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Applications relying on mid-infrared radiation ( λ  ~ 3-30 μm) have progressed at a very rapid pace in recent years, stimulated by scientific and technological breakthroughs like mid-infrared cameras and quantum cascade lasers. On the other side, standalone and broadband devices allowing control of the beam amplitude and/or phase at ultra-fast rates (GHz or more) are still missing. Here we show a free-space amplitude modulator for mid-infrared radiation ( λ  ~ 10 μm) that can operate at room temperature up to at least 1.5 GHz (−3dB cutoff at ~750 MHz). The device relies on a semiconductor heterostructure enclosed in a judiciously designed metal–metal optical resonator. At zero bias, it operates in the strong light-matter coupling regime up to 300 K. By applying an appropriate bias, the device transitions towards the weak-coupling regime. The large change in reflectance is exploited to modulate the intensity of a mid-infrared continuous-wave laser up to 1.5 GHz. Broadband integrated electrical modulators are key components for photonic systems. Here, the authors present a room temperature mid-IR free-space amplitude modulator based on a semiconductor heterostructure that exploits the change in reflectance occurring at the change between weak and strong coupling.

A highly sensitive immunosensor using streptavidin-conjugated quantum dots (QDs/SA) was developed to detect dengue biomarker of non-structural protein 1 (NS1) at very low concentration, so that it can probe dengue infection even in the early stage. The QDs/SA were first bound to biotinylated NS1 antibody (Ab) and the QDs/SA-Ab conjugates were then used to detect the NS1 antigen (Ag) in the Ag concentration range of 1 pM to 120 nM. The formation of QDs/SA-Ab and QDs/SA-Ab-Ag conjugates was confirmed by the measurements of field emission scanning electron microscopy (FF-SEM), field emission transmission electron microscopy (FE-TEM), dynamic light scattering (DLS), and zeta-potential. Fluorescence emission spectra of QDs/SA-Ab-Ag conjugates showed that the magnitude of fluorescence quenching was linearly proportional to the NS1 Ag concentration and it nicely followed the Stern–Volmer (SV) equation in phosphate buffer solution. However, in human plasma serum solution, the fluorescence quenching behavior was negatively deviated from the SV equation presumably due to interference by the serum component biomolecules, and it was well explained by the Lehrer equation. These results suggest that the current approach is promising because it is highly sensitive, fast, simple, and convenient, and thus it has a potential of application for point-of-care.

This paper aims to develop a practical artificial neural network (ANN) model for predicting the punching shear strength (PSS) of two-way reinforced concrete slabs. In this regard, a total of 218 test results collected from the literature were used to develop the ANN models. Accordingly, the slab thickness, the width of the column section, the effective depth of the slab, the reinforcement ratio, the compressive strength of concrete, and the yield strength of reinforcement were considered as input variables. Meanwhile, the PSS was considered as the output variable. Several ANN models were developed, but the best model with the highest coefficient of determination (R2) and the smallest root mean square errors was retained. The performance of the best ANN model was compared with multiple linear regression and existing design code equations. The comparative results showed that the proposed ANN model was provided the most accurate prediction of PSS of two-way reinforced concrete slabs. The parametric study was carried out using the proposed ANN model to assess the effect of each input parameter on the PSS of two-way reinforced concrete slabs. Finally, a graphical user interface was developed to apply for practical design of PSS of two-way reinforced concrete slabs.

This study quantifies the projected impacts of climate change on crop yields across temperature rise regimes and climatic zones, using the latest global dataset of site-level process-model simulations of crop responses to climate scenarios. We employed a threshold regression technique to identify and estimate temperature change thresholds and used linear mixed-effects models to assess the climate impacts on crop yields across different levels of temperature rise. The results indicated that warmer temperatures are detrimental to crop yields across countries, with negative impacts exacerbated when temperature increase exceeds threshold values. For instance, for wheat, a 1 °C temperature increase would result in a 6.1% yield loss when the temperature rise is below 2.38 °C; however, when it exceeds 2.38 °C, yield loss would rise to 8.2% per 1 °C warming. Similarly, the loss in rice yields for each °C increase in temperature would increase from 1.1 to 7.1% per °C when the temperature rise surpasses the 3.13 °C threshold. For maize, no threshold effect is found; instead, temperature increase would reduce yields by an average of 4.03% per °C. We also conducted impact assessments by climate zone, categorizing studied sites according to the Köppen climate classification system. We found that crop yields in arid regions are most adversely affected by global warming compared to other zones, while adaptive potential is higher for rice and wheat in temperate zones and for maize in continental zones. This study highlights the existence of threshold effects of temperature rise on crop yields and the varying yield impacts among climate zones, informing effective adaptation strategies to enhance global food security.

The ultimate bond strength of corroded steel reinforcement and surrounding concrete critically affects the load carrying capacity and eventually serviceability of the reinforced concrete structures. This study constructs and verifies a data-driven method for estimating ultimate bond strength. The proposed method is a hybridization of least squares support vector regression (LSSVR) and differential flower pollination (DFP) computational intelligence approaches. Since the problem of ultimate bond strength prediction involves nonlinear and multivariate data modeling, the LSSVR is employed to infer the mapping function between ultimate bond strength and its influencing factors of concrete compressive strength, concrete cover, steel type, diameter of steel bar, bond length, and corrosion level. Moreover, in order to overcome the very challenging task of fine-tuning the LSSVR model training, the DFP algorithm, as a population-based metaheuristic, is utilized to optimize the performance of the LSSVR prediction model. A dataset including 218 experimental tests has been collected from the literature to construct and verify the proposed hybrid method. Experimental results supported by the Wilcoxon signed-rank test point out that the hybridization of LSSVR and DFP can deliver predictive results (root-mean-square error = 2.39, mean absolute percentage error = 33.82%, and coefficient of determination = 0.84) superior to those of benchmark models including the artificial neural network, the multivariate adaptive regression splines, and the regression tree. Additionally, a software program based on the LSSVR model and the DFP optimization result has also been developed and compiled in Visual C#.Net to ease the model implementation. Hence, the hybrid model of DFP and LSSVR can be a promising alternative to assist engineers in the task of evaluating the health of reinforced concrete structures.

This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The WOA, which is configured to search for an optimal set of XGBoost parameters, helps increase the model’s accuracy and robustness. The hybrid method is constructed by a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the hybrid model consistently outperforms the default XGBoost model and deep neural network (DNN) regression. In an experiment of 20 runs, the proposed model has gained roughly 12, 11.7, 9, and 12% reductions in root mean square error compared to the DNN with 2, 3, 4, and 5 hidden layers, respectively. The Wilcoxon signed-rank tests confirm that the proposed model is highly suitable for concrete pile capacity prediction.

Tissue mimics are of great interest in understanding diseases. Here, organoids were developed that resemble polycystic kidney disease cysts and it was demonstrated how material environment and adhesion can affect cystogenesis and disease progression. Polycystic kidney disease (PKD) is a life-threatening disorder, commonly caused by defects in polycystin-1 (PC1) or polycystin-2 (PC2), in which tubular epithelia form fluid-filled cysts 1 , 2 . A major barrier to understanding PKD is the absence of human cellular models that accurately and efficiently recapitulate cystogenesis 3 , 4 . Previously, we have generated a genetic model of PKD using human pluripotent stem cells and derived kidney organoids 5 , 6 . Here we show that systematic substitution of physical components can dramatically increase or decrease cyst formation, unveiling a critical role for microenvironment in PKD. Removal of adherent cues increases cystogenesis 10-fold, producing cysts phenotypically resembling PKD that expand massively to 1-centimetre diameters. Removal of stroma enables outgrowth of PKD cell lines, which exhibit defects in PC1 expression and collagen compaction. Cyclic adenosine monophosphate (cAMP), when added, induces cysts in both PKD organoids and controls. These biomaterials establish a highly efficient model of PKD cystogenesis that directly implicates the microenvironment at the earliest stages of the disease.

Purpose: Based on research data from 24 listed commercial banks in the period 2012-2022, via regression using the Bayesian approach, the study provided evidence of the optimal threshold in capital structure to improve the stability of banks. Design/methodology/approach: Regarding macroeconomic factors, economic growth tends to erode the banking system's stability, while inflation has a vague impact. Furthermore, through the Bayesian approach via the Monte Carlo algorithm, the study has proposed a method to determine the optimal capital structure for each specific bank to cope with risk. Findings: The results show that the deposit-to-asset value of ACB (Asia Commercial Bank) and CTG (Vietnam Joint Stock Commercial Bank for Industry and Trade) has exceeded the optimal threshold. For non-deposit-to-asset, ACB is approximately at the optimal level; for CTG, this ratio is significantly lower than the optimal level; hence, they could increase this ratio to control risks and create more capital to finance their activities. Research limitations/implications: This research result is an essential practical contribution; it could help specific banks determine the appropriate capital structure to maintain operational stability. Research results could reflect the characteristics of the market being studied. Then, we would use this research result as prior information and combine it with data from each specific bank to estimate the posterior probability of the impact of capital on risk, thereby estimating the appropriate capital structure for the bank that needs to be researched. Originality/value: The paper provided evidence of an optimal capital structure that is associated with lower risk in Vietnamese banks. In addition, foreign capital also tends to improve the stability of the banking system, while bank size increases risks. KCI Citation Count: 0

Plants use multi-step phosphorelay (MSP) systems in response to exogenous and endogenous stimuli. Cytokinin and ethylene are among the factors that engage MSP signaling cascades but examples independent of phytohormones also exist. The MSP signaling involves four consecutive phosphorylation events at: (i) the kinase domain of the sensory histidine kinase, (ii) the receiver domain of the latter protein, (iii) the histidine-containing phosphotransfer protein, and (iv) the response regulator. In Arabidopsis thaliana , there are eight canonical histidine kinases, five histidine-containing phosphotransfer proteins (AHPs), one pseudo AHP, and 23 response regulators (ARRs). This redundancy suggests complex interactions between signaling pathways, including those involved in phytohormone cross-talk. To bring new insights at the molecular level, we investigated the structural and biophysical characteristics of the AHP1/ARR1 complex. ARR1, a type-B ARR, contains the GARP domain for DNA binding, in addition to the canonical receiver domain that mediates AHP1 interaction. We compared the ARR1 affinities across all five active AHPs and found a modest, two-fold higher affinity for AHP1. This result suggests that while ARR1 shows a slight preference for AHP1, it can also interact with AHP2-5, which potentially makes ARR1 a central node in signaling and a cross-talk modulator. In addition, we discuss the oligomerization state of AHP and related proteins utilizing all available experimental data to conclude that free AHPs are most likely monomeric.

Abstract Supervised machine learning methods, such as convolutional neural networks (CNNs), that use haplotype matrices as input data have become powerful tools for population genomics inference. However, these methods often lack interpretability, making it difficult to understand which population genetics features drive their predictions—a critical limitation for method development and biological interpretation. Here, we introduce a systematic permutation approach that progressively disrupts population genetics features within input test haplotype matrices, including linkage disequilibrium, haplotype structure, and allele frequencies. By measuring performance degradation after each permutation, the importance of each feature can be assessed. We applied our approach to three published CNNs for positive selection and demographic history inference. We found that the positive selection inference CNN ImaGene critically depends on haplotype structure and linkage disequilibrium patterns, while the demographic inference CNN relies primarily on allele frequency information. Surprisingly, another positive selection inference CNN, disc-pg-gan, achieved high accuracy using only simple allele count information, suggesting its training regime may not adequately challenge the model to learn complex population genetic signatures. Our approach provides a straightforward, model-agnostic, and biologically-motivated framework for interpreting any haplotype matrix-based method, offering insights that can guide both method development and application in population genomics.