•Deep learning models have excellent performance for estimating ETo beyond study areas.•Temporal convolution neural network outperformed markedly empirical equations.•T-test method was used to test ...the performance of proposed models.•Temporal convolution neural network outperformed classical machine learning models.
To evaluate the performance of deep learning methods (DL) for reference evapotranspiration estimation and to assess the applicability of the developed DL models beyond the study areas where they were trained, three popular DL models named deep neural network (DNN), temporal convolution neural network (TCN), and long short-term memory neural network (LSTM) were developed to estimate daily reference evapotranspiration (ETo) using incomplete meteorological data in the Northeast plain, China. The performances of the three DL models were compared to two classical machine learning models (CML)—support vector machine (SVM) and random forest (RF)—and empirical equations, including two temperature-based (Hargreaves (H) and modified Hargreaves (MH)), three radiation-based (Ritchie (R), Priestley-Talor (P), and Makkink (M)), and two humidity-based (Romanenko (ROM) and Schendel (S)) empirical models, in two strategies: (1) all proposed models were trained, tested, and compared in each single weather station, and (2) all-weather stations were split into several groups using the K-means method with their mean climatic characteristics. Then, in each group, stations took turns testing the proposed models which were trained by rest of the stations. The results showed that (1) the coefficient of determination (R2) values of the TCN and RF were 0.048 and 0.035 significantly higher than that of MH, respectively, and the relative root mean error (RMSE) values of TCN and RF were substantially 0.096, and 0.074 mm/d lower than that of MH, indicating that TCN and RF performed better than empirical models in the first strategy, and TCN and LSTM exhibited an RMSE that was significantly decreased by 0.069 and 0.079 mm/d, showing that TCN and LSTM outperformed empirical models in the second strategy, compared with the MH method; (2) in both strategies, compared with the Ritchie (R) model, TCN, LSTM, DNN, RF, and SVM increased R2 and decreased RMSE significantly, especially the TCN model; (3) similarly, TCN, LSTM, DNN, RF, and SVM models all augmented R2 and reduced RMSE substantially in comparison to humidity-based empirical models in both strategies, especially the TCN model. Overall, when temperature-based features were available, the TCN and LSTM models performed markedly better than temperature-based empirical models beyond the study areas, and when radiation-based or humidity-based features were available, all of the proposed DL and CML models outperformed radiation-based or humidity-based empirical equations beyond the study areas in which they were trained.
We study a model where each competing firm has a target segment where it has full consumer information and can exercise personalized pricing, and consumers may engage in identity management to bypass ...the firm’s attempt to price discriminate. In the absence of identity management, more consumer information intensifies competition because firms can effectively defend their turf through targeted personalized offers, thereby setting low public prices offered to nontargeted consumers. But the effect is mitigated when consumers are active in identity management because it raises the firm’s cost of serving nontargeted consumers. When firms have sufficiently large and nonoverlapping target segments, identity management can enable firms to extract full surplus from their targeted consumers through perfect price discrimination. Identity management can also induce firms not to serve consumers who are not targeted by either firm when the commonly nontargeted market segment is small. This results in a deadweight loss. Thus, identity management by consumers can benefit firms and lead to lower consumer surplus and lower social welfare. Our main insight continues to be valid when a fraction of consumers are active in identity management or when there is a cost of identity management. We also discuss the regulatory implications for the use of consumer information by firms as well as the implications for management.
This paper was accepted by Juanjuan Zhang, marketing.
Nature provides an almost limitless supply of sources that inspire scientists to develop new materials with novel applications and less of an environmental impact. Recently, much attention has been ...focused on preparing natural‐product‐derived carbon dots (NCDs), because natural products have several advantages. First, natural products are renewable and have good biocompatibility. Second, natural products contain heteroatoms, which facilitate the fabrication of heteroatom‐doped NCDs without the addition of an external heteroatom source. Finally, some natural products can be used to prepare NCDs in ways that are very green and simple relative to traditional methods for the preparation of carbon dots from man‐made carbon sources. NCDs have shown tremendous potential in many fields, including biosensing, bioimaging, optoelectronics, and photocatalysis. This Review addresses recent progress in the synthesis, properties, and applications of NCDs. The challenges and future direction of research on NCD‐based materials in this booming field are also discussed.
On the dot: In this Review, methods for the preparation of natural‐product‐derived carbon dots (NCDs) are summarized. Then, the physical and optical properties of the NCDs are outlined, and finally, several applications of NCDs are noted, including their use in sensors, bioimaging, and solar cells and their use as catalysts.
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•Magnetic Fe3O4-PSS@ZIF-67 composites with core-shell structure were prepared.•Fe3O4-PSS@ZIF-67 exhibited highly adsorption capacity for MO.•Fe3O4-PSS@ZIF-67 exhibited excellent ...selective adsorption of MO from mixture solution.•Adsorption kinetic and mechanism were studied.
Efficacious and convenient removal of organic dye contaminants from wastewater is a challenge for public health and ecosystem protection. Here we fabricate a novel type of Fe3O4@MOFs (Metal-organic frameworks) magnetic porous composite materials. ZIF-67 (Zeolitic imidazolate framework-67) nano-crystals as an attractive subfamily of MOF was selected to fabricate Fe3O4-PSS@ZIF-67 composites (defined as MZIF-67). MZIF-67 composites are core-shell structure, for which the aggregation core of Fe3O4 nanoparticles is coated with petal-like ZIF-67 crystals, in which Co2+ firstly combines with SO32− provided by PSS (poly (styrenesulfonate, sodium salt)) to form nucleation. MZIF-67 composites perform well on methyl orange (MO) adsorption, which could be attributed to the highly porosity and the nature of Lewis base of coordinated Co2+ centrals. The results show that the equilibrium adsorption capacity for MO is up to 738mg·g−1 (when pH=8.0, contact time is 7h, adsorbent dose is 5mg and initial MO concentration is 400mg·L−1). In addition, MZIF-67 composites could selectively separate MO from the mixture solution of MO and MB (methylene blue). The removal rate of MO is up to 92%. The concentration ratio of MO/MB is 0.04. And the separation efficiency is up to 96%. The results suggest MZIF-67 composites could be a good candidate for treatment of dye-bearing wastewater.
Carbon dots (CDs) are a new representative in the carbon-based material family, attracting tremendous interest in a large variety of fields, including biomedicine. In this report, we described a ...facile and green system for synthesizing DNA–CDs using genomic DNA isolated from Escherichia coli. DNA–CDs can be purified using a simple column centrifugation-based system. During DNA–CD synthesis, ribose was collapsed, accompanied by the release of nitrogen, and several new bonds (C–OH, N–O, and N–P) were formed, while the other covalent bonds of DNA were largely maintained. The presence of abundant chemical groups, such as amino or hydroxyl groups on DNA–CDs, may facilitate their future functionalization. These highly biocompatible DNA–CDs can serve as a new type of fluorescent vehicle for cell imaging and drug delivery studies. Our research may hasten the development of CDs for prominent future biomedical applications.
Data‐driven mergers and personalization Chen, Zhijun; Choe, Chongwoo; Cong, Jiajia ...
The Rand journal of economics,
03/2022, Letnik:
53, Številka:
1
Journal Article
Recenzirano
Odprti dostop
This article studies tech mergers that involve a large volume of consumer data. The merger links the markets for data collection and data application through a consumption synergy. The ...merger‐specific efficiency gains exist in the market for data application due to the consumption synergy and data‐enabled personalization. Prices fall in the market for data collection but generally rise in the market for data application as the efficiency gains are extracted away through personalized pricing. When the consumption synergy is large enough, the merger can result in monopolization of both markets. We discuss policy implications including various merger remedies.
We show that large retailers, competing with smaller stores that carry a narrower range, can exercise market power by pricing below cost some of the products also offered by the smaller rivals, in ...order to discriminate multistop shoppers from one-stop shoppers. Loss leading thus appears as an exploitative device rather than as an exclusionary instrument, although it hurts the smaller rivals as well; banning below-cost pricing increases consumer surplus, rivals' profits, and social welfare. Our insights extend to industries where established firms compete with entrants offering fewer products. They also apply to complementary products such as platforms and applications.
Photon upconversion lithography is demonstrated for the patterning of proteins using near‐infrared light. Proteins and an upconverting‐nanoparticle‐decorated substrate are linked via ...blue‐light‐cleavable Ru complexes. The substrate is irradiated using near‐infrared light with a photomask. In the exposed areas, upconverting nanoparticles convert the near‐infrared light into blue light, which induces cleavage of the Ru complexes and release of the proteins.
Tumor‐associated macrophages (TAMs) are vital constituents in mediating cell‐to‐cell communication within the tumor microenvironment. However, the molecular mechanisms underlying the interplay ...between TAMs and tumor cells that guide cell fate are largely undetermined. Extracellular vesicles, also known as exosomes, which are derived from TAMs, are the components exerting regulatory effects. Thus, understanding the underlying mechanism of “onco‐vesicles” is of crucial importance for prostate cancer (PCa) therapy. In this study, we analyzed micro RNA sequences in exosomes released by THP‐1 and M2 macrophages and found a significant increase in miR‐95 levels in TAM‐derived exosomes, demonstrating the direct uptake of miR‐95 by recipient PCa cells. In vitro and in vivo loss‐of‐function assays suggested that miR‐95 could function as a tumor promoter by directly binding to its downstream target gene, JunB, to promote PCa cell proliferation, invasion, and epithelial–mesenchymal transition. The clinical data analyses further revealed that higher miR‐95 expression results in worse clinicopathological features. Collectively, our results demonstrated that TAM‐mediated PCa progression is partially attributed to the aberrant expression of miR‐95 in TAM‐derived exosomes, and the miR‐95/JunB axis provides the groundwork for research on TAMs to further develop more‐personalized therapeutic approaches for patients with PCa.
Our results demonstrated that TAM‐mediated prostate cancer (PCa) progression is partially attributed to the aberrant expression of miR‐95 in TAM‐derived exosomes, and the miR‐95/JunB axis provides the groundwork for research on tumor‐associated macrophages (TAMs) to further develop more‐personalized therapeutic approaches for patients with PCa.
For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful ...effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.
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