This study investigated the conversion of cellulose from rice husk (RH) and straw (RS), two types of agricultural waste, into Carboxymethyl cellulose (CMC). Cellulose was extracted using KOH and NaOH, hydrolyzed, and bleached to increase purity and fineness. The cellulose synthesis yielded a higher net CMC content for RH-CMC (84.8%) than for RS-CMC (57.7%). Due to smaller particle sizes, RH-CMC exhibited lower NaCl content (0.77%) and higher purity. FT-IR analysis confirmed similar functional groups to commercial CMC, while XRD analysis presented a more amorphous structure and a higher degree of carboxymethylation. A biodegradable film preparation of starch-based CMC using citric acid as a crosslinking agent shows food packaging properties. The biodegradable film demonstrated good swelling, water solubility, and moisture content, with desirable mechanical properties, maximum load (6.54 N), tensile strength (670.52 kN/m2), elongation at break (13.3%), and elastic modulus (2679 kN/m2), indicating durability and flexibility. The RH-CMC film showed better chemical and mechanical properties and complete biodegradability in soil within ten days. Applying the biodegradable film for tomato preservation showed that wrapping with the film reduced weight loss more efficiently than dip coating. The additional highlight of the work was a consumer survey in Thailand that revealed low awareness but significant interest in switching to alternative uses, indicating commercial potential for eco-friendly packaging choices and market opportunities for sustainable materials.

In recent decades, buried flexible corrugated metal culverts (CMCs) and corrugated metal pipes (CMPs) have increasingly contributed to the development of infrastructure networks. The primary design aspect of these structures is the soil-structure interaction under different modes of loading. Surface static loading caused by traffic flow frequently leads to the development of deformations and internal forces in buried structures. Thus, the investigation of the soil-structure interaction mechanism under surface static loading can yield a deeper understanding of the culvert response, to enhance current design approaches. Furthermore, to assure their continued serviceability over time, the regular inspection of in-service culverts is vital to assess their status in terms of potential damage and material deterioration due to aging factors such as corrosion and/or mechanical abrasion. In this study, laboratory tests were used to monitor the performance of buried flexible open-bottom arch corrugated metal culverts under surface static loading. Following the backfilling of soil surrounding each culvert, surface static loading was initiated via a top loading steel plate. Impacts of the soil cover depth and culvert condition (i.e., intact or deteriorated) were investigated via three test configurations: an intact culvert with a cover depth of 600 mm (C-01), an intact culvert with a cover depth of 300 mm (C-02), and a deteriorated culvert with a cover depth of 300 mm (C-03). During each static loading test, the load-settlement curve of the top loading steel plate, the increase in vertical soil stresses, and culvert deformations and internal forces were recorded. Furthermore, 3D finite element models of the three test configurations were developed by simulating the culvert responses to surface static loading, and the numerical modelling results were then validated against the laboratory measurements. In addition, to investigate the impact of culvert deterioration on the performance of the soil-culvert interaction, numerical models were used to simulate different damage scenarios.

利用GNSS-R(全球导航卫星系统反射测量)技术进行准确的雪深监测已成为传统雪深测量的重要补充手段。本文使用GNSS-R技术反演了2012—2018年美国阿拉斯加州4个GPS观测站附近的雪深结果,结合加拿大气象中心(Canadian Meteorological Centre, CMC)提供的雪深模型数据产品,以PBO(Plate Boundary Observatory)H2O项目组提供的雪深资料为参考值,分析了不同手段获取的雪深值在不同时间尺度上的变化特征,同时评估了GNSS-R反演雪深结果作为独立数据集验证CMC模型数据的能力。结果表明:GNSS-R、CMC和PBO得到的长时间序列雪深结果均具有较为一致的明显周期性变化,整体上GNSS-R反演结果比CMC数据精度更高,更能反映雪深的年际变化情况。GNSS-R反演值和CMC模拟值均能够反映各测站PBO雪深值的逐月变化规律,但GNSS-R反演值的精度和稳定性总体上优于CMC模拟值。GNSS-R反演结果比CMC模拟值与PBO雪深值的季节性变化更具一致性,且对于本文研究的4个测站,GNSS-R反演雪深的精度和稳定性在雪深值较大的春季和冬季...

We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root-mean-square errors = 0.13-0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai-Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.