Co-delivery of paclitaxel and α-tocopherol succinate by novel chitosan-based polymeric micelles for improving micellar stability and efficacious combination therapy

The aim of this study was to develop chitosan derivative polymeric micelles for co-delivery of paclitaxel (PTX) and α-tocopherol succinate (α-TS) to the cancer cells to improve the therapeutic efficiency and reduce side effects of PTX. In this study, amphiphilic tocopheryl succinate-grafted chitosan oligosaccharide was synthesized and physically loaded by PTX and α-TS with entrapment efficiency of 67.9% and 73.2%, respectively. Physical incorporation of α-TS into the micelles increased the hydrophobic interaction between PTX and the micelles core, which improved micelle stability, reduced the micelle size and also sustained the PTX release from the micelles. The mean particle size and zeta potential of αTS/PTX-loaded micelles were about 133?nm and +25.2?mV, respectively, and PTX release was completed during 6–9?d from the micelles. Furthermore, the cytotoxicity of α-TS/PTX-loaded micelles against human ovarian cancer cell line cancer cell in vitro was higher than that of PTX-loaded micelles and the free drug solution. Half maximal inhibitory concentration values of PTX after 48-h exposure of the cells to the PTX-loaded micelles modified and unmodified with α-TS were 110 and 188?ng/ml, respectively.     

城市框架

这个装置提升了基础设计在城市公共空间和城市化中的重要性。这个结构既是城市性的也像住宅一样私密,围合但是开放,完整地形式却具有无限感。它同时唤起对住宅和城市的想象。结构的层叠和不同尺度的内部空间是经过者产生视幻效果。参观者必须微微弯下腰进入这个装置。这个装置的框架像建筑的脚手架或剧场的背景。内部空间的形状和尺寸给人们提供了进行不同活动的机会,对成年人和小孩创造了不一样的空间体验,其中秋千一样的座椅可供人休憩：特别设计的灯具分布在有顶的小空间中：几个悬挂的花园在装置中引入绿色。通过编织各种元素,这个框架创造了一个丰富的环境供人交流、放松和玩耍。     

Accurate determination of chemical composition of urinary calculi by spiral computerized tomography

PURPOSE: Choice of efficacious clinical management of symptomatic renal calculi can be facilitated by ascertaining the precise chemical composition of the calculus. Spiral computerized tomography (CT) is becoming a frequently used radiographic examination to establish the diagnosis and severity of calculus disease. Our objective for this study was to determine the precision of spiral CT in identifying the chemical composition of 6 different types of urinary calculi with region of interest measurements using spiral CT. MATERIALS AND METHODS: A total of 102 chemically pure stones were separated into 6 groups. The stones along with phantoms containing butter (fat) and jello (water) were mounted vertically in the scanner gantry. Then 1 mm. thickness scanning was performed with a high speed scanner at the 2 energy levels of 80 and 120 kV. The determination of the chemical composition was performed using the absolute CT value measured at 120 kV. and the dual kilovolt CT values measured at 80 and 120 kV. Hounsfield unit at 80 kV.-Hounsfield unit at 120 kV.). RESULTS: The absolute CT value measured at 120 kV. was able to identify precisely the chemical composition of uric acid, struvite and calcium oxalate stones. It was imprecise in differentiating calcium oxalate from brushite stone and struvite from cystine stone. However, dual kilovolt CT value was able to differentiate these latter stones with statistical significance (p < 0.03). Uric acid stones were easily differentiated from all other stones using the absolute CT value. CONCLUSIONS: This study demonstrates that the chemical composition of urinary calculi can be accurately determined by CT scanning in an in vitro setting.     

Large-scale building damage assessment using a novel hierarchical transformer architecture on satellite images

This paper presents damage assessment using a hierarchical transformer architecture (DAHiTrA), a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale postdisaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the feature domain after applying a transformer encoder to the spatial features. The proposed network achieves state-of-the-art performance when tested on a large-scale disaster damage data set (xBD) for building localization and damage classification, as well as on LEVIR-CD data set for change detection tasks. In addition, this work introduces a new high-resolution satellite imagery data set, Ida-BD (related to 2021 Hurricane Ida in Louisiana) for domain adaptation. Further, it demonstrates an approach of using this data set by adapting the model with limited fine-tuning and hence applying the model to newly damaged areas with scarce data.     

A graph‐based method for social sensing of infrastructure disruptions in disasters

Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in recent studies, the application of social sensing in detecting disruptions and analyzing evolutions of the situation about critical infrastructure is limited. To address this limitation, this study developed a graph‐based method for detecting credible situation information related to infrastructure disruptions in disasters. The proposed method was composed of data filtering, burst time‐frame detection, content similarity calculation, graph analysis, and situation evolution analysis. The application of the proposed method was demonstrated in a case study of Hurricane Harvey in 2017 in Houston. The findings highlighted the capability of the proposed method in detecting credible situational information and capturing the temporal and spatial patterns of critical infrastructure events that occurred in Harvey, including disruptive events and their adverse impacts on communities. The proposed methodology can improve the ability of community members, volunteer responders, and decision makers to detect and respond to infrastructure disruptions in disasters.     

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas.     

Energy consumption in greenhouses and selection of an optimized heating system with minimum energy consumption

In this paper, heat loss is precisely computed by a proposed code considering different climates. Estimating the costs of different central heating system, unit heaters were selected as the most feasible system. Finally, considering the heating capacity and unit heater's dimensions a computational fluid dynamics model was developed to find the optimized configuration of unit heaters in a typical greenhouse. Using this model, the required thermal load for a greenhouse based on the daily temperature of Arak city in 2017 was computed with a smart control system. It was found that savings in energy consumption were approximately 5447 m³ of natural gas each year.     

Game‐theoretic auction design for bandwidth sharing in helper‐assisted P2P streaming

Video traffic offloading through the helper peers can significantly alleviate the server workload and enhance users' quality of experience. In this paper, the complex interactions of a number of helpers seeking to trade their bandwidth surplus in a peer‐to‐peer market are studied using non‐cooperative game theory and double auction. In particular, a non‐cooperative game is formulated among the helpers in which each helper can make a decision on the maximum amount of upload bandwidth surplus that it is willing to sell so as to maximize its utility function that reflects the tradeoff between the benefits from bandwidth sharing and its associated costs. Then, the price governing the bandwidth trading market between the helpers and peers is determined via a truthful double auction mechanism. To solve the game, an algorithm based on the best response dynamics is proposed using which helpers can reach a Nash equilibrium point. Simulation results exhibit that the proposed algorithm achieves significant performance improvements in terms of utility per selling helpers. Copyright © 2015 John Wiley & Sons, Ltd.     

Effect of geometrical parameters on thermal efficiency and fuel consumption of heaters of a natural gas pressure reduction station using Sobol statistical sensitivity analysis

The main objective of natural gas pressure reduction station or city gate stations (CGSs) is to adjust gas pressure within a standard range for home usage. CGSs usually utilize heaters for pressure tuning, which generally consume a considerable amount of energy. In this paper, the effect of six influential parameters, namely, the length of gas-coil, the diameter of gas-coil, the radius of heater shell, length of heater shell, fire tube diameter, and length, on the amount of fuel consumption and thermal efficiency is investigated using Sobol statistical sensitivity analysis. The results show that gas-coil length and diameter are not effective in fuel consumption and thermal efficiency. In contrast, increasing the heater shell radius, heater shell length, and fire tube radius has the same effect as they cause a linear increase in fuel consumption. According to results taken from Sobol analysis, they similarly have an influence of 19%–23% on fuel consumption and 21%–22% on thermal efficiency. Finally, it was revealed that the effect of fire tube length on fuel consumption and thermal efficiency of the heater was 37% and 35%, respectively.