Compensation negotiation and corporate governance: the evidence from China

This paper examines CEO pay dispersion for the listed companies in China. We apply a two-tier stochastic frontier model to the CEO compensation framework where asymmetric information generates a surplus between the minimum wage that CEOs accept and the maximum payment that firms offer. This surplus leads to CEO pay dispersion coming from the negotiation power between the CEO and the firm. We generate the surplus extracted by each CEO-firm pair and analyze how corporate governance affects them. An empirical analysis finds that: (1) On average, CEOs are paid 23.26% more than the benchmark; (2) additionally, we examine the bargaining power in state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). We find that CEOs in SOEs have less bargaining power due to compensation regulations. We then examine compensation for new CEOs hired externally and find that CEOs hired externally have less bargaining power on average; and (3) corporate governance has a significant effect on the salary bargaining power of each agent. More specifically, the CEO-Chairman dummy has a significant positive effect on the bargaining power of firms and CEOs, but the latter is larger. Board size has a negative effect on both. Independent directors help improve the bargaining power of the firms and board meeting times help enhance the bargaining power of the CEOs. Equity concentration has a significant negative effect on both sides

Thermal strain induced large electrocaloric effect of relaxor thin film on LaNiO3/Pt composite electrode with the coexistence of nanoscale antiferroelectric and ferroelectric phases in a broad temperature range

Ferroelectric/antiferroelectric thin/thick films with large electrocaloric (EC) effect in a broad operational temperature range are very attractive in solid-state cooling devices. We demonstrated that a large positive electrocaloric (EC) effect (maximum ΔT ~ 20.7 K) in a broad temperature range (~ 110 K) was realized in Pb0.97La0.02(Zr0.65Sn0.3Ti0.05)O3 (PLZST) relaxor antiferroelectric (AFE) thin film prepared using a sol-gel method. The large positive EC effect may be ascribed to the in-plane residual thermal tensile stress during the layer-by-layer annealing process, and the high-quality film structure owing to the utilization of the LaNiO3/Pt composite bottom electrode. The broad EC temperature range may be ascribed to the great dielectric relaxor dispersion around the dielectric peak because of the coexistence of nanoscale multiple FE and AFE phases. Moreover, a large pyroelectric energy density (6.10 Jcm−3) was harvested by using an Olsen cycle, which is much larger than those (usually less than 10− Jcm−3) obtained by using direct thermal-electrical, Stirling and Carnot cycles, etc. These breakthroughs enable the PLZST thin film an attractive multifunctional material for applications in modern solid-state cooling and energy harvesting

Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models

Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the interruption of the supply chain in 2022 led to a severe contrast media shortage in the US. Both factors justify the necessity of contrast media reduction in relevant clinical applications. In this study, we propose a diffusion model-based deep learning framework to address this challenge. First, we simulate different levels of low contrast dosage DECT scans from the standard normal contrast dosage DECT scans using material decomposition. Conditional denoising diffusion probabilistic models are then trained to enhance the contrast media and create contrast-enhanced images. Our results demonstrate that the proposed methods can generate high-quality contrast-enhanced results even for images obtained with as low as 12.5% of the normal contrast dosage. Furthermore, our method outperforms selected competing methods in a human reader study

Rate of Decline in Serum PFOA Concentrations after Granular Activated Carbon Filtration at Two Public Water Systems in Ohio and West Virginia

Drinking water in multiple water districts in the Mid-Ohio Valley has been contaminated with perfluorooctanoic acid (PFOA), which was released by a nearby DuPont chemical plant. Two highly contaminated water districts began granular activated carbon filtration in 2007.To determine the rate of decline in serum PFOA, and its corresponding half-life, during the first year after filtration.Up to six blood samples were collected from each of 200 participants from May 2007 until August 2008. The primary source of drinking water varied over time for some participants; our analyses were grouped according to water source at baseline in May-June 2007.For Lubeck Public Service District customers, the average decrease in serum PFOA concentrations between May-June 2007 and May-August 2008 was 32 ng/mL (26%) for those primarily consuming public water at home (n = 130), and 16 ng/mL (28%) for those primarily consuming bottled water at home (n = 17). For Little Hocking Water Association customers, the average decrease in serum PFOA concentrations between November-December 2007 and May-June 2008 was 39 ng/mL (11%) for consumers of public water (n = 39) and 28 ng/mL (20%) for consumers of bottled water (n = 11). The covariate-adjusted average rate of decrease in serum PFOA concentration after water filtration was 26% per year (95% confidence interval, 2528% per year).The observed data are consistent with first-order elimination and a median serum PFOA half-life of 2.3 years. Ongoing follow-up will lead to improved half-life estimation

Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential

The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on using ChatGPT to translate radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest CT lung cancer screening scans and 76 brain MRI metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.27 in the five-point system with 0.08 places of information missing and 0.07 places of misinformation. In terms of the suggestions provided by ChatGPT, they are general relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential

Influence of Tap Water Quality and Household Water Use Activities on Indoor Air and Internal Dose Levels of Trihalomethanes

Individual exposure to trihalomethanes (THMs) in tap water can occur through ingestion, inhalation, or dermal exposure. Studies indicate that activities associated with inhaled or dermal exposure routes result in a greater increase in blood THM concentration than does ingestion. We measured blood and exhaled air concentrations of THM as biomarkers of exposure to participants conducting 14 common household water use activities, including ingestion of hot and cold tap water beverages, showering, clothes washing, hand washing, bathing, dish washing, and indirect shower exposure. We conducted our study at a single residence in each of two water utility service areas, one with relatively high and the other low total THM in the residence tap water. To maintain a consistent exposure environment for seven participants, we controlled water use activities, exposure time, air exchange, water flow and temperature, and nonstudy THM sources to the indoor air. We collected reference samples for water supply and air (pre–water use activity), as well as tap water and ambient air samples. We collected blood samples before and after each activity and exhaled breath samples at baseline and postactivity. All hot water use activities yielded a 2-fold increase in blood or breath THM concentrations for at least one individual. The greatest observed increase in blood and exhaled breath THM concentration in any participant was due to showering (direct and indirect), bathing, and hand dishwashing. Average increase in blood THM concentration ranged from 57 to 358 pg/mL due to these activities. More research is needed to determine whether acute and frequent exposures to THM at these concentrations have public health implications. Further research is also needed in designing epidemiologic studies that minimize data collection burden yet maximize accuracy in classification of dermal and inhalation THM exposure during hot water use activities

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019