High channel count and high precision channel spacing multi-wavelength laser array for future PICs

Multi-wavelength semiconductor laser arrays (MLAs) have wide applications in wavelength multiplexing division (WDM) networks. In spite of their tremendous potential, adoption of the MLA has been hampered by a number of issues, particularly wavelength precision and fabrication cost. In this paper, we report high channel count MLAs in which the wavelengths of each channel can be determined precisely through low-cost standard μm-level photolithography/holographic lithography and the reconstruction-equivalent-chirp (REC) technique. 60-wavelength MLAs with good wavelength spacing uniformity have been demonstrated experimentally, in which nearly 83% lasers are within a wavelength deviation of ±0.20 nm, corresponding to a tolerance of ±0.032 nm in the period pitch. As a result of employing the equivalent phase shift technique, the single longitudinal mode (SLM) yield is nearly 100%, while the theoretical yield of standard DFB lasers is only around 33.3%

III-V-on-silicon photonic devices for optical communication and sensing

In the paper, we review our work on heterogeneous III-V-on-silicon photonic components and circuits for applications in optical communication and sensing. We elaborate on the integration strategy and describe a broad range of devices realized on this platform covering a wavelength range from 850 nm to 3.85 μm

Demonstration of a discretely tunable III-V/SOI sampled grating distributed feedback laser

A novel III-V/SOI sampled grating DFB laser is experimentally demonstrated. Two input currents allow wavelength tuning over a 55 nm wide range in discrete wavelength steps of 5 nm. A side mode suppression ratio larger than 33 dB is obtained for all wavelength channels

A simplified climate change model and extreme weather model based on a machine learning method

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change

Post-disaster functional recovery of the built environment: A systematic review and directions for future research

Life safety has been a primary design requirement in codes and standards for the built environment. However, over the past several years, better building performance goals that consider acceptable recovery times and continued functionality following major disasters have been advocated. Functional recovery, a new design philosophy that establishes holistic performance goals, and focuses on the robustness of structures, enhanced safety, and, specifically, fast return to operation post-disaster, has been introduced in earthquake engineering to govern future building designs. This article utilised the systematic review procedures as a tool to provide a state-of-the-art review of functional recovery research within the built environment. A critical review of 78 publications was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The evolution of paradigm shifts from seismic resilience to functional recovery in earthquake engineering research has been discussed in detail. Two frameworks, namely the Federal Emergency Management Agency's (FEMA) P-58 and Arup's Resilience-Based Earthquake Design Initiative (REDi), have been recognised as the most commonly utilised frameworks for modelling the functional recovery of buildings post-earthquake due to their effectiveness and widespread adoption. However, it is essential to acknowledge that recently developed frameworks, such as the F-Rec framework, ATC-138, and TREADS, which explicitly formulate functional recovery calculation procedures, have the potential to replace FEMA P-58 and REDi and advance functional recovery research in the future. Moreover, aligned with modular-based characteristics of existing frameworks, indicators required in functional recovery analysis have been extracted and classified into four distinct categories: 1) hazard analysis, 2) structural response analysis, 3) damage analysis, and 4) recovery analysis. This categorisation enables a comprehensive and systematic approach to understanding the multifaceted aspects of functional recovery in a structured manner. Detailed investigation of frameworks and indicators offers insights for future research exploration. These include (a) expanding the fragility library of components to permit more widespread recovery analysis, (b) comparing, validating and optimising existing frameworks and models, (c) enhancing the modelling of interdependencies between the building and its adjacent buildings and services, (d) improving the capability for uncertainty analysis, and (e) acquiring empirical data to enable predictability of the existing frameworks and models for functional recovery

Modulating Cell Biology with Carbohydrates and their Derivatives

With the rise of antibiotic resistance, there is a growing need for compounds capable of non-bactericidal mechanisms that do not exert evolutionary pressure on the bacterium, such as biofilm modulators. Human milk oligosaccharides (HMO) exhibit antimicrobial and antibiofilm activity against Group B Streptococcus (GBS). Chemical derivatization of 2’-fucosyllactose (2’-FL), a known prebiotic oligosaccharide, via Kochetkov amination generated β-amino-2’-fucosyllactose (βA-2’-FL). Interestingly, βA-2’-FL significantly reduces biofilm formation. Application of the Kochetkov amination produced three additional βA-HMOs, which were evaluated against GBS and an additional gram-positive bacterium, methicillin-resistant Staphylococcus aureus (MRSA). While the parent HMOs showed no antibiofilm activity, βA-HMOs display potent antibiofilm activity against GBS and MRSA. Currently, anthracycline chemotherapy is limited by dose-dependent cardiotoxicity. With the rising need for novel therapeutics to address this issue, we focused on a differing scaffold, saponins. Desgalactotigonin (DGT) is a saponin known to exhibit cytotoxicity against various cancer cell lines. DGT is a glycosteroid featuring Tigogenin as the aglycon, conjugated to lycotetraose (O-β-D-glucopyranosyl-(1→2)-O-[β-D-xylopyranosyl-(1→3)]-O-β-D-glucopyranosyl-(1→4)-D-galactose). We synthesized XX number of glycosylated tigogenyl saponins. Structure-activity relationship was conducted through anticancer assays against multidrug resistant lung and colorectal cancer. Gal-Tig and Gal-NMe2-Tig exhibited potent anticancer activity against both lung and colorectal cancer

A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.</jats:p

A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth