The Monitoring of the Livestock Product Market for the Formation of Food Security

Introduction. The imbalance of the livestock product market due to significant challenges, such as large-scale military actions, reduced purchasing power, and changes in citizens' diets, presents other threats to food security. The influx of threats leads not only to a significant differentiation in the balance of demand and supply for meat and meat products by regions and strata of the population but also to a significant dynamism of the entire livestock market. This determines the need for changes in the methodology for monitoring and evaluating dynamic changes in the parameters affecting the production and consumption of livestock products. Aim and tasks. This study aims to establish the peculiarities of monitoring the livestock market under significant dynamic changes in the main parameters affecting production and consumption volumes. The objectives of this study were to establish integral trends in the production of the main types of livestock products and to develop a mathematical model for evaluating the balance of production and consumption of livestock products. Results. A break in the trend of stability of the pig and poultry population in 2021 and the continuation of the long-term trend of reduction in the cattle population was established, as evidenced by changes in the number of animals from 2023 to 2021: for cattle, by 20%; for pigs, by 16%; and for poultry, by 11%. This indicates that significant short-term fluctuations in influencing factors cause the production and consumption forecasts to be irrelevant. The influence of threats determines the deformation of markets for the production and consumption of livestock. This necessitates assessing dynamic changes in parameters affecting the volume of production and consumption of livestock products and the prompt formation of forecasts. Conclusions. The developed mathematical model for assessing the balance of production and consumption of livestock products allows for considering dynamic changes in the main parameters of influence, which ensures the relevance of forecasts. This will enable prompt implementation of measures to regulate food security

Differential Recurrent Neural Networks for Action Recognition

The long short-term memory (LSTM) neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model any sequential time-series data, where the current hidden state has to be considered in the context of the past hidden states. This property makes LSTM an ideal choice to learn the complex dynamics of various actions. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient motion patterns, when they gate the information that ought to be memorized through time. To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive frames. This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed as differential Recurrent Neural Network (dRNN). We demonstrate the effectiveness of the proposed model by automatically recognizing actions from the real-world 2D and 3D human action datasets. Our study is one of the first works towards demonstrating the potential of learning complex time-series representations via high-order derivatives of states

Macroprudential policy and bank systemic risk

This paper investigates the effectiveness of macroprudential policy to contain the systemicrisk of European banks between 2000 and 2017. We use a new database (MaPPED) collected by experts at the ECB and national central banks with narrative informationon a broad range of instruments which are tracked over their life cycle. Using a dynamicpanel framework at a monthly frequency we assess the impact of macroprudential tools and their design on the banks’ systemic risk both in the short and the long run. We furthermore decompose the systemic risk measure in an individual bank risk component and a systemic linkage component. This is of particular interest because microprudential policy focuses on the tail risk of an individual bank while macroprudential policy targets systemic risk by addressing the interlinkages and common exposures across banks. In general, the announcements of macroprudential policy actions have a downward effect on bank systemic risk. On average, all banks benefit from macroprudential tools in terms oftheir individual risk. We find that credit growth tools and exposure limits exhibit the most pronounced downward effect on the individual risk component. However, we find evidence for a risk-shifting effect which is more pronounced for retail-oriented banks. The effects are heterogeneous across banks with respect to the systemic linkage component. Liquidity tools and measures aimed at increasing the resilience of banks decrease the systemic linkage of banks. Moreover, these tools appear to be most effective for distressed banks.Our results have implications for the optimal design of macroprudential instruments

The impact of cellular characteristics on the evolution of shape homeostasis

The importance of individual cells in a developing multicellular organism is well known but precisely how the individual cellular characteristics of those cells collectively drive the emergence of robust, homeostatic structures is less well understood. For example cell communication via a diffusible factor allows for information to travel across large distances within the population, and cell polarisation makes it possible to form structures with a particular orientation, but how do these processes interact to produce a more robust and regulated structure? In this study we investigate the ability of cells with different cellular characteristics to grow and maintain homeostatic structures. We do this in the context of an individual-based model where cell behaviour is driven by an intra-cellular network that determines the cell phenotype. More precisely, we investigated evolution with 96 different permutations of our model, where cell motility, cell death, long-range growth factor (LGF), short-range growth factor (SGF) and cell polarisation were either present or absent. The results show that LGF has the largest positive impact on the fitness of the evolved solutions. SGF and polarisation also contribute, but all other capabilities essentially increase the search space, effectively making it more difficult to achieve a solution. By perturbing the evolved solutions, we found that they are highly robust to both mutations and wounding. In addition, we observed that by evolving solutions in more unstable environments they produce structures that were more robust and adaptive. In conclusion, our results suggest that robust collective behaviour is most likely to evolve when cells are endowed with long range communication, cell polarisation, and selection pressure from an unstable environment