Towards sustainable development in a transition economy: The case of eco-industrial parks in Ukraine

Sustainable development for transition economies is an opportunity to accelerate and complete socio-economic transformations and at the same time an additional responsibility in situations of instability and uncertainty. The chances for strengthening sustainability are growing within the organized innovation space, which makes it possible to model scenarios of ecologically oriented development and, with the help of state and international support, to start their implementation. The paper aims to analyze the possibilities and directions of creating eco-industrial parks in a transition economy. It uses an innovative helix model in its triple, quadruple and quintuple variations for functioning and sustainable development of industrial parks in Ukraine.The study adopts a descriptive comparative analysis of data on the planning and implementation of economic, primarily environmentally relevant, activities. Based on the analysis and description of exogenous factors, in particular within GEIPP, a SWOT table on the potential of eco-industrial parks was formed. The directions of development of industrial, technological, and scientific parks in Ukraine are determined using the quintuple helix model on the plane of “knowledge-innovation”, in particular on quadruple helix transition to sustainability through the simultaneous development of socially oriented and environmental activities. Within the legislation, it is proposed to approve a sustainable form of artificially separated innovation parks, namely the “eco-industrial park”. One of the conditions for advanced sustainable development in Ukraine is the creation of a national program to support the transformation of innovation parks into their environmental versions 2.0 and 3.0, as well as investing in greenfield eco-industrial parks.

Numerical Modeling for Region of Freshwater Influence by Han River Discharge in the Yeomha Channel, Gyeonggi Bay

This study estimates the region of freshwater influence (ROFI) by Han River discharge in the Yeomha channel, Gyeonggi Bay. A 3-D numerical model, which is validated for reproducibility of variation in current velocity and salinity, is applied in Gyeonggi Bay. Distance of freshwater influence (DOFI) is defined as the distance from the entrance of Yeomha channel to the point where surface salinity is 28 psu. Model scenarios were constructed by dividing the Han River discharge into 10 categories (200~10,000 m3/s). The relation equation between freshwater discharge and DOFI was calculated based on performing a non-linear regression analysis. ROFI in Yeomha channel expands from the southern sea area of Ganghwa-do to the northern sea area of Yeongheung- do as the intensity of Han River discharge increases. The discharge and DOFI are a proportional relationship, and the increase rate of DOFI gradually decreases as discharge increases. Based on the relation equation calculated in this study, DOFI in the Yeomha channel can be estimated through the monthly mean Han River discharge. Accordingly, it will be possible to respond and predict problems related to damage to water quality and ecology due to rapid freshwater runoff.

Bioenergetic Model Sensitivity to Diet Diversity Across Space, Time and Ontogeny

Consumption is the primary trophic interaction in ecosystems and its accurate estimation is required for reliable ecosystem modeling. When estimating consumption, species’ diets are commonly assumed to be the average of those that occur among habitats, seasons, and life stages which introduces uncertainty and error into consumption rate estimates. We present a case study of a teleost (Yellowfin Bream Acanthopagrus australis) that quantifies the potential error in consumption (in mass) and growth rate estimates when using diet data from different regions and times and ignoring ontogenetic variability. Ontogenetic diet trends were examined through gut content analysis (n = 1,130 fish) and incorporated into a bioenergetic model (the “primary” model) that included diet variability (n = 144 prey sources) and ontogenetic changes in metabolism (1–7 year) to estimate lifetime consumption. We quantified error by building nine model scenarios that each incorporated different spatiotemporal diet data of four published studies. The model scenarios produced individual lifetime consumption estimates that were between 25% lower and 15% higher than the primary model (maximum difference was 53%, range 11.7–17.8 kg). When consumption (in mass) was held constant, differences in diet quality among models caused a several-fold range in growth rate (0.04–1.07 g day–1). Our findings showcase the large uncertainty in consumption rate estimates due to diet diversity, and illustrate that caution is required when considering bioenergetic results among locations, times, and ontogeny.

Importance of epidemic severity and vaccine mode of action and availability for delaying the second vaccine dose

Following initial optimism regarding the potential for rapid vaccination, delays and shortages in vaccine supplies have occurred in many countries. Various strategies to counter this gloomy reality and speed up vaccination have been set forth, of which the most popular approach has been to delay the second vaccine dose for a longer period than originally recommended by the manufacturers. Controversy has surrounded this strategy, and overly simplistic models have been developed to shed light on this issue. Here we use three different epidemic models, all accounting for the actual COVID-19 epidemic in the Czech Republic, including the rise and eventual prevalence of the B.1.1.7 variant of SARS-CoV-2 virus and real vaccination rollout strategy, to explore when delaying the second vaccine dose from 21 days to 42 days is advantageous. Using the numbers of COVID-19-related deaths as a quantity for comparing various model scenarios, we find that vaccine mode of action at the beginning of the infection course (preventing contagion and symptom appearance), mild epidemic and sufficient vaccine supply rate call for the original inter-delay scenario of 21 days regardless of vaccine efficacy. On the contrary, for vaccine mode of action at the end of infection course (preventing severe symptoms and death), severe epidemic and low vaccine supply rate, the 42-day inter-dose period is preferable, at any plausible vaccine efficacy.

Constructive criticism of “Misinterpreting carbon accumulation rates in records from near-surface peat” by Young et al: Further evidence of charcoal impacts in relation to long-term carbon storage on blanket bog under rotational burn management

t is with great interest that we read the recent paper by Young et al. entitled “Misinterpreting carbon accumulation rates in records from near-surface peat”. However, we have some concerns about: (i) the use of an unvalidated deep drainage model to criticise studies investigating the impact of heather burning; (ii) the model scenarios and underlying model assumptions used; and (iii) misleading claims made about net C budgets and deep C losses. We feel that these issues require clarification and, in some cases, correction, especially as Young et al. has been used by a leading peatland policy and conservation body (IUCN UK Peatland Programme) to incorrectly characterise two recent studies by Heinemeyer et al. and Marrs et al. as having “presented misleading conclusions”. We strongly believe that one of the main ways to increase our scientific understanding is through vigorous and factual debate. Whilst we are open to and welcome criticism, such criticism needs to be accurate, balanced and evidence-based. Criticism must avoid unfounded or speculative accusations, especially when based on unrelated and unvalidated model scenarios. Indeed, study aims, hypotheses and discussion sections all need to be considered to ensure any criticism is applicable. We accept that deep C losses can be caused by peatland drainage and that this can lead to the misinterpretation of peat surface C accumulation rates or peatland C budgets. But these issues do not apply to the Heinemeyer et al. study, which investigated two specific and clearly stated burn-related hypotheses (charcoal impacts on peat properties and thus peat C accumulation), which only required comparisons of C accumulation rates within recent peat layers. Moreover, using peat core data collected by Heinemeyer et al., we provide strong evidence that the accusations of deep C losses by Young et al. are unfounded. However, the peat core data from Heinemeyer et al. does highlight the value of the Young et al. model scenarios for predicting short-term C loss caused by recent drainage. Finally, we also highlight the value of a detailed peat layer organic C content (%Corg) assessments to detect potential management (i.e. drainage) induced deep peat C loss.

Futures Planning at the AAN

We describe a process of organizational strategic future forecasting, with a horizon of 2035, as implemented by the American Academy of Neurology (AAN) on behalf of its members, and as a model approach for other organizations. The participants were members of the 2018–2020 AAN Boards of Directors and Executive Team, moderated by a consultant with expertise in future forecasting. Four predetermined model scenarios of import to our field (1 “expectable,” 1 “challenging,” and 2 “visionary”) were discussed in small groups, with alternative scenarios developed in specific domains. Common themes emerged among all scenarios: the importance of thoughtful integration of biomedical and information technology tools into neurologic practice; continued demonstration of the value of neurologic care to society; and emphasis on population management and prevention of neurologic disease. Allowing for the inherent uncertainties of predicting the future, the AAN's integration of structured forecasting into its strategic planning process has allowed the organization to prepare more effectively for change, such as the disruptions stemming from the coronavirus disease 2019 (COVID-19) pandemic. The approaches outlined here will be integrated into future AAN operations and may be implemented to a similar effect by other organizations.

Model-Based Predictive Detector of a Fire inside the Road Tunnel for Intelligent Vehicles

The paper proposes a method for detection of a fire inside the road tunnel without direct view on the fire, using on-board vehicle technologies. The system is based on comparing the measured development of temperature and smoke with model scenarios precomputed for a given road tunnel. The fire scenarios are computed by HW/SW tool TuSim regarding the parameters of the real road tunnel and then the results are presented to the vehicles via car-to-infrastructure communication link. The proper detection of the fire allows early evacuation of the vehicle passengers, which will significantly increase chance of their survival. The computed scenarios also provide supporting information for the rescue teams.

The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios

The exchange of ideas between computer science and statistical physics has advanced the understanding of machine learning and inference significantly. This interdisciplinary approach is currently regaining momentum due to the revived interest in neural networks and deep learning. Methods borrowed from statistical mechanics complement other approaches to the theory of computational and statistical learning. In this brief review, we outline and illustrate some of the basic concepts. We exemplify the role of the statistical physics approach in terms of a particularly important contribution: the computation of typical learning curves in student teacher scenarios of supervised learning. Two, by now classical examples from the literature illustrate the approach: the learning of a linearly separable rule by a perceptron with continuous and with discrete weights, respectively. We address these prototypical problems in terms of the simplifying limit of stochastic training at high formal temperature and obtain the corresponding learning curves.