Motivated by a common Mathematical Finance topic, this paper surveys several results concerning windings of 2-dimensional processes, including planar Brownian motion, complex-valued Ornstein-Uhlenbeck processes and planar stable processes. In particular, we present Spitzer's asymptotic Theorem for each case. We also relate this study to the pricing of Asian options.

After UV excitation, gas phase thymine returns to ground state in 5 to 7 ps, showing multiple time constants. There is no consensus on the assignment of these processes, with a dispute between models claiming that thymine is trapped either in the first (S1) or in the second (S2) excited states. In the present study, nonadiabatic dynamics simulation of thymine is performed on the basis of ADC(2) surfaces, to understand the role of dynamic electron correlation on the deactivation pathways. The results show that trapping in S2 is strongly reduced in comparison to previous simulations considering only non-dynamic electron correlation on CASSCF surfaces. The reason for the difference is traced back to the energetic cost for formation of a CO p bond in S2.

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Conditional heteroskedastic financial time series are commonly modelled by (G)ARCH processes. ARCH(1) and GARCH were recently established in C[0,1] and L^2[0,1]. This article provides sufficient conditions for the existence of strictly stationary solutions, weak dependence and finite moments of (G)ARCH processes for any order in C[0,1] and L^p[0,1]. It deduces explicit asymptotic upper bounds of estimation errors for the shift term, the complete (G)ARCH operators and the projections of ARCH operators on finite-dimensional subspaces. The operator estimaton is based on Yule-Walker equations, and estimating the GARCH operators also involves a result estimating operators in invertible linear processes being valid beyond the scope of (G)ARCH. Moreover, our results regarding (G)ARCH can be transferred to functional AR(MA).

Combining modern thermodynamics theory branches, including finite time thermodynamics or entropy generation minimization, constructal theory and entransy theory, with metallurgical process engineering, this paper provides a new exploration on generalized thermodynamic optimization theory for iron and steel production processes. The theoretical core is to thermodynamically optimize performances of elemental packages, working procedure modules, functional subsystems, and whole process of iron and steel production processes with real finite-resource and/or finite-size constraints with various irreversibilities toward saving energy, decreasing consumption, reducing emission and increasing yield, and to achieve the comprehensive coordination among the material flow, energy flow and environment of the hierarchical process systems. A series of application cases of the theory are reviewed. It can provide a new angle of view for the iron and steel production processes from thermodynamics, and can also provide some guidelines for other process industries.

Hydrological processes are complex to compute on hilly areas when compared to the plain areas. Most of the hydrological model do not take into account the critical rainfall-runoff generation processes such as subsurface storm flow, saturation excess flow, overland flow, return flow and pipe storage. The simulations of the above processes in the soil matrix requires detailed hillslope hydrological modelling. In present study, a hillslope experimental plot is designed to study the runoff generation processes on the plot scale. The setup is designed keeping in view the natural hillslope conditions prevailing in the north western Himalayas, India where high intensity storm event occurs frequently. Using the experimental data and the developed conceptual model, the overland flow and the subsurface flow through macropore dominated area has been estimated/analyzed on the pixel basis. Over the experimental hillslope plot, a rainfall simulator was installed to generate the rainfall intensity in the range of 15 to 150 mm/hr which represented the dominating rainfall intensity range in the region. Soil moisture sensors were also installed at 100 mm and 300 mm depth at different locations of the plot to observe soil moisture variations. It was found that once the soil is saturated, it remains in the field capacity for next 24-36 hours. Such antecedent moisture conditions are most favorable for the generation of rapid stormflow from hillslopes. Dye infiltration test was also performed on the undisturbed soil column to observe the macropore fraction variability over the vegetated hillslopes. The surface runoff predicted using the developed hillslope hydrological model compared well with the observed surface runoff under high intensity rainfall conditions.

Local Productive Systems (hereinafter LPS) based on agro-food industries constitute alternative models of development in peripheral rural areas that are subject to internal and external dynamics and processes. The main objective of the research is to investigate the processes and their consequences on four SPLs based on the Iberian Pig Transformation Industry (hereinafter LPS-IPTI) in SW Spain: Fregenal de la Sierra, Higuera la Real, Cumbres Mayores and Jabugo. Using secondary data, a comparison is made between 2002 and 2020 to establish the changes, causes and consequences on the LPS-IPTI studied. The results obtained indicate (1) business and territorial concentration of LPS-IPTI; (2) productive and territorial specialisation in standardised products and quality products; (3) simplification of industrial processes; (4) loss of employment, especially female; (5) external control of companies in the sector which, accordingly, results in the loss of prominence of local actors in favour of foreign companies, reduced social capital and the progressive loss of ownership of the LPS.

Many of engineering’s problems are about to optimise equations, functions and process models equations which appear on common or even complex science’s cases. Most applied optimisation methods nowadays can only be used on particular cases, so the main objective of this article is to define a computational method that can optimise or even find target values for specified objective functions and variables, by just using computer effort. Chemical plants involve many differential equations and they can be optimised with more facility now. On this paper, there are shown as example, an optimisation for a chemical reactor, which is found the optimal temperature and volumetric flow rate of feed, for the given objective function being the composition of the desired product. Named Domain’s Sweep, it is an algorithm that evaluate given(s) mathematical function(s) and/or equation(s) by varying your(s) independent variable(s) through loops with a given step size and solving it after closing the degrees of freedom, and finally, with some condition statements, store all the optimum values of given or created objective functions with its respective independent variables. In another words, the user create an objective function and this method find the function’s maximum, minimum or a certain chosen target value, even if it does not have an inflection point in the given search interval of the independent variables.

In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”.

This review focus on a critical analysis of nanocatalysts for Advanced Reductive Processes (ARP) and Oxidation Processes (AOP) designed for the degradation of poly/perfluoroalkyl substances (PFAS) in water. Ozone, ultraviolet and photocatalyzed ARP and/or AOP will be the basic treatment technologies. Besides the review of the nanomaterials with greater potential as catalyst for advanced processes of PFAS in water, the perspectives for its future development considering sustainability considerations will be discussed. Moreover, a brief analysis of the current state of the art of the ARP and AOP for the treatment of PFAS in water will be presented.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “Most Cited Publications” is positively associated, among others, to “Innovation Index” and “Enterprise Birth” and negatively associated, among others, to “Government Procurement of Advanced Technology Products” and “Human Resources”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “Most Cited Publication” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “Most Cited Publication” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “Most Cited Publications” with Augmented Data-AD.

Knowledge management gains space within the school organization and can contribute satisfactorily to the quality of teaching. In everyday life the school manager intuitively uses knowledge management practices without exploiting the potentials they offer or allow. In this context, the purpose of this work is to identify the level of implementation of knowledge management practices aimed at structuring the organizational processes used by the public school manager. The methodology adopted was exploratory, with a qualitative and quantitative approach. For data collection, an already validated instrument with twenty-seven questions was used. Respondents to the questionnaires, one hundred and eleven managers of the basic education schools that make up the public school system in a city in the northwestern region of Paraná, Southern Brazil. In analyzing the results, it was only at this point that we investigated only those practices that were related to the structuring of organizational processes. This decision is justified because of the responsibilities of the school manager in the execution of his work. The results indicated that the school in its daily life, makes use of practices of Knowledge Management related to the structuring of the organizational processes and that many are already applied by the managers.

This paper proposes that intelligent processes can be completely explained by thermodynamic principles. They can equally be described by information-theoretic principles that, from the standpoint of the required optimizations, are functionally equivalent. The underlying theory arises from two axioms regarding distinguishability and causality. Their consequence is a theory of computation that applies to the only two kinds of physical processes possible—those that reconstruct the past and those that control the future. Dissipative physical processes fall into the first class, whereas intelligent ones comprise the second. The first kind of process is exothermic and the latter is endothermic. Similarly, the first process dumps entropy and energy to its environment, whereas the second reduces entropy while requiring energy to operate. It is shown that high intelligence efficiency and high energy efficiency are synonymous. The theory suggests the usefulness of developing a new computing paradigm called Thermodynamic Computing to engineer intelligent processes. The described engineering formalism for the design of thermodynamic computers is a hybrid combination of information theory and thermodynamics. Elements of the engineering formalism are introduced in the reverse-engineer of a cortical neuron. The cortical neuron provides perhaps the simplest and most insightful example of a thermodynamic computer possible. It can be seen as a basic building block for constructing more intelligent thermodynamic circuits.

The interaction between single or a fixed number of photons with a single absorber is of fundamental interest in quantum technology. The harnessing of light matter interactions at the single particle limit has several potential applications ranging from quantum communication and quantum metrology to quantum imaging. In this letter, a setup for heralded two-photon absorption at the single absorber level is proposed. The setup is based on a heralded two-photon source utilizing spontaneous parametric down-conversion, entanglement swapping and sum frequency generation for joint detection. The feasibility of the scheme is discussed by reviewing recent achievements in utilizing entangled and correlated photons for two-photon absorption as well as single photon absorption experiments at the limit of single absorbers in the context of applications in imaging (here mainly microscopy) and spectroscopy.

A multiscale modeling concept for numerical simulation of multiphysics turbulent flow utilizing map-based advection is described. The approach is outlined with emphasis on its theoretical foundations and physical interpretations in order to establish the context for subsequent presentation of the associated numerical algorithms and the results of validation studies. The model formulation is a synthesis of existing methods, modified and extended in order to obtain a qualitatively new capability. The salient feature of the approach is that time advancement of the flow is fully resolved both spatially and temporally, albeit with modeled advancement processes restricted to one spatial dimension. This one-dimensional advancement is the basis of a bottom-up modeling approach in which three-dimensional space is discretized into under-resolved mesh cells, each of which contains an instantiation of the modeled one-dimensional advancement. Filtering is done only to provide inputs to a pressure correction that enforces continuity and to obtain mesh-scale-filtered outputs if desired. The one-dimensional advancement, the pressure correction, and coupling of one-dimensional instantiations using a Lagrangian implementation of mesh-resolved volume fluxes is sufficient to advance the three-dimensional flow without time advancing coarse-grained equations, a feature that motivates the designation of the approach as autonomous microscale evolution (AME). In this sense, the one-dimensional treatment is not a closure because there are no unclosed terms to evaluate. However, the approach is additionally suitable for use as a subgrid-scale closure of existing large-eddy-simulation methods. The potential capabilities and limitations of both of these implementations of the approach are assessed conceptually and with reference to demonstrated capabilities of related methods.

We address the process of discounting in random environments which allows to value the far future in economic terms. We review several approaches to the problem regarding different well-established stochastic market dynamics in the continuous-time context and include the Feynman-Kac approach. We also review the relation between bond pricing theory and discount and introduce the market price of risk and the risk neutral measures from an intuitive point of view devoid of excessive formalism. We provide the discount for each economic model and discuss their key results. We finally present a summary of our previous empirical studies on several countries of the long-run discount problem.

Partial differential representation of self-similarity feature has been derived from notion of the homogenous function in general sense. This representation allows consideration of stochastic self-similar systems. As well as the partial differential representation allows consideration of Stochastic Partial Differential Equation.

By using a systems biological perspective and available literature on human social interaction, grouping, and cohesiveness, a new coherent model is proposed that integrates existing social integration and neurobiological research into a theoretical neurobiological framework of personality and social interaction. This model allows for the coherent analysis of complex social systems and interactions within them, and proposes a framework for estimating group cohesiveness and evaluating group structures in order to build and organize optimized social groups. This „Neurobiological-Associative“ model proposes two primary feedback loops, with environmental conditioning (learning) being sorted into an associative model that modulates interaction with the social environment, and which impacts the second feedback loop involving the individuals' neurobiological capacity. In this paper, the concept of neurobiological capacity is developed and based upon contemporary research on intelligence, personality, and social behavior with a focus on the oxytocin, serotonin, and dopamine systems. The basis of social exclusion and group structure is thus, expressed in the very most simple terms, neurobiological compatibility and risk assessment modulated by an internal associative model.

Research into the neurobiological and psychosocial mechanisms involved in fibromyalgia (FM) has progressed remarkably in recent years. Despite this, currents accounts of FM fail to capture the complex, dynamic and mutual crosstalk between neurophysiological and psychosocial domains. We conducted a comprehensive review of the existing literature in order to synthesise current knowledge on FM, explore and highlight multi-level links and pathways among different systems and build bridges between existing approaches. An extensive panel of international experts in neurophysiology and psychosocial aspects of FM discussed the collected evidence and progressively refined and conceptualized its interpretation. Fibromyalgia is a complex condition resulting from the dynamic interplay between multiple systems and processes. We provided an updated overview of the most relevant observations in FM to date as well as the potential pathways by which they exert they are related and exert their mutual influence, to produce the manifestations commonly associated with FM. This review constituted the first step towards and supported the development of a much needed model capable of integrating the main factors implicated in FM into a single, unified model that may prove valuable in understanding and managing FM.

The paper is based on the research project P2Endure funded within the H2020-EE-2016-2017 call. The main aim of the research is to propose innovative solutions for deep renovation that are applicable and replicable for the widest range of building typologies, from public to residential and historic buildings. In this paper, the methodology adopted to perform the energy analyses and to verify the achievement of the expected targets in terms of savings is defined, based on a range of real case studies. The main aim is to promote a guideline for designers showing specific instruments to perform towards the attainment of the 60% threshold of the energy savings giving advice on how to further improve the energy performance of the buildings, basing on the overview of the energy analyses run for similar cases.

In Italy, the number of buildings that have fallen into disuse is huge; also the normative and regulative framework promoting public portfolio’s re-use and revitalisation is still unclear and blurred. Nevertheless, in the last ten years, these buildings have become fertile ground for innovation and creative experiences by civic actors. The rise of this new category of civic actors plays an important role, both from the institutional side and the kind of initiatives and practices they process. They act in different manners but according to an in-depth analysis they share similar patterns of behaviour. What has emerged is that, although the different contexts, where they operate, the institutional performances might be successful if only certain kinds of conditions are taken into consideration. Based on some basic features, civic actors and their dynamics with public administrations are analysed to understand conditions that allow revitalisation processes to be successful in unused public buildings.

We compute explicitly traces of one-dimensional diffusion processes. The obtained trace forms can be regarded as Dirichlet forms on graphs. Then we discuss conditions ensuring the trace forms to be conservative. Finally, the obtained results are applied to the one-dimensional diffusion related to the Bessel’s process of order $\nu$.

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

Human behaviour was tipped as the mainstay in the control of further SARS-CoV-2 (COVID-19) spread especially after the lifting of restrictions by many countries. Countries in which restrictions were lifted soon after the first wave, had subsequent waves of the COVID-19 infections and it remains to be seen whether there will be more waves. In this paper, we formulate a deterministic model for COVID-19 incorporating dynamic non-pharmaceutical interventions, dubbed social dynamics. The model steady states are determined and their stability analysed. Numerical simulations are carried out to determine the pack of various parameters that influence the social dynamics. In South Africa, the first wave was the only wave in which the only interventions rested solely on human behavior. The model is thus fitted to COVID-19 data on the first wave in South Africa. The results presented in this paper have implications on the trajectory of the pandemic in the presence of changing social processes.

Systems Engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address Systems Engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in Industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks; along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

The paper describes a method of solving some stochastic processes using generating functions. A general theorem of generating functions of a particular type is derived. A generating function of this type is applied to a stochastic process yielding polynomial time algorithms for certain partitions. The method is generalized to a stochastic process describing a rather general linear transform. Finally, the main idea of the method is used in deriving a theoretical polynomial time algorithm to the knapsack problem.

We investigate the innovational determinants of “Patent Applications” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel. We found that the variables that have a deeper positive association with “Patent Applications” are “Human Resources” and “Intellectual Assets”, while the variables that show a more intense negative relation with Patent Applications are “Employment Share in Manufacturing” and “Total Entrepreneurial Activity”. A cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient has been realized. The results show the presence of two clusters. A network analysis with the distance of Manhattan has been performed and we find three different complex network structures. Finally, a comparison is made among eight machine learning algorithms for the prediction of the future value of the “Patent Applications”. We found that PNN-Probabilistic Neural Network is the best performing algorithm. Using PNN the results show that the mean future value of “Patent Applications” in the estimated countries is expected to decrease of -0.1%.

In this article we have estimated the value of “Opportunity Driven Entrepreneurship” in Europe. We use data from European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, Pooled OLS, and Dynamic Panel. Our results show that “Opportunity Driven Entrepreneurship” is positively associated, among others, to “Innovation Friendly Environment” and “Turnover Share Large Enterprises”, while it is negatively associated, among others, to “Sales Impacts” and “R&D Expenditure Business Sectors”.

In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.

The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%.

In the following article, the value of the "Knowledge Intensive Services Exports in Europe" in 36 European countries is estimated. The data were analyzed through a set of econometric models or: Poled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. The results show that “Knowledge Intensive Services Exports” is negatively associated, among others, with "Buyer Sophistication", "Government Procurement of Advanced Technology Products", and positively associated with the following variables i.e. "Innovation Index", "Sales Impacts" and "Total Entrepreneurial Activity". Then a clusterization with k-Means algorithm was made with the Elbow method. The results show the presence of 3 clusters. A network analysis was later built and 4 complex network structures and three structures with simplified networks were detected. To predict the future trend of the variable, a comparison was made with eight different machine learning algorithms. The results show that prediction with Augmented Data-AD is more efficient than prediction with Original Data-AD with a reduction of the mean of statistical errors equal to 55,94%.

The following article indicates the determinants of “Internet User Skills” among European countries based on the application of the database deriving from the DESI-Index. The data were analyzed using the following econometric models, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, WLS corrected for heteroskedasticity. The Elbow method and the Silouette coefficient method were compared for the optimization of the number of clusters obtained by the k-Means algorithm. The result shows the presence of 5 clusters. A network analysis was carried out using the Euclidean distance with the result of identifying two network structures between some analyzed countries. subsequently a comparison was made between six different machine learning algorithms for the prediction of the future value of the variable of interest. The result shows that the best predictor algorithm is Gradient Boosted Tree Regression with an expected value of the predicted variable increasing by a value of 1.75%. Later a further comparison was made by comparing 6 algorithms with the increased data. The result shows that the best predictor is Simple Regression Tree. The interest variable is predicted to decrease by an amount equal to -6.099%. Statistical errors improve on average by 32.43% in the transition between the original data and the increased data.

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

The following article estimates the value of ICT Specialists in Europe between 2016 and 2021 for 28 European countries. The data were analyzed using the following econometric techniques, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, WLS and Pooled OLS. The results show that the value of ICT Specialists in Europe is positively associated with the following variables: "Desi Index", "SMEs with at least a basic level of digital intensity", "At least 100 Mbps fixed BB take-up" and negatively associated with the following variables: "4G Coverage","5G Coverage", "5G Readiness", "Fixed broadband coverage", "e-Government", "At least Basic Digital Skills", "Fixed broadband take-up", "Broadband price index", "Integration of Digital Technology". Subsequently, two European clusters were found by value of "ICTG Specialists" using the k-Means clustering algorithm optimized by using the Silhouette coefficient. Finally, eight different machine learning algorithms were compared to predict the value of "ICT Specialists" in Europe. The results show that the best prediction algorithm is ANN-Artificial Neural Network with an estimated growth value of 12.53%. Finally, "augmented data" were obtained through the use of the ANN-Artificial Neural Network, through which a new prediction was made which estimated a growing value of the estimated variable equal to 3.18%.

The following article analyzes the determinants of e-government in 28 European countries between 2016 and 2021. The DESI-Digital Economy and Society Index database was used. The econometric analysis involved the use of the Panel Data with Fixed Effects and Panel Data with Variable Effects methods. The results show that the value of “e-Government” is negatively associated with “Fast BB (NGA) coverage”, “Female ICT specialists”, “e-Invoices”, “Big data” and positively associated with “Open Data”, “e-Government Users”, “ICT for environmental sustainability”, “Artificial intelligence”, “Cloud”, “SMEs with at least a basic level of digital intensity”, “ICT Specialists”, “At least 1 Gbps take-up”, “At least 100 Mbps fixed BB take-up”, “Fixed Very High Capacity Network (VHCN) coverage”. A cluster analysis was carried out below using the unsupervised k-Means algorithm optimized with the Silhouette coefficient with the identification of 4 clusters. Finally, a comparison was made between eight different machine learning algorithms using "augmented data". The most efficient algorithm in predicting the value of e-government both in the historical series and with augmented data is the ANN-Artificial Neural Network.

This article analyzes the determinants of the "Broadband Price Index" in Europe. The data used refer to 28 European countries between 2016 and 2021. The database used is the Digital, Economy and Society Index-DESI of the European Commission. The data were analyzed using the following econometric techniques, namely Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS and Dynamic Panel. The value of the "Broadband Price Index" is positively associated with the DESI Index, and "Connectivity" while it is negatively associated with "Fixed Broadband Take Up", "Fixed Broadband Coverage", "Mobile Broadband", "e-Government", "Advanced Skills and Development", "Integration of Digital Technology", "At Least Basic Digital Skills ", "Above Basic Digital Skills "," At Least Basic Software Skills ". A cluster analysis was carried out below using the k-Means algorithm optimized with the Silhouette coefficient. The analysis revealed the existence of three clusters. Finally, an analysis of the machine learning algorithms was carried out to predict the future value of the "Broadband Price Index". The result shows that the most useful algorithm for prediction is the Artificial Neural Network-ANN with an estimated value equal to an amount of 9.21%.

The determinants of the presence of “Foreign Doctorate Students” among 36 European Countries for the period 2010-2019 are analyzed in this article. Panel Data with Fixed Effects, Random Effects, WLS, Pooled OLS, and Dynamic Panel are used to investigate the data. We found that the presence of Foreign Doctorate Students is positively associated to “Attractive Research Systems”, “Finance and Support”, “Rule of Law”, “Sales Impacts”, “New Doctorate Graduates”, “Basic School Entrepreneurial Education and Training”, “Tertiary Education” and negatively associated to “Innovative Sales Share”, “Innovation Friendly Environment”, “Linkages”, “Trademark Applications”, “Government Procurement of Advanced Technology Products”, “R&D Expenditure Public Sectors”. A cluster analysis was then carried out through the application of the unsupervised k-Means algorithm optimized using the Silhouette coefficient with the identification of 5 clusters. Finally, eight different machine learning algorithms were used to predict the value of the "Foreign Doctorate Students" variable. The results show that the best predictor algorithm is the "Tree Ensemble Regression" with a predicted value growing at a rate of 114.03%.

An increasing ecological phase-shift from coral dominated reefs to macroalgae dominated reefs as a result of anthropogenic impacts, such as eutrophication, sedimentation, and overfishing, has been observed in many reef systems around the world. Ocean warming is a universal threat to both corals and macroalgae, which may alter the outcome of competition between them. Therefore, in order to explore the effects of indirect and direct exposure to macroalgae on the physiological, biochemical, and genetic expression of corals at elevated temperature, the coral Acroproa hyacinthus and highly invasive green algae Caulerpa taxifolia have been chosen. Physiologically, the results exhibited that distinguish from control and direct contact treatments, the density and chlorophyll a content of zooxanthella decreased by 53.1% and 71.2% respectively, when coral indirect contacted with algae at ambient temperature (27°C). Besides, enzyme activities of superoxide dismutase (SOD) and catalase (CAT) in coral tissue were enhanced by interacting with algae. After an increase of 3°C, the density and chlorophyll a content of zooxanthella reduced by 84.4% and 93.8% respectively, whereas the enzmy activities of SOD and CAT increased by 2.3 and 3.1-fold. However, only the zooxanthellae density and pigment content decreased when C.taxifolia co-culture with A.hyacinthus at 30°C. Molecularly, different from the control group, the differentially expressed genes (DEGs) such as Rab family, ATG family and Casp7 were significantly enriched in endocytosis, autophagy and apoptosis pathways , regardless whether A.hyacinthus was indirect or indirect exposure to C.taxifolia at 27°C. Under thermal stress without algae interaction, the DEGs were significantly enriched in microbial immune signal transduction pathways, such as Toll-like receptor signaling pathway and TNF signaling pathway, while multiple cellular immunity (IFI47, TRAF family) and oxidative stress (CAT, SODC, HSP70) genes were up-regulated. Inversely, compared with corals without interaction with algae at 30°C, the DEGs of corals interacted with C.taxifolia at 30°C, were remarkably enriched in apoptosis, NOD-like receptor signaling pathway, including the transcription factors such as Casp family, TRAF family. In conclusion, the density and chlorophyll a content of zooxanthella remained a fading tendency induced by macroalgae at ambient temperature. The oxidative stress and immune response levels of coral has been elevated at 30°C, but macroalgae alleviated the negative effects triggered by thermal stress.

. This paper features an analysis of the relative effectiveness of a variety of methods of modelling Realised Volatility (RV), namely: the use of Gegenbaur processes in Auto-Regressive Moving Average format, GARMA, as opposed to Heterogenous Auto-Regressive HAR models and simple rules of thumb. The analysis is applied to two data sets that feature the RV of the S&P500 index, as sampled at 5 minute intervals, provided by the Oxford Man RV database. The GARMA model does perform slightly better than the HAR model, but both models are matched by a simple rule of thumb regression model based on the application of lags of squared, cubed and quartic, demeaned daily returns.

Engineering properties of bedrock materials at Badlands National Park were used to develop models for Park managers to assess slope erosion and stability for fossil resource protection. Six fully instrumented sites were used to document slope conditions. Bedrock consisted of Oligocene White River Group rocks. Bulk erosion rates correlated to grain size with silty-sandy materials producing higher mass erosion rates as a function of the silt-to-clay ratio and plastic index. Data indicated that as grain size decreased, plastic index increased leading to a decrease in erodibility. These parameters were used to construct a grain-size proxy, ψ, that was substituted for grain size, D, in Bagnold’s entrainment equation and provided significant improvement in calculation of critical entrainment velocities for fine-grained materials. Hydraulic analyses of slope and pediment surface processes indicated surface roughness was a controlling factor and materials washed from rough steep slopes were effectively transported across smooth low-angle pediments with slope-to-pediment angle ratios of nearly 6:1. Slope stability modeling of ten slopes produced high factors of safety for all slopes, even under saturated conditions and was attributable to clay cohesion. All results were used to construct models that predicted years until net slope erosion equaled 2.5 cm (1 inch). Using these results, Park managers were advised to visit erosion-prone sites on a 1 to 6 year schedule, based on site geology and slope aspect, to adequately protect critical fossil resources from destruction.

Salinity acts as a critical environmental filter on microbial communities in natural systems, negatively affecting microbial diversity. However, how salinity affects the community assembly remains unclear. This study used Wendeng multi-pond saltern as a model to evaluate the prokaryotic community composition and diversity and quantify the relative importance of ecological processes across salinity gradients. Results showed that low saline salterns (45-80 g/L) exhibited higher bacterial diversity than those in high saline salterns (175-265 g/L). The relative abundance of taxa assigned to Halanaerobiaceae, Haloferacaceae, Desulfohalobiaceae, Phormidiaceae, Rohodobacteraceae, and Nitrococcaceae was higher with increasing salinity. Salinity and pH were the primary environmental factors that directly or indirectly determined the composition and diversity of prokaryotic communities. Microbial co-occurrence network dynamics were more complex in the sediment than in water of salterns. An infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis (iCAMP) showed that microbial community assembly in sediment and water differed. Our findings provide more information about microbial community structure and the importance of various ecological processes in controlling microbial community diversity and succession along salinity gradients in water and sediment environments.

We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Simultaneously with the development of industrial society, wastewater with organic pollutants has caused various environmental problems. The most majority of organic pollutants in water and wastewater are persistent, reason which can cause serious problems for human health, animal health, and for the whole environment. Therefore, efficient treatment methods for wastewater with highly concentration of organic compounds are immediately necessary. During the last years, the prescribed and non-prescribed consumption of antibiotics has grown a lot worldwide. Big quantities of antibiotics are discharged into wastewater because their incomplete absorption by living organisms, but at small concentrations present in aquatic environments represents a major risk for the human health and environment protection. The paper presents the main advantages and disadvantages of advanced oxidation processes, but also current state and new perspectives in the field of environment protection. Advanced oxidation processes (AOPs) are often used in the field of treatment of different types of wastewater. AOPs are based on physicochemical processes that create significant structural changes in chemical species, their commercialization at a wide scale may result in cost reductions that are desirable for environmental applications. The majority of antibiotics may be eliminated using physicochemical processes, such as photo-Fenton, photolysis, ozonation, electrooxidation, heterogeneous catalysis, and other bio processes. In comparison to conventional chemical processes, AOPs provide superior oxidation efficiency, ideal operating costs, and zero secondary pollutants.

Carbamazepine (CBZ) is one of the most common emerging contaminants released to the aquatic environment through domestic and pharmaceutical wastewater. Due to its high persistence through conventional degradation treatments, is considered a typical indicator for anthropogenic activities. This study tested the removal of CBZ through two different clay-based purification techniques: adsorption of relatively large concentrations (20-500 μmol L-1) and photocatalysis of lower concentrations (<20 μmol L-1). The sorption mechanism was examined by FTIR measurements, exchangeable cations released, and colloidal charge of the adsorbing clay materials. Photocatalysis was performed in batch experiments under various conditions. Despite the neutral charge of carbamazepine, the highest adsorption was observed on negatively charged montmorillonite-based clays. Desorption tests indicate that adsorbed CBZ is not released by washing. The adsorption/desorption processes were confirmed by ATR-FTIR analysis of the clay-CBZ particles. A combination of synthetic montmorillonite or hectorite with low H2O2 concentrations under UVC irradiation exhibits efficient homo-heterogeneous photodegradation at μM CBZ levels. The two techniques presented in this study suggest solutions for both industrial and municipal wastewater, possibly enabling water reuse.

The historic western edge of bison (Bison bison) range and the ecological processes that caused its formation are frequently debated with important implications for bison restoration across North America. We test the hypothesis that a combination of bottom-up habitat suitability and top-down harvest pressure from humans were important processes in forming the western edge of bison distribution. Using 9,384 historical journal observations from 1691 – 1928, we employ MaxEnt ecological niche modelling to identify suitable bison habitat across the Western Cordillera from bottom-up climatic, land cover, and topographic factors. We then use mixed-effect logistic regression to test if bison occurrence in journal records can be in part explained by the abundance of Indigenous humans, wolves, or grizzly bears, in addition to MaxEnt-derived habitat suitability. We find support for our hypothesis because of the limited suitable habitat in the Rocky Mountains that likely prevented westward bison dispersal from core habitat, and there was a negative relationship between bison occurrence and human harvest pressure. On this basis, we propose that intensive human harvest from large populations in the Western Cordillera, subsidized by other wildlife, salmon, and vegetation resources, is an underappreciated socioecological process that needs to be restored alongside bison populations. Co-managing bison with Indigenous people will also mitigate the adverse effects of overabundant bison and maximize the ecological and cultural benefits of bison restoration.

Starting from the environmental issues related to global warming, climate change and reduction of greenhouse gas emissions, this review paper describes how CO₂ recycling can represent a challenging strategy suitable to explore new concepts and opportunities for catalytic and industrial development. In this view, the production of dimethyl ether (DME) from catalytic hydrogenation of CO₂ appears as a viable technology, able to meet also the ever-increasing need for alternative environmentally-friendly fuels and energy carriers. Basic considerations on thermodynamic aspects controlling DME production from CO₂ are presented, then summarizing the main catalytic systems developed in such a field. Special attention is paid on the role assumed during last years by zeolite-based systems, either in the methanol-to-DME dehydration step or in the one-pot CO₂-to-DME hydrogenation. On the whole, the productivity of DME results significantly to be dependent on several catalyst features, linked not only to the metal-oxide phase responsible for CO₂ activation/hydrogenation, but also to specific properties of the zeolites (i.e., topology, porosity, surface area, acidity, interaction with active metals, distributions of metal particles, …) influencing activity and stability of hybridized bifunctional heterogeneous catalysts.

Mangroves and salt marshes are among the most productive ecosystems in the global coastal ocean. Mangroves store more carbon (739 Mg CORG ha-1) than salt marshes (334 Mg CORG ha-1), but the latter sequester proportionally more (24%) net primary production (NPP) than mangroves (12%). Mangroves exhibit greater rates of gross primary production (GPP), above-ground net primary production (NPP) and plant respiration (RC) with higher PGPP/RC ratios, but salt marshes exhibit greater rates of below-ground NPP. Mangroves have greater rates of subsurface DIC production and, unlike salt marshes, exhibit significant microbial decomposition to a soil depth of 1 m. Salt marshes release more soil CH4 and export more dissolved CH4 , but mangroves release more CO2 from tidal waters and export greater amounts of POC, DOC and DIC to adjacent waters. Both ecosystems contribute only a small proportion of GPP, RE (ecosystem respiration) and NEP (net ecosystem production) to the global coastal ocean due to their small global area, but contribute 72% of air-sea CO2 exchange from the world’s wetlands and estuaries and contribute 34% of DIC export and 17% of DOC + POC export to the world’s coastal ocean. Thus, both wetland ecosystems contribute disproportionately to carbon flow of the global coastal ocean.

The risk factors associated with COVID-19 related severity, morbidity, and mortality, i.e., obesity (often associated with NAFLD), hyperglycemia, hypertension and dyslipidemia all cluster together as metabolic syndrome (MetS). Instead of studying association of these risk factors with COVID-19, it makes sense studying the association between MetS on one hand and COVID-19 on the other. This study explores a molecular basis underpinning the above association. Severity of COVID-19 patients with MetS could be due to functional alterations of host proteins due to their interactions with viral proteins. We collected data from Enrichr (https://amp.pharm.mssm.edu/Enrichr/), DisGeNET (https://www.disgenet.org/) and others and carried out enrichment analysis using Enrichr. Various biological processes and pathways associated with viral protein interacting partners are known to involve in metabolic diseases. The molecular pathways underlying insulin resistance, insulin signaling and insulin secretion are not only involved in diabetes but also in CVD and obesity (associated with non-alcoholic fatty liver disease; NAFLD). Lipid metabolism/lipogenesis, fatty acid oxidation and inflammation are associated with MetS. Viral interacting host proteins are associated and enriched with terms like hyperglycemia, coronary artery disease, hypertensive disease related to CVD and liver diseases in DisGeNET. Association of viral interacting proteins with disease-relevant biological processes, pathways and disease-related terms suggests that altered host protein function following interaction with viral proteins might contribute to frequent occurrence and/or severity of COVID-19 in subjects with MetS. Such analysis not only provides a molecular basis of comorbidity but also incriminates host proteins in viral replication, growth and identifies possible drug targets for intervention.

The diagnostics of natural landscapes geoecological state in the course of the permafrost zone economic development should be based on the assessment of the destructive cryogenic processes formation. It is expedient to consider it in terms of landscapes resistance to cryogenic processes increase. The choice of lithocryogenic stability parameters is substantiated. These are: permafrost distribution over the area, its annual mean temperature and ice content, protective properties of the vegetation and etc. Cryogenic processes activation is estimated with regard to their area, development rate and attenuation, natural landscapes damage, hazard for engineering facilities operation. The assessment procedure, and the way of improving expert numerical scores are shown. At Geography Faculty of Lomonosov Moscow State University, the authors have developed a number of the approved methods of making evaluation maps of different scales applying the methods of landscape indication, modern methods of space images decoding, methods of expert scores, the method statistical calculations and methods of spatial GIS-analysis. The technique of Atlas geo-information mapping on the landscape basis has been introduced in seven atlases at scales from 1:3 000 000 to 1:20 000 000. These maps are widely used in design and survey organizations and in education.

Here we are concerning with two problems of a coupled system of random and stochastic nonlinear differential equations with two coupled systems of nonlinear nonlocal random and stochastic integral conditions. The existence of solutions will be studied. The sufficient condition for the uniqueness of the solution will be given. The continuous dependence of the unique solution on the nonlocal conditions will be proved.

Congestion pricing has received lots of attention in the scientific discussion. Congestion pricing means that the operator increases prices at the time of congestion and the traffic demand is expected to decrease. In a certain sense, shadow prices are an optimal way of congestion pricing: users are charged shadow prices, i.e., the expectations of future losses because of blocked connections. The shadow prices can be calculated exactly from Howard’s equation, but this method is difficult. The paper presents simple approximations to the solution of Howard’s equation and a way to derive more exact approximations. If users do not react by lowering their demand, they will receive higher bills to pay. Many users do not react to increased prices but would want to know how the congestion pricing mechanism affects the bills. The distribution of the price of a connection follows from knowing the shadow prices and the probability of a congestion state. There is another interesting distribution. The network produces profit to the operator, or equivalently, blocked connections produce a cost to the operator. The average cost rate can be calculated from Howard’s equation, but the costs have some distribution. The distribution gives the risk that the actual costs exceed the average costs, and the operator should include this risk to the prices. The main result of this paper shows how to calculate the distribution of the costs in the future for congestion pricing by shadow prices and for congestion pricing with a more simple pricing scheme that produces the same average costs.

It is widely accepted that the post-harvest processes of coffee are key factors in determining the final quality of the product. In the department of Cauca in Colombia, this stage is carried out empirically by the farmers of the region, using old methods that do not assure consistent quality. We propose a study to determine the best conditions of temperature and time in post-harvest for the coffee produced in the region. For this purpose, we carried the fermentation and honey process out on different samples of coffee of the Coffea Arabica species of the Castillo variety. Subsequently, the quality of the samples was determined through sensory evaluation by experts. Finally, descriptive statistical techniques applied to the resulting data, and component and hierarchical cluster analysis to find similarities between the samples. The results suggest that the honey process gets better evaluations in the cup profile over any fermentation condition.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used to define the building blocks to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

Currently, the use of numerical models for reproducing the landscape evolution of a river basin is part of the day-by-day research activities of fluvial engineers and geomorphologists. However, despite landscape modelling is based on a rather long tradition, and scientists and practitioners are trying to schematize the processes involved in the evolution of a landscape since decades, there is still the need for improving both the knowledge of the physical mechanisms and their numerical coding. The present review focuses on the first aspect, discussing the main components of a landscape evolution model and their more common schematizations, presenting possible open questions to be addressed towards an improvement of the reliability of such kind of models in describing the fluvial geomorphology.

Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one. Recent advances in big data provide an opportunity to characterize the commonly observed patterns of nature. Those observed regularities set the challenge of understanding the mechanistic processes that generate common patterns. This article illustrates the problem with the recent big data analysis of collective memory. Collective memory follows a simple biexponential pattern of decay over time. An initial rapid decay is followed by a slower, longer lasting decay. Candia et al. successfully fit a two stage model of mechanistic process to that pattern. Although that fit is useful, this article emphasizes the need, in big data analyses, to consider a broad set of alternative causal explanations. In this case, the method of signal frequency analysis yields several simple alternative models that generate exactly the same observed pattern of collective memory decay. This article concludes that the full potential of big data will require better methods for developing alternative, empirically testable causal models.

Many species of plants are found in regions to which they are alien. Their global distributions are characterised by a family of exponential functions of the kind that arise in elementary statistical mechanics (an example in ecology is MacArthur's broken stick). We show here that all these functions are quantitatively reproduced by a model containing a single parameter – some global resource partitioned at random on the two axes of species number and site number. A dynamical model generating this equilibrium is a two fold stochastic process and suggests a curious and interesting biological interpretation in terms of niche structures fluctuating with time and productivity; with sites and species highly idiosyncratic. Idiosyncrasy implies that attempts to identify a priori those species likely to become naturalized are unlikely to be successful. Although this paper is primarily concerned with a particular problem in population biology, the two fold stochastic process may be of more general interest.

Conventional academic debate on sustainability and transformation has begun to be disputed by researchers, policymakers and other stakeholders from the private sector. The complexity of linking sustainability with transformation necessitates the development of new ways of understanding and explaining actors, processes, issues, structures, and outcomes related to transformation to sustainability (T2S). A persistent problem in understanding and managing the T2S is the implied notion of a monolithic transformation to achieve sustainability. This notion underestimates the diversity of actors as well as the historical contexts of transformations. At the same time, achieving T2S is highly dependent on technical solutions as well as policies that can stir needed behavioural change, whereas these technical solutions and policies are not always socially acceptable or politically implementable. Therefore, achieving T2S also calls for evaluating normative foundations of policies and actions needed to advance T2S. This article argues that T2S is composed of different phases, each of which having a different set of actors, resources and audience. This article explains how collective decision can be achieved through multiple levels and types of negotiations that occur in an orchestrated manner. To understand this, this article introduces a theoretical model as an analytical framework to structure how T2S unfolds. This model uses insights from negotiation studies to focus on the actors’ perspectives on T2S. It proposes the division of the transformation process into phases – entry point, learning, sequencing, disrupting and fortifying. Using the Germany’s energy transition (Energiewende), each of these phases is analysed to determine the “quality” of cooperation that can help fulfil the tasks required to master the so-called “cognitive games” of T2S (ripeness game, power game, bargaining game, policy game, scaling game). Moreover, insights are presented to explain how the designated milestone can be achieved that indicates the advancing to the next phase and eventually entrench the transformation process. The lessons and findings resulting from the analysis of the phases of T2S suggest that there is also the need to revisit underlying assumptions on negotiations, sustainability and transformation.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

: Efficient or at least rational use of energy resources is a problem that occupies an overwhelming number of people in modern society – from major scientists and politicians to journalists and schoolchildren who have a poor understanding of the basic laws of physics.(the knowledge of politicians in this area is also sometimes doubtful). At the same time, the main focus of the discussions is on the resource base, possible waste and consequences. According to these features, they want to see the future of energy. However, the division of "energetik" follows different principles. On the one hand, these are thermal, nuclear and hydropower sources. On the other – wind and solar energy. Obviously, this separation is not only related to the renewable resources or the absence of consequences for the environment and humans. If we do not touch on geopolitical problems. which are not the subject of this article, it is obvious that the main dividing feature of these types of energy production will be their controllability. The theory of automatic control (TAU) is one of the most complex modern sciences that has been developing for many years to solve precisely the problems of energy Now, when considering energy problems, this science is most often not remembered. Maybe because the most active participants in the discussions don't know much about it? Or is this situation beneficial to someone? How efficiency and controllability are related to some problems with controllability in the "household" energy sector, which concerns almost every inhabitant of the planet and in the global energy sector, this article is devoted to

This study investigated the efficacy of advanced oxidation process (AOP) for the reduction of pollution loads in mixed agro-food industrial wastewaters (dairy and slaughterhouse) in Nablus city, Palestine. Bench-scale Jar tests using an advanced oxidation process (AOP) were performed as a pretreatment stage. Initial results on direct applications of Fenton’s process on mixed agro-food wastewater (COD: 15400-18200 mg/l) were unsatisfactory. Hence, the performance of the Fenton process was applied on three mixed wastewater samples with different pre-treatment trials: (A) coagulant (FeCl3.6H2O) addition, (B) settling (2h), and use of flocculent (lime Ca(OH)2) in sample (C). Preceded with lime, Fenton`s process (Sample C) was most effective in the removal of organic carbon and nitrogen (89% COD; 80% TKN). The removal efficiency in inorganic loads (91% TSS; 62% TS) were achieved under H2O2/COD (w/w ratio 2:1), H2O2/Fe+2 (w/w ratio 10:1) and acidic conditions (pH = 3). The adoption of AOP technology by agro-food industries could ensure compliance with municipal by-laws and acquire connection permits to sewerage networks.

In large industrial plants, the trend is to move the position of the human to the area of service and diagnostics, in medium and smaller plants the trend is different and is mainly directed to the sphere of close cooperation between the human and the robotic system. This paper deals with the implementation of human-cooperating robots in manufacturing processes. The analytical part of the paper deals with theoretical knowledge, taking into account the latest trends in the subject area. To achieve a successful implementation of cobots based on the above-mentioned operating systems in manufacturing systems, it is necessary to pay attention specifically to the interfacing of the robotic operating system with the control systems of the manufacturing systems at the process level of the enterprise. Within the practical part, an algorithm with well-defined steps towards the successful implementation of cobots in holonic manufacturing processes is proposed. By setting up an experimental workstation in the laboratory, the proposed procedures are verified at the end of the paper. In the context of the implementation of collaborative robots in enterprises, the paper also discusses cloud computing. The creation of a design for the implementation of a collaborative robot with a human represents the missing link in the whole chain of commercial applications of the latest trends from the field of robotic systems in the industrial sphere.

The basis of this project is a health literacy cohort study (LISA) being developed in the county of Leiria. The study aims to reach and monitor the adult population of the county over time. Counting with a multidisciplinary team, the study stakeholders asked the designers to develop the study’s brand so it could be more advertisable to the population, turning the communication between the stakeholders and the public easier. The development of the brand identity and elements of communication used some graphic design approaches. These approaches were reinforced by the opinions given by the interviewed sample of the population, that answered some of the study’s questions. The interviews held by designers allowed the team to receive information about what was expected the study to be, and how and where to better communicate the LISA’s Study. Trough the aforementioned process resulted in the current finalized outcomes of the study’s brand and communication materials. Both printable and digital communication elements (i.e., roll-up, id cards, flyers, QR Code, and website) were developed and disseminated according to the brand's strategy. Concluding, the communication elements created are seen as the first phase of this project, leaving a second phase related to the design and structuration of the Study to be developed, and published in future articles.

Natural systems of units $\{U_i\}$ need to be overhauled to include the dimensionless coupling constants $\{\upalpha_{U_i}\}$ of the natural forces. Otherwise, they cannot quantify all forces in a unified manner. Thus, each force must furnish a system with at least one dimensional and one dimensionless constant. We revisit three natural systems of units (atomic, cosmological, and Planck). The Planck system is easier to rectify, and we do so in this work. The atomic system discounts $\{ G, \upalpha_G \}$, thus it cannot account for gravitation. The cosmological system discounts $\{ \slashed{h}, \upalpha_\slashed{h} \}$, thus it cannot account for quantum physics. Here, the symbols have their usual meanings; in particular, $\upalpha_G$ is the gravitational coupling constant and $\upalpha_\slashed{h}$ is the fine-structure constant. The speed of light $c$ and the impedance of free space $Z_0$ are resistive properties of the vacuum, thus they must be present in all systems of units. The upgraded Planck system $${\rm UPS} \,:=\,\{ c, Z_0, G, \upalpha_G, \slashed{h}, \upalpha_\slashed{h}, \,\dots\,\!\},$$ describes all physical scales in the universe---it is nature's system of units. As such, it reveals a number of properties, most of which have been encountered previously in seemingly disjoint parts of physics, and some of which have been designated as mere coincidences. Based on the UPS results, that relate (sub)atomic scales to the Planck scale and the fine-structure constant to the Higgs field, we can state with confidence that no observed/measured physical properties are coincidental in this universe.

To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, Hybrid Monte Carlo on Hilbert spaces [A. Beskos, F.J. Pinski, J.-M. Sanz-Serna and A.M. Stuart, Stoch. Proc. Applic. 121, 2201 - 2230 (2011); doi:10.1016/j.spa.2011.06.003] that provides finite-dimensional approximations of measures π which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method which is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous method, the phase space π was augmented with Brownian bridges. With the new choice for the mass operator, π is augmented with Ornstein-Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the Metropolis-Hasting acceptance rate. This contrasts with the covariance of OU bridges which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally the Metropolis-Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier.

Boreal forests are one of the largest stores of carbon on Earth, and two-thirds of them are located in Siberia. Despite the fact that these forests have a significant influence on the global climate, they continue to remain understudied. Chernevaya taiga is a unique example of a highly productive Siberian boreal ecosystem. This type of forest is characterized by a series of unique ecological traits, the most notable of which are the gigantism of the perennial herbaceous plants and bushes, complete lack of moss cover on soil surface, and the type of soil it grows on, notable for its particularly high rate of decomposition of vegetative remains and low humic acid content. Abundant rainfall actively washes out nutrients from the top layers of the soil, but its fertility level remains very high. In fact, based on the existing data, it is twice as high as that of fertilized agricultural lands. In some ways the conditions within this type of forest closely resemble those observed in tropical rainforests. Microbiota associated with soil and plants represent an integral part of an interconnected system and contribute significantly to productive processes in soils. Its impacts on the environment require further study, since it could lead to discoveries that will help improve soil fertility without harming the natural environment.

In this study, TiO2 nanostructured coatings on Ti-6A-4V alloys were fabricated by two methods: H2O2 oxidation and RF sputtering. In the annealing temperature range of 25 C - 500 C, there were the peaks at 35, 37, 40 and 52 corresponding to {100}, {002}, {101} and {102} crystal planes of hcp structure of α-Ti. At the annealing temperature of 600 C, there was the presence of peaks corresponding to crystal planes of anatase and rutile TiO2. The relative intensities of anatase and rutile phases of the sample fabricated by RF sputtering were 3.62 and 10.25 %, respectively; while those of the sample fabricated by H2O2 oxidation were 21.27 and 3.20 %, respectively (The relative intensity of α-Ti phase was 100 %). The results investigated the peak shift of α-Ti phase in TiO2/Ti-6Al-4V nanostructured coatings fabricated by the two methods which was reasonably explained from the difference in the thermal expansion coefficients of Ti alloy and TiO2 components, as well as the difference in the ratio of anatase to rutile phases.

Multi-task Gaussian processes (MTGPs) are a powerful approach for modeling structured dependencies among multiple tasks. Researchers on MTGPs have contributed to enhance this approach in various ways. Current MTGP methods, however, cannot model nonlinear task correlations in a general way. In this paper we address this problem. We focus on spectral mixture (SM) based kernels and propose an enhancement of this type of kernels, called multi-task generalized convolution spectral mixture (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and mixtures dependency, including time and phase delay, not only between different tasks but also within a task at the spectral mixture level. Each task in MT-GCSM has its own generalized convolution spectral mixture kernel (GCSM) with a different number of convolution structures and all spectral mixtures from different tasks are dependent. Furthermore, the proposed kernel uses inner and outer full cross convolution between base spectral mixtures, so that the base spectral mixtures in the tasks are not necessarily aligned. Extensive experiments on synthetic and real-life datasets illustrate the difference between MT-GCSM and other kernels as well as the practical effectiveness of MT-GCSM.

Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations have been used to accelerate and optimize bioprocess development. As implementation of fed-batch conditions, multiple options of process control and sample analysis are possible, these systems represent valuable screening tools for large-scale production. However, the dynamic behavior of cultivations has not yet been considered regarding data evaluation and decision making during high-throughput screening in mini-bioreactors. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed in 48 parallel fed-batch cultivations regarding 16 experimental conditions. Automated parallel process control, frequent sampling and analysis were implemented. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable and suitable for automatized process development reducing the experimental times and costs.

We proposed in this work the introduction of a new vision of stochastic processes through geometry induced by dilation. The dilation matrices of a given process are obtained by a composition of rotation matrices built in with respect to partial correlation coefficients. Particularly interesting is the fact that the obtention of dilation matrices is regardless of the stationarity of the underlying process. When the process is stationary, only one dilation matrix is obtained and it corresponds therefore to Naimark dilation. When the process is nonstationary, a set of dilation matrices is obtained. They correspond to Kolmogorov decomposition. In this work, the nonstationary class of periodically correlated processes was of interest. The underlying periodicity of correlation coefficients is then transmitted to the set of dilation matrices. Because this set lives on the Lie group of rotation matrices, we can see them as points of a closed curve on the Lie group. Geometrical aspects can then be investigated through the shape of the obtained curves, and to give a complete insight into the space of curves, a metric and the derived geodesic equations are provided. The general results are adapted to the more specific case where the base manifold is the Lie group of rotation matrices, and because the metric in the space of curve naturally extends to the space of shapes, this enables a comparison between curves’ shapes and allows then the classification of processes’ measure.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

Plant reproduction is a very important process on Earth from the perspective of biodiversity, biomass gain and crop productivity. It is therefore important to understand sex determination process and many researchers are investigating the molecular basis of this phenomenon. However, information on the influence of transcription factors (TFs) on this process is limited, although cucumber is a model plant in this regard. In the present study, based on RNA-seq data for differential gene expression (DEG) analyses, we aimed to investigate the regulatory TFs that may influence the metabolomic processes in the shoot apex containing the forming flower buds. Therefore, a robust TF database was established for the B10v3 cucumber genome. Sex-specific interactome network maps were generated, indicating the regulatory TFs by their effects on DEGs and further on processes leading to the formation of different sex flowers. The network analysis identified major families of regulatory TFs. The most abundant families were: MYB, AP2/ERF, NAC and bZIP, and those with the greatest impact on developmental processes were identified, namely the AP/ERF family, followed by DOF, MYB, MADS and others. Thus, the central nodes and key regulators in the networks were identified with respect to male, female and hermaphrodite. Here, we proposed the first model of the regulatory network of TFs that influences the metabolism of sex development in cucumber. These findings may help to understand the molecular genetics and functional mechanisms underlying sex determination processes.

A probabilistic description is essential for understanding the dynamics of stochastic systems far from equilibrium. To compare different Probability Density Functions (PDFs), it is extremely useful to quantify the difference among different PDFs by assigning an appropriate metric to probability such that the distance increases with the difference between the two PDFs. This metric structure then provides a key link between stochastic processes and geometry. For a non-equilibrium process, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory of the system quantifies the total number of different states that the system undergoes in time and is called the information length. By using this concept, we investigate classical and quantum systems and demonstrate the utility of the information length as a unique Lagrangian diagnostic to quantify the information change as a system continuously evolves in time and to map out attractor structure. We further elucidate quantum effects (uncertainty relation) and the dual role of the width of PDF in quantum systems.

Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.

The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher Information Matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs since the FIM only accounts for the linear variation of the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, where model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model curvature. We demonstrated the advantages of the MOO MBDOE over existing FIM-based and other curvature-based MBDOEs in an application to the kinetic modeling of fed-batch fermentation of Baker's yeast.

The spectra currently emerging from modern ground- and space-based astronomical instruments are of exceptionally high quality and resolution. To meaningfully analyse these spectra researchers utilise complex modelling codes to replicate the observations. The main inputs to these codes are atomic data such as excitation and photoionisation cross sections as well as radiative transition probabilities, energy levels and line strengths. In this publication the current capabilities of the numerical methods and computer packages used in the generation of these data are discussed. Particular emphasis is given to Fe-peak species and the heavy systems of tungsten and molybdenum. Some of the results presented to highlight certain issues and/or advances have already been published in the literature, while other sections present, for the first time, new recently evaluated atomic data.

In recent years Pd-containing catalytic systems for tandem processes has drawn special attention due to the enhanced catalytic properties and possibility to perform several reactions without the necessity to separate the intermediates. In this review, recent progress in Pd-catalyzed tandem processes is considered. Three types of catalytic systems were described: homogeneous catalysts (including immobilized Pd complexes); heterogeneous catalysts supported on oxides, MOFs, COFs, etc. with particular attention to the supports containing acid/base sites; metal-enzyme catalysts for chemoenzymatic tandem processes applied in fine organic synthesis and biotechnology. For homogeneous Pd-catalyzed reactions different tandem reactions were considered, i.e. cross-coupling, cyclization, carbonylation, isomerization, alkylation, arylation, etc.

Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).

Small boat fishers are often the lifeblood of remote coastal communities in Iceland, contributing to employment, jobs and economic prosperity. This study conducts exploratory but highly practical research into the efficiency of onboard catch handling practices by Icelandic small boat fishers using fish handling tools called automatic jigging machines. Using applied research methods, this study researches whether standardisation of operations could be applied to make catch handling practices on small boats more time and resource efficient, leading to reduced waste, a consistently higher quality product, and potential increases in economic efficiency and sustainability. Thematic analysis, value stream mapping, flow analysis and Kaizen ideology were adopted to identify gaps and continuous improvement opportunities to standardise processes, leading to exemplary performance. Eight core recommendations are identified, seven of which are classed as straight-forward, ‘do now’ measures according to a Kaizen Priority Matrix. These include human and technological interventions in the areas of safety, organisational arrangements, hygiene, fish handling and bleeding, and cooling. Questionnaire responses reveal four main themes of importance to the sub-sector: changes in recent decades; the importance of small boat fishers; education and improvement; and the particularities of the sub-industry. The latter include the perception of a ‘race against time’ to land the catch, an issue that sometimes contributes to sub-optimal catch handling practices. Although this study has decidedly practical connotations for small boat fishers, its outcomes are also likely to be of interest to academics, particularly those focused on the organisational management of natural resources and general applications of the project management methodology and applied research methods as a means of solving practical problems in everyday life.

Blatter radicals 1-(3,4-difluorophenyl)- (1a) and 1-(2,4-difluorophenyl)-3-phenyl-1,4-dihydrobenzo[e][1,2,4]triazin-4-yl (1b) were prepared in good yields through oxidation of the corresponding amidrazones using MnO2 in dry CH2Cl2. Cyclic voltammetry showed that both radicals are oxidized and reduced chemically and electrochemically reversibly in accordance with –1/0 and 0/+1 processes. EPR spectroscopy indicated that spin density is mainly delocalized on the triazinyl moiety of the heterocycle. Structure of all paramagnets was unambiguously confirmed by single-crystal X-ray diffraction, and two different 1D chains of alternating radicals were identified. 3,4-Difluorophenyl-derivatives 1a are packed into columns composed of two kinds of alternating centrosymmetric dimers, having comparatively short intermolecular distances. In crystals of 2,4-difluorophenyl-derivative 1b, the parallel arrangement of bicyclic moieties and phenyl rings favors the formation of 1D regular chains wherein the radicals are related by translation parallel to the crystallographic stacking axis. Magnetic susceptibility measurements in the 2–300 K region showed that in crystals of the radicals, strong antiferromagnetic interactions are dominant. Subsequent fitting of the dependence of T on T with 12-membered looped uniform stacks gave the following best-fit parameters: for 1a, g = 2.01 ± 0.05, J/kB = –292 ± 10 K; for 1b, g = 2.04 ± 0.1J/kB = –222 ± 17 K. For comparison, in a nonfluorinated related radical, there are only very weak intermolecular interactions along the columns (J/kB = –2.2 ± 0.2 K). These results illustrate the magnitude of the influence of the fluorine atoms introduced into Blatter radicals on their structure and magnetic properties.

The Covid-19 has hit Italy in February 2020 after its outbreak in China at the beginning of January. But why Italy first among Western countries? What are the conditions that made Italy more vulnerable and the first target of such disease? What characteristics and what diffusion patterns could be highlighted and hypothesized, from the outbreak to the end of March 2020, after containment measures - including a national lock down – were introduced? In this paper we try to provide some answers to these questions, analyzing the issue from the medical, geographical and planning points of view. In particular, we started from a hypothesis of very similar economic, geographical, climatic and environmental conditions of the areas of Wuhan – in Hubei Province in China, the outbreak of the epidemics – and the Po Valley area – in Italy – where most cases and deaths were registered. Adopting an ecological approach, we compared the spatial distribution and pattern of Covid-19-related mortality in Italy with several geographical, environmental and socio-economic variables at Provincial level, analyzing them by means of spatial analytical techniques as LISA – Local Indicators of Spatial Association. Possible evidence relating Covid-19 cases and Nitrogen-related pollutants and land take arise, particularly in the Po Valley area.

The water quality index (WQI) and irrigational index for groundwater were studied in the northeastern section of the Manipur Valley in northeast India. Water samples were collected in 2022 during the pre-monsoon season. To compute the water quality index for drinking water, the basic chemical parameters of total hardness, pH, electrical conductivity, total dissolved solids, Ca2+, Na+, Fe, Mg2+, Mn, Cl-, and HCO3, were used. The assessment of bacteriological quality is also done, which is crucial for overall water quality evaluation, alongside physical and chemical analyze. For determining irrigation suitability, irrigational indices such as sodium absorption ratio, sodium percentage, and magnesium hazard were calculated. WQI, %Na+, SAR, magnesium hazard, permeability Index and total hardness indicate that most water samples are harmless for irrigation and drinking purpose. They have affirmative relationships indicating that these characteristics are interdependent. Approximately 25% of the Piedmont zone groundwater is found to be unfit for agricultural and drinking usage. The encrustation of gypsum, halite, and evaporation into the Disang shares accelerate the dissolution of ions in Piedmont water, resulting in quality degradation. According to Gibbs plots, Durov Scatter Plot, and Hill Piper Trilinear Diagram of water dominated the rock-weathering process, while hydro chemical facies progressed from the beginning to the intermediate stage of Chadha's graphical representation illustrates the progression of hydro chemical processes in surface water. Therefore, proper integrated water resource management and development are required for effective water resource utilization, particularly around the Piedmont zone. To prevent a water crisis, groundwater resources must be managed sustainably. The current study also concentrated on a bibliometric examination of groundwater management and access to gauge the state of the field.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

We have detected a large Nernst effect in the CDW state of the multiband organic metal α−(BEDT−TT)2KHg(SCN)4. We find that there exists a significant gradient of charge relaxation processes that can generate a sizeable contribution adding especially to the transverse thermoelectric signal. Apart from the phonon drag, the energy relaxation processes governing the electron-phonon interactions and the momentum relaxation processes governing the charge mobility of the q1D group of carriers have a significant role in observing the large Nernst signal in the CDW state. The emphasised momentum relaxation dynamics in the low field CDW state is a clear indicator of a significant carrier mobility that can lead to the largest Nernst signal in this state. The momentum relaxation is reduced with increasing angle and magnetic field, i.e., in the high field CDW state which is reflected in the smaller Nernst effect amplitude. In this case only the phonon drag and electron-phonon interactions are contributing to the transverse thermoelectric signal. Our findings advance and change the previous observations on the complex properties of this organic metal.

This communication investigates possible anomalies in the lithosphere atmosphere and ionosphere on the occasion of the ML=3.3 earthquake that occurred on 1st January 2023 close to Guidonia Montecelio (Rome, Italy). This earthquake followed another event on 23 December 2022 of magnitude ML=3.1 with a very close epicentre (distance less than 1km). Seismological investigations clearly show an acceleration of seismicity in the last six months in the area. Two solutions of fitting time to failure power law on the Cumulative Benioff strain curve are the more likelihood: the ML3.3 of 1 January is the mainshock of seismic sequence or incoming earthquakes of a magnitude of about 4.1 provides a slightly better fit of the seismic data. Further investigation are necessary to assess if the accumulated stress have been totally released or not. No atmospheric anomalies related to this seismic activity have been identified even if some SO2 emissions could come from tectonic or volcanic sources in the South-Tyrrhenian Sea. Swarm satellites' magnetic data shows an anomalous track on 16 December 2022, which is temporally compatible with the seismic acceleration but other sources for the anomalous signal are also possible.

Background: Kidney damage is considered to be one of the risk factors for severity and mortality among COVID-19 patients. However, molecular nature of such observations remains unknown. Hypothesis: Altered gene expressions due to infection and in chronic kidney disease could explain severity in COVID-19 with kidney defects. Methods: We collected gene expression data from publicly available resources Gene Expression Omnibus CKD, Enrichr for deregulated genes in SARS-CoV infected cells in vitro, DisGeNET and others and carried out enrichment analysis using Enrichr. Result: Number of common genes altered in chronic kidney disease (CKD) and SARS-CoV infected cells was 2834. Enrichment analysis revealed that biological processes related viral life cycle and growth, cytokines, immunity, interferon, inflammation, apoptosis, autophagy, oxidative stress and others were significantly enriched with common deregulated genes. Similarly, significantly enriched pathways related to viral and bacterial infections, immunity and inflammation, cell cycle, ubiquitin mediated proteolysis, signaling pathways like Relaxin signaling pathway, mTOR signaling pathway, IL-17 signaling pathway, NF-kappa B signaling pathway were enriched with the common deregulated genes. These processes and pathways are known to be related to kidney damage. DisGeNET terms enriched include and related to Dengue fever, chronic Hepatitis, measles, retroviridae infections, respiratory syncytial virus Infections and many others. Kidney dysfunction related terms ischemia of kidney, renal fibrosis and diabetic nephropathy. Conclusion: Common deregulated genes in SARS-CoV infected cells and chronic kidney disease, as well as their enrichment with molecular processes and pathways relevant for viral pathogenesis and renal dysfunctions, could explain the severity of COVID-19 with kidney disease. This observation not only provides molecular relation of severity in COVID-19 with renal dysfunctions but might also help in the management and treatment targets for these cases.

The commonly used words ‘transformation’ and ‘transition’ tend to lose their edge when used for any significant change process as is rather often the case. Partners and wider stakeholders in initiatives related to ‘sustainability transitions’ therefore often entertain different perspectives on what the strategic orientation of an initiative is or is meant to be. Common planning and design processes such as situation analysis and theories of change, however, often do not sufficiently cater to this dynamic. As a result, different actors may be pulling the initiative in different directions, undermining the overall partnership efforts. In this short contribution a strategic scoping canvas and an associated facilitation process are presented as a way of addressing such situations. Illustrations are provided of initial application in three cases related to food system transitions in Peru, Ethiopia, and Bangladesh, exploring the connectivity with approaches commonly used in the context of system transformations, including the Multi-Level Perspective on sustainability transitions, the Leverage Points approach, Capability Approach, and the theory of Large System Change. We conclude that the canvas and associated facilitation approach has proved useful in different contexts, offering opportunities for complementing existing methodologies, and potentially enhancing their efficacy in facilitated multi-stakeholder processes.

The present paper deals with research of maintenance strategy and maintenance process improvement procedure to increase the efficiency and quality of the production system. It includes the design of maintenance performance indicators relevant to today's manufacturing systems to track improvements in the sustainable development of the manufacturing system. Describes the basic principles of maintenance strategy with linkages to the manufacturing system and selecting the right strategy for the organization. The paper emphasizes maintenance management auditing and quality implementation in maintenance. Further, there is the process of changing the maintenance strategy described. This process includes reassessing the criticality of machines and equipment and their design units, then resource and capacity planning and input for maintenance management. It is described the impact of changing the maintenance management organization and maintenance capacity in the light of the change of strategy and the evaluation of maintenance choice alternatives, together with an analysis of the optimization procedure. The paper concludes by presenting the achieved impact of maintenance strategy change on the production efficiency and production quality of the selected enterprise.

Red mud (RM) is a residue obtained from the production of alumina. It contains a high concentration of metal oxides and waste concentrated sodium hydroxide solution (pH = 13). To increase the value of RM, an environmentally friendly process of transesterification using waste cooking oil (WCO), MeOH and concentrated sodium hydroxide solution (CSHS) from aluminum production was proposed. Triglycerides of WCO reacted with MeOH at 60 oC to yield mixtures of fatty acid methyl esters (FAMEs) in the presence of 2.03% (w/w) CSHS/WCO using the CSHS (0.204 mol L-1, predetermined by potentiometric titration) from aluminum production by the Bayer process or with the addition of 0.68% (w/w) fabric softener (3% w/w cetyltrimethylammonium chloride in solution) as a phase transfer (PTA) agent. The addition of PTA to the catalyst resulted in a better yield of the products (greater than 98% yield). A simplified mechanism is presented to account for the experimental results.

We report the results of an analysis of the functional capabilities of the KTP crystal and its isomorphs for nonlinear-optical frequency conversion of all types of interaction in the transparency range of the crystal. The possibility of implementing angle-, wavelength- (frequency-) and temperature-noncritical phase matching is shown.

The capacity and the mechanism of the adsorption of aqueous barium (Ba), cobalt (Co), strontium (Sr) and zinc (Zn) by Ecuadorian (NatAllo) and synthetic (SynAllo-1 and SynAllo-2) allophanes were studied as a function of contact time, pH and metal ion concentration using kinetic and equilibrium experiments. The mineralogy, nano-structure and chemical composition of the allophanes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and specific surface area analyses. The evolution of adsorption fitted to a pseudo-first-order reaction kinetics, where equilibrium between aqueous metal ions and allophane was reached within < 10 min. The metal ion removal efficiencies varied from 0.7 to 99.7 % at pH 4.0 to 8.5. At equilibrium, the adsorption behavior is better described by the Langmuir model than by the Dubinin-Radushkevich model, yielding sorption capacities of 10.6, 17.2 and 38.6 mg/g for Ba^(2+), 12.4, 19.3 and 29.0 mg/g for HCoO_2^-, 7.2, 15.9 and 34.4 mg/g for Sr^(2+) and 20.9, 26.9 and 36.9 mg/g for Zn^(2+), respectively, by NatAllo, SynAllo-2 and SynAllo-1. The uptake mechanism is based on a physical adsorption process. Allophane holds great potential to remove aqueous metal ions and could be used instead of zeolites, montmorillonite, carbonates and phosphates for wastewater treatment.

Extrusion-based additive manufacturing (AM) technologies (i.e., fused filament fabrication, FFF) mostly produce thermoplastic parts. However, producing metallic or ceramic parts by FFF is also a sintered-based AM process. FFF for metallic parts can be divided into five steps: (1) raw materials selection and feedstock mixture (including palletization), (2) filament production (extrusion), (3) production of AM components using the filament extrusion process, (4) debinding, and (5) sintering. These steps are interrelated, where the parameters interact with the other ones and have a key role in the integrity and quality of the final metallic parts. FFF can produce high-accuracy and complex metallic parts, potentially revolutionizing the manufacturing industry and taking AM components to a new level. In the FFF technology for metallic materials, material compatibility, production quality, and cost-effectiveness are the challenges to overcome to make it more competitive compared to other AM technologies, like the laser processes. This review provides a comprehensive overview of the recent developments in FFF for metallic materials, including the metals and binders used, the challenges faced, potential applications, and the impact of FFF on the manufacturing (prototyping and end parts), design freedom, customization, sustainability, supply chain, among others.

Steady-state one-dimensional flows of five-component air behind a normal shock wave are considered with one-temperature model. A mathematical model is formulated to describe the relaxation of a five-component air mixture with a one-temperature non-equilibrium approximation. A numerical study of non-equilibrium flows of a reacting five-component air mixture behind shock waves at different heights and velocities of free flow is performed. The contribution of different types of reactions to the overall relaxation of the mixture is discussed, and the distributions of macro-parameters of the flow behind the shock wave front are calculated. The lengths of the relaxation zones behind the shock wave front are compared at different initial conditions.

The characteristics of the eddy mass transport are estimated depending on the values of the parameters of a large-scale flow that forms under the conditions of the shelf seas in the Arctic. For this, the results of numerical simulation of the Kara Sea with a horizontal resolution permitting the development of mesoscale eddies are used. The parameters resulting from numerical experiment are considered as a statistical sample and are analyzed using methods of sensitivity study and clustering of sample elements. Functional dependencies are obtained that are closest to the simulated distributions of quantities. These expressions make it possible, within the framework of large-scale models, to evaluate the characteristics of the cross-isobatic eddy mass transport in the diffusion approximation with a counter-gradient flux. Numerical experiments using the SibCIOM model showed that areas along the Fram branch of the Atlantic waters trajectory in the Arctic as well as the shelf of the East Siberian and Laptev seas with adjacent deep water areas are most sensitive to proposed parameterization of eddy exchanges. Accounting for counter-gradient eddy fluxes turned out to be less important.

The physicalchemistry properties and crystal structure of were nano-ZrO₂+3mol.%Y₂O₃ determined. The kinetics of the formation of H₂ as a result of the decomposition of H₂O on the surface of nano-ZrO₂+3mol.%Y₂O₃ was studied. Effects of adsorption and desorption process on ZrO₂+3 mol.%Y₂O₃ nanoparticles were studied at different (T=400÷1000⁰C) temperature. The study of H₂ in thermal processes at nano-ZrO₂+3 mol.%Y₂O₃ system increased. Such an increase in H₂ generation in comparison with a pure H₂O as thermal processes had formedactive centers for H₂O decomposition on the surface of the catalyst at the expense of δ-electrons emitted on the surface of nano-ZrO₂+3 mol.%Y₂O₃. This showed that the dimensions of the studied nanoscale particles systems are comparable to the free running distance of energy carriers generated by of nano-ZrO₂+3 mol.%Y₂O₃ as a result of thermal processes. These results are promising for hydrogen generation by waer spliting in near future.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

Creative thinking is increasingly recognised as an essential ability that should be part of school curricula. Given the move towards online learning and assessment, we investigate whether mathematical creativity can be assessed at-scale in the Numbers game, an arithmetic game in Math Garden, a popular online math practice platform. In the Numbers game, a generalisation of the 24 Game, children are asked to figure out how to compute a target number using basic arithmetic operations and a given set of numbers. We argue that creative thinking is required when the search space is complex, and propose that the base-pattern, i.e., the sequence of the operations needed to solve a Numbers game item, indicates search space complexity. We then demonstrate that items with disordered base-patterns are more likely to require mathematical creativity to figure out. Specifically, our analysis shows that for items with only one solution sequence, those with disordered base-patterns are more difficult and take longer to solve compared to items with ordered base-patterns. For items where multiple solution sequences are possible, nine times out of ten children choose ordered over disordered base-patterns. We conclude that the Numbers game has potential for assessing mathematical creativity at-scale.

This paper introduces a closed-loop control strategy for maintaining consistency of liquid temperature in commercial Drop on Demand (DoD) inkjet printing. No additional sensors or additional actuators are installed in the printhead while achieving the consistency in liquid temperature. To this end, this paper presents a novel in situ sensing-actuation policy at every individual liquid-nozzle, where the jetting mechanism has three distinct roles. It is used for jetting liquid droplet onto the print media based on the print-job. It is used as a soft sensor to estimate the real-time liquid temperature of the jetting nozzle. While not jetting liquid, it is used as a heating actuator to minimize the gradient of liquid temperature among nozzles. The soft sensing based in situ controller is implemented in an experimentally validated digital twin that models the thermo-fluidic processes of the printhead. The digital twin is scalable and flexible to incorporate an arbitrary number of liquid-nozzles, making the control strategy applicable for future designs of the printhead.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.