Consumers’ willingness to pay for upcycled foods

Research on food waste reduction suggests that solutions aimed at reducing waste early in the consumption cycle are the most impactful. Based on this premise, food research labs and food manufacturers have started creating and selling foods made from food ingredients that are generally discarded. Such foods, termed upcycled foods, are safe for human consumption and provide a promising solution to reduce food waste. However, the commercial success of this new category of foods will depend on consumers’ acceptance. This research examines a key indicator of acceptance – consumers’ willingness to pay. We find that although consumers are willing to pay less for upcycled foods compared to conventional alternatives, messaging increases consumers’ willingness to pay. Specifically, we find that rational messaging is more effective than emotional messaging. Overall, our findings suggest that upcycled foods may command good acceptance among consumers.     

煤与不同原料重整气化制甲醇对CO2排放的影响

通过介绍煤制甲醇对整个煤化工行业CO2排放的影响,选择以煤制甲醇工艺CO2排放为研究对象,分别叙述了煤气化制合成气,煤与天然气、煤与焦炉气以及煤与重质油重整气化制合成气联合生产甲醇过程中,吨甲醇产品工艺CO2排放情况,探讨不同煤气化形式对工艺CO2排放的影响因素,为如何通过气化工艺选型,降低煤制甲醇生产过程中工艺CO2排放量提供理论判断依据。     

用于嵌入系统的超声电机驱动设计

为了在嵌入式控制系统中充分利用超声电机特有功能,对超声电机的驱动原理进行了研究,在分析了超声电机的驱动控制原理的基础上,提出了一种基于单片机控制超声电机的软硬件控制实现方法。利用单片机输出数字量作为控制信号,通过串行输入并行输出芯片扩展后,输出到超声电机驱动器,实现超声电机启动/停止/正反转的功能;通过扩展单片机数模转换,将单片机控制超声电机速度的数字信号转换为电压信号,实现超声电机的速度控制功能;利用运行脉冲宽度控制实现超声电机的位置控制功能,并将光电编码器实现超声电机的反馈,最后通过试验验证了单片机系统实现超声电机驱动设计。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。     

An investigation on the microstructural and electrical properties of La0.85DxSr0.15–xGa0.8Mg0.2O2.825 (D = Ba and Ca) electrolytes in solid oxide fuel cells

La_0.85D_xSr_0.15–xGa_0.8Mg_0.2O_2.825 (D = Ba and Ca, x?=?0, 0.01, 0.03, 0.05, and 0.07) electrolytes were synthesized using a solid-state reaction method, calcined at 1400?°C for 5?h, and sintered at 1400?°C for 5?h. The microstructures, electrical properties, and cell performances of the electrolytes and fuel cells were analyzed by X-ray diffraction, scanning electron microscopy, impedance analysis, and electrochemical analysis. La_0.85Ba_xSr_0.15–xGa_0.8Mg_0.2O_2.825 (LBSGM) and La_0.85Ca_xSr_0.15–xGa_0.8Mg_0.2O_2.825 (LCSGM) exhibit a dense structure and a cubic perovskite phase. Further, they contain small amounts of a secondary phase. The lattice constants of LBSGM and LCSGM are 0.3913–0.3914?nm and 0.3906–0.3909?nm, respectively. The average grain size of the sample increases with increasing Ba²⁺ or Ca²⁺ content. The conductivity of LCSGM (0.197–0.174?S/cm) is usually higher than that of LBSGM (0.181–0.162?S/cm) at 800?°C. The cells with La_0.85Sr_0.15Ga_0.8Mg_0.2O_2.825 and La_0.85Ca_0.03Sr_0.12Ga_0.8Mg_0.2O_2.825 electrolytes exhibit high open-circuit voltages and maximum power densities of 0.96?V and 542?mW/cm² and 0.94?V and 567?mW/cm², respectively, at 800?°C.     

综合改进奇异谱分解和奇异值分解的齿轮故障特征提取方法

针对齿轮故障特征微弱，在强背景噪声下难以有效提取的问题，提出了一种改进奇异谱分解（ISSD）结合奇异值分解（SVD）的齿轮故障特征提取方法。针对奇异谱分解（SSD）算法中模态参数需凭经验选取的缺陷，基于散布熵优化算法对SSD算法进行了改进，在得到既定的一组奇异谱分量的基础上，根据峭度值最大准则筛选出了最佳奇异谱分量并进行了SVD处理，采用奇异值能量标准谱自适应地确定了信号重构阶数以还原信号和提高降噪效果。最后对信号进行包络解调以提取齿轮故障特征，将所提方法运用到仿真信号和齿轮实测信号中，并同传统包络谱、SSD包络谱以及经验模态分解结合SVD(EMD-SVD)方法进行了对比分析，结果表明，所提方法的降噪和特征提取效果更佳，能够更加有效地实现齿轮故障的判别。     

Tangeretin Ameliorates Glucose-Induced Podocyte Injury through Blocking Epithelial to Mesenchymal Transition Caused by Oxidative Stress and Hypoxia

Podocyte injury inevitably results in leakage of proteins from the glomerular filter and is vital in the pathogenesis of diabetic nephropathy (DN). The underlying mechanisms of podocyte injury facilitate finding of new therapeutic targets for DN treatment and prevention. Tangeretin is an O-polymethoxylated flavone present in citrus peels with anti-inflammatory and antioxidant properties. This study investigated the renoprotective effects of tangeretin on epithelial-to-mesenchymal transition-mediated podocyte injury and fibrosis through oxidative stress and hypoxia caused by hyperglycemia. Mouse podocytes were incubated in media containing 33 mM glucose in the absence and presence of 1–20 μM tangeretin for up to 6 days. The in vivo animal model employed db/db mice orally administrated with 10 mg/kg tangeretin for 8 weeks. Non-toxic tangeretin inhibited glucose-induced expression of the mesenchymal markers of N-cadherin and α-smooth muscle actin in podocytes. However, the reduced induction of the epithelial markers of E-cadherin and P-cadherin was restored by tangeretin in diabetic podocytes. Further, tangeretin enhanced the expression of the podocyte slit diaphragm proteins of nephrin and podocin down-regulated by glucose stimulation. The transmission electron microscopic images revealed that foot process effacement and loss of podocytes occurred in diabetic mouse glomeruli. However, oral administration of 10 mg/kg tangeretin reduced urine albumin excretion and improved foot process effacement of diabetic podocytes through inhibiting loss of slit junction and adherenes junction proteins. Glucose enhanced ROS production and HIF-1α induction in podocytes, leading to induction of oxidative stress and hypoxia. Similarly, in diabetic glomeruli reactive oxygen species (ROS) production and HIF-1α induction were observed. Furthermore, hypoxia-evoking cobalt chloride induced epithelial-to-mesenchymal transition (EMT) process and loss of slit diaphragm proteins and junction proteins in podocytes, which was inhibited by treating submicromolar tangeretin. Collectively, these results demonstrate that tangeretin inhibited podocyte injury and fibrosis through blocking podocyte EMT caused by glucose-induced oxidative stress and hypoxia.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Arabic sign language intelligent translator

ABSTRACT

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.