Asymmetric Risk and CEO Compensation

This paper aims to understand how firms’ asymmetric risk environment affects the CEO compensation structure. I investigate how firm’s downside risk and upside potential differentially affect the choice between cash and equity compensation and the choice between stock options and restricted stock compensation. First, I show that, as downside risk (upside potential) increases, boards grant more cash compensation (more equity compensation) and less equity compensation (less cash compensation). Second, I show that the proportion of CEO option compensation in total equity compensation increases with downside risk and decreases with upside potential. My findings support the idea that boards respond to changes in their firms’ risk environments by adjusting the structure of CEO compensation to reflect risk-averse CEOs’ risk preferences

Two essays in asset pricing

The first essay, Knowledge Capital and Innovation Efficiency Effects on Stock Returns, provides a novel framework for understanding innovation in the asset pricing literature. Prior research shows that stock returns are increasing in firms' innovative efficiency. In a dynamic model of investment in physical and knowledge capital, this effect can arise rationally as innovative efficiency amplifies risks associated with investment and investors require compensation for these risks. I identify operating leverage and expansion option channels as the main drivers of the risk premium. Simulations of panels of firms with heterogeneous technology can reproduce the economic magnitude of the empirical return effect. The model further implies that the effect should be stronger for firms with high operating leverage and low book-to-market ratios. These predictions are supported by the data. The second essay, Operating Leverage, R&D Intensity, and Stock Returns, studies interaction effects of operating leverage and R&D intensity on stock returns. A production-based asset pricing model with knowledge capital has an implication that R&D intensive firms earn higher expected stock returns among high fixed costs firms. An investment strategy that bought R&D intensive firms and sold R&D weak firms earn 0.67% to 1.26% per month in high fixed cost portfolios, while the strategy is not profitable in low fixed costs portfolios. In regression analysis, one standard deviation increase in R&D expenditures is associated with 1.85% to 2.12% increase in yearly stock returns for above median fixed costs firms. By the recursive nature of knowledge capital accumulation, the value of knowledge capital itself is sensitive to the economic situation. R&D intensive firms' values aggravate faster with fixed costs in bad times and investors require compensation for the risk. In short, the value of knowledge capital itself is risky, R&D intensive firms are more exposed to the risky nature of knowledge capital, and fixed costs amplify the risk

Recalibration and validation of the Charlson Comorbidity Index in acute kidney injury patients underwent continuous renal replacement therapy

Background Comorbid conditions impact the survival of patients with severe acute kidney injury (AKI) who require continuous renal replacement therapy (CRRT). The weights assigned to comorbidities in predicting survival vary based on type of index, disease, and advances in management of comorbidities. We developed a modified Charlson Comorbidity Index (CCI) for use in patients with AKI requiring CRRT (mCCI-CRRT) and improved the accuracy of risk stratification for mortality. Methods A total of 828 patients who received CRRT between 2008 and 2013, from three university hospital cohorts was included to develop the comorbidity score. The weights of the comorbidities were recalibrated using a Cox proportional hazards model adjusted for demographic and clinical information. The modified index was validated in a university hospital cohort (n = 919) using the data of patients treated from 2009 to 2015. Results Weights for dementia, peptic ulcer disease, any tumor, and metastatic solid tumor were used to recalibrate the mCCI-CRRT. Use of these calibrated weights achieved a 35.4% (95% confidence interval [CI], 22.1%–48.1%) higher performance than unadjusted CCI in reclassification based on continuous net reclassification improvement in logistic regression adjusted for age and sex. After additionally adjusting for hemoglobin and albumin, consistent results were found in risk reclassification, which improved by 35.9% (95% CI, 23.3%–48.5%). Conclusion The mCCI-CRRT stratifies risk of mortality in AKI patients who require CRRT more accurately than does the original CCI, suggesting that it could serve as a preferred index for use in clinical practice

Chemokine (C-C Motif) Ligand 8 and Tubulo-Interstitial Injury in Chronic Kidney Disease

Kidney fibrosis has been accepted to be a common pathological outcome of chronic kidney disease (CKD). We aimed to examine serum levels and tissue expression of chemokine (C-C motif) ligand 8 (CCL8) in patients with CKD and to investigate their association with kidney fibrosis in CKD model. Serum levels and tissue expression of CCL8 significantly increased with advancing CKD stage, proteinuria level, and pathologic deterioration. In Western blot analysis of primary cultured human tubular epithelial cells after induction of fibrosis with rTGF-β, CCL8 was upregulated by rTGF-β treatment and the simultaneous treatment with anti-CCL8 mAb mitigated the rTGF-β-induced an increase in fibronectin and a decrease E-cadherin and BCL-2 protein levels. The antiapoptotic effect of the anti-CCL8 mAb was also demonstrated by Annexin V/propidium iodide staining assay. In qRT-PCR analysis, mRNA expression levels of the markers for fibrosis and apoptosis showed similar expression patterns to those observed by western blotting. The immunohistochemical analysis revealed CCL8 and fibrosis- and apoptosis-related markers significantly increased in the unilateral ureteral obstruction model, which agrees with our in vitro findings. In conclusion, CCL8 pathway is associated with increased risk of kidney fibrosis and that CCL8 blockade can ameliorate kidney fibrosis and apoptosis

In Vitro Cellular Uptake and Transfection of Oligoarginine-Conjugated Glycol Chitosan/siRNA Nanoparticles

Chitosan and its derivatives have been extensively utilized in gene delivery applications because of their low toxicity and positively charged characteristics. However, their low solubility under physiological conditions often limits their application. Glycol chitosan (GC) is a derivative of chitosan that exhibits excellent solubility in physiological buffer solutions. However, it lacks the positive characteristics of a gene carrier. Thus, we hypothesized that the introduction of oligoarginine peptide to GC could improve the formation of complexes with siRNA, resulting in enhanced uptake by cells and increased transfection efficiency in vitro. A peptide with nine arginine residues and 10 glycine units (R9G10) was successfully conjugated to GC, which was confirmed by infrared spectroscopy, 1H NMR spectroscopy, and elemental analysis. The physicochemical characteristics of R9G10-GC/siRNA complexes were also investigated. The size and surface charge of the R9G10-GC/siRNA nanoparticles depended on the amount of R9G10 coupled to the GC. In addition, the R9G10-GC/siRNA nanoparticles showed improved uptake in HeLa cells and enhanced in vitro transfection efficiency while maintaining low cytotoxicity determined by the MTT assay. Oligoarginine-modified glycol chitosan may be useful as a potential gene carrier in many therapeutic applications

Sequential Targeted Delivery of Liposomes to Ischemic Tissues by Controlling Blood Vessel Permeability

Delivery systems for therapeutic angiogenesis that deliver angiogenic factors to ischemic tissues have recently been fabricated. However, these systems are designed for surgical implantation or multiple local injections which can cause pain and potential physical burden in patients. Here, we propose a minimally invasive sequential nanoparticle-mediated delivery strategy for ischemic tissue using a murine hindlimb ischemic model. Intravenously injected liposomes that encapsulate VEGF, an angiogenic factor, first target the ischemic sites via the enhanced permeability and retention (EPR) effect in early stages of ischemia. VEGF released from the targeted liposomes maintains the blood vessel permeability for a longer period of time compared to the delivery of empty liposomes. This first nanoparticle-mediated delivery of VEGF to the ischemic site enables extending the temporal window of leaky blood vessel up to 7 days so that the second liposomes could be targeted to the ischemic sites via EPR effect. This strategy will provide opportunities for the targeted delivery of other vessel maturation agents loaded in nanoparticles to ischemic tissue

Machine learning-based weld porosity detection using frequency analysis of arc sound in the pulsed gas tungsten arc welding process

Automatic welding equipment has replaced human welders in the nuclear industry for safety issues and uniform and high welding quality. However, automatic welding equipment cannot predict porosity defects. So, the weldment must be inspected by non-destructive testing. This inspection was a costly and time-consuming process, and it applies to each weldment even if it welded same material. To improve the welding efficiency, a weld porosity detection system of the same weld material with different material thicknesses was needed. This paper proposed a machine-learned porosity detection system for 3.0 mm plates with welding arc sound data from the pulsed gas tungsten arc welding (P-GTAW) process of 1.6 mm plates. Ensemble-Empirical Mode Decomposition (EEMD) was used to divide the arc sound signal according to the pulse period of P-GTAW. Fast Fourier transform (FFT) was used to convert the arc sound into frequencies for features extraction according to porosity. The validity of these weld frequency features was confirmed through k-fold cross-validation across various machine learning techniques, with evaluation of F-1 scores against experimental weld sounds

The effect of nanoparticle properties, detection method, delivery route and animal model on poly(lactic-co-glycolic) acid nanoparticles biodistribution in mice and rats

We demonstrate the impact of engineering molecular structures of poly(acrylamide) (PAAm) and poly(-isopropylacrylamide) (PNIPAm) hydrogel composites on several physical properties. The network structure was systematically varied by (i) the type and the concentration of difunctional cross-linkers and (ii) the type of native or chemically modified natural polymers, including sodium alginate, methacrylate/dopamine-incorporated porcine skin gelatin and fish skin gelatin, and thiol-incorporated lignosulfonate, which are attractive biopolymers generated in pulp and food industries because of their abundance, rich chemical functionalities, and environmental friendliness. First, we added cross-linking agents of varying lengths at different concentrations to assess how the cross-linking agent modulates the mechanical properties of acrylamide-based composites with alginate. After chemically modifying gelatins from fish or porcine skin with methacrylate and/or dopamine, the acrylamide-based composites were fabricated with the chemically modified gelatins and thiolated lignosulfonate to assess the stress-strain behavior. Furthermore, swelling ratios were measured with respect to temperature change. The mechanical properties were systematically modulated by the changes in the molecular structure, that is, the length of the chemical unit between two end alkene groups in the difunctional cross-linker and the types of the additive natural polymers. Overall, PAAm hydrogel composites exhibit a significant, negative correlation between toughness and the volume fraction of the swollen state and between strain at fracture and the volume fraction of the swollen state. In contrast, PNIPAm hydrogel composites showed positive, but only moderate correlations, which is attributed to the difference in the network polymer structure

Prediction of internal welding penetration based on IR thermal image supported by machine vision and ANN-model during automatic robot welding process

Welding quality is a critical criterion for evaluating welding operations. Traditional evaluation methods suffer from drawbacks such as lack of objectivity, untimeliness, and high costs. Therefore, real-time monitoring and assessment of the weld pool have become the mainstream trend in welding technology. This study introduces a novel method for defining weld pool width boundaries. It utilizes an infrared (IR) camera to capture the weld pool temperature clusters and employs the Sobel operator for convolution to generate the gradient map of the weld pool temperature clusters. Through enhanced processing in the gradient map, the width boundaries of the weld pool are more effectively detected compared to previous methods. Previous studies defined weld pool width boundaries by identifying characteristic points with the most distinct temperature fluctuations, caused by the different radiative properties of the same material in different states. However, practical tests revealed susceptibility to interference from reflected arc light. The proposed method mitigates the impact of reflected arc light and is applicable to complex multilayer welding scenarios. To address the lag in quality monitoring, reduce welding costs, and achieve real-time monitoring of the weld pool process, we employed machine vision and an artificial neural network (ANN) model. This led to the development of a weld penetration assessment system based on infrared thermal images. The system successfully predicted the penetration depth for 4 mm carbon steel with an accuracy of 86.6 %. This validates the feasibility of estimating and predicting weld performance using the surface temperature characteristics of the weld pool. The newly proposed weld pool boundary definition method holds promise for real-time monitoring in more complex multilayer pipe welding scenarios. It lays the groundwork for predicting and fusing the weld depth in intricate multi-pass pipe welding