The plane of nutrition in deer may affect body condition and lactation in hinds and calf growth both through long‐term density‐dependent effects and by shortterm abiotically originated falls in food supply. Our study examines the effect of low nutrient availability after calving on lactation in captive Iberian red deer Cervus elaphus hispanicus. Twelve hinds and their calves were allotted to a food restricted (50–60% daily energy requirements) or a control group just after calving. Hinds in the food‐restricted group showed a greater body mass loss, produced less milk and yield of milk fat, protein and lactose, and a different lactation curve shape, which resulted in reduced calf growth. However, the time course of lactation variables appeared to show a compensatory response up to week 4: a greater milk fat content in low‐nutrition hinds than in the control group appeared to compensate for lower milk production, as neither calf nor hind mass differed from the control group, and lactation variables in both groups showed a standard lactation pattern. In contrast, as milk fat content fell below that of the control group after week 4, the low nutrition plane overcame a standard lactation pattern and groups differed in most variables (e.g. calf and hind mass and percentage of calf growth). Our results appear to show that deer mobilise body reserves in lactation to maintain offspring growth under temporary reductions in food intake, which may be a strategy of securing investment in current offspring at the expense of reproducing the following season.

As we know, some global optimization problems cannot be solved using analytic methods, so numeric/algorithmic approaches are used to find near to the optimal solutions for them. A stochastic global optimization algorithm (SGoal) is an iterative algorithm that generates a new population (a set of candidate solutions) from a previous population using stochastic operations. Although some research works have formalized SGoals using Markov kernels, such formalization is not general and sometimes is blurred. In this paper, we propose a comprehensive and systematic formal approach for studying SGoals. First, we present the required theory of probability (σ-algebras, measurable functions, kernel, markov chain, products, convergence and so on) and prove that some algorithmic functions like swapping and projection can be represented by kernels. Then, we introduce the notion of join-kernel as a way of characterizing the combination of stochastic methods. Next, we define the optimization space, a formal structure (a set with a σ-algebra that contains strict ǫ-optimal states) for studying SGoals, and we develop kernels, like sort and permutation, on such structure. Finally, we present some popular SGoals in terms of the developed theory, we introduce sufficient conditions for convergence of a SGoal, and we prove convergence of some popular SGoals.

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The transmission dynamics of the coronavirus—COVID-19—have challenged humankind at almost every level. Currently, research groups around the globe are trying to figure out such transmission dynamics under special conditions such as separation policies enforced by governments. Mathematical and computational models, like the compartmental model or the agent-based model, are being used for this purpose. This paper proposes an agent-based model, called INFEKTA, for simulating the transmission of infectious diseases, not only the COVID-19, under social distancing policies. INFEKTA combines the transmission dynamic of a specific disease, (according to parameters found in the literature) with demographic information (population density, age, and genre of individuals) of geopolitical regions of the real town or city under study. Agents (virtual persons) can move, according to its mobility routines and the enforced social distancing policy, on a complex network of accessible places defined over an Euclidean space representing the town or city. The transmission dynamics of the COVID-19 under different social distancing policies in Bogotá city, the capital of Colombia, is simulated using INFEKTA with one million virtual persons. A sensitivity analysis of the impact of social distancing policies indicates that it is possible to establish a ‘medium’ (i.e., close 40% of the places) social distancing policy to achieve a significant reduction in the disease transmission.

Two incubation trials were carried out with the rumen-simulation technique (RUSITEC). In each trial, four vessels received a diet of grass hay and concentrate (600 and 400 g/kg DM, respectively; diet F), and the other four were fed a diet composed of concentrate and barley straw (900 and 100 g/kg DM, respectively; Diet C). Vessels were given 20 g of the corresponding diet daily, and half of them were supplemented with disodium malate to achieve a final concentration of 6·55 mM. There were no effects (P.0·05) of malate either on pH or on the daily production of NH 3 -N, but malate treatment increased (P,0·05) DM, neutral detergent and acid detergent fibre disappearance after 48 h incubation. The daily production of propionate and butyrate increased (P,0·001), and the ratio CH 4 :volatile fatty acids decreased (P,0·001) by supplementing both diets with malate. Whereas adding malate to the F diet produced an increase in acetate production (P¼ 0·011) and the growth of solid-associated micro-organisms (P¼ 0·037), no effects (P.0·05) were observed for diet C. For both diets, there were no differences (P.0·05) between treatments in the daily flow of liquid-associated micro-organisms measured using 15 N as a microbial marker. These results indicate that malate stimulated the in vitro fermentation of both diets by increasing the apparent disappearance of the diet and decreasing the ratio of CH 4 :volatile fatty acids, but a greater response was observed with diet F. If these results are confirmed in vivo, malate could be used as a feed additive for ruminants fed diets containing medium proportions of forage (i.e. dairy animals) and not only in animals fed high-concentrate diets, as has so far been proposed.