Starch: a potential substrate for biohydrogen production

Hydrogen is a clean energy carrier with great potential to be an alternative fuel. Anaerobic hydrogen fermentation seems to be more favorable, since hydrogen is yielded at high rates and various organic waste and wastewater enriched with carbohydrates as substrate result in low cost for hydrogen production. Abundant biomass from various industries could be a source for biohydrogen production where combination of waste treatment and energy production would be an advantage. Carbohydrate‐rich nitrogen‐deficient solid wastes such as starch residues can be used for hydrogen production by using suitable bioprocess technologies. Alternatively, converting biomass into gaseous fuels, such as biohydrogen, is possibly the most efficient way to use these agroindustrial residues. This review summarizes the potential of starch agroindustrial residues as a substrate for biohydrogen production. Types of potential starch agroindustrial residues, recent developments and bio‐processing conditions for biohydrogen production will be discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

Harvesting small trees and forest residues

Eight countries collaborated and shared technical information on the harvesting of small trees and forest residues in a three year program. Proceedings and reports from workshops and reviews are summarized in a review of activities and harvesting systems of the participating countries. Four databases were developed for harvesting and transportation of these materials.     

Effects of Contaminants on Toxicity of the Lampricides TFM and Bayer 73 to Three Species of Fish

Waters in the Great Lakes basin contain more than 400 contaminant chemicals that potentially affect fishery resources, commerce, and human inhabitants. We determined in the laboratory the effects of selected contaminants on the toxicity of the widely used lampricides TFM (3-trifluoromethyl-4-nitrophenol) and Bayer 73 (2′,5-dichloro-4′-nitrosalicylanilide) to three species of fish—rainbow trout (Salmo gairdneri), white sucker (Catostomus commersoni), and fathead minnow (Pimephales promelas). The fish were exposed to paired mixtures of lampricides and selected contaminants in standardized, acute static toxicity tests to determine the resulting type of response—less than additive, additive, or greater than additive (synergistic). As expected, the toxicities of combinations of lampricides with organic pesticides, metal, industrial or municipal pollutants, and tannic acid were mostly additive. However, the toxicity of a combination of TFM, Delnav, and malathion was synergistic, and extremely small quantities of each chemical became lethal when mixed. The concentration that produced 50% mortality was 1.64 mg/L for TFM alone but only 0.041 mg/L for TFM with the pesticides. Toxicities of the pesticides in the combination also increased commensurately. The triple combination of chemicals produced extraordinary synergism and effectively demonstrated the hazards that may result if certain chemical combinations occur in the aquatic environment. However, synergism is not the only kind of toxic action that produces hazards to aquatic organisms. All three types of toxic action are of concern because toxic units produced by contaminant chemicals add to the toxic units of applied management chemicals. Since the toxicity of the majority of chemical combinations is simply additive, this cumulative toxic action contributes more total units to aquatic environments than the extreme actions of less than additive and synergism. The toxicity of the lampricide TFM, as well as other management chemicals, is reinforced by the presence of any contaminant that contributes additional units of toxicity. Therefore, all types of cumulative toxic action should be of concern to people and agencies involved with protecting the environment.     

Effects of home preparation on pesticide residues in cabbage

Experiment was carried out to evaluate the pesticides (chlorpyrifos, p,p-DDT, cypermethrin, chlorothalonil) residue levels in cabbage in the process of home preparation by washing with different concentrations of acetic acid and sodium chlorine, and tap water, preserving in refrigerator, and stir-frying for different time. Results showed that washing by tap water and/or detergent solution for cooking are necessary to decrease the concentration of pesticide residues in cabbage. Washing with acetic acid solutions (at 10% concentration for 20 min) caused 79.8%, 65.8%, 74.0% and 75.0% loss of the above pesticides, respectively. Washing with NaCl solutions (at 10% concentration for 20 min) produced 67.2%, 65.0%, 73.3% and 74.1% loss, respectively, and washing by tap water (for 20 min) were 17.6%, 17.1%, 19.1% and 15.2% loss, respectively. The reductions due to the refrigeration (for 48 h) were 3.4%, 2.6%, 3.1% and 3.6%, respectively, and those due to the stir-frying (for 5 min) were 86.6%, 67.5%, 84.7% and 84.8%, respectively. The data indicated that washing by detergent solutions and stir-frying of cabbage are the most effective home preparations for the elimination of pesticide residues.     

中国水稻害虫发生与防治研究进展

本文介绍了我国水稻害虫发生的种类，主要稻虫的种群消长，对水稻危害的损失，在各个不同年代，通过研究和防治，总结出对抗虫品种评价与利用的研究发展迅速，新农药的试验开发效果明显。７０年代以来，按我国稻区生态条件和稻虫为害的特点，形成了江淮、太湖平原、川西平原、洞庭湖平原和珠江三角洲五大稻区的水稻病虫综合防治技术体系，主要技术路线是以农业防治为基础，抗性品种为主体，依靠经济防治指标，协调农药防治和利用天敌     

Occurrence of Lawn Care and Agricultural Pesticides in the Don River and Humber River Watersheds (1998–2002)

Between 1998 and 2002, surface water samples were collected from several sites in the Don River and Humber River watersheds, both tributaries to Lake Ontario, and analyzed for a variety of pesticides, including those used for urban lawn care. Analyses included 152 pesticide active ingredients and eight metabolites. Samples were collected during base flow periods (i.e., dry events) and rainfall events (i.e., wet events). The objectives of the study were to determine which pesticides were detectable, whether there was a difference in the detection frequency between the two watersheds and between upstream and downstream in each river, and whether precipitation influenced the frequency of detection. Eleven pesticides and one metabolite were detected in surface waters of the Don and Humber rivers or their tributaries, with approximately 72% of samples containing at least one pesticide attributable to lawn care use. The pesticides and pesticide metabolite detected in this study included 2,4-D, atrazine, bromacil, carbofuran, chlorpyrifos, cypermethrin, diazinon, dicamba, MECOPROP, metolachlor, metribuzin, and an atrazine metabolite (des-ethyl atrazine). Four pesticides exceeded federal or provincial water quality guidelines/objectives. Diazinon exceeded the provincial water quality objective in 28% of the samples taken. For the three other pesticides (atrazine, carbofuran and chlorpyrifos) exceedance of a water quality criteria occurred in less than 1% of the samples.     

一种在秘密共享中骗子的检测方案

提出了一种能正确确定欺骗者的方案。此方案具有在验证时运算简单、快速的特点。对此方案的攻击如同大数分解一样困难。     

Productivity and costs of slash bundling in Nordic conditions

The number of slash bundlers and the volume of slash bundling have been rapidly increasing during the last few years in Finland. However, no comprehensive time or follow-up studies have been carried out on slash bundling technology in Finland or in any other country. Metsäteho Oy carried out studies on the productivity and costs of slash bundling in different Nordic recovering conditions. The study methods included both time and follow-up studies. Data were collected during the summer and winter period primarily in Norway spruce (Picea abies L. Karst.) dominated clear cutting sites. The bundling techniques performed by different types of bundler (Fiberpac 370, Timberjack 1490D, Pika RS 2000, Valmet WoodPac) were studied.

The average productivity of slash bundling was 18.1 bundles per operating (E₁₅, including delays shorter than 15 min) hour with the Timberjack 1490D and Fiberpac 370 bundlers in the follow-up study. The operator of the slash bundler had the greatest effect on the productivity of bundling. The prerequisite for increased bundling volumes is a reduction in the costs of the most expensive sub-stage of the bundling supply chain, i.e. bundling itself. This requires improved recovery conditions at bundling sites, increased bundling productivity, larger sized bundles, and the execution of bundling operations in two work shifts using an efficient bundler and effective operator working methods. Implementation of these development measures will bring the bundling supply chain up to a speed that makes it the most competitive supply chain for forest chips in terms of total supply costs for long-distance transportation distances of more than 60 km.     

铝电解槽修槽废渣的特性分析及处理

分析了铝电解槽修槽废渣的产生量、基本构成及污染特性，重点介绍了铝电解槽修槽废渣的处理处置方法和综合利用途径。     

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L⁻¹) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.