• Korean Journal of Materials Research
• /
• v.34 no.6
• /
• pp.267-274
• /
• 2024

In this study, four different samples of Se₆₀Ge_40-xBi_x chalcogenides glasses were synthesized by heating the melt for 18 h in vacuum Pyrex ampoules (under a 10^-4 Torre vacuum), each with a different concentration (x = 0, 10, 15, and 20) of high purity starting materials. The results of direct current (DC) electrical conductivity measurements against a 1,000/T plot for all chalcogenide samples revealed two linear areas at medium and high temperatures, each with a different slope and with different activation energies (E₁ and E₂). In other words, these samples contain two electrical conduction mechanisms: a localized conduction at middle temperatures and extended conduction at high temperatures. The results showed the local and extended state parameters changed due to the effective partial substitution of germanium by bismuth. The density of extended states N(E_ext) and localized states N(E_loc) as a function of bismuth concentration was used to gauge this effect. While the density of the localized states decreased from 1.6 × 10¹⁴ to 4.2 × 10¹² (ev^-1 cm^-3) as the bismuth concentration increased from 0 to 15, the density of the extended states generally increased from 3.552 × 10²¹ to 5.86 × 10²¹ (ev^-1 cm^-3), indicating a reduction in the mullet's randomness. This makes these alloys more widely useful in electronic applications due to the decrease in the cost of manufacturing.

• Geomechanics and Engineering
• /
• v.37 no.4
• /
• pp.307-321
• /
• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.5
• /
• pp.1390-1411
• /
• 2024

Underwater Wireless Sensors Networks (UWSNs) are deployed in remotely monitored environment such as water level monitoring, ocean current identification, oil detection, habitat monitoring and numerous military applications. Providing scalable and efficient routing is very challenging in UWSNs due to the harsh underwater environment. The biggest difficulties are the nodes inherent movement due to water current, long delay in data transmission, low bandwidth of the acoustic signal, high error rate and energy scarcity in battery powered nodes. Many routing protocols have been proposed to solve the aforementioned problems. There are three broad categories of routing protocols namely depth based, energy based and vector-based routing. Vector Based Forwarding protocols perform routing through virtual pipeline by defining their radius which give proper direction to packets communication. We proposed a routing protocol termed as Path-Oriented Energy Scaled Expanded Vector Based Forwarding (PESEVBF). PESEVBF takes into account all parameters; holding time, the source nodes packets routing path and void holes creation on the second hop; PESEVBF not only considers the packet upward advancement but also focus on density of the forwarded nodes in terms of number of potential forwarding and suppressed nodes for path selection. Node selection in resultant holding time is based on minimum Path Factor (PF) value. Moreover, the suppressed node will be selected for packet forwarding to avoid the void holes occurrences on the second hop. Performance of PESEVBF is compared with other routing protocols using matrices such as energy consumption, packet delivery ratio, packets dropping ratio and duplicate packets creation indicating considerable performance improvement.

• Geomechanics and Engineering
• /
• v.37 no.1
• /
• pp.65-72
• /
• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• Journal of IKEEE
• /
• v.28 no.1
• /
• pp.38-45
• /
• 2024

Most previous works about magnetic effect on plasma emission were interested in emission enhancement which was useful to various fields of plasma application. On the contrary, the following work is interested in plasma dissipation rarely reported in prior researches and expected to help advance plasma-controlling technique. Nd:YAG laser (1064 nm, 6 ns) was focused on three kinds of metals (Al, Ti and STS) and air. The permanent magnetic field (0.4 T) of Nd₂Fe₁₄B magnet was provided passing throughout laser-induced plasma. The spectra of plasma in both the presence and absence of the magnetic field were observed with varying laser power and delay time of the spectrograph. In this work it was uniquely discovered that the plasma always dissipated easily in the presence of magnetic field irrespective of the laser power. With the O I(777.42 nm)-line shape function fitted to Lorentz profile, its half width at half maximum (HWHM) was evaluated to verify that the magnetic field increased the plasma density. It is concluded that magnetic field facilitates not only plasma emission enhancement but also plasma dissipation, increasing recombination rate which is proportional to plasma density.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.17 no.3
• /
• pp.155-165
• /
• 2024

A hybrid thin film was prepared by doping reduced graphene oxide (rGO) into a sol-gel solution mixed with aluminum, titanium, and strontium using a brush coating method. The annealing temperature was carried out at 160, 260, and 360℃, and the difference in oxidation reaction was observed. The sol-gel solution created during the membrane manufacturing process generates a contractile force due to the shear stress of the brush bristles, forming a microgroove structure. This structure was confirmed through scanning electron microscopy analysis, and the presence of rGO was clearly revealed. As the annealing temperature increases, the oxidation and reduction reactions on the thin film surface become more active, so the intensity of the surface mixture increases. Moreover, the electro-optical properties were stabilized and improved by increasing the intensity of the mixtures. Likewise, the voltage-capacitance values are also significantly improved. Lastly, the transmittance measurement showed that it was suitable for liquid crystal display application.

• Nuclear Engineering and Technology
• /
• v.56 no.7
• /
• pp.2584-2594
• /
• 2024

A novel approach to incorporating oxide formers into ferritic ODS production has been developed using the co-precipitation technique. This method enables the tailored design of complex nano-oxides, integrated during Mechanical Alloying (MA) and precipitated during Spark Plasma Sintering (SPS) consolidation. Findings illustrate that co-precipitation effectively produces nano-powders with customised compositions, enriching Y, Ti, and Zr in the ferritic grade to condition subsequent oxide precipitation. While the addition of Y-Ti-Zr-O nano-oxides did not prevent the formation of Y-Al-O and Al-containing nano-oxides, these were refined thanks to the presence of well-dispersed Zr. Additionally, the Spark Plasma Sintering (SPS) parameters were optimised to tailor the bimodal grain size distribution of the ODS steels, aiming for favourable strength-to-ductility ratios. Comprehensive microstructural analyses were performed using SEM, EDS, EBSD, and TEM techniques, alongside mechanical assessments involving microtensile tests conducted at room temperature and small punch tests carried out at room temperature, 300 ℃, and 500 ℃. The outcomes yielded promising findings, showcasing similar or better performance with conventionally manufactured ODS steels. This reinforces the effectiveness and success of this innovative approach.

• Journal of the Korean Applied Science and Technology
• /
• v.41 no.1
• /
• pp.58-68
• /
• 2024

The purpose of this study was to investigate the effects of treadmill exercise treadmill exercise (TE) and environmental enrichment (EE) interventions on cognitive function, muscle function, and the expression of tight junction proteins in an Alzheimer's disease (AD) animal model. To create the AD animal model, aluminum chloride (AlCl₃) was administered for 90 days (40mg/kg/day), while simultaneously exposing the animals to TE (10-12m/min, 40-60min/day) or EE. The results showed that cognitive impairment and muscle dysfunction induced by AlCl₃ administration were alleviated by TE and EE. Furthermore, TE and EE reduced the increased expression of β-amyloid(Aβ), alpha-synuclein, and tumor necrosis factor-α (TNF-α) proteins observed in AD pathology. Additionally, TE and EE significantly increased the expression of decreased adhesive adjacent proteins (Occludin, Claudin-5, and ZO-1) induced by AlCl₃ administration. Lastly, correlation analysis between Aβ protein and tight junction proteins showed negative correlations (Occludin: r=-0.853, p=0.001; Claudin-5: r=-0.352, p=0.915; ZO-1: r=-0.424, p=0.0390). In conclusion, TE or EE interventions are considered effective exercise methods that partially alleviate pathological features of AD, improving cognitive and muscle function.

• Journal of the Korean Ceramic Society
• /
• v.47 no.4
• /
• pp.283-289
• /
• 2010

Physical properties of Plasma electrolytic oxidized 8 different kinds of Al alloys, A-1100, A-2024, A-5052, A-6061, A-6063, A-7075, ACD-7B and ACD-12 were investigated. The electrolyte for PEO was $Na_2SiO_3$ and NaOH and some alkali earthen metal salts system solution. $\eta$-alumina, as well as $\gamma$-alumina, was main crystal phase, which were ever reported. Also, $Al_{4.95}Si_{1.05}O_{9.52}$ was found only in this research. So we can conclude that the process conditions of PEO apparatus and composition and concentration of its electrolyte affects crystal structure and physical properties of PEO layers much more than the compositions of Al alloy.

• Journal of Korean Neurosurgical Society
• /
• v.67 no.2
• /
• pp.202-208
• /
• 2024

Objective : Failed back surgery syndrome (FBSS) is a common long-term complication following spine surgeries characterized by chronic persistent pain; different strategies of management were employed to deal with it. This clinical trial aims to compare the efficacy of Pregabalin and Gabapentin in the management of this condition. Methods : A double-blind, randomized, comparative study (clinical trial registry NCT05324761 on 11th April 2022) with two parallel arms with Pregabalin and Gabapentin were used in arms one and two, respectively. Visual analog scale was used for basal and endpoint assessment of pain. T-test and analysis of covariance were used to deal with different variables. A pairwise test was used to compare pairs of means. Results : Of 66 patients referred to the trial, 64 were eligible, with 60 patients completing the 30 days trial. Both pregabalin and gabapentin effectively reduce pain, with significant p-values of 0.001 for each group. However, the pregabalin group was superior to gabapentin in pain reduction (p=0.001). Gender was an insignificant factor (p=0.574 and p=0.445 for the pregabalin and gabapentin groups, respectively, with a non-significant reduction (p=0.393) for both groups in total. Location of stenosis before surgery and type of surgery performed show non-significant effect on pain reduction for both groups. Conclusion : Both pregabalin and gabapentin effectively and safely relieve neuropathic pain associated with FBSS; pregabalin was significantly more effective irrespective of the patients' gender.