Estimating the External Returns to Education: Evidence from China

Using longitudinal data from the China Health and Nutrition Survey, we examine how individual wages change in line with the share of college graduates in a given province. The individual fixed effect model shows that the external returns to education in China appear to be zero. We estimate an instrumental variables fixed effects model where share of college graduates is instrumented by the number of universities with special status and find positive external returns to education of about 10 per cent to 14 per cent. We also find that the returns are affected by individual heterogeneity. While negligible returns are found for urban, women, and high-educated workers, the returns are positive and statistically significant for rural, men, and low-educated workers. This finding provides the motivation for increasing education investment in rural China and targeting it more toward poorly educated workers

Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the 'predict, then optimize' paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts in operation. In this paper, we design a value-oriented point forecasting approach for energy dispatch problems with renewable energy sources (RESs). At the training phase, this approach incorporates forecasting with day-ahead/real-time operations for power systems, thereby achieving reduced operation costs of the two stages. To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages. Under mild assumptions, we propose a novel iterative solution strategy for this bilevel program. Under such an iterative scheme, we show that the upper level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used point forecasting methods, the forecasts obtained by the proposed approach result in lower operation costs in the subsequent energy dispatch problems. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic program.Comment: submitted to European Journal of Operational Researc

Efficient semi-supervised inference for logistic regression under case-control studies

Semi-supervised learning has received increasingly attention in statistics and machine learning. In semi-supervised learning settings, a labeled data set with both outcomes and covariates and an unlabeled data set with covariates only are collected. We consider an inference problem in semi-supervised settings where the outcome in the labeled data is binary and the labeled data is collected by case-control sampling. Case-control sampling is an effective sampling scheme for alleviating imbalance structure in binary data. Under the logistic model assumption, case-control data can still provide consistent estimator for the slope parameter of the regression model. However, the intercept parameter is not identifiable. Consequently, the marginal case proportion cannot be estimated from case-control data. We find out that with the availability of the unlabeled data, the intercept parameter can be identified in semi-supervised learning setting. We construct the likelihood function of the observed labeled and unlabeled data and obtain the maximum likelihood estimator via an iterative algorithm. The proposed estimator is shown to be consistent, asymptotically normal, and semiparametrically efficient. Extensive simulation studies are conducted to show the finite sample performance of the proposed method. The results imply that the unlabeled data not only helps to identify the intercept but also improves the estimation efficiency of the slope parameter. Meanwhile, the marginal case proportion can be estimated accurately by the proposed method

Degradation of Grassland Ecosystems in the Developing World: The Tragedy of Breaking Coupled Human-Natural Systems

Since Hardin (1968) published his famous theory Tragedy of the Commons supported by examples showing that communal grasslands can be easily overgrazed when herdsman increase their herd numbers, a lot of research has supported the viewpoint that rangeland degradation and desertification in much of the pastoral areas in the developing world are caused by overgrazing (Arnalds and Archer 2000). With increasing focus on change at the global scale, many scientists, guided by the disequilibrium theory, hypothesized that climatic variability and change rather than overgrazing is associated with rangeland degradation. We argue that neither overgrazing nor climate change can alone explain the degradation of rangelands worldwide. In contrast, failure to reconcile emergent issues at the interface between the ecological, economic and social aspects has repeatedly resulted in management and policy actions that do not achieve the objectives of optimizing yield of rangeland products in a sustainable manner. The coupled human and natural systems (CHANS) approach proposed by Liu et al. (2007) can be used to identify applicable approaches for helping pastoral societies worldwide cope with global change by facilitating effective collaboration among social scientists, bio/physical scientists, practitioners, managers, and users to protect and sustain pastoral environments (Dong et al. 2011)

Comparison of the safety and efficacy of propofol and dexmedetomidine as sedatives when used as a modified topical formulation

Purpose: To evaluate the safety and efficacy of propofol and dexmedetomidine as sedatives in patients with anticipated difficult airways, used as a modified topical preparation.Methods: A total of 432 patients were enrolled in this study. They were classified as ASA I and ASA II. The patients were equally divided into group A (propofol group) and group B (dexmedetomidine group). A modified Awake Fiberoptic Intubation (AFOI) was carried out for these patients, followed by airway assessment and evaluation of clinical outcome based on intubation scores, adverse events, and postoperative data.Results: Patients in both groups had successful intubation at the first attempt. There was no significant difference in baseline characteristics between the two groups. The SARI scores which characterized the overall score for tracheal intubation were 4.6 and 4.2 for groups A and B, respectively. With respect to rescue infusion and consciousness, 11 patients (5.09 %) in group A required rescue, as against 5 patients (2.31 %) in group B. Seven (7) patients (3.24 %) in group A (propofol group) had severe airway obstruction, while only 4 patients (1.85) in group B had the same adverse reaction. Patients in group B had more satisfactory and favourable outcomes than those in group A who were treated with modified AFOI.Conclusion: The use of dexmedetomidine based on modified topical anaesthesia is safe and comfortable in terms of patient convenience and difficult airway management. Thus, dexmedetomidine is a safe, feasible and effective method for managing difficult airway when applied using the modified AFOI

Potentials of neuron-specific enolase as a biomarker for gastric cancer

Purpose: To investigate the potentials of neuron-specific enolase (NSE) as a biomarker for gastric cancer (GC). Methods: Gastric cancer (GC) patients (n = 412) who underwent gastrectomy were recruited over a 3- year period for this study. Their clinicopathological data such as age, sex, histological type, depth, tumor invasion, lymph node metastasis, and distant metastasis were analyzed. The patients were followed up for four years and the outcomes were also assessed. Histological changes in biopsies and levels of expression of NSE in biopsies and serum of patients were determined using immunohistochemical staining, western blotting and enzyme-linked immunosorbent assay (ELISA), respectively. Results: Immunohistochemical staining showed that NSE was differentially expressed in the cytoplasm of GC cells. Histological changes in biopsies of patients in the overexpression group were not significantly different from those of patients in under-expression group (p > 0.05). In NSE overexpression group, the number of patients in early stage GC subgroup (n = 186, 86.10 %, T1) were significantly higher than that in advanced GC subgroup (n = 124, 62.20 % T2–T4) (p < 0.05). However, in NSE under-expression group, there were more patients in advanced GC subgroup (n = 72, 37.70 %) than in early GC subgroup (n = 30, 13.80 %) (p < 0.05). Patients in NSE overexpression group survived longer than those in NSE under-expression group (p < 0.05). The level of expression of NSE significantly decreased with increase in TNM stage (p < 0.05). There was no significant difference in serum NSE level between GC patients and healthy control (p > 0.05). The results of the correlation analysis indicated that NSE levels were positively associated with GC. Conclusion: The results obtained in this study suggest that NSE could serve as a potential biomarker for GC. Keywords: Biomarker, Gastric cancer, Neuron-specific enolase, Overexpression, TNM stagin

Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy

Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs) have automated the radiotherapy plan-making by predicting the dose maps. However, current CNN-based methods ignore the remarkable dose difference in the dose map, i.e., high dose value in the interior PTV while low value in the exterior PTV, leading to a suboptimal prediction. In this paper, we propose a triplet-constraint transformer (TCtrans) with multi-scale refinement to predict the high-quality dose distribution. Concretely, a novel PTV-guided triplet constraint is designed to refine dose feature representations in the interior and exterior PTV by utilizing the explicit geometry of PTV. Furthermore, we introduce a multi-scale refinement (MSR) module to effectively fulfill the triplet constraint in different decoding layers with multiple scales. Besides, a transformer encoder is devised to learn the important global dosimetric knowledge. Experiments on a clinical cervical cancer dataset demonstrate the superiority of our method.Comment: accepted by 2024 IEEE ISB

Cancer Nanotechnology: Enhancing Tumor Cell Response to Chemotherapy for Hepatocellular Carcinoma Therapy

Abstract Hepatocellular carcinoma (HCC) is one of the deadliest cancers due to its complexities, reoccurrence after surgical resection, metastasis and heterogeneity. In addition to sorafenib and lenvatinib for the treatment of HCC approved by FDA, various strategies including transarterial chemoembolization, radiotherapy, locoregional therapy and chemotherapy have been investigated in clinics. Recently, cancer nanotechnology has got great attention for the treatment of various cancers including HCC. Both passive and active targetings are progressing at a steady rate. Herein, we describe the lessons learned from pathogenesis of HCC and the understanding of targeted and non-targeted nanoparticles used for the delivery of small molecules, monoclonal antibodies, miRNAs and peptides. Exploring current efficacy is to enhance tumor cell response of chemotherapy. It highlights the opportunities and challenges faced by nanotechnologies in contemporary hepatocellular carcinoma therapy, where personalized medicine is increasingly becoming the mainstay. Overall objective of this review is to enhance our understanding in the design and development of nanotechnology for treatment of HCC