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The present study simultaneously optimizes the three objective functions of pre-tensioned concrete (PT) beams, such as construction and material cost, beam weights, and beam depth, where multiple objectives optimizations (MOO) conflicting with each other are performed. MOO is performed based on 21 input parameters including tendon parameters and the 21 output parameters. Preassigned input parameters are defined by 16 equalities and design requirements are also implemented during the optimization through 19 code-based inequalities. Finally, the five-step ANN-based algorithms are presented to find optimized design parameters subject to external loads within ranges prescribed by inequalities. A Pareto frontier is presented based on the combinations of weight fractions representing contributions made by the three objective functions. ANN-based optimizations are capable of quantifying tendon ratios and rebar areas when concrete sections crack under service loads while optimizing multi-design targets and objective functions at the same time. The ANN-based optimized designs are verified with a structural mechanics-based software, AutoPT. Reductions of 9.1%, 30.1%, and 10.4% in beam depths, costs, and weights, respectively, obtained based on a Pareto frontier which simultaneously optimizes multi-objective functions are compared with probable designs by human engineers, demonstrating a design efficiency for a pre-tensioned concrete (PT) beam.

Design optimizations of round reinforced concrete columns based on artificial neural networks (ANNs) have been investigated in previous studies with only one pair of axial load (${P_u}$) and bending moment (${M_u}$). In this study, ANNs are generalized to be applicable to multiple load pairs by reshaping weight matrices of ANNs to prevent retraining of ANNs on the large datasets. Generalized ANN-based Lagrange optimizations are proposed for structural designs of round reinforced concrete columns with multiple load combinations. The present study modularizes the weight matrix of ANNs which considers one load pair to completely capture multiple factored loads. An optimal design by ANNs based on the modularized weight matrix and Lagrange optimization techniques using the Karush–Kuhn–Tucker (KKT) conditions was performed and validated with large datasets. Design examples performed by an ANN-based method and structural mechanics demonstrate accuracies of safety factors (SF) as small as 1% − 2%, which confirms the applicability of the proposed ANNs. Based on the present study, ANNs with modularized weight matrices aid engineers in optimizing round reinforced concrete columns subject to multiple loads.

In this study, the evolution of the frame in a structural behavior’s perspective is focused, identifying how cracks influence the degradations of the frame from the camber cracks to plastic hinges. An extensive experimental investigation for the H-shaped post-tensioned precast frame module with monolithic beam-column connections using bolted plate connections is performed to identify the ductile behavior of the H-shaped post-tensioned precast frame module. The inelastic stresses are observed to be redistributed until the failure mechanism with the three plastic hinges are formed in the prestressed beam, which dissipates an inelastic energy in a ductile fashion. No noticeable deformation in plates and bolts for both the first and second floor of the precast frame is found at the final stroke when the experiment was terminated, elucidating that bolted plate connections provide the frame with connections similar to the concrete monolithic joints. Structural behaviors are also investigated using nonlinear finite element analyses based on a damaged concrete plasticity model, unraveling the principles governing crack formations within tested frames. This investigatory approach yields valuable insights for designing post-tensioned precast frames with mechanical column joints. The proposed frame is verified as an option for modular off-site construction to replace cast-in-concrete monolithic frames.

Influenza, a historical and contemporary public health challenge, is an acute respiratory infection caused mainly by influenza A or B viruses. This descriptive case series describes 194 children treated for seasonal influenza at the Department of Pediatrics, 103 Military Hospital, Vietnam Military Medical University, from May 2022 to May 2023. Influenza primarily occurred during winter and early spring, peaking in December and January, 57.2% were aged 12-60 months, boys. Vaccination coverage was limited (36.3%), especially post-COVID-19. Complications were seen in 75.3% of the cases, including febrile seizures (42.3%), bronchopneumonia (40.2%), respiratory failure (8.2%), acute myocarditis, influenza-associated encephalopathy, and septic shock. The study reveals bacterial coinfections, affected 20% of the cases, mainly by Streptococcus pneumoniae and S. aureus, and atypical bacteria Mycoplasma pneumoniae. Non-vaccinated children faced significantly higher risks of complications, oseltamivir within 48 hours reducing the risks of complications. Influenza predominantly affects 12-60 month-olds, males, and peaks annually in winter. Severe conditions stem from bacterial coinfections, emphasizing the importance of vaccination and prompt antiviral treatment to mitigate complications. The study highlights a critical need for increased vaccination, especially in the post-COVID-19 era, to manage influenza in children effectively.

Influenza, a historical and contemporary public health challenge, is an acute respiratory infection caused mainly by influenza A or B viruses. This descriptive case series describes 194 children treated for seasonal influenza at the Department of Pediatrics, 103 Military Hospital, Vietnam Military Medical University, from May 2022 to May 2023. Influenza primarily occurred during winter and early spring, peaking in December and January, 57.2% were aged 12-60 months, boys. Vaccination coverage was limited (36.3%), especially post-COVID-19. Complications were seen in 75.3% of the cases, including febrile seizures (42.3%), bronchopneumonia (40.2%), respiratory failure (8.2%), acute myocarditis, influenza-associated encephalopathy, and septic shock. The study reveals bacterial coinfections, affected 20% of the cases, mainly by Streptococcus pneumoniae and S. aureus, and atypical bacteria Mycoplasma pneumoniae. Non-vaccinated children faced significantly higher risks of complications, oseltamivir within 48 hours reducing the risks of complications. Influenza predominantly affects 12-60 month-olds, males, and peaks annually in winter. Severe conditions stem from bacterial coinfections, emphasizing the importance of vaccination and prompt antiviral treatment to mitigate complications. The study highlights a critical need for increased vaccination, especially in the post-COVID-19 era, to manage influenza in children effectively.

Conventional cast-in-place frames using a pair of steel plates are replaced by a post-tensioned H-shaped precast frame with mechanical joints in which plates are used to transfer loads between them. The mechanical joints strengthened by interior bolts are used, providing a flexural capacity of columns to a level similar to that of conventional monolithic connections. In the present study, strain evolutions of the proposed frames are explored using a finite element analysis with a concrete plasticity model (CP) applying a 0% damage index representing undamaged concrete for the first floor and a concrete damaged plasticity model (CDP) model implementing 80% damage index for the second floor when using ABAQUS. FEA-based strain evolutions are, then, verified by experimental results obtained from full-scale tests, showing agreements except for places where gauges malfunctioned. This study identifies structural behaviors and contributions of each structural element of the mechanical joint to flexural capacity. Microscopic strain evolutions of the post-tensioned H-shaped precast frames with mechanical joints agreed with the test data. Both experimental and numerical investigations demonstrate the structural performance of the proposed prestressed frames with mechanical joints is predictable and stable under service loads.

This research demonstrates how pre-tensioned concrete beams (PT beams) are designed holistically using artificial neural networks (ANNs). To establish reverse design scenarios, large input and output data are generated using the mechanics-based software AutoPTbeam. ANN-trained reverse-forward networks are proposed to solve reverse designs with 15 input and 18 output parameters for engineers. ANNs for reverse designs pre-tensioned concrete beams are formulated based on 15 input structural parameters to investigate the performances of PT beams with pin-pin boundaries. Useful reverse designs based on neural networks can be established by relocating preferable control parameters on an input-side, such as when four output parameters ($${q_{L/250, }} {q_{0.2mm, }} {q_{str, }} {\mu _{\rm{\Delta }}}$$) (reverse scenario) are reversely pre-assigned on an input-side, all associated design parameters, including crack width, rebar strains at transfer load stage, rebar strains, and displacement ductility ratio at ultimate load stage are computed on an output-side. Deep neural networks trained by chained training scheme with revised sequence (CRS) for the reverse network of Step 1 show the better design accuracies when compared to those obtained based on ANNs trained by parallel training method (PTM) and based on shallow neural networks trained by CRS when the deflection ductility ratios (μΔ) within generated big datasets are reversely pre-assigned on an input-side.

The alkylation reaction was used to couple zerumbone with the 3-substituted quinazolinone-4(3H)-ones and quinolines, resulting in the formation of 11 new conjugates. Their structures were fully characterized by 1D-, 2D-NMR, and HRMS spectral data. The evaluation of their anti-inflammatory activity was examined by inhibiting NO production in RAW 267.4 cells. Screening for their cytotoxic activity was performed using three human cancer cell lines HepG2, SK-LU-1, and MCF-7. The results indicated that all 11 novel conjugates exhibited potent cytotoxic activity against the tested cell lines with IC50 values in the range of 1.01–9.86 µg/mL, which were stronger than those of the parent compound zerumbone. In silico EGFR inhibitory activity was also performed by docking and molecular dynamics simulation studies to find out the most potent compounds based on the main interactions of zerumbone derivatives 16a-k with important amino acids of EGFR (PDB ID 4HJO) protein in its active site.

Structural engineers must encounter multiobjective optimization (MOO) problems in designs including a design of reinforced concrete (RC) columns, where several design objectives must be satisfied to meet contractors’ interests and produce a sustainable design. MOO applications in structural engineering practice are uncommon despite their high demand. Multiobjective population methods have been mainly investigated. However, previous studies have mostly neglected the stopping criteria and convergence of these algorithms. This study proposes a novel gradient-based algorithm – ANN-based Lagrange optimization and its implementation in designs of circular reinforced concrete columns where three objectives, such as cost, CO2 emissions, and column weight, are minimized simultaneously. This study integrates each single-objective function extracted from an artificial neural network (ANN) into a global Lagrange function, a unified function of objectives (UFO), using the trade-off fractions. The Lagrange multiplier method is adopted to deal with constrained conditions by using Newton–Raphson iteration to solve Karush-Kuhn-Tucker (KKT) conditions. The proposed algorithm brought in a set of optimal results capturing multiple objectives, known as a Pareto frontier, which are compared with those obtained using the Nondominated Sorting Genetic Algorithm – II (NSGA-II), showing that two algorithms calibrated to each other. Overall, the ANN-based Lagrange optimization algorithm exhibited better convergence than NSGA-II.

shaped steel sections subjected to biaxial loads. The Lagrange multiplier method is used to optimize cost index (CIc), and CO2 emission of the columns. ANNs can be implemented to generalize functions for objective parameters CIc, and CO2 emission. Generalized functions can replace complex analytical functions that are difficult to derive when optimizing objective functions. In the AI-based Lagrange multiplier method, dimensions of columns and steel sections are calculated as output parameters corresponding to minimized CIc, and CO2 emission. Note that 3D interaction diagrams of SRC columns subjected to biaxial bending and concentric axial loads are also formulated based on optimal results. An accuracy of the ANN-based optimal designs is demonstrated using structural mechanics based on a strain compatibility. A hybrid network based on both ANNs and Lagrange multiplier method identified design parameters which reduced CIc, and CO2 emission of a column by 30.7% and 40.4% respectively, when compared with those of a conventionally designed column.

This study aims to propose artificial neural networks (ANNs) to solve structural engineering problems and to provide optimal designs for reinforced concrete columns with H-shaped steel sections. A reverse scenario based on preassigned safety factor ($$SF$$) for a factored biaxial load, steel ratio ($${\rho _s}$$), and aspect ratio of columns ($$b/h$$) is presented, meeting code requirements. A back-substitution (BS) method using a chained training scheme with a revised sequence (CRS) is implemented to optimize training. Effects of rebar and steel ratios on objective functions, cost index ($$C{I_c}$$), $$C{O_2}$$ emission, and column weight ($${W_c}$$) are identified. Three-dimensional interaction diagrams are obtained based on optimized $$C{I_c}$$, $$C{O_2}$$ emission, and $${W_c}$$ based on preassigned $$SF$$ equivalent to 1.0. Predictions from ANNs are ascertained using structural mechanics, demonstrating a significance of an accuracy of the optimized design obtained by the proposed ANNs. This study provides a useful and practical design for SRC columns by lessening engineer’s effort while increasing design accuracies.

This study proposes an artificial neural network for a design of reinforced concrete (RC) columns for structural engineers who are interested in performing reverse designs, exploring influences of structural parameters (e.g., $$\phi {P_n},{\ }\phi {M_n}$$, and $${{\rm{\varepsilon }}_{\rm{s}}}$$) or code requirements on structural performances. The proposed networks enable both forward and reverse designs for an RC column, which is challenging to be achieved using conventional designs. An AI-based surrogate model of RC columns with sufficient training accuracy can comprehensively replace conventional design software, exhibiting excellent productivity for both forward and reverse designs. In addition, useful reverse design models based on neural networks can be established by relocating preferable control parameters, including safety factor (SF = $${\ }\phi {M_n}/{M_u}$$) and an aspect ratio of column sections, into the input region. All associated design parameters, including $$b$$, $$h$$, and $${\rho _s}$$, are computed on an output side. Design charts, such as the P–M diagram, are constructed, demonstrating design moment strength equal to the factored moment demand by specifying SF = 1. The design scenarios can be extended as further as possible to meet the requirements of engineers.

Structural engineers face several code-restricted design decisions. Codes impose many conditions and requirements to the designs of structural frames, such as columns and beams. However, it is difficult to intuitively find optimized solutions, while satisfying all code requirements simultaneously. Engineers commonly make design decisions based on empirical observations. Optimization techniques can be employed to make more rational engineering decisions, which result in designs that can meet various code restrictions simultaneously. Lagrange optimization techniques with constraints, not based on explicit parameterization, are implemented to make rational engineering decisions and find minimized or maximized design values by solving nonlinear optimization problems under strict constraints imposed by design codes. It is difficult to express objective functions analytically directly in terms of design variables to use derivative methods, such as Lagrange multipliers. This study proposes the use of neural network to approximate well-behaved objective functions and other output parameters into one universal function that can also give a generalizable solution for operating Jacobian and Hessian matrices to solve the Lagrangian function. The proposed method was applied successfully in optimizing a cost of a reinforced concrete column under various design requirements. An efficacy of optimal results was also proven by 5 million datasets.

This study aims to develop artificial neural networks (ANNs)-based forward Lagrange networks optimizing ductile doubly reinforced concrete (RC) beams. Cost (CIb) of materials and manufacture were established as an objective function, which is minimized considering constraints according to engineer’s needs. Large datasets of 100,000 were used to derive an AI-based objective function for cost (CIb, minimizing cost of an RC beams) as a function of forward input parameters, replacing complex analytical objective functions. A resilient design capable of finding concrete beams beyond human efficiency is based on equality and inequality constraints, which are implanted in AI-based Lagrange. Optimal designs are not simple especially when multiple constraining conditions are to be considered. Neither is it possible for engineers to pre-assign constraining conditions which can be sequentially calculated in an output of a conventional design. Cost of RC beams minimized by forward Lagrange networks was reduced 18%–26% compared to probable beam designs. AI-based design charts with eight forward outputs (ϕMn,Mu,Mcr,εrt_0.003,εrc_0.003,Δimme,Δlong,CIb) based on nine forward inputs (L, h, b, fy,f’c, ρrt,ρrc,MD,ML) are proposed to assist engineers to design ductile doubly RC beams, presenting minimized CIb in the preliminary design stage.

This study proposes artificial neural networks (ANNs) that can solve structural engineering problems in general and design ductile concrete beams in particular. ANNs demonstrating a learning and memory capability similar to that of the human brain were implemented in the design of doubly reinforced concrete beams. ANNs are developed to map inputs into outputs based on large structural design datasets for engineering applications rather than being based on structural mechanics or knowledge. This study aims to develop design of doubly reinforced concrete beams based on ANN. In this study, a reverse design was presented with design charts in which the sequences of calculating inputs and outputs are exchanged, which is challenging to perform in conventional designs. Design charts suggested based on ANN for a design of doubly reinforced concrete beams were implemented with a design example. A prediction of multiple design parameters in an order that design charts offer consecutively is now possible in this study to help engineers solve reverse design problems in an accurate and rapid way. Accuracies were verified with structural calculations. AI-based reverse design provides engineers with preliminary design. Reverse techniques can be implemented in many areas of design.

This is a proposal for a nonlinear analytical model that was developed to predict the structural response of post-tensioned beam with unbonded tendon. The structural response includes deflections under the service load before or after cracking, as well as the ultimate load capacity. A comprehensive procedure for the deflection calculation was implemented, considering the additional shear deformation and tension stiffening effect. The shear deformation calculation is based on the Timoshenko beam theory, considering the shear modulus reduction upon cracking. Furthermore, a modification of the tension stiffening model from CEB–FIP Model Code 1990 is proposed to account for the significant strain concentration in the post-yielding region. A fixed-point iterative procedure is introduced in the prediction model to compute the incremental strain in the unbonded tendon. Each iteration brings the estimated strain increase in the tendon closer to the actual value until it is reasonably accurate. Predictive accuracy of the proposed model in both load–deflection relationships and strain rates, as a function of deflections, is successfully validated against experimental beam tests and a nonlinear finite element analysis considering concrete plasticity, respectively.

CuSO4/hydrazine hydrate was used as a catalyst system for copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) of AZT and 5′-azido adenosine with terminal alkynes to give 30 novel 1,2,3-triazole derivatives. Screening for their anticancer, anti-inflammatory, angiotensin-converting enzyme 2 (ACE2), and 3C-like protease (3CLpro) inhibitory activities showed that several triazoles of AZT containing murayafoline A and indirubin-3′-oxime inhibited the growth of HepG2 and LU-1 with the IC50 values ranging from 11.01 to 19.87 μg/mL. Besides that, some triazole derivatives of adenosine exhibited anti-inflammatory activity against RAW264.7 cells with the IC50 values within an interval of 12.00–59.48.00 μg/mL. Especially, two triazoles of adenosine with indirubin-3′-oxime at O- and N1 positions expressed the ACE2 and 3CLpro inhibitory activities in which the triazole of adenosine with indirubin-3′-oxime at N1 inhibited both ACE2 and 3CLpro inhibitory activities with IC50 values of 135.62 and 142.95 μg/mL, respectively.

[This corrects the article DOI: 10.1371/journal.pcbi.1006425.].

Pinus sylvestris. var mongolica is a major source of timber in Mongolia. The logging process makes many kinds of valuable biomass including bark, cones, and needles, which can be used for obtaining essential oil. The essential oil from the needles of wild growing Pinus sylvestris. var mongolica growing in Mongolia was chemically analyzed and its antibacterial, antifungal and cytotoxic activities were evaluated. The chemical analyses identified 101 compounds in the essential oil with the major compounds of α-pinene (29.87 %), limonene + β-phellandrene (16.15 %), camphene (4.95 %), bornylacetate (4.34 %), and β-pinene (3.88 %). This oil possessed the inhibitory activity against B. subtilis, S. cerevisiae, S. aureus and E. coli, successively with minimum inhibition concentration of 0.125, 0.1, 3.0, and 10.0 µg/mL. Importantly, the oil at 50 µg/mL and 100 µg/mL inhibited the growth of MCF-7 cells by 45.3 % and 99.7 %. The half of inhibition concentration of DPPH (2,2-Diphenyl-1-picrylhydrazyl) radical scavenging was 14.36 ± 0.28 mg/mL. The results, therefore, suggested that the essential oil of a Mongolian Scotch pine could potentially be used as a preservative material in cosmetic and food products, as a bioactive agent in anti-inflammatory and wound healing products in view of its antibacterial activity. Given our findings that this essential oil has such profound activity against MCF-7 cancer cells, a further investigation concerning the full extent of this essential oil’s anticancer activities seems warranted. Furthermore, given the promising antimicrobial effects of this essential oil against various bacterial species, an investigation concerning its effect against drug-resistant bacteria would be of immense interest.

Abstract The narwhal (Monodon monoceros) is a high‐Arctic species inhabiting areas that are experiencing increases in sea temperatures, which together with reduction in sea ice are expected to modify the niches of several Arctic marine apex predators. The Scoresby Sound fjord complex in East Greenland is the summer residence for an isolated population of narwhals. The movements of 12 whales instrumented with Fastloc‐GPS transmitters were studied during summer in Scoresby Sound and at their offshore winter ground in 2017–2019. An additional four narwhals provided detailed hydrographic profiles on both summer and winter grounds. Data on diving of the whales were obtained from 20 satellite‐linked time‐depth recorders and 16 Acousonde™ recorders that also provided information on the temperature and depth of buzzes. In summer, the foraging whales targeted depths between 300 and 850 m where the preferred areas visited by the whales had temperatures ranging between 0.6 and 1.5°C (mean = 1.1°C, SD = 0.22). The highest probability of buzzing activity during summer was at a temperature of 0.7°C and at depths > 300 m. The whales targeted similar depths at their offshore winter ground where the temperature was slightly higher (range: 0.7–1.7°C, mean = 1.3°C, SD = 0.29). Both the probability of buzzing events and the spatial distribution of the whales in both seasons demonstrated a preferential selection of cold water. This was particularly pronounced in winter where cold coastal water was selected and warm Atlantic water farther offshore was avoided. It is unknown if the small temperature niche of whales while feeding is because prey is concentrated at these temperature gradients and is easier to capture at low temperatures, or because there are limitations in the thermoregulation of the whales. In any case, the small niche requirements together with their strong site fidelity emphasize the sensitivity of narwhals to changes in the thermal characteristics of their habitats.

Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours.

We report the fabrication of a set of new rare-earth-free magnetic compounds, which form the Fe3Co3Ti2-type hexagonal structure with P-6m2 symmetry. Neutron powder diffraction shows a significant Fe/Co anti-site mixing in the Fe3Co3Ti2 structure, which has a strong effect on the magnetocrystalline anisotropy as revealed by first-principle calculations. Increasing substitution of Fe atoms for Co in the Fe3Co3Ti2 lattice leads to the formation of Fe4Co2Ti2, Fe5CoTi, and Fe6Ti2 with significantly improved permanent-magnet properties. A high magnetic anisotropy (13.0 Mergs/cm3) and saturation magnetic polarization (11.4 kG) are achieved at 10 K by altering the atomic arrangements and decreasing Fe/Co occupancy disorder.