Spatial and Temporal Distinction of Microelemental Signatures of Missouri River Tributaries

The microchemical composition of a fish's calcified structure, such as an otolith, fin ray, or scale, is a ‘biological tag’ that reflects the use of different habitats throughout its life history. Hard‐part microchemistry has the potential to address many fish conservation, management, and behavior questions. In order to use hard‐part microchemistry to address fisheries research questions, elemental signatures of water must be distinct at the spatial scale of interest and ideally temporally stable. The goal of this study was to assess whether spatial differences existed in the concentrations of five elements [barium (¹³⁷Ba), magnesium (²⁴Mg), manganese (⁵⁵Mn), sodium (²³Na), and strontium (⁸⁸Sr)] between and within eight Missouri River tributaries and whether those signatures were temporally stable. All elemental concentrations were converted to molar ratios (mmol mol^?1) using calcium (⁴³Ca) concentrations as the base. Canonical correspondence analysis showed distinct differences in Mg:Ca, Na:Ca, and Sr:Ca concentrations between the eight tributaries. Cochran–Mantel–Haenszel tests showed that sites within a tributary were distinguished using Mg:Ca and Na:Ca concentrations. However, only Mg:Ca concentrations were temporally stable. Results from this study demonstrate the potential for using hard‐part microchemistry to address various questions at multiple spatial scales in the Missouri River riverscape but also highlight the importance of evaluating water microelemental signatures prior or simultaneous to any hard‐part microchemistry study. Copyright © 2016 John Wiley & Sons, Ltd.     

Impact of initial moisture content levels,freezing rate and instant controlled pressure drop treatment (DIC) on dehydrofreezing process and quality attributes of quince fruits

Dehydrofreezing process involves water partial removal before freezing. This treatment has been proposed in order to reduce the negative impacts of conventional or even accelerated freezing, especially on the textural quality of high water content fruits and vegetables. Indeed, in such cases, freezing and thawing processes result in severe damage of the integrity of product’s cell structure due to the formation of ice crystals. For this purpose, quince fruits (7?g H₂O/g db) were subjected to convective air drying of 40?°C and 3m/s to reach different water content levels of 2, 1, and 0.3?g H₂O/g db. Freezing profiles obtained at various freezing rates (V₁, V₂, and V₃) for different water contents allowed the main freezing characteristics such as the Initial Freezing Temperature (IFT), the Practical Freezing time (PFt), and the Specific Freezing time (SFt) to be assessed. The impact of freezing rate was important on PFt and SFt, and more pronounced for high water contents (W between 7 and 2?g H₂O/g db (dry basis)). Furthermore, IFT decreased sharply when initial sample water content decreased. Indeed, it started at ?0.8?°C for W?=?7g H₂O/g db, while it reached a value of ?8.2?°C for samples of W?=?1g H₂O/g db. Since convective air drying normally triggers shrinkage which causes a detrimental deformation of fruit structures, instant controlled pressure drop (DIC) treatment was used to improve the texture and enhance the whole dehydrofreezing performance and the final frozen-thawed product quality. Moreover, DIC implied a slight increase of PFt compared to untreated ones. On the other hand, quality attributes were estimated through the assessment of thawed water exudate (TWE g H₂O/100?g db), color and texture (maximum puncture force as index of firmness): freezing rate and water content had great impacts on TWE. Hence, the lower the water content, the weaker the TWE. Furthermore, the TWE of the pre-dried quince (0.3?g H₂O/g db) had higher value for DIC-textured samples than for the un-treated ones. Indeed, DIC-texturing leads to a well-controlled structure expansion of the cell wall. These textural changes resulted in more lixiviation of residual water. Consequently, water becomes more available, hence more releasable after thawing. Finally, the partial removal of water by air drying before freezing remarkably reduced the negative impact of freezing/thawing processes on final quince color. Decisively, the firmness of quince fruit increased with the decrease of water content level.

Abbreviations: DMC: Dry Matter Concentration (%); DIC: Instant controlled pressure drop; W: Water content dry basis (g H₂O/g db); IFT: Initial Freezing Temperature (°C); PFt: Practical Freezing time (min); SFt: Specific Freezing time (min); TWE: Thawed Water Exudate (g H₂O/100?g db); L, a, and b: Color coordinates; (L): The degrees of lightness; (a) and (–a): The redness (a) or greenness (?a), respectively; (b) and (?b): The yellowness (b) or blueness (?b), respectively; ΔE*_ab: Total color difference; L₀, a₀, and b₀: Color coordinates of fresh or dried quince samples; SD: Standard Deviation; ANOVA: Analysis of variances; LSD: Least Significant Differences; cp: Specific Heat of the product depending on composition (dry material and water content)(KJ/kg K); cp_d: Specific Heat of the dry material (KJ/kg K); cp_W: Specific Heat of water (KJ/kg K); V₁: Freezing rate without insulation; V₂: Freezing rate with a food stretch film insulation with thickness e2?=?3?mm and thermal conductivity λ₂?=?0.17 W/m K; V₃: Freezing rate with a versatile flexible insulation (Armacell) with thickness e3?=?13mm and weak thermal conductivity λ₃?=?0.036 W/m K; v_d: Volume of dry material of quince sample (mm³); v_H2O: Volume of quince sample water (mm³); v_t: Total volume of quince sample (mm³); e₀: Quince sample thickness (mm); e₂: Insulation thickness in the case V2; = 3?mm; ; e₃: Insulation thickness in the case V3; = 13?mm; ; λ₀: Quince sample conductivity (W/m K); λ₂: Insulation conductivity in the case V2;?=?0.17 W/m K; ; λ₃: Insulation conductivity in the case V3;?=?0.036 W/m K; λ_d: Conductivity of quince sample dry material (W/m K); λ_H2O: Conductivity of water (W/m K); λ_equiv: Equivalent conductivity of quince sample versus water content (W/m K); m_i and m_f: Weights of the frozen and thawed samples, respectively     

基于时间冗余的显著测量误差处理方法

提出基于时间冗余的不可删显著误差处理方法。对于该类特殊变量，采用线性回归的方法，根据历史数据进行预测，进而代替实际到量值进行协调运算。大大降低显著误差对协调数据的污染，确保协调物度。通过实例数据仿真计算证实该方法的有效性。     

一种基于优先级表的实时调度方法

针对单处理器实时系统动态调度问题进行了研究,分析任务的到达时间、执行时间、截止时间和空闲时间等任务属性的敏感度和影响度,提出了一种基于优先级表的调度算法PTBM,使相对截止期越小、空闲时间越大的任务优先级越高。对关于实时任务属性敏感度和影响度的结论验证和与传统的EDF、LLF和PTD算法的对比进行仿真实验,仿真结果表明基于优先级表设计的实时调度算法PTBM具有较高的调度成功率。该方法可应用于实时系统的实时任务的动态调度中。     

基于复合左右手传输线结构的新型微带天线

基于复合左右手传输线结构的零阶谐振理论,提出了一种新型双层结构的微带天线,仿真结果表明该天线具有比单层微带天线更小的尺寸。研究了天线各结构参数对天线特性的影响,并对各参数进行了优化仿真。仿真结果表明该天线具有全向辐射特性,与传统微带天线相比,该天线具有体积小的特点。     

基于差异路径权重的二部图网络推荐算法

针对二部图网络结构推荐算法中资源分配不合理的现象,同时为了丰富推荐结果多样性和促进冷门物品的推荐,提出了一种利用差异路径权重改变资源传递的二部图网络结构算法。利用用户相似性构造路径权重改变第一阶段资源传递规则,使资源较多地流向与目标用户相似的用户节点。通过物品属性相似的构造路径权重,使第二阶段资源更多地流向与目标用户已购物品具有相似属性的物品。实验结果表明,本算法相对于其他流行网络结构算法提高了推荐的综合性能,并且更好地解决推荐中的相关问题。     

怒江流域云南区段降雨时空变化分析

利用怒江流域云南区段及其毗邻地区77个站点1980~2009年雨量资料,以及怒江干流道街坝站1957~2015年和南汀河支流姑老河站1960~2015年降雨、径流资料,研究该流域降雨区域分异特征及变化趋势。结果表明:怒江流域云南区段多年平均面雨量达1 336.7 mm,远大于全省平均降雨量;怒江干流云南区段上游贡山地区和下游支流苏帕河流域,以及支流南汀河流域下游多年平均降雨量达1 500 mm以上,而干流的中游怒江坝干热河谷不足1 000 mm,降水空间分布格局与西南季风强弱及纵向岭谷地形密切相关;怒江流域云南区段降水量呈下降趋势,与怒江上游西藏区域相反,支流南汀河流域面雨量减少速率(56 mm/10 a)大于怒江干流云南区段(29 mm/10 a);怒江流域云南区段夏季降水占全年降水量的51%,怒江干流云南区段与支流南汀河流域降水量差异主要表现在夏季;怒江流域云南区段春季降水量呈增加趋势,夏季、秋季和冬季降水量呈减少趋势;怒江干流道街坝站、支流南汀河姑老河站降雨量长周期分别为36 a和33 a左右,从降雨变化长周期来看,2002年以来的少水期将持续到2018~2020年。怒江干流云南区段降水量变化的Hurst指数值为0.69,表明其未来趋势与过去一致,仍将延续降水量减少的变化趋势。     

鄱阳湖流域水分盈亏时空变化特征分析

根据1960~2014年鄱阳湖流域13个气象站点的气象资料,采用FAO推荐的Penman-Monteith法、Mann-Kendall趋势检验法和相关分析法,研究鄱阳湖流域水分盈亏量时空分布变化趋势及其与气象因子的关系。研究结果表明:鄱阳湖流域近55 a来水分盈亏量多年平均盈余为565.88 mm,空间上呈由西北向东北和由西南向东北逐渐递增的趋势;水分盈亏量年内分布呈秋季亏缺,其他季节盈余,水分盈余量表现为春季夏季冬季秋季;在研究时段内流域各地水分盈余量均呈增加趋势,但各季节水分盈余量变化存在一定的差异性,其中春季和秋季呈减少趋势,但趋势不明显,而夏季和冬季均呈显著的增加趋势(p0.05)。鄱阳湖流域水分盈亏量与降雨、日照时数和日最高气温关系密切,流域年水分盈余量的增加是由于夏季和冬季降水量的增加、日照数和风速的减少。     

向家坝右岸电站水轮机低水位运行性能预测分析

以向家坝右岸电站水轮发电机组模型试验数据为基础,对其在初期低水位状态下运行的水力特性进行了初步预测,并通过低水位下水轮机实际运行试验数据与模型试验预测结果的对比分析,验证了模型试验预测的准确性,可以作为向家坝右岸水轮发电机组在蓄水初期低水位下运行情况的初步参考依据。     

Attitudes towards mobile banking: are there any differences between users and non-users?

Extant research has focused on monitoring the behaviour of people who use mobile banking (MB) but it has paid little attention to understanding the impact of information technology usage behaviour due to cultural differences. Humans are the weakest link in information technology adoption; past research has shown that not all users are predisposed to change their behaviour radically and adopt new channels of banking. This paper examines the demographic patterns of users and non-users of MB. The paper also investigates the attitudinal influences of users and non-users of MB based on innovation attributes. Using empirical research, the study identifies constructs of innovation attributes that were perceived to be significantly different among the users and non-users of MB. The study provides valuable insights into MB in Saudi Arabia that have not been previously investigated. From a practical point, findings of this study will be particularly useful to banks, financial institutions and telecommunication service providers.