Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text

Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings

New potential carbon emission reduction enterprises in China: deep geological storage of CO2 emitted through industrial usage of coal in China

Deep geological storage of carbon dioxide (CO2) could offer an essential solution to mitigate greenhouse gas emissions from the continued use of fossil fuels. Currently, CO2 capture is both costly and energy intensive; it represents about 60% of the cost of the total carbon capture and storage (CCS) chain which is causing a bottleneck in advancement of CCS in China. This paper proposes capturing CO2 from coal chemical plants where the CO2 is high purity and relatively cheap to collect, thus offering an early opportunity for industrial-scale full-chain CCS implementation. The total amount of high concentration CO2 that will be emitted (or is being emitted) by the coal chemical factories approved by the National Development and Reform Commission described in this paper is 42 million tonnes. If all eight projects could utilize CCS, it would be of great significance for mitigating greenhouse gas emissions in China. Basins which may provide storage sites for captured CO2 in North China are characterized by large sedimentary thicknesses and several sets of reservoir-caprock strata. Some oil fields are nearing depletion and a sub-set of these are potentially suitable for CO2 enhanced oil recovery (EOR) and CCS demonstration but all these still require detailed geological characterization. The short distance between the high concentration CO2 sources and potential storage sites should reduce transport costs and complications. The authors believe these high purity sources coupled with EOR or aquifer storage could offer China an opportunity to lead development in this globally beneficial CCS optio

trans-Bis(5,5-diphenyl­hydantoinato-κN 3)bis­(propane-1,2-diamine-κ2 N,N′)nickel(II)

The asymmetric unit of the title complex, [Ni(pht)2(pn)2] (pht is 5,5-diphenyl­hydantoinate and pn is propane-1,2-diamine) or [Ni(C15H11N2O2)2(C3H10N2)2], contains one-half [Ni(pht)2(pn)2] mol­ecule. The NiII atom is situated on a crystallographic center of inversion and shows a distorted octa­hedral coordination geometry. A three-dimensional network structure is assembled by inter- and intra­molecular N—H⋯O=C inter­actions

Exploring human resource management in the top five global hospitals: a comparative study

BackgroundThe pivotal role of Human Resource Management (HRM) in hospital administration has been acknowledged in research, yet the examination of HRM practices in the world’s premier hospitals has been scant.ObjectiveThis study explored how the world’s leading hospitals attain operational efficiency by optimizing human resource allocation and melding development strategies into their HRM frameworks. A comparative analysis of the HRM frameworks in the top five global hospitals was undertaken to offer a reference model for other hospitals.MethodsThis research offers a comparative exploration of the HRM frameworks utilized by the top five hospitals globally, underscoring both shared and distinct elements. Using a multi-case study methodology, the research scrutinized each hospital’s HRM framework across six modules, drawing literature from publicly accessible sources, including websites, annual reports, and pertinent English-language scholarly literature from platforms such as Google Scholar, PubMed, Medline, and Web of Science.ResultsThe analyzed hospitals exhibited inconsistent HRM frameworks, yet all manifested potent organizational cultural attributes and maintained robust employee training and welfare policies. The design of the HR systems was strategically aligned with the hospitals’ objectives, and the study established that maintaining a sustainable talent system is pivotal to achieving hospital excellence.ConclusionThe HRM frameworks of the five analyzed hospitals align with their developmental strategies and exhibit unique organizational cultural attributes. All five hospitals heavily prioritize aligning employee development with overall hospital growth and place a spotlight on fostering a healthy working environment and nurturing employees’ sense of achievement. While compensation is a notable performance influencer, it is not rigorously tied to workload in these hospitals, with employees receiving mid-to-upper industry-range compensation. Performance assessment criteria focus on job quality and aligning employee actions with organizational values. Comprehensive welfare and protection are afforded to employees across all five hospitals

Individual and Joint Effects of Early-Life Ambient PM2.5 Exposure and Maternal Prepregnancy Obesity on Childhood Overweight or Obesity

Background: Although previous studies suggest that exposure to traffic-related pollution during childhood increases the risk of childhood overweight or obesity (COWO), the role of early life exposure to fine particulate matter (aerodynamic diameter <2.5μm; PM2.5) and its joint effect with the mother's prepregnancy body mass index (MPBMI) on COWO remain unclear. Objectives: The present study was conducted to examine the individual and joint effects of ambient PM2.5 exposures and MPBMI on the risk of COWO. Methods: We estimated exposures to ambient PM2.5 in utero and during the first 2 y of life (F2YL), using data from the U.S. Environmental Protection Agency’s (EPA's) Air Quality System matched to residential address, in 1,446 mother–infant pairs who were recruited at birth from 1998 and followed up prospectively through 2012 at the Boston Medical Center in Massachusetts. We quantified the individual and joint effects of PM2.5 exposure with MPBMI on COWO, defined as the child's age- and sex-specific BMI z-score ≥85th percentile at the last well-child care visit between 2 and 9 y of age. Additivity was assessed by estimating the reduced excess risk due to interaction. Results: Comparing the highest and lowest quartiles of PM2.5, the adjusted relative risks (RRs) [95% confidence intervals (CIs)] of COWO were 1.3 (95% CI: 1.1, 1.5), 1.2 (95% CI: 1.0, 1.4), 1.2 (95% CI: 1.0, 1.4), 1.3 (95% CI: 1.1, 1.6), 1.3 (95% CI: 1.1, 1.5) and 1.3 (1.1, 1.5) during preconception; the first, second, and third trimesters; the entire period of pregnancy; and F2YL, respectively. Spline regression showed a dose–response relationship between PM2.5 levels and COWO after a threshold near the median exposure (10.46μg/m3–10.89μg/m3). Compared with their counterparts, children of obese mothers exposed to high levels of PM2.5 had the highest risk of COWO [RR≥2.0, relative excess risk due to interaction (RERI) not significant]. Conclusions: In the present study, we observed that early life exposure to PM2.5 may play an important role in the early life origins of COWO and may increase the risk of COWO in children of mothers who were overweight or obese before pregnancy beyond the risk that can be attributed to MPBMI alone. Our findings emphasize the clinical and public health policy relevance of early life PM2.5 exposure. https://doi.org/10.1289/EHP26

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Surface Modification and Characterisation of Silk Fibroin Fabric Produced by the Layer-by-Layer Self-Assembly of Multilayer Alginate/Regenerated Silk Fibroin.

Silk-based medical products have a long history of use as a material for surgical sutures because of their desirable mechanical properties. However, silk fibroin fabric has been reported to be haemolytic when in direct contact with blood. The layer-by-layer self-assembly technique provides a method for surface modification to improve the biocompatibility of silk fibroin fabrics. Regenerated silk fibroin and alginate, which have excellent biocompatibility and low immunogenicity, are outstanding candidates for polyelectrolyte deposition. In this study, silk fabric was degummed and positively charged to create a silk fibroin fabric that could undergo self-assembly. The multilayer self-assembly of the silk fibroin fabric was achieved by alternating the polyelectrolyte deposition of a negatively charged alginate solution (pH = 8) and a positively charged regenerated silk fibroin solution (pH = 2). Finally, the negatively charged regenerated silk fibroin solution (pH = 8) was used to assemble the outermost layer of the fabric so that the surface would be negatively charged. A stable structural transition was induced using 75% ethanol. The thickness and morphology were characterised using atomic force microscopy. The properties of the self-assembled silk fibroin fabric, such as the bursting strength, thermal stability and flushing stability, indicated that the fabric was stable. In addition, the cytocompatibility and haemocompatibility of the self-assembled silk fibroin fabrics were evaluated. The results indicated that the biocompatibility of the self-assembled multilayers was acceptable and that it improved markedly. In particular, after the self-assembly, the fabric was able to prevent platelet adhesion. Furthermore, other non-haemolytic biomaterials can be created through self-assembly of more than 1.5 bilayers, and we propose that self-assembled silk fibroin fabric may be an attractive candidate for anticoagulation applications and for promoting endothelial cell adhesion for vascular prostheses

A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems

Mobile node location predication is critical to efficient data acquisition and message forwarding in participatory sensing systems. This paper proposes a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). The SMLPR algorithm models application scenarios based on geographic locations and extracts social relationships of mobile nodes from nodes' mobility. After considering the dynamism of users' behavior resulting from their daily routines, the SMLPR algorithm preliminarily predicts node's mobility based on the hidden Markov model in different daily periods of time and then amends the prediction results using location information of other nodes which have strong relationship with the node. Finally, the UCSD WTD dataset are exploited for simulations. Simulation results show that SMLPR acquires higher prediction accuracy than proposals based on the Markov model