were significantly improved after operation (American Shoulder and Elbow Surgeons score: 58.4 ± 6.2 preoperatively, 85.2 ± 6.4 at the final, P < .001; Rowe score: 49.0 ± 12.2 preoperatively, 86.8 ± 9.1 at the final, P < .001) and postoperative recurrence was occurred in 1patient (2%). No differences were found in clinical outcomes and recurrence rate (4% vs 0%) according to patient positioning, despite the larger number of suture anchors used in group II (6.2 ± 1.5 in group I, 7.6 ± 1.1 in group II, P < .001). It remained difficult to preoperatively diagnose panlabral tear using standard physical examinations and imaging studies. Nevertheless, arthroscopic stabilization procedures for patients with panlabral tear provided satisfactory clinical outcomes with a low recurrence rate. Patient position during surgery did not alter clinical outcomes and recurrence rate, despite the use of different numbers of suture anchors. Level III, Retrospective comparative therapeutic trial.

An Ambient RF Energy Harvesting System Where the Number of Antenna Ports is Dependent on Frequency

We describe the design of a multiport rectenna system for ambient radio frequency (RF) energy harvesting where the number of ports utilized is dependent on frequency. A unique aspect of the design is the use of different numbers of antenna ports for harvesting RF energy at different frequencies. This allows the available area for the rectenna to be fully utilized at all frequencies. In particular, the proposed antenna is designed to have four ports for harvesting energy from the GSM-900 frequency band and 12 ports for the GSM-1800 frequency band in the same area. The design for the rectifiers for the GSM-900 and GSM-1800 frequency bands with direct current (dc) combining is provided and prototypes are demonstrated. Field-test measurements show that the proposed rectenna can provide an output dc voltage of more than 3.2 V, an output dc power of more than −10 dBm, and an RF-to-dc efficiency of greater than 42% when the power density is greater than $1400~\mu \mathrm {W/m^{2}}$ . Measurements in an ambient RF environment at a university campus with commercial GSM-900 and GSM-1800 systems in operation show that the proposed rectenna can achieve output dc voltages of up to 2.2 V and dc power up to −13.6 dBm.

Colored Fused Filament Fabrication

Fused filament fabrication is the method of choice for printing 3D models at low cost and is the de facto standard for hobbyists, makers, and schools. Unfortunately, filament printers cannot truly reproduce colored objects. The best current techniques rely on a form of dithering exploiting occlusion, which was only demonstrated for shades of two base colors and behaves differently depending on surface slope. We explore a novel approach for 3D-printing colored objects, capable of creating controlled gradients of varying sharpness. Our technique exploits off-the-shelf nozzles that are designed to mix multiple filaments in a small melting chamber, obtaining intermediate colors once the mix is stabilized. We apply this property to produce color gradients. We divide each input layer into a set of strata, each having a different constant color. By locally changing the thickness of the stratum, we change the perceived color at a given location. By optimizing the choice of colors of each stratum, we further improve quality and allow the use of different numbers of input filaments. We demonstrate our results by building a functional color printer using low-cost, off-the-shelf components. Using our tool, a user can paint a 3D model and directly produce its physical counterpart using any material and color available for fused filament fabrication.

Analysis of Congestion using mobile nodes in Wireless Network

This article discusses the work plan of wireless ad hoc network and the problems related to it during congestion. The congestion in wireless network congestion will vary with different topologies, methods of network deployment, mobility, channel utilization and power consumption at node level. This might lead to degradation of TCP performance of packet loss. In this research the proposed work plan the use of different number of nodes and their effect on congestion in wireless network. The reason for loss in wireless network could be overload of packets in node buffer space. This research will also enable us to conduct a deeper study to understand network congestion in ad-hoc networks. We will try to simulate various network environments with different number of users and then study the network on parameters like throughput, good put; packets dropped etc.

The effect of planting density on ‘BRS Rubimel’ peach trained as a “Y-shaped” system

Abstract The densification of orchards has become a viable alternative for producers seeking a greater use of current area, as well as greater profitability. In this sense, the spacing and training system to be used in the peach tree planting are extremely important to facilitate orchard management and, above all, to maximize tree yield. Based on this, the present research aimed to evaluate the possibility of orchard densification and the use of different numbers of main scaffolds to recommend the best way of training and spacing the ‘BRS Rubimel’ peach cultivar. The planting was carried out in 2010, in the city of Fraiburgo - SC, and eight treatments with different spacings between the plants in the rows and number of scaffolds were evaluated during four harvests (2013, 2014, 2015 and 2016): T1- 2 scaffolds and 0.75 m; T2- 2 scaffolds and 1.00 m; T3- 2 scaffolds and 1.25 m; T4- 2 scaffolds and 1.50 m; T5- 4 scaffolds and 1.00 m; T6- 4 scaffolds and 1.50 m; T7- 4 scaffolds and 1.75 m; T8- 4 scaffolds and 2.00 m, totaling a plant density of 2667, 2000, 1600, 1333, 2000, 1333, 1143 and 1000 per hectare, respectively. The analyzed variables were the number of fruits per plant, production per plant (kg); productivity (t ha-1); fresh fruit mass (g), total soluble solids content (ºBrix), total acidity (meq L-1), epidermal coloring and pulp firmness (pounds). The highest yields were found in the treatment with two scaffolds and 0.75m between plants, as well as that with 4 scaffolds in 1.0 m spacing in the 2014 and 2015 crops and two scaffolds in 1.0m in the 2015 harvest. The fresh mass, soluble solids, total acidity and fruit firmness were not influenced by the different treatments. It was concluded that the densification of orchards is feasible for peach trees of the ‘BRS Rubimel’ cultivar due to the increase in productivity, without decreasing the quality of the fruits, indicating a spacing of 0.75 cm between plants and two scaffolds in the “Y-shape” as ideal. Another option with good results is the use of the four-scaffold “Y-shaped” training system, indicated for the ‘BRS Rubimel’ peach trees, due to the maintenance of high yields and reduction in the number of plants per hectare when compared to treatments with two scaffolds.

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

AbstractControlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.

On the reduced Redlich-Kister excess properties for 1,2-dimethoxyethane with propylene carbonate binary mixtures at temperatures (from 298.15 to 318.15) K.

Values of excess properties in 1,2-dimethoxyethane + propylene carbonate binary liquid mixtures at different temperatures from experimental density and viscosity values presented in earlier work, were used to test the applicability of the correlative reduced Redlich-Kister functions and the Belda equation, and to reveal eventual specific interaction hidden by the classical treatment of direct excess Redlich-Kister functions. Their correlation ability at different temperatures, and the use of different numbers of parameters, is discussed for the case of limited experimental data. The relative Redlich-Kister functions are important to reduce the effect of temperature and, consequently, to reveal the effects of different types of interactions. Values of limiting excess partial molar volume at infinite dilution deduced from different methods were discussed. Also, the activation parameters and partial molar Gibbs free energy of activation of viscous flow against compositions were investigated. Correlation between the two Arrhenius parameters of viscosity shows the existence of main different behaviors separated by a stabilized structure in a short range of mole fraction in 1,2-dimethoxyethane (0.45 to 0.83). In this context, the correlation Belda equation has also been applied to the present system for thermodynamic properties in order to reveal eventual solute-solvent interaction at high dilution.