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Abstract. The impact of multiphase reactions involving nitrogen dioxide (NO2) and aromatic compounds was simulated in this study. A mechanism (CAPRAM 2.4, MODAC Mechanism) was applied for the aqueous phase reactions, whereas RACM was applied for the gas phase chemistry. Liquid droplets were considered as monodispersed with a mean radius of 0.1 µm and a liquid content (LC) of 50 µg m-3. The multiphase mechanism has been further extended to the chemistry of aromatics, i.e. reactions involving benzene, toluene, xylene, phenol and cresol have been added. In addition, reaction of NO2 with dissociated hydroxyl substituted aromatic compounds has also been implemented. These reactions proceed through charge exchange leading to nitrite ions and therefore to nitrous acid formation. The strength of this source was explored under urban polluted conditions. It was shown that it may increase gas phase HONO levels under some conditions and that the extent of this effect is strongly pH dependent. Especially under moderate acidic conditions (i.e. pH above 4) this source may represent more than 75% of the total HONO/NO2 - production rate, but this contribution drops down close to zero in acidic droplets (as those often encountered in urban environments).

The net energy (NE) system takes into account the metabolic utilisation of energy and has been proposed as a superior system for characterising the energy value of feeds. In growing pigs, the inefficiency of ME utilisation for NE (or the heat increment, HI) is dependent on many factors, among them the genotype, which implies that published NE prediction equations may not apply across all genotypes. We conducted a study to investigate the effect of two genotypes (Yorkshire-Hampshire♀ × Duroc♂; YH × D) and Large white♀ × Landrace♂; LW × LR) on heat production (HP) and NE value of a corn soybean meal-based diet fed to growing pigs. The diet met or exceeded the nutrient specifications of 20-50 kg b.w. pigs according to NRC (1998). A total of sixteen barrows were used, eight of each genotype (initial b.w. of 20.1 ± 1.1 and 19.0 ± 0.9 kg for YH ×D and LW × LR, respectively). Pigs were initially fed at 550 kcal/kg b.w.-0.60/day (high ME intake) for determination of DE and ME in metabolism crates. Thereafter, HP was measured using an indirect calorimeter at either high ME or 330 kcal/kg b.w.-0.60/day (low ME intake) to estimate fasting HP (FHP) by regression. Pigs were allowed a 3-d adaptation period at low ME intake before measurement of HP. Irrespective of the genotype, a reduction of ME intake resulted in a decrease (P < 0.0001) of HP (352 for high ME vs. 292 kcal/kg b.w.-0.60/day for low ME). Pigs of LW × LR tended (P = 0.07) to have higher HP than those of YH× D and their estimated FHP was 175 and 103 kcal/kg b.w.-0.60/day, respectively. The determined diet NE value was lower for the YHxD genotype (2,307 vs. 2633 kcal/kg DMI, P = 0.01) than for the LW × LR genotype. Pigs of LW × LR genotype showed lower (179 vs. 226 kcal/kg b.w.-0.60/day, P = 0.003) HI than YH × D genotype and were determined to retain less energy as protein (100 vs. 123 kcal/kg b.w.-0.60/day, P =0.04) and more energy as fat (73 vs. 42 kcal/kg b.w.-0.60/day, P = 0.04). The diet NE value was 96% (LW × LR) and 81% (YH × D) of the predicted NE from published equations. In conclusion, a corn-soybean meal fed at equal amounts resulted in different HP and NE value depending on genotype.

Even with the benefits of sustainability and climate change reporting, there is limited information on how municipalities are reporting on performance for external stakeholders in comparison with private sector organizations. The purpose of this research was to gain an understanding of the current state of sustainability and climate change reporting at the local level and to investigate the extent to which municipalities across Ontario, Canada, report. We used content analysis to identify the presence or non-presence of information on the websites of 38 municipalities and analyzed the results using descriptive statistics. Our analysis showed that the sample municipalities were not widely reporting on sustainability or climate change performance. Also, we identified a gap between the number of plans and reports produced by sample municipalities, with the latter being less common, indicating a need for an improved evaluation of plan implementation. Further, we found that a provincial regulation that required municipalities to make their energy conservation and demand management plans public did not guarantee publication of the plan on a municipality’s website. This study contributes to the growing field of sustainability and climate change planning and reporting by local governments and offers empirical evidence specific to Ontario, Canada.

Abstract Motivation: The steady growth of the number of available protein structures has constantly motivated the development of new algorithms for detecting structural correspondences in proteins. Detecting structural equivalences in two or more proteins is computationally demanding as it typically entails the exploration of the combinatorial space of all possible amino acid pairings in the parent proteins. The search is often aided by the introduction of various constraints such as considering protein fragments, rather than single amino acids, and/or seeking only sequential correspondences in the given proteins. An additional challenge is represented by the difficulty of associating to a given alignment, a reliable a priori measure of its statistical significance. Results: Here, we present and discuss MISTRAL (Multiple STRuctural ALignment), a novel strategy for multiple protein alignment based on the minimization of an energy function over the low-dimensional space of the relative rotations and translations of the molecules. The energy minimization avoids combinatorial searches and returns pairwise alignment scores for which a reliable a priori statistical significance can be given. Availability: MISTRAL is freely available for academic users as a standalone program and as a web service at http://ipht.cea.fr/protein.php. Contact: michelet@sissa.it Supplementary information: Supplementary data are available at Bioinformatics online.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.

Camptotheca acuminata is considered a natural medicinal plant with antitumor activity. The assessment of climate change impact on its suitable habitats is important for cultivation and conservation. In this study, we applied a novel approach to build ecological niche models with both climate and soil variables while the confounding effects between the variables in the two categories were avoided. We found that the degree-days below zero and mean annual precipitation were the most important climatic factors, while the basic soil saturation, soil gravel volume percentage, and clay content were the main soil factors, determining the suitable habitats of C. acuminata. We found that suitable habitats of this species would moderately increase in future climates under both the RCP4.5 and RCP8.5 climate change scenarios for the 2020s, 2050s, and 2080s. However, substantial shifts among levels of habitat suitability were projected. The dual high-suitable habitats would expand, which would be favorable for commercial plantations. Our findings contribute to a better understanding of the impact of climate change on this species and provide a scientific basis for the cultivation and conservation purposes.