Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode

We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation. This DRL approach has the potential to lead to a large management model based on certain transformer neural network structures, resulting in an artificial general intelligence paradigm for various management tasks. Traditional methods have limitations for solving complex real-world problems, and we demonstrate how DRL can surpass existing heuristic approaches for solving management tasks. We aim to solve the problems in a unified framework, considering the interconnections between different tasks. Central to our methodology is the development of a foundational decision model coordinating decisions across the different domains through generative decision-making. Our experimental results affirm the effectiveness of our DRL-based framework in complex and dynamic business environments. This work opens new pathways for the application of DRL in management problems, highlighting its potential to revolutionize traditional business management

Meta contrastive label correction for financial time series

Financial applications such as stock price forecasting, usually face an issue that under the predefined labeling rules, it is hard to accurately predict the directions of stock movement. This is because traditional ways of labeling, taking Triple Barrier Method, for example, usually gives us inaccurate or even corrupted labels. To address this issue, we focus on two main goals. One is that our proposed method can automatically generate correct labels for noisy time series patterns, while at the same time, the method is capable of boosting classification performance on this new labeled dataset. Based on the aforementioned goals, our approach has the following three novelties: First, we fuse a new contrastive learning algorithm into the meta-learning framework to estimate correct labels iteratively when updating the classification model inside. Moreover, we utilize images generated from time series data through Gramian angular field and representative learning. Most important of all, we adopt multi-task learning to forecast temporal-variant labels. In the experiments, we work on 6% clean data and the rest unlabeled data. It is shown that our method is competitive and outperforms a lot compared with benchmarks

Evaluation of six satellite-based terrestrial latent heat flux products in the vegetation dominated Haihe river basin of north China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p 2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget

Recurrent renal secondary hyperparathyroidism caused by supernumerary mediastinal parathyroid gland and parathyromatosis: A case report

BackgroundSurgical parathyroidectomy (PTX) is necessary for patients with severe and progressive secondary hyperparathyroidism (SHPT) refractory to medical treatment. Recurrence of SHPT after PTX is a serious clinical problem. Both supernumerary mediastinal parathyroid gland and parathyromatosis are the rare causes of recurrent renal SHPT. We report a rare case of recurrent renal SHPT due to supernumerary mediastinal parathyroid gland and parathyromatosis.Case presentationA 53-year-old man underwent total parathyroidectomy with autotransplantation due to the drug-refractory SHPT 17 years ago. In the last 11 months, the patient experienced symptoms including bone pain and skin itch, and the serum intact parathyroid hormone (iPTH) level elevated to 1,587 pg/ml. Ultrasound detected two hypoechoic lesions located at the dorsal area of right lobe of the thyroid gland, and both lesions presented as characteristics of hyperparathyroidism in contrast-enhanced ultrasound. 99mTc-MIBI/SPECT detected a nodule in the mediastinum. A reoperation involved a cervicotomy for excising parathyromatosis lesions and the surrounding tissue and a thoracoscopic surgery for resecting a mediastinal parathyroid gland. According to a histological examination, two lesions behind the right thyroid lobe and one lesion in the central region had been defined as parathyromatosis. A nodule in the mediastinum was consistent with hyperplastic parathyroid. The patient remained well for 10 months with alleviated symptoms and stabilized iPTH levels in the range of 123–201 pg/ml.ConclusionAlthough rare, recurrent SHPT may be caused by a coexistence of both supernumerary parathyroid glands and parathyromatosis, which should receive more attention. The combination of imaging modalities is important for reoperative locations of parathyroid lesions. To successfully treat parathyromatosis, all the lesions and the surrounding tissue must be excised. Thoracoscopic surgery is a reliable and safe approach for the resection of ectopic mediastinal parathyroid glands

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Research progress in application of novel two-dimensional MXenes material for fuel cells

MXenes are a new type of two-dimensional layered transition metal carbides and nitrides prepared by selective etching of MAX phase materials. Due to their excellent physical, electronic and chemical properties, MXenes have been widely used in electromagnetic shielding, biomedicine, energy storage, sensors, water purification and other fields. At the same time, MXenes and their composites can effectively improve the catalytic efficiency of noble metal catalysts or directly serve as a class of non-precious metal catalysts due to their large specific surface area, excellent electrical conductivity and stability, and are regarded as a promising class of fuel cells electrocatalysts or supports. The structure, properties and preparation methods of MXenes were introduced in this paper, and the latest application research results of MXenes and their composites in the fields of oxygen reduction, formic acid oxidation, methanol oxidation and ethanol oxidation reactions were overviewed, and the main problems existing in MXenes materials were pointed out (for example, it is difficult to preparing uniformly dispersed multi-layer MXenes flakes or few or even single-layer MXenes flakes, which are easy to re-stack due to higher surface energy, etc.), preparing more new MXenes and composite them with various materials were put forward, in order to promote the application of MXenes and their composites in the field of fuel cells