Bacterial spores in spices and dried herbs: The risks for processed food

Production and world consumption of spices are constantly increasing. Although the antimicrobial properties of some spices are well documented, their use in the agri-food industry is also responsible for microbial contamination and spoilage. Bacterial spores introduced by spices can withstand different preparation processes, particularly thermal treatments, leading to food alterations during storage. This review brings together data from the literature about the prevalence and concentrations of spore-forming bacteria in all commercially available spices. The sporeformers found in spices belong mainly to the genera Bacillus and Clostridium. Such contaminations are very common and sometimes reach high levels, as in pepper and turmeric. Bacillus licheniformis and Bacillus cereus are the most frequently detected species. Studying the harvesting, processing, and storage procedures for spices provides elements to explain why high prevalence and concentrations are observed. Spices are mostly produced in developing countries on small farms using traditional production methods. Spices become contaminated by bacterial spores in two main ways: by contact with soil during harvesting or drying, as for pepper, or by cross-contamination during the water-cooking step, as for turmeric. From these observations, we propose some recommendations. Different methods that can be used to eliminate bacterial spores from spices are presented indicating their efficiency and the limitations of their use.     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

Iodine from brown algae in human nutrition,with an emphasis on bioaccessibility,bioavailability, chemistry,and effects of processing: A systematic review

Brown algae are becoming increasingly popular as a food source and dietary supplement in Europe and other Western countries. As they are highly rich in iodine, they represent a potential new dietary iodine source. Iodine deficiency has been re-emerging in Europe, and it is important to ensure adequate intake through one's diet. However, macroalgae, and especially brown algae, may contain very high amounts of iodine, and both iodine deficiency and excessive iodine may increase the risk of negative health effects. The iodine content of algae or foods containing algae is currently not regulated in the European Union. The aim of this paper is to review the literature to determine the chemical species of iodine in brown algae, the loss of iodine during processing, and the bioavailability and bioaccessibility of iodine. A systematic search of the literature was performed in April 2021, via the databases Web of Science and PubMed. The review includes studies of iodine in brown macroalgae in relation to bioavailability, bioaccessibility, processing and speciation. A meta-analysis was conducted in relation to the following topics: (i) the correlation between total iodine and iodide (I^–) content in brown algae; (ii) the correlation between the loss of iodine during processing and the I^– content; and (iii) the correlation between bioavailability and the I^– content. The bioavailability of iodine from brown algae was generally high, with in vivo bioavailability ranging from 31% to 90%. The in vitro bioavailability of iodine (2%–28%) was systematically lower than in vivo bioavailability (31%–90%), indicating an inadequate in vitro methodology. Processing may reduce the iodine content of brown algae, and a higher I^– content was positively correlated with increased iodine loss during processing. Although processing strategies may reduce the iodine content of brown algae significantly, the iodine content may still be high after processing. These findings may be used in food safety evaluations of brown algae as well as in the development of macroalgae-containing foods with iodine contents suitable for human consumption. Further research on processing techniques to reduce the iodine content in brown macroalgae are warranted.     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Synthesis of thermoresponsive copolymer/silica-coated magnetite nanoparticle composite and its application for heavy metal ion recovery

To enhance chemical stability and suppress of aggregation of magnetite nanoparticles (MNPs), which are used as a support for thermoresponsive copolymer immobilization, silica coating of the MNPs is applied via the electrooxidation method. Although the resulting silica coated-MNPs also formed aggregates, the size distribution of the aggregate shifted to smaller size range. Because of that, the surface area available for copolymer immobilization increased approximately 6.7 times at maximum as compared with that of the uncoated MNPs. It contributed to the increase of the amount of the immobilized copolymer on the silica-coated MNPs, which is approximately four times larger than that on the uncoated MNPs. Fe₃O₄ dissolution test confirmed enhancement of chemical stability of MNPs. The thermoresponsive copolymer immobilized on the silica-coated MNPs shows the ability to recycle Cu(II) ion from Cu(II) containing solution by changing temperature with significantly shorter time than those in other thermoresponsive adsorbents in gel form.     

Patterns in environmental priorities revealed through government open data portals

The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance--the proliferation of government open data portals (ODPs)--has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.     

Investigation the correlation between chemical structure and swelling,thermal and flocculation properties of carboxymethylcellulose hydrogels

Hydrophilic polymer networks (hydrogels) based on sodium carboxymethylcellulose (NaCMC) and polycarboxylic acids (oxalic, succinic, citric and adipic) as cross-linking agents are synthesized by esterification reaction; one series of NaCMC hydrogels cross-linked with citric acid is prepared with acrylamide and acrylic acid (Aam/Aac) copolymers using the design of semi-interpenetrating polymer networks (semi-IPN), in order to increase their potential application for flocculation purposes. The Infrared spectroscopy (FTIR) of hydrogels confirms the esterification reaction between NaCMC and cross-linking agents. Results of swelling measurements show that citric acid in the amount of 15 wt% gives the hydrogels with the best absorption capacity. The results of Differential scanning calorimetry (DSC) and Thermal gravimetric analysis (TGA) show no significant difference in thermal properties of neat and semi-interpenetrating NaCMC hydrogels. The amorphous nature of hydrogels is confirmed by X-ray diffraction analysis (XRD). The results of flocculation study show that combination of NaCMC network and Aam/Aac copolymer with initial mass ratio of 10/90 creates a theoretical platform for the production of flocculant which could show high efficacy in purifying of water dominated by positively charged particles.     

Emergency egress for the elderly in care home fire situations

Practitioners are continuing to develop egress modelling software for the design of the built environment. These models require data about human behaviour and factors for calibration, validation and verification. This study aims to address the specific data and knowledge gap: emergency egress of the elderly. Such data are difficult to collect given privacy and consent concerns, with strong relationships generally being required between residences and researchers. Through the observation of nine fire drills at six Canadian long-term care (LTC) and retirement homes, specific evacuation actions and behaviour were observed for 37 staff members and information about the evacuation of 56 residents was collected. These drills demonstrated that emergency egress in LTC and retirement homes is highly staff dependent with 72% of residents recorded requiring full assistance at all stages of movement in evacuation, and that the type of announced/unannounced drill and level of resident care will affect the type of data collected. The development of travel speed and pre-movement is discussed subject to limitation with qualitative behavioural insights of residents that were observed. This study provides valuable methodological discussion on how to conduct behavioural studies in similar highly restricted research environments. Specific attention is given to understanding the considerations that must be made when using fire drills as data sources, and the impact that these can have on using such data for modelling. This study may inform the initial setup and programming of evacuation models from an actions and behavioural perspectives of staff members and residents.     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.