Stacking sequence determines Raman intensities of observed interlayer shear modes in 2D layered materials - A general bond polarizability model

2D layered materials have recently attracted tremendous interest due to their fascinating properties and potential applications. The interlayer interactions are much weaker than the intralayer bonds, allowing the as-synthesized materials to exhibit different stacking sequences (e.g. ABAB, ABCABC), leading to different physical properties. Here, we show that regardless of the space group of the 2D material, the Raman frequencies of the interlayer shear modes observed under the typical configuration blue shift for AB stacked materials, and red shift for ABC stacked materials, as the number of layers increases. Our predictions are made using an intuitive bond polarizability model which shows that stacking sequence plays a key role in determining which interlayer shear modes lead to the largest change in polarizability (Raman intensity); the modes with the largest Raman intensity determining the frequency trends. We present direct evidence for these conclusions by studying the Raman modes in few layer graphene, MoS2, MoSe2, WSe2 and Bi2Se3, using both first principles calculations and Raman spectroscopy. This study sheds light on the influence of stacking sequence on the Raman intensities of intrinsic interlayer modes in 2D layered materials in general, and leads to a practical way of identifying the stacking sequence in these materials.Comment: 30 pages, 8 figure

Exposure Test on Two Surface Anticorrosion Technologies for Marine Concrete Structure

This paper is to study the effect of surface coating and silane hydrophobic agents for high performance concrete durability in a marine environment of tidal zone and splash zone by exposure test in JiaoZhou Bay. The results indicated that surface coating had good protection and coating quality after a 5-year period and the adhesive strength with concrete surface was more than 2.5 MPa. Surface coating can effectively improve chloride ion penetration resistance of concrete structures. The substrate concrete of specimen treated with silane had some chloride ion penetration, but compared with untreated concrete, chloride content of silane-treated concrete within 10 mm depth from surface was reduced by 43 and 67% in the tidal zone and the splash zone, respectively. Two surface anticorrosion measures technologies were effective in reducing the chloride erosion and improved the service life of marine concrete structure

Artificial intelligence-based prediction for cancer-related outcomes in Africa: status and potential refinements

[Extract] Globally, cancer ranks among the most common causes of death, especially among people under 70 years of age [1]. With this burden rapidly increasing globally, utilizing prediction tools to assist decision-making and encourage individualized treatment planning is gradually becoming paramount in cancer diagnosis and management. Notably, many tools constructed on the backend of artificial intelligence (AI) algorithms have been shown to improve the predictive accuracy and clinical impact of risk prediction compared to clinical scenarios not utilizing these models [2]. However, the actualisation of the potential of health care AI has mostly been assessed in high-income and resource-driven centres. The impact and efficiency of oncological AI-based prediction tools are expected to be better realised when applied in low-resource and rural settings fraught with a paucity of experienced clinicians and specialists

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs. Methods: PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study. Results: ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy. Conclusion: Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial design

Radioprotective effect of lidocaine on neurotransmitter agonist-induced secretion in irradiated salivary glands.

Previously we verified the radioprotective effect of lidocaine on the function and ultrastructure of salivary glands in rabbits. However, the underlying mechanism of lidocaine's radioprotective effect is unknown. We hypothesized that lidocaine, as a membrane stabilization agent, has a protective effect on intracellular neuroreceptor-mediated signaling and hence can help preserve the secretory function of salivary glands during radiotherapy. Rabbits were irradiated with or without pretreatment with lidocaine before receiving fractionated radiation to a total dose of 35 Gy. Sialoscintigraphy and saliva total protein assay were performed before radiation and 1 week after the last radiation fraction. Isolated salivary gland acini were stimulated with either carbachol or adrenaline. Ca(2+) influx in response to the stimulation with these agonists was measured using laser scanning confocal microscopy. The uptake of activity and the excretion fraction of the parotid glands were significantly reduced after radiation, but lidocaine had a protective effect. Saliva total protein concentration was not altered after radiation. For isolated acini, Ca(2+) influx in response to stimulation with carbachol, but not adrenaline, was impaired after irradiation; lidocaine pretreatment attenuated this effect. Lidocaine has a radioprotective effect on the capacity of muscarinic agonist-induced water secretion in irradiated salivary glands

Developing construction defect management system using BIM technology in quality inspection

Defect management (DM) for quality inspection (QI) is a major strategy employed by general contractors to enhance construction management of building projects. However, there are significant issues in construction DM in standard practice that affects quality inspection, including protracted procedures, data entry redundancies, confusion, and inefficient information management. Recognition of these construction DM issues, this paper proposes a new and practical approach that applies Building Information Modeling (BIM) technology to quality inspection and defect man­agement. Specifically, BIM digitally contains precise geometry and relevant data needed to support building structures to describe 3D object-oriented CAD. Using BIM technology, this study proposes a BIM-based Defect Management (BIMDM) system by on-site quality managers during the construction phase. The intended approach integrates web and BIM technologies in the BIMDM system to illustrate and analyze defect information at the jobsite in real time. The anticipated result is the effectively managed status and results of the corrective works performed. The BIMDM system is applied in a selected case study of a building project in Taiwan to verify the proposed approach and demonstrate the effectiveness of defect management practice. Utilizing the BIMDM system, on-site quality managers are better able to track and manage defects with BIM models through accurate records and photos. The combined results of the study demonstrate that a BIMDM-like system can be an effective visual defect management platform when integrating BIM and web technologies. The advantage of the BIMDM system lies not only in improving defect management efficiency for on-site quality engineers and managers, but also in facilitating easy quality inspection while identifying and com­municating in the 3D BIM environment. As such, authors expect that effective use of the proposed BIMDM would significantly assist on-site quality engineers and managers to systematically handle defect management work using BIM technologies in future construction projects