Риски, вызовы и механизмы ESG-трансформации систем управления

Purpose: the article aims at justification and identification of the factors hindering the effective implementation of the management systems ESG-transformation, taking into account new risks and threats to sustainable development, and substantiation of the mechanisms that ensure its implementation.Methods: along with the traditional methods of scientific analysis, interdisciplinary approach typical for the study of sustainable development problems and the diagnosis of key factors associated with ESG-transformation of management systems, carried out a review of scientific literature, used various rating models, regulatory documents and guidelines for sustainable development, corporate social responsibility and diagnostics of ESG-factors.Results: the article performed diagnostics of managed and unmanaged risks of ESG-transformation of management systems, identified trends in the development of managerial personnel competencies that carry out such a transformation, and disclosed the features of achieving sustainable development goals. The essence of the author's position is that in order to achieve any of the sustainable development goals, two mandatory conditions must be met: ensuring effective interaction between the state, business and civil society and applying an integrated approach to considering economic, social and environmental aspects that reflect its specifics.Сonclusions and Relevance: the proposed approach makes it possible to develop scientifically based tools for minimizing risks and mechanisms for achieving sustainable development goals based on the ESG-transformation of management systems. Results obtained in the article may be useful for the professional community interested in promoting the ESG-agenda and achieving sustainable development goals based on the ESG-transformation of public and corporate governance.Цель статьи – выявление и идентификация факторов, препятствующих эффективному проведению ESGтрансформации систем управления, с учетом новых рисков и вызовов устойчивому развитию, и обоснование механизмов, обеспечивающих ее реализацию.Методы или методология проведения работы. Наряду с традиционными методами научного анализа, а также междисциплинарного подхода, характерного для исследования проблем устойчивого развития и диагностики ключевых факторов, связанных с ESG-трансформацией систем управления, в работе выполнен обзор научной литературы. В рамках исследования использовались различные рейтинговые модели, нормативные документы и руководящие принципы устойчивого развития, корпоративной социальной ответственности и диагностики ESG-факторов.Результаты работы. В работе проведена диагностика управляемых и неуправляемых рисков ESGтрансформации систем управления, определены тренды развития компетенций управленческих кадров, такую трансформацию осуществляющих, и раскрыты особенности достижения целей устойчивого развития. Суть авторской позиции заключается в том, что для достижения любой из целей устойчивого развития необходимо выполнение двух обязательных условий: обеспечение эффективного взаимодействия государства, бизнеса и гражданского общества и применение комплексного подхода к рассмотрению экономических, социальных и экологических аспектов, отражающих ее специфику.Выводы. Предложенный подход дает возможность разработки научно обоснованного инструментария минимизации рисков и механизмов достижения целей устойчивого развития на основе ESG-трансформации систем управления. Результаты, полученные в статье, могут быть использованы профессиональным сообществом, заинтересованном в продвижении ESG-повестки и достижении целей устойчивого развития на основе ESG-трансформации государственного и корпоративного управления

Residence Time Statistics for Normal and Fractional Diffusion in a Force Field

We investigate statistics of occupation times for an over-damped Brownian particle in an external force field. A backward Fokker-Planck equation introduced by Majumdar and Comtet describing the distribution of occupation times is solved. The solution gives a general relation between occupation time statistics and probability currents which are found from solutions of the corresponding problem of first passage time. This general relationship between occupation times and first passage times, is valid for normal Markovian diffusion and for non-Markovian sub-diffusion, the latter modeled using the fractional Fokker-Planck equation. For binding potential fields we find in the long time limit ergodic behavior for normal diffusion, while for the fractional framework weak ergodicity breaking is found, in agreement with previous results of Bel and Barkai on the continuous time random walk on a lattice. For non-binding potential rich physical behaviors are obtained, and classification of occupation time statistics is made possible according to whether or not the underlying random walk is recurrent and the averaged first return time to the origin is finite. Our work establishes a link between fractional calculus and ergodicity breaking.Comment: 12 page

Insights gained from the reverse engineering of gene networks in keloid fibroblasts

<p>Abstract</p> <p>Background</p> <p>Keloids are protrusive claw-like scars that have a propensity to recur even after surgery, and its molecular etiology remains elusive. The goal of reverse engineering is to infer gene networks from observational data, thus providing insight into the inner workings of a cell. However, most attempts at modeling biological networks have been done using simulated data. This study aims to highlight some of the issues involved in working with experimental data, and at the same time gain some insights into the transcriptional regulatory mechanism present in keloid fibroblasts.</p> <p>Methods</p> <p>Microarray data from our previous study was combined with microarray data obtained from the literature as well as new microarray data generated by our group. For the physical approach, we used the fREDUCE algorithm for correlating expression values to binding motifs. For the influence approach, we compared the Bayesian algorithm BANJO with the information theoretic method ARACNE in terms of performance in recovering known influence networks obtained from the KEGG database. In addition, we also compared the performance of different normalization methods as well as different types of gene networks.</p> <p>Results</p> <p>Using the physical approach, we found consensus sequences that were active in the keloid condition, as well as some sequences that were responsive to steroids, a commonly used treatment for keloids. From the influence approach, we found that BANJO was better at recovering the gene networks compared to ARACNE and that transcriptional networks were better suited for network recovery compared to cytokine-receptor interaction networks and intracellular signaling networks. We also found that the NFKB transcriptional network that was inferred from normal fibroblast data was more accurate compared to that inferred from keloid data, suggesting a more robust network in the keloid condition.</p> <p>Conclusions</p> <p>Consensus sequences that were found from this study are possible transcription factor binding sites and could be explored for developing future keloid treatments or for improving the efficacy of current steroid treatments. We also found that the combination of the Bayesian algorithm, RMA normalization and transcriptional networks gave the best reconstruction results and this could serve as a guide for future influence approaches dealing with experimental data.</p

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks

Background: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters. Results: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach. Conclusions: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes

On the continuing relevance of Mandelbrot’s non-ergodic fractional renewal models of 1963 to 1967

The problem of “1∕ƒ” noise has been with us for about a century. Because it is so often framed in Fourier spectral language, the most famous solutions have tended to be the stationary long range dependent (LRD) models such as Mandelbrot’s fractional Gaussian noise. In view of the increasing importance to physics of non-ergodic fractional renewal models, and their links to the CTRW, I present preliminary results of my research into the history of Mandelbrot’s very little known work in that area from 1963 to 1967. I speculate about how the lack of awareness of this work in the physics and statistics communities may have affected the development of complexity science, and I discuss the differences between the Hurst effect, “1∕ƒ” noise and LRD, concepts which are often treated as equivalent