Search Results (9)

This research introduces a novel approach using silver (Ag) nanostructures generated through electrochemical deposition and photo-reduction of Ag on fluorine-doped tin oxide glass substrates (denoted as X-Ag-Ag_yFTO, where ‘X’ and ‘y’ represent the type of light source and number of deposited cycles, respectively) for surface-enhanced Raman spectroscopy (SERS). This study used malachite green (MG) as a Raman probe to evaluate the enhancement factors (EFs) in SERS-active substrates under varied fabrication conditions. For the substrates produced via electrochemical deposition, we determined a Raman EF of 6.15 × 10⁴ for the Ag₂FTO substrate. In photo-reduction, the impact of reductant concentration, light source, and light exposure duration were examined on X-Ag nanoparticle formation to achieve superior Raman EFs. Under optimal conditions (9.0 mM sodium citrate, 460 nm blue-LED at 10 W for 90 min), the combination of blue-LED-reduced Ag (B-Ag) and an Ag₂FTO substrate (denoted as B-Ag-Ag₂FTO) exhibited the best Raman EF of 2.79 × 10⁵. This substrate enabled MG detection within a linear range of 0.1 to 1.0 µM (R² = 0.98) and a detection limit of 0.02 µM. Additionally, the spiked recoveries in aquaculture water samples were between 90.0% and 110.0%, with relative standard deviations between 3.9% and 6.3%, indicating the substrate’s potential for fungicide detection in aquaculture. Full article

Growing rates of childhood obesity globally create concern for individuals’ health outcomes and demands on health systems. While many policy approaches focus on macro-level interventions, we examine how the type of stability of a family structure might provide opportunities for policy interventions at the micro level. We examine the association between family structure trajectories and childhood overweight and obesity across three Anglophone countries using an expanded set of eight family structure categories that capture biological relationships and instability, along with potential explanatory variables that might vary across family trajectories and provide opportunities for intervention, including access to resources, family stressors, family structure selectivity factors, and obesogenic correlates. We use three datasets that are representative of children born around the year 2000 and aged 11 years old in Australia (n = 3329), the United Kingdom (n = 11,542), and the United States (n = 8837) and nested multivariate multinomial logistic regression models. Our analyses find stronger relationships between child overweight and obesity and family structure trajectories than between child obesity and obesogenic factors. Children in all three countries are sensitive to living with cohabiting parents, although in Australia, this is limited to children whose parents have been cohabiting since before their birth. In the UK and US, parents starting their cohabitation after the child’s birth are more likely to have children who experience obesity. Despite a few differences across cross-cultural contexts, most of the relationship between family structures and child overweight or obesity is connected to differences in families’ access to resources and by the types of parents who enter into these family structures. These findings suggest policy interventions at the family level that focus on potential parents’ education and career prospects and on income support rather than interventions like marriage incentives. Full article

The Raman phenomenon is based on the spontaneous inelastic scattering of light, which depends on the molecular characteristics of the dispersant. Therefore, Raman spectroscopy and imaging allow us to obtain direct information, in a label-free manner, from the chemical composition of the sample. Since it is well established that the development of many brain diseases is associated with biochemical alterations of the affected tissue, Raman spectroscopy and imaging have emerged as promising tools for the diagnosis of ailments. A combination of Raman spectroscopy and/or imaging with tagged molecules could also help in drug delivery and tracing for treatment of brain diseases. In this review, we first describe the basics of the Raman phenomenon and spectroscopy. Then, we delve into the Raman spectroscopy and imaging modes and the Raman-compatible tags. Finally, we center on the application of Raman in the study, diagnosis, and treatment of brain diseases, by focusing on traumatic brain injury and ischemia, neurodegenerative disorders, and brain cancer. Full article

Interorganelle membrane contact sites (MCS) are areas of close vicinity between the membranes of two organelles that are maintained by protein tethers. Recently, a significant research effort has been made to study MCS, as they are implicated in a wide range of biological functions, such as organelle biogenesis and division, apoptosis, autophagy, and ion and phospholipid homeostasis. Their composition, characteristics, and dynamics can be studied by different techniques, but in recent years super-resolution fluorescence microscopy (SRFM) has emerged as a powerful tool for studying MCS. In this review, we first explore the main characteristics and biological functions of MCS and summarize the different approaches for studying them. Then, we center on SRFM techniques that have been used to study MCS. For each of the approaches, we summarize their working principle, discuss their advantages and limitations, and explore the main discoveries they have uncovered in the field of MCS. Full article

The development of many intelligent technologies, such as artificial intelligence and the Internet of Things, has brought new opportunities for water industry intelligence. Based on intelligent pressure regulation technology, this paper built an intelligent management platform, designed an intelligent pressure-regulating device, and combined both to form an intelligent pressure-regulating vehicle (IPRV). The IPRV has the functions of developing a pressure-regulating scheme, equipment selection, pressure reduction potential analysis, etc. It can bring convenience to the field test of the water supply network. In the field test, an intelligent pressure-regulating device was used to obtain the network data in the pilot site called S-cell. After utilizing the intelligent management platform to analyze the measured data, the water usage pattern and pressure reduction potential of the S-cell were obtained, and an optimal pressure-regulating strategy was formulated. The water pressure at the critical node always met the water demand at the critical node during the field test. In addition, no complaints were received from other users. The results show that the IPRV is not only convenient for utility managers to make decisions on building pressure-reducing stations, but also meets user needs, realizing a win–win situation for both users and companies. Full article

Accurate forecasting of hourly water demand is essential for effective and sustainable operation, and the cost-effective management of water distribution networks. Unlike monthly or yearly water demand, hourly water demand has more fluctuations and is easily affected by short-term abnormal events. An effective preprocessing method is needed to capture the hourly water demand patterns and eliminate the interference of abnormal data. In this study, an innovative preprocessing framework, including a novel local outlier detection and correction method Isolation Forest (IF), an adaptive signal decomposition technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and basic forecasting models have been developed. In order to compare a promising deep learning method Gated Recurrent Unit (GRU) as a basic forecasting model with the conventional forecasting models, Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been used. The results show that the proposed hybrid method can utilize the complementary advantages of the preprocessing methods to improve the accuracy of the forecasting models. The root-mean-square error of the SVR, ANN, and GRU models has been reduced by 57.5%, 27.8%, and 30.0%, respectively. Further, the GRU-based models developed in this study are superior to the other models, and the IF-CEEMDAN-GRU model has the highest accuracy. Hence, it is promising that this preprocessing framework can improve the performance of the water demand forecasting models. Full article

This article summarizes the proceedings of the four-session meeting (webinar) conducted by the Alliance of International Science Organizations on Disaster Risk Reduction (ANSO-DRR) on 18 May 2020. ANSO-DRR is an international, nonprofit and nongovernmental scientific alliance bringing together academies of science, research organizations and universities which share a strong interest in disaster risk reduction in the regions along the land-based and maritime routes of the Belt and Road Initiative. ANSO-DRR convenes an annual meeting to review its work progress and discuss its scientific programs. The first session was the opening statements and was followed by the introduction and updates on ANSO-DRR in the second session. The third session was the depiction of the big picture of ANSO, the umbrella organization of ANSO-DRR, led by the Assistant Executive Director of ANSO, while the fourth session was a presentation of perspectives on the strategic development of ANSO-DRR. One of ANSO-DRR’s key strategies is to enhance disaster mitigation and response through multidisciplinary cooperation among disaster and healthcare sciences (i.e., health emergency and disaster risk management (Health-EDRM)). It aims to enhance DRR efforts by performing as an instrument in connecting people along the Belt and Road regions, focusing on DRR resource and database development, involving higher education institutions in DRR efforts and increasing disaster resilience in built infrastructures. Full article

Fire following earthquake (FFE) is a common secondary disaster that can inflict great damage to humans. A large number of seismic resilience evaluation methods have been proposed, but few of them consider the influence of FFE. In this study, a multi-scenario simulation based model was developed to evaluate the post-disaster performance of water distribution networks (WDNs) in supplying both firefighting flow and original demand under the effect of seismic damage and FFEs. Hypothetical earthquakes were generated and the spatial–temporal distribution of FFEs was simulated by the Poisson distribution model and the Weibull distribution model. The post-disaster performance was evaluated by two types of seismic reliability metrics. The developed model was applied to a WDN currently operating in China with eight pre-determined earthquake scenarios. The results showed that the firefighting flow was concentrated in the first few hours after the earthquake. Thus, the serviceability of both original demand and firefighting flow was influenced significantly within the first few hours, while little impact was observed after the concentrated firefighting flow was delivered. The proposed model quantified the WDN’s performance under specific seismic damage and potential FFEs, and can be used for the planning, design, and maintenance of WDNs. Full article

Artichoke waste represents a huge amount of discarded material. This study presents the by-products (bracts, exterior leaves, and stalks) of the “Blanca de Tudela” artichoke variety as a potential source of phenolic compounds with promising antioxidant properties. Artichoke residues were subjected to different extraction processes, and the antioxidant capacity and phenolic composition of the extracts were analyzed by spectrophotometric methods and high performance liquid chromatography (HPLC) analyses, respectively. The most abundant polyphenols in artichoke waste were chlorogenic acid, luteolin-7-O-rutinoside, and luteolin-7-O-glucoside. Minor quantities of cynarin, luteolin, apigenin-7-O-glucoside, apigenin-7-O-rutinoside, and naringenin-7-O-glucoside were also found. The antioxidant activity of the obtained extracts determined by ABTS [2, 2′-azinobis (3-ethylbenzothiazoline-6-sulphonic acid)], DPPH (2,2-diphenyl-1-pycrilhydracyl), and FRAP (Ferric Ion Reducing Antioxidant Power) was highly correlated with the total concentration of phenolic compounds. Chlorogenic acid, luteolin-7-O-glucoside, and luteolin-7-O-rutinoside, the most abundant compounds in 60% methanol extracts, are the components most responsible for the antioxidant activity of the artichoke waste extracts. The extract with the best antioxidant capacity was selected to assay its antioxidant potential on a model intestinal barrier. This action of the hydroxycinnamic acids on intestinal cells (Caco-2) was confirmed. In summary, artichoke waste may be considered a very interesting ingredient for food functionalization and for therapeutic purposes. Full article