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Accurate classification and localization of faults in power transmission lines are critical for maintaining grid stability and minimizing downtime. However, it remains challenging due to the variability of fault conditions and system complexities. In this paper, we present a novel method to classify and localize faults in power transmission line using machine learning algorithms. The IEEE 9 Bus is simulated in MATLAB Simulink and faults are generated with various conditions including fault types, load level, fault resistance, and fault location. More than 300 thousand fault datasets are created for both classifying and localizing tasks. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), XGBoost, and Artificial Neural Network (ANN) are used for fault classification while Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and combined CNN-LSTM models are used for fault localization. The results show that XGBoost can classify fault with the accuracy of 99.82 %, while the CNN-LSTM can localize fault with MAPE < 1 % and MAE < 0,16 km.

A dual-band frequency-reconfigurable flexible antenna with an extensive frequency tuning range of approximately 83% is proposed. This unique frequency agility feature is realized by implementing frequency tunability into two overlapping frequency bands and then combining them. The antenna is optimized to operate from 1.42 GHz to 3.43 GHz, which is centered on the 2.45 GHz Industrial, Scientific, and Medical bands for demonstration purposes. A prototype is manufactured and measured to validate the operating concept.

Purpose: This work is the first to use the finite element technique to analyze the vibrational and static bending responses of multi-layer composite nanobeams. Each layer is made from microporous material and has variable mechanical properties. Method: The formulae for the calculations are derived from the enhanced third-order shear strain theory, which accounts for the impact of size effects and the imperfections in the initial shape. Because of its dependability in demonstrating the validity of the theory of computing, the two-node element is chosen to implement the solution. Results: Numerical computations are performed to provide commentary on the effect of certain geometrical factors and bonding circumstances on the vibrational and static bending responses of nanobeams. Research has shown that the mechanical response of multi-layer beams also depends greatly on the shape of the interface between the layers. Conclusion: Some main points in this work are concluded as follows: the natural frequency of the beams will drop if the values of k0and V0are raised in either direction. When k0has a small value, if V0increases, the maximum deflection of the beam increases, but when k0has a large value, if V0increases, the deflection of the beam decreases. There is a nonlinear relationship between the parameter Nand the beams’ maximum deflection as well as their natural frequencies. It is able to find a suitable value of Nthat allows for the beam rigidity to be at both its maximum and its lowest. These are useful results for the practical design of multi-layer beam structures.

In this letter, we presents a polarization-reconfigurable series-fed patch antenna with high gain and a simple switching mechanism. The proposed design comprises of four slotted circular patches arranged in a 1 × 4 configuration. For excitation, the array is capacitive fed by a single T-junction power divider.   By controlling the bias state of three p-i-n diodes on the divider, the antenna's polarization can be switched among a single linear and dual-sense circular polarization. To validate the design concept, an array prototype is fabricated and measured at the operating frequency of 5.0 GHz. The measurements result in a common operating bandwidth for all radiating modes of about 1.8%, ranging from 4.95 GHz to 5.04 GHz. Besides, a stable broadside gain of around 12 dBi is also obtained.

We reported unprecedented volatile conductance switching in molecular device based on viologen (VIO2+) molecules, typically HS-C6-VIO2+-C6–SH. In symmetrical Ag/S–C6-VIO2+-C6–S/Ag junction, we observed bipolar, volatile conductance switching with the on/off ratio reaching up to 106, and the on-state conductance was approximately 35.7 μS at the threshold voltage of ±0.6 V. In asymmetrical junctions, Au/S–C6-VIO2+-C6–S/Ag or Ag/S–C6-VIO2+-C6–S/Au, we observed unipolar, volatile switching with the on/off ratio of 106at the absolute threshold voltage of 0.6 V. The conductance switching direction and the stability and reproducibility depend on the Ag–S interfacial bonding, the ionic conductor, VIO2+, and the material of electrode. We assigned the volatile, bipolar, or unipolar switching behavior to the formation of the weak silver filament. Our result proves that interfacial bonding in combination with the ionic conductor medium should be considered in the design and analysis of molecular electronics.

The aim of this study was to determine factors including biochemical markers, dietary habits, and social-anthropometric associated with uncontrolled hypertension among elderly hypertension in rural regions of Vietnam.

The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random excitation. The key idea is to design a deep learning architecture to leverage the relationships, between external excitations and structure’s vibration signals, and between historical values and future values, within multiple time-series data. The proposed method consists of two main steps: the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture; the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future values. The viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional (3D) reinforced concrete structure and experimental data from an 18-story steel frame. Furthermore, comparison and robustness studies are carried out, showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

Here, the role of pores in the structure of inverse opal photonic crystals (IOPC) in binding proteins in comparison with their parent photonic crystal (PC) templates has been investigated. For this purpose, polyethyleneglycol diacrylate (PEGDA) coated with SiO2‐based PC (PEGDA/SiO2‐based PC) and PEGDA‐based IOPC were attached with fluorophore Alexa 488, which is a polyclonal secondary antibody, to investigate their fluorescence emission. Scanning electron microscopy (SEM) images showed face centered cubic (fcc) packing of the PEGDA/SiO2‐based PC. And it remained after the formation of PEGDA‐based IOPC. The presence of 3‐aminopropyl triethoxysilane (APTES) and Alexa 488 that immobilized the PEGDA‐based PC and PEGDA‐based IOPC was recognized by the appearance of bands at 850, 1175, and a dominant increase in the band at 1750 cm−1. Those were attributed to (N─H) wagging, (C─N) stretching, and (C═O) stretching vibrations, respectively. Reflectance spectra showed a blue shift of the Bragg photonic band gap of the PEGDA‐based IOPC in comparison with that of the PEGDA/SiO2‐based PC. The fluorescence images showed a significant increase in the fluorescence intensity of PEGDA‐based IOPC owing to the resonance effect. These obtained results indicated the role of pores in structure of PEGDA‐based IOPC in improvement of the attachment of Alexa 488 protein.

The circular steel arches are large-span structures, which are shaped into a circular or semicircular form. The circular steel arch is widely used in bridges, tunnels, architectural design, industrial and warehouse buildings, and aqueducts. Circular steel arches are known for their strength and durability, making them a popular choice in architecture and civil engineering. The safety of circular steel arches bearing radial load with elastic rotational restraints depends on material properties, geometric dimensions, and boundary conditions. The objective of this research is to perform a reliability assessment of the in-plane elastic buckling critical load of circular steel arches with elastic rotational restraints considering random input parameters. For that, the Artificial Neural Network (ANN) algorithm is used to construct a model for estimating the in-plane elastic buckling critical load of the circular steel arches, while Monte Carlo Simulation (MCS) is used to simulate the in-plane elastic buckling critical load and assess structural reliability. The calculated results of the proposed model are compared with FORM, SORM, and MCS. Eventually, the influence of random input parameters on the reliability of circular steel arches is evaluated using the first order and total Solol’s indices.

Reinforced concrete (RC) shear walls are very commonly used in buildings and nuclear power plants. Shear strength is one of the critical parameters in the design of RC walls, especially considering the influence of horizontal loads such as wind or earthquakes. The objective of this paper is to build machine learning (ML) models to predict the shear capacity of rectangular RC squat walls. A dataset of 312 experimental results in previous studies were collected and used for training ML models. Six ML models including Artificial neural network-Levenberg Marquardt (ANN-LM), Artificial neural network-Bayesian regularization (ANN-BR), Artificial neural network-Gene algorithm (ANN-GA), Adaptive neuro fuzzy inference system (ANFIS), Random Forest (RF), and Gradient boosting regression tree (GBRT), were developed to predict the shear strength of RC walls. The prediction results of the proposed ML models were compared with that from eight empirical formulas in design standards and published studies. From the comparison results, the RF and GBRT models predicted the shear capacity of RC walls much more accurately than existing formulas. Furthermore, a graphical user interface has been established based on an efficient ML model to facilitate the actual design process of rectangular RC short walls.

This study presents the performance of three machine learning (ML) models including gradient boosting regression trees (GBRT), artificial neural network model (ANN), and artificial neural network–particle swarm optimization (ANN-PSO) for predicting the axial compression capacity (ACC) of cold‑formed steel elliptical hollow section (EHS) columns. To achieve the goal, a set of 291 data is collected from previous studies to develop GBRT, ANN, and ANN-PSO models. The performance of GBRT, ANN, and ANN-PSO models is evaluated based on the statistical indicators, which are R2,RMSE,MAPE,and i20-index. The results show that the ANN-PSO model with R2=1.00,RMSE=41.3631,MAPE=1.3689,and i20-index=0.9966has the best performance compared to GBRT and ANN models. Moreover, a graphical user interface tool is developed based on the ANN-PSO model for practical designs.

STREAM, developed by the Computational Reactor Physics and Experiment laboratory (CORE) of the Ulsan National Institute of Science and Technology (UNIST), is a deterministic neutron- and photon-transport code primarily designed for light water reactor (LWR) analysis. Initially, the photon module in STREAM did not account for fluorescence and bremsstrahlung photons. This article presents recent developments regarding the integration of atomic relaxation and bremsstrahlung models into the existing photon module, thus allowing for the transport of secondary photons. The photon flux and photon heating computed with the newly incorporated models is compared to results obtained with the Monte Carlo code MCS. The incorporation of secondary photons has substantially improved the accuracy of photon flux calculations, particularly in scenarios involving strong gamma emitters. However, it is essential to note that despite the consideration of secondary photon sources, there is no noticeable improvement in the photon heating for LWR problems when compared to the photon heating obtained with the previous version of STREAM.

The indirubin (2) derivative undergoes condensation with hydroxylamine hydrochloride, resulting in the formation of a novel indirubin derivative (3) that incorporates both the oxime group and the piperidine heterocycle. This compound is subsequently treated with hydrochloric acid in ethanol, leading to the formation of the hydrochloride salt (3.HCl), which exhibits excellent solubility in water. The chemical structures of compounds (3) and (3.HCl) were determined using 1D and 2D nuclear magnetic resonance (NMR) spectroscopy, as well as high-resolution mass spectrometry with electrospray ionization (ESI). To evaluate their potential as anticancer agents, in vitro assays were conducted using three human cancer cell lines (A549, Hep-G2, SW480) and one murine cancer cell line (B16F10). These compounds, along with indirubin (1) and Bortezomib (BTZ), were tested. The results revealed that compounds (3) and (3.HCl) demonstrated significant antitumor activity against all four cancer cell lines, with concentrations ranging from 0.37 ± 0.01 to 0.53 ± 0.06 μM. Importantly, their activity surpassed that of indirubin (1) and BTZ. Furthermore, an in vivo anticancer activity assay was performed on compound (3.HCl) using the B16F10 cell line. After 18 days, the tumor size in the group treated with compound (3.HCl) was approximately eight times smaller than that of the control group, and nearly three times smaller than the group treated with BTZ.

Spirolobium cambodianumBaill., belonging to Apocynaceae family, was used as a traditional medicine to treat various diseases. This study is the first report on antioxidant activities and chemical composition of S. cambodianumBaill. ethanolic leaf extract. The results showed that ethanolic extract had potent in vitroantioxidant activities in DPPH, ABTS•+assays (the low concentrations of IC50were 19.51 ± 0.78 µg/mL and 4.89 ± 0.07 µg/mL, respectively), and in RP, TAC methods (the Abs0.5were 20.80 ± 0.26 µg/mL and 36.15 ± 1.03 µg/mL). Moreover, investigating in vivoantioxidant activity under oxidative stress conditions caused by paraquat and H2O2using Drosophila melanogastershowed that flies were raised in the medium supplemented with a concentration of extract (1 mg/mL) lived longer than standard medium. Notably, the chemical investigation of S. cambodianumextract, ten identified compounds were reported for the first time from this plant and from the genus Spirolobium, including 27‐p‐Z‐coumaroyloxyursolic acid (1), lupeol (2), p‐hydroxybenzoic acid (3), ursolic acid (4), tectoquinone (5), daucosterol (6), asiatic acid (7), naringenin (8), 1‐O‐β‐D‐glucopyranosyl‐(2S,3S,4R,9Z)‐2‐[(2′R)‐2′‐hydroxytetracosanoylamino]‐octadec‐9‐en‐1,3,4‐triol (9), quercetin (10). This study suggests that the antioxidant efficacy of S. cambodianumleaf extract and its constituents can be explored as a potential antioxidant agent to treat various diseases, especially oxidative stress. Ten identified compounds were isolated for the first time from Spirolobium cambodianum Baill. and from the genus Spirolobium using chromatography column. S. cambodianumehthanolic extract exert its antioxidant activities in vitro using DPPH, ABTS, RP, TAC methods and antioxidative stress in vivo using Drosophila melanogaster as an experimental model.

The research activity in progress and the advancements in concrete technology are leading to an increased use of ultra-high-performance fiber-reinforced concrete (UHPFRC) in structural engineering. UHPFRC is characterized by steel fibers, cement, silica fume, fine sand, superplasticizer, and very low water–cement ratio. UHPFRC is defined as a new material, with unique properties (high ductility, low permeability, very high strength capacity in compression, higher toughness) in comparison with conventional concrete (Buttignol et al, Revista IBRACON de Estruturas e Materiais 10(4):957–971, 2017). This makes it possible to make slender constructions because a smaller cross section can transfer the same force as a larger cross section. However, in case of fire safety design a disadvantageous behavior of UHPC compared to normal-strength concrete is well known and documented. The high packing density of the cement matrix is the main reason for explosive spalling behavior when exposed to fire. In this paper, the numerical simulation using finite-element method has been used to evaluate the behavior of UHPFRC frame structure under fire action. According to results, the three-dimensional finite-element model can be used for calculating the mechanical localized of UHPFRC concrete frame structures subjected to nominal fire curves. The results of numerical analysis under fire action are also compared to the other materials for more understanding the fire resistance between UHPFRC material (with PP fiber, without PP fiber) and high-strength concrete.

Slender reinforced concrete (RC) walls are popularly employed to improve the lateral loading capacity of high-rise buildings. Shear strength is an important target in designing RC walls subjected to horizontal loads such as earthquakes. This study aims to develop machine learning (ML) models for estimating the shear capacity of RC slender walls. A total of 154 test results, which were published in the literature, are gathered for training ML models. Two neural network-based models, i.e., Artificial neural network- Levenberg Marquardt (ANN-LM) and Artificial neural network-Bayesian regularization (ANN-BR), and two tree-based ML models including Random Forest (RF) and Gradient boosting regression tree (GBRT), are developed to predict the shear strength of RC walls. The predicted results obtained from ML models are then compared with those from empirical formulas in design codes. It shows that ML models are superior in predicting the shear capacity of RC slender walls compared to other code-based models, especially, RF and GBRT highly efficient models. Moreover, a practical graphical user interface tool is proposed to simplify the practical design of RC slender walls.

Accurate node positioning in wireless sensor networks (WSNs) is essential for optimising monitoring and tracking applications. This becomes increasingly challenging, especially in large-scale WSNs where precise location data is needed for unknown nodes. Traditional methods often struggle with computational complexity, particularly in enlarged network setups. To address this issue, we introduce an innovative adaptive optimisation approach called crossover mutation differential evolution (ACMDE) tailored for node localisation in WSNs. ACMDE rapidly localises unknown nodes by leveraging location data and employing adaptive optimisation strategies, including enhanced crossover, mutation, and reinitialisation techniques. The objective function is modelled for the WSNs node localisation to minimise localisation errors between actual and detected node positions that are obtained optimisation targets through ACMDE's superior capabilities. The ACMDE's effectiveness is evaluated in the test suits and node localisation through comprehensive comparisons with existing strategies using various metrics. Experimental results unequivocally demonstrate that ACMDE outperforms competing algorithms in node localisation within WSNs.

A dual-band multifunctional body-worn textile antenna is proposed. The antenna has the capability to tune its resonance frequency at its lower band, which aims for applications in the upper range of the ultrahigh-frequency (UHF) radio frequency band. At the same time, it independently operates with a fixed resonance frequency at 2.45 GHz for general communications or data transfer purpose. The antenna concept is first proposed with a single feeding port design, which achieves a frequency tuning range of 60% in the lower band. Then, an alternative configuration with two independent feeding ports is demonstrated, which is able to efficiently use the fixed frequency at the higher band. This second antenna is optimized to have an isolation of at least 15 dB between its two ports for operation with two coexisting transceiver systems. At the lower band, the fractional tuning range is approximately 37%. The two antennas are fabricated and measured to validate the design concepts.

The aim of this study is to develop an efficient hybrid machine learning (ML) model, which combines the genetic algorithm (GA) and artificial neural network (ANN) for rapidly calculating the load-bearing capacity (LBC) reinforced concrete driven piles. An extensive database including 470 static tests is collected to train the hybrid ML model. The predicted results of the GA–ANN model in this study are compared to those of the pure ANN model. Statistical indicators containing the coefficient of determination (R2), root-mean-squared error (RMSE), and a20-indexare determined to assess the prediction performance of the ML models. The comparison emphasizes that the GA–ANN model predicts the LBC of the pile accurately with a very high R2value of 0.99 and small RMSEof 49 kN. Furthermore, the effects of input variables on the predicted LBC are evaluated. Finally, to apply the ML model, a graphical user interface tool is developed for simplifying the LBC of reinforced concrete driven piles.

A low-profile ( $0.035\lambda _{0}$ ) frequency-reconfigurable filtering patch antenna with a single-layered substrate is proposed in this communication. Parasitic strips and stubs are utilized to realize the filtering characteristics while using a simple probe feed. Frequency agility is implemented by adding three sets of varactor diodes on different parts of the radiating structure. These sets of varactors control the lower radiation null, the resonance frequency, and the upper radiation null, respectively. By optimizing the tuning characteristics of each set, only two biasing voltages are required, which simplifies the biasing circuit. The proposed antenna exhibits a tuning range of 21.9% with approximately 10 dB gain suppression level in its stopband, which is validated by experimental characterization.

Graphical abstract:

This study presented the performance of the gradient boosted regression trees (GBRT) model for predicting the axial load capacity (ALC) of the cold-formed steel semi-oval hollow section (CFS-SOHS) columns. A total of 219 datasets were carefully collected from previous publications including experimental and numerical results, which were used to develop the GBRT model. Of these, 209 datasets were used for training, while 10 datasets were used for testing. The results obtained from the GBRT model were compared with two existing formulas based on the statistical metrics (R2,RMSE,anda20-index). The statistical metrics from the GBRT model (R2=0.9999,RMSE=29.458,and i20-index=1.000)have been the best-predicting to the ALC of the CFS-SOHS column. Finally, a new graphical user interface GUItool was built based on the regression model for practice design.

This study develops an artificial neural network (ANN) to estimate the critical buckling load (CBL) of corroded web-tapered steel I-section (WTSI) columns in pre-engineered steel buildings. A total of 387 datasets are employed to develop the ANN model. The datasets are generated from the proposed analytical model and Newton–Raphson method. The input parameters of the developed ANN model contain the cross-sectional dimensions of the steel column (i.e., the top and bottom flange width, top and bottom flange thickness, maximum section height, minimum section height, and web thickness), elastic modulus of material, and the column height. Meanwhile, the CBL is the output parameter of the ANN model. A predictive process for the CBL of the corroded WTSI columns has been proposed based on the ANN model and previous corrosion model. Results reveal that the ANN model showed an excellent performance in predicting the CBL of the corroded steel columns. The R2values of the training, testing, and validation data are 0.99975, 0.99916, and 0.99951, respectively. The root-mean-squared errors of the training, testing, and validation data are 96.705 kN, 103.402 kN, and 103.200 kN, respectively. Additionally, the a20-index is very close to 1.0. Moreover, a graphical user interface tool is constructed to facilitate the CBL calculation of the corroded WTSI columns.

A dual-band multiport antenna with three orthogonal polarizations is proposed for 2.45 and 5.8 GHz bands. It consists of a monopolar slotted patch with shorting vias for vertically polarized omnidirectional radiations and differential-fed stacked patches for $x$- and $y$-polarized broadside radiations. To avoid undesired effects among different radiation modes, four shorting vias in the monopolar patch are utilized to feed the stacked patches. The final design with 0.07$\lambda _{2.45-\text{GHz}}$ profile achieves measured 10 dB return loss bandwidths of 50 and 180 MHz at the two target bands, respectively, and an isolation of $\ge$40 dB among all the ports, showing significant improvement as compared to the existing designs for the same application in the literature. Its radiation characteristics are validated by the far-field measurements, which illustrate highly symmetrical patterns and low cross polarization in all the operation modes.

Composite nanomaterials of ZnO/chitosan oligosaccharide (ZnO/COS) and ZnO/chitosan oligosaccharide iodine (ZnO/CTS‐I2) were expected to have effective antimicrobial effects due to the synergistic effects of the components. In this work, we present a method to synthesize ZnO nanoparticles with size of 12.3‐15.0 nm stabled in COS with low molecular weight (Mw) 3,321 g/mol and COS‐I2complex by precipitation of Zn2+with NaOH at pH 9, and then ultrasonic treatment to form ZnO nanoparticles. The UV‐Vis spectra of ZnO/COS and ZnO/COS‐I2composite nanomaterials both appeared absorption peaks typical for surface plasmon resonances of ZnO nanoparticles in solution at 372 and 370 nm, respectively. X‐ray diffraction (XRD) patterns confirmed the formation of ZnO/COS‐I2with wurtzite crystal structure characterized in both materials. The FT‐IR spectra of COS, COS‐I2, nano ZnO/COS, and nano ZnO/COS‐I2were measured. The characteristic peaks for the bonding vibrations of –OH and –NH2groups of ZnO/COS and ZnO/COS‐I2shifted to a larger wavenumber than that of COS. The prepared ZnO/COS and ZnO/COS‐I2nanocomposite materials may have potential for application as antibacterial agents used in medicine, food, and agricultural product preservation, control of harmful microorganisms on plants, and plant growth promotion.

In recent decades, the dramatic transformation from conventional energy to renewables, such as photovoltaic (PV), has been extensively occurred to address the increasing electricity demand and environmental issues. Nevertheless, the operation of PV systems is adversely affected by the intermittence of meteorological factors, such as solar irradiance. Therefore, accurate PV power generation forecast has played a vital role to stimulate the integration of renewable energy sources into the power grid. This study proposes a novel scheme for short-term PV power forecast based on Transformer Neural Network (TransNN) integrated with Convolutional Neural Network (CNN). In addition, Variational Mode Decomposition (VMD) is adopted in the data pre-processing stage to enhance the forecast accuracy. The performance of the proposed model is validated utilizing two real-world datasets, and five benchmark models are dedicated to forecast outcome comparison. The predicted results indicated the superiority of the proposed model compared to the referenced models by achieving the Mean Absolute Error (MAE) values under 1 kW in all cases.

A pattern-reconfigurable flexible textile antenna covering the 2.45-GHz industrial, scientific, and medical (ISM) band is presented. The design utilizes RF-switch integrated circuits (ICs) packaged into reconfiguration modules as switchable vias to change between the omnidirectional and broadside radiation modes of a center-shorted patch. Analytical models to calculate the resonance frequency of each mode are developed to give more physical insights into the mode resonance and assist the design optimization. An antenna prototype is experimentally characterized to further validate the concept and analysis of the proposed antenna structure. The stable performance under an on-body environment and various bending test conditions demonstrates the antenna operation in wearable scenarios.

AbstractThis study investigates the effectiveness of sustainability labels, environmental attitudes, food safety concerns, and knowledge on Vietnamese consumer preferences for sustainably farmed shrimp. Mixed logit and latent class models were applied to estimate utility functions based on 459 samples collected using a choice experiment. The results indicate that Vietnamese consumers prefer sustainably farmed shrimp to conventionally farmed shrimp. Also, both food safety concerns and consumer knowledge vigorously promote sustainably farmed shrimp choices. Notably, the latent class results show that 73.5% of the respondents consider their environmental concerns about shrimp production when making decisions about consumption. The results indicate that placing information related to eco-friendly and food safety attributes on sustainability labels provides Vietnamese consumers with additional choices in the form of sustainably farmed shrimp. Finally, the simulated MXL results in the WTP space are more significant than those in the preference space, indicating that it is necessary to estimate the MXL in the WTP space to avoid specifying the parameter distribution subjectively by analysts.

The cold-formed Steel Semi-oval Hollow Section (SSOHS) column is a new cross-section column and has been used a lot in construction projects. However, the design standards for steel structures in the world have not covered the cross-section classifications for the SSOHS columns in the design process. Therefore, the Axial Load Capacity (ALC) of the SSOHS column has been different between the design standards and experiments. This paper develops predictive tools (formula and graphical user interface) for calculating the ALC of the SSOHS columns based on an Artificial Neural Network (ANN) model. The ANN model has been developed with 219 datasets. The input parameters of the ANN model include the overall depth (D), the overall width (B), thickness (t) of the sections, and the length of the pin-ended columns (L). Meanwhile, the ALC of the SSOHS column is the output parameter of the ANN model. The predictive formula based on an ANN model is compared with three regression models and two existing formulas. The comparison results reveal that the performance of the ANN model outperform three regression models and two existing formulas through indicators: R-squared, RMSE, and a20-index. The sensitivity analyses of the input parameters to the ALC of the SSOHS column are also performed. Finally, a mathematical formula and graphical user interface program are developed to practically calculate the ALC of the SSOHS column.

The crane runway girder is an indispensable structure in industrial buildings with cranes, which is responsible for transferring the load from the crane and itself loads. The geometry properties, materials properties, and load input parameters are random variables in practice. Therefore, it must ensure the necessary reliability. This study presented the reliability assessment of the crane runway girder. To achieve the goal, the deterministic model is built based on the design approach for the crane runway girder according to EC-3. Meanwhile, the deterministic model with random input parameters has been called a stochastic model. Random variables for input design parameters are considered in the proposed procedure. The reliability assessment of the crane runway girder was based on the stochastic model and Monte Carlo simulation method. Finally, the effect of random input parameters investigated based on global Sobol sensitivity analysis was also presented.

Biohydrogen (H2) is considered as a prospective energy source for altering of exhausting fossil fuel-based hydrogen in the coming years. Among biomaterials, lignocellulose is the most abundant renewable feedstock for the second generation biohydrogen production through many processes. The conventional microbial fermentation and the photocatalytic reforming have received considerable attention that could convert the monosugars (mainly glucose and xylose) of the lignocellulosic materials into biohydrogen. In general, to obtain these monosugars, the lignocellulosic materials must be pre-treated through various complicated processes. This review focuses on various integrated pre-treating lignocellulosic material techniques and their advantages and disadvantages, including pretreatment, hydrolysis, and detoxification methods to get monosugars. Additionally, the state-of-the-art accomplishments in the post-methods, including microbial fermentation (including photo-fermentation and dark fermentation), microbial electrolysis, and photocatalytic reforming, for further converting monosugars into sustainable biohydrogen, are favorably highlighted. Finally, the perspectives for material pre-treating techniques and future challenges for post-methods to enhance biohydrogen are also discussed and intensified.

The on-the-fly energy release per fission (OTFK) model is implemented in STREAM to continuously update the Kappa values during the depletion calculation. The explicit neutron and photon energy distribution, which has not been considered in previous STREAM versions, is incorporated into the existing on-the-fly model. The impacts of the modified OTFK model with explicit neutron and photon heating in STREAM on the power distribution, fuel temperature, and other core parameters during depletion with feedback calculations are studied using several problems from the VERA benchmark suit. Overall, the explicit heating calculation provides a better power map for the feedback calculations particularly when strong gamma emitters are present. Generally, the fuel temperature decreases when neutron and photon heating is employed because fission neutrons and gamma rays are transported away from their points of generation. This energy release model in STREAM indicates that gamma energy accounts for approximately 9.5%–10% of the total energy released, and approximately 2.4%–2.6% of the total energy released will be deposited in the coolant for the VERA 5, NuScale, and Yonggwang Unit 3 2D cores.