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Executive Summary: The EC Directive 2013/59/Euratom states in article 56 that exposures of target volumes in nuclear medicine treatments shall be individually planned and their delivery appropriately verified. The Directive also mentions that medical physics experts should always be appropriately involved in those treatments. Although it is obvious that, in nuclear medicine practice, every nuclear medicine physician and physicist should follow national rules and legislation, the EANM considered it necessary to provide guidance on how to interpret the Directive statements for nuclear medicine treatments. For this purpose, the EANM proposes to distinguish three levels in compliance to the optimization principle in the directive, inspired by the indication of levels in prescribing, recording and reporting of absorbed doses after radiotherapy defined by the International Commission on Radiation Units and Measurements (ICRU): Most nuclear medicine treatments currently applied in Europe are standardized. The minimum requirement for those treatments is ICRU level 1 ("activity-based prescription and patient-averaged dosimetry"), which is defined by administering the activity within 10% of the intended activity, typically according to the package insert or to the respective EANM guidelines, followed by verification of the therapy delivery, if applicable. Non-standardized treatments are essentially those in developmental phase or approved radiopharmaceuticals being used off-label with significantly (> 25% more than in the label) higher activities. These treatments should comply with ICRU level 2 ("activity-based prescription and patient-specific dosimetry"), which implies recording and reporting of the absorbed dose to organs at risk and optionally the absorbed dose to treatment regions. The EANM strongly encourages to foster research that eventually leads to treatment planning according to ICRU level 3 ("dosimetry-guided patient-specific prescription and verification"), whenever possible and relevant. Evidence for superiority of therapy prescription on basis of patient-specific dosimetry has not been obtained. However, the authors believe that a better understanding of therapy dosimetry, i.e. how much and where the energy is delivered, and radiobiology, i.e. radiation-related processes in tissues, are keys to the long-term improvement of our treatments. [ABSTRACT FROM AUTHOR]

Various studies have been conducted in the fields of sustainable operations management (SOM), optimization, and wastewater treatment, yielding unsubstantiated recovery. In the context of Europe's climate neutrality vision, this paper reviews effective decarbonization strategies and proposes sustainable approaches to mitigate carbonization in various sectors such as buildings, energy, industry, and transportation and how these interlink with wastewater management. The study also explores the role of digitalization in decarbonization and reviews policies that can direct governments' actions towards a climate-neutral society. This paper presents a review of optimization approaches applied in the fields of science and technology, incorporating modern optimization techniques based on various peer-reviewed published research papers. It emphasizes non-conventional energy and distributed power-generating systems along with the deregulated and regulated environment. Additionally, this paper critically reviews the performance and capability of the micellar-enhanced ultrafiltration (MEUF) process in the treatment of dye wastewater. The review presents evidence of the simultaneous removal of co-existing pollutants and explores the feasibility and efficiency of biosurfactants instead of chemical surfactants. Lastly, the paper proposes a novel Firm–Regulator–Consumer-Technology Enablers/Facilitators interaction framework to study operations, decisions and interactive cooperation considering the relationships between the four agents through a comprehensive literature review of SOM. The proposed framework provides support for exploring future research opportunities and holistic sustainability initiatives. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of recent literature on European energy system modeling and analysis with special focus on grid development. Spanning the years from 2013 to 2023, we analyze 59 selected articles, organizing them by geographical scope, grid expansion strategies, research focus, and methodology. Additionally, we provide an overview of established and recurring frameworks, including ELMOD, EMPIRE, AnyMOD, LIMES, TIMES, FlexPlan, PyPSA, REMix, and Balmorel. Further, we elaborate on the recent trends in research and modeling. Based on our observations, we propose avenues for future research. For instance, considering recent changes in the geopolitical environment, we suggest shifting the geographical research focus from the North Sea region to the Central and Eastern European regions. Other suggestions include investigating grid development under imperfect market competition, merging the study of grid development with sector coupling, and increasing the focus on blue hydrogen, which appear to not receive much focus, as opposed to green hydrogen. Overall, this work may serve as a useful resource for newcomers to grid-related research and a practical guide for seasoned researchers in the field. [ABSTRACT FROM AUTHOR]

In this paper we are researching the optimality of developing counties for investing in their energy sector as a way of diversifying portfolio by applying the multicriteria decision making model There are multiple quantitative and qualitative criteria that can be considered when finding the adequate market for investment, other than its natural potential, such as the level that its energy sector is developed, legal framework that surrounds this sector, market openness of the observed economy, ease of investment and market liquidity. The four sources of the energy sector that are considered are oil, gas, coal and renewable energy sources. Even though the renewable energy sources aren't sufficiently exploited, the countries that have high potential could provide significant financial profits by exploiting them. Through this study we are using the most significant and impacting criteria in order to explore and deduce the developing country that has the highest potential of the energy sector and represents the most optimal market to invest. [ABSTRACT FROM AUTHOR]

In the recent era of smart city ecosystems and the Internet of Things (IoT), innovative, intelligent parking systems must make cities more sustainable. Every year, the increasing number of city vehicles requires more time to search for parking slots. In large cities, 10% of the traffic congestion occurs because of cruising; drivers spend almost 20 minutes searching for free space to park their vehicles. The passing time of waiting for parking in the traffic leads the issues such as energy, pollution, and stress. There needs to be more than the developed solutions. Therefore, the necessary to create a parking slot availability detection system that informs the drivers in advance about the free parking slot based on location. This paper introduces an enhanced ensemble Deep Learning (DL) model designed to forecast parking slot availability through the integration of IoT, cloud technology, and sensor networks. The devised model, known as Ensemble CNN-Boosted Graph LSTM (ECNN-BGLSTM), is optimized using a Genetic Algorithm (GA) framework. The model's performance is rigorously evaluated using a dataset from Europe, and various metrics, including Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE), are employed for assessment. The experimental findings demonstrate the superior performance of the proposed model compared to existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Moving towards regional Supergrids, an increasing number of interconnections are formed by High Voltage Direct Current (HVDC) lines. Currently, in most regions, HVDC losses are not considered in market operations, resulting in additional costs for Transmission System Operators (TSOs). Nordic TSOs have proposed the introduction of HVDC loss factors in the market clearing algorithm, to account for the cost of losses and avoid HVDC flows between zones with zero price difference. In this paper, we introduce a rigorous framework to assess the introduction of loss factors, in particular HVDC loss factors, in nodal and zonal pricing markets. First, we focus on the identification of an appropriate loss factor. We propose and compare three different models: constant, linear, and piecewise linear. Second, we introduce formulations to include losses in market clearing algorithms. Carrying numerical tests for a whole year, we find that accounting only for HVDC or AC losses may lead to lower social welfare for a non-negligible amount of time. To counter this, this paper introduces a framework for including both AC and HVDC losses in a zonal or nodal pricing environment. We show both theoretically and through simulations that such a framework is guaranteed to increase social welfare. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing, and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse, and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system, and comment on the impacts of both proactive, and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

The paper addresses a real-time optimization-based reference calculation integrated with a control structure for Modular Multilevel Converters (MMC) operating under normal and constrained situations (where it has reached current and/or voltage limitations, as it may occur during system faults). Firstly, a nonlinear optimization problem has been developed in which it prioritizes to satisfy the AC grid current set-points imposed by the transmission System Operator (TSO). The constrained nonlinear optimization problem is formulated based on the steady-state model of the MMC, whereby the prioritization is achieved through distinct weights defined in the Objective Function’s (OF) terms. The resultant optimization problem, however, is highly nonlinear requiring high computation burden to be solved in real-time. To cope with this issue, this paper applies a Linear Time-Varying (LTV) approximation, which permits to represent the nonlinear dynamics of the system as constant parameters, while a Linear Time-Invariant (LTI) system is used to formulate the optimization constraints. The converter’s current references are determined in real-time by solving a constrained linearized optimization problem at each control time step, which considers the TSO’s demands, the current MMC operating point and its physical limitations. Theoretical analyses comparing the responses of the linear and nonlinear optimization problems are performed to validate the accuracy of the LTV approximation. Finally, the linearized-optimization problem is integrated with the MMC controllers, evaluated under different AC and DC network conditions and compared with conventional control strategies, where it is shown that the presented method can be potentially employed to obtain the MMC current references for distinct network scenarios. [ABSTRACT FROM AUTHOR]

The need to optimize the deployment and maintenance costs for service delivery in wireless networks is an essential task for each service provider. The goal of this paper was to optimize the number of service centres (gNodeB) to cover selected customer locations based on the given requirements. This optimization need is especially emerging in emerging 5G and beyond cellular systems that are characterized by a large number of simultaneously connected devices, which is typically difficult to handle by the existing wireless systems. Currently, the network infrastructure planning tools used in the industry include Atoll Radio Planning Tool, RadioPlanner and others. These tools do not provide an automatic selection of a deployment position for specific gNodeB nodes in a given area with defined requirements. To design a network with those tools, a great deal of manual tasks that could be reduced by more sophisticated solutions are required. For that reason, our goal here and our main contribution of this paper were the development of new mathematical models that fit the currently emerging scenarios of wireless network deployment and maintenance. Next, we also provide the design and implementation of a verification methodology for these models through provided simulations. For the performance evaluation of the models, we utilize test datasets and discuss a case study scenario from a selected district in Central Europe. [ABSTRACT FROM AUTHOR]

Glued laminated timber made from hardwood is a sustainable structural product that is currently gaining momentum due to climatic changes and afforestation policy. Diversities in hardwood species have led to a lack of a complete standard for the production of glued laminated timber from hardwood in Europe. This paper addresses hardwood species available in Croatian forests, namely Carpinus Betulus L. (European hornbeam), Quercus Cerris L. (Turkey oak), and Acer Campestre L. (Maple), in terms of their suitability for the production of glued laminated timber. The suitability is assessed by a multi‐criteria analysis with seven criteria. Visual and physical properties include density, the occurrence of knots, and grain deviation in lamellas. Mechanical properties include the bending strength, modulus of elasticity, and shear strength of glue lines. In order to obtain physical and mechanical properties, corresponding experimental research has been carried out. Production costs have been evaluated in collaboration with the manufacturer. Results indicate a very high potential of European hornbeam and Turkey oak for the production of glued laminated timber. [ABSTRACT FROM AUTHOR]

Determining contingency aware dispatch decisions by solving a security-constrained optimal power flow (SCOPF) is challenging for real-world power systems, as the high problem dimensionality often leads to impractical computational requirements. This problem becomes more severe when the SCOPF has to be solved not only for a single instance, but for multiple periods, e.g. in the context of electricity market analyses. This paper proposes an algorithm that identifies the minimal set of constraints that exactly define the space of feasible nodal injections for a given network and contingency scenarios. By internalizing the technical limits of the nodal injections and enforcing a minimal worst-case impact of contingencies to line flows, computational effort can be further improved. The case study applies and analyzes the methods on the IEEE 118 and A&M 2000 bus systems, as well as the German and European transmission systems. In all tested cases the proposed algorithm identifies at least 95% of the network and security constraints as redundant, leading to significant SCOPF solve time reductions. Scalability and practical implementation are explicitly discussed. The code and input data of the case study is published supplementary to the paper under an open-source license. [ABSTRACT FROM AUTHOR]

A novel model for the clearing of European day-ahead electricity markets, with all associated types of orders, namely simple hourly orders, block orders, complex orders and PUN (“Prezzo Unico Nazionale”) orders is presented in this paper. An iterative algorithm is employed for the market clearing, bearing an inner iteration process for the handling of Paradoxically Accepted block and MIC orders, and an outer iteration process for the handling of PUN orders. During the iterative process a Master Problem and a PUN subproblem are sequentially solved and coordinated, for the efficient handling of all order clearing conditions and market and/or system constraints. The clearing conditions of each order type are explicitly incorporated in the PUN subproblem, formulating a Mixed Complementarity Problem bearing both primal and dual decision variables. The PUN subproblem concerns the sequential clearing of 24 hourly subproblems, employing a special technique for handling the possible inefficiencies due to the myopic nature of the hourly subproblem solutions, leading to enhanced behavior in terms of social surplus with respect to the state-of-the-art day-ahead market clearing solver. The model and the algorithmic process are evaluated in a west-European test case, demonstrating minimal computational requirements and proving its excellent escalation capabilities. [ABSTRACT FROM AUTHOR]

With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation. The new challenges arising from the energy transition will require a better coordination between neighboring system operators to maintain system security. To this end, this paper studies the benefit of exchanging primary frequency reserves between asynchronous areas using the Supplementary Power Control (SPC) functionality of High-Voltage Direct-Current (HVDC) lines. First, we focus on the derivation of frequency metrics for asynchronous AC systems coupled by HVDC interconnectors. We compare two different control schemes for HVDC converters, which allow for unilateral or bilateral exchanges of reserves between neighboring systems. Second, we formulate frequency constraints and include them in a unit commitment problem to ensure the N-1 security criterion. A data-driven approach is proposed to better represent the frequency nadir constraint by means of cutting hyperplanes. Our results suggest that the exchange of primary reserves through HVDC can reduce up to 10% the cost of reserve procurement while maintaining the system N-1 secure. [ABSTRACT FROM AUTHOR]

Securing profits from energy, reserve capacity and balancing markets is critical to ensure the profitability of battery energy systems (BES). However, the intimate connection between offers on these trading floors combined with the limited energy storage capacity of BES renders its scheduling very complex. In this paper, we develop a bilevel optimization problem for strategic participation of a BES in the day-ahead energy-reserve and balancing markets, improving the state-of-the-art by (i) considering the conditional-value-at-risk; (ii) ensuring the real-time feasibility of the obtained day-ahead schedule; (iii) addressing the operational underperformance risk stemming from inaccurate battery modeling. In a case study, we illustrate how the proposed model allows risk-averse BES owners to hedge their day-ahead position without jeopardizing their expected profit, while ensuring the feasibility of their day-ahead schedule. [ABSTRACT FROM AUTHOR]

In this paper, shaped continuous parallel plate waveguide delay lenses are proposed. The previous configurations introduced by the authors displayed a residual side lobe imbalance, showing the limitations of elliptical delay lens profiles, derived from a starting bifocal constrained lens. Polynomial profiles are proposed here as a way to enhance the scanning performance and the pattern shape. Optimized configurations demonstrate a reduction in the phase aberration levels over a large scanning range ([−30°, 30°]). The associated radiation patterns demonstrate lower and more balanced first side lobe levels (SLLs) ($\cong -18$ dB), as compared to the previous elliptical approach ($\cong -13.2$ dB). A validation has been proposed through the manufacturing and test of a prototype over the down-link Ka-band ([27.5–31] GHz). Excellent radiation performance [half-power beamwidth (HPBW), SLL] has been experimentally obtained over a wide angular range ([−31.5°, 31.5°]), including low scanning loss and high radiation stability over the entire frequency range. High radiation efficiencies are demonstrated with this fully metallic design, particularly suitable for space applications. The mechanical simplicity offered by the concept is also quite attractive for low-cost multi-beam platforms. [ABSTRACT FROM AUTHOR]

Assessing the performance of transit systems operating under different regulatory regimes is a topic of considerable interest in the literature. The goal of this paper is to investigate the performance characteristics and factors affecting this performance for European transit systems operating in vastly different regulatory environments. The initial results of data envelopment analysis application indicate that privately operated systems tend to be more efficient while publicly operated systems tend to be more effective; transport operators in cities where open entry form of competition has been introduced were found to perform both efficiently and effectively. Further, as the Tobit regressions focused on the effect of ownership indicate, most systems in the sample demonstrate a positive trend in both efficiency and effectiveness, a finding with important practical implications for financially strained systems. [ABSTRACT FROM AUTHOR]

This article analyzes the temporal programming of sales for a horticultural marketing company, e.g. a cooperative. The empirical study references the European tomato market, where most of the production is sold through the retail channel dominated by large distribution chains. We study the marketing schedule for an individual company, or even a prominent farmer, using a modified Markowitz model, assuming that his decisions do not affect the balance of market prices. As a result, this model can manage risk and improve decision-making. The data also provide information on the risk borne by marketers depending on their sales calendar, which often depends on their geographic location. [ABSTRACT FROM AUTHOR]

Transitioning from fossil fuel dominated power systems to high penetrations of intermittent renewable generation is affecting classical electricity market designs. Here, a method is proposed to model and assess the absence of co-optimization of energy and reserve that prevails in the European system. Co-optimization is formulated through an optimization problem (COM), and its absence as an equilibrium problem (EQM) built on Karush-Kuhn-Tucker conditions of agents’ optimization and market clearing equations. EQM cannot be reformulated as a single optimization problem. Market distortions are identified by comparing the complementarity conditions of both models. These are then discussed on system with Feed in Premium to wind generation. Parameters in the models allow to represent different market configurations regarding: available wind generation, Feed in Premium to wind, generators’ risk aversion, and required reserve from wind generation. [ABSTRACT FROM AUTHOR]

• A single IDF model valid for various return periods and durations is developed. • New scaling models of design rainfall curves are presented. • Adjusted rainfall event durations are considered in the context of scale invariance. • Parsimonious intensity–duration–frequency (IDF) structure is obtained. • Good fit to quantile predictions of extreme rainfall data for specified durations. This paper deals with intensity–duration–frequency (IDF) curves, which express the relationship between average rainfall intensity and event duration for various probabilities of non-exceedance (or return periods) for the design/analysis of hydraulic interventions and infrastructures in riverine and urban drainage contexts. New scaling models are proposed to develop a single IDF model valid for all durations, from below 1 h to 24 h. In these models, the scale invariance is applied to rainfall intensity to obtain a parsimonious IDF structure capable of defining a family of IDF curves at various return periods. The main novelty consists of the formulation of simple and multiple scale invariance based on adjusted design event durations. The parameterization is carried out in two phases: in the first phase, the adjustment size is searched for iteratively while the other parameters of the IDF structure are directly obtained in cascade; in the second phase, which enables considering different reliability levels for extreme rainfall data as a function of sample length at different durations, parameter refinement is carried out by means of a local optimization algorithm. The second step accommodates data samples of different extension as a function of available data at different durations. The application to four case studies at various latitudes in Europe, namely Helsinborg (Sweden), Frauenwald (Germany), Pavia and Erice (Italy), proves the IDF structure to fit well the quantile predictions of extreme rainfall data for specified durations below and above one hour. [ABSTRACT FROM AUTHOR]

In situations where it is necessary to consider a variety of options when making decisions in forestry (and in general), with the choice influenced by hardly comparable criteria and a number of conflicting interests, a possible solution is to use multiple criteria methods. One of these methods, which can be applied in forestry, is mathematical programming (in particular, linear programming). Linear programming has a long tradition of being used in the U.S. and European forestry, whereas in the forestry of Serbia it still represents a theoretically and practically unknown tool. Therefore, in this paper we analyze the possibility of applying the methods of linear programming in developing a plan of regeneration cutting in the poplar plantations of FMU "Topolik" managed by PE "Vojvodinašume." Using the aimed function (linear programming) and the corresponding software package the maximum yield that can be achieved by cutting the plantation was obtained. The planned management period was from 2012 to 2021 and its volume was 155 852 m3. The preset condition that the yield in half-periods remains equal was fulfilled (halfperiod I 77,925 m3, half-period II 77,927 m3). The maximum yield obtained with this methodology was by 4,040 m3 lower than the theoretically possible yield that would be obtained if all stands were cut down at the end of the second half-period, i.e. higher by 8,430 m3 than the yield that would be obtained if cutting of the stands were performed at the start of the management period. The results obtained and foreign experience in this area clearly indicate that linear programming can successfully be used to solve this problem and even more complex problems (than the one presented in this paper) in our local forest practice (multidimensional planning with a series of constraints). [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Among several hundred steel grades, 70MnVS4 steel is also produced. In the paper optimization of steelmaking of 70MnVS4 steel is presented. 70MnVS4 is a high-strength microalloyed steel which is used for forging of connecting rods in the automotive industry. During 70MnVS4 ladle treatment, the sulfur addition in the melt should be conducted only once. For several reasons the sulfur is repeatedly added and therefore threatening clogging during continuous casting and as such influencing surface defects occurrence and steel cleanliness. Accordingly, the additional sulfur addition was predicted using linear regression and genetic programming. Following parameters were collected within the period from January 2018 to December 2018 (78 consequently cast batches): sulfur and carbon cored wire addition after chemical analysis after tapping, carbon, manganese and sulfur content after tapping, time between chemical analysis after tapping and starting of the casting, ferromanganese and ferrosilicon addition and additional sulfur cored wire addition. Based on modelling results it was found out that the ferromanganese is the most influential parameter. Accordingly, 12 consequently cast batches (from February 2019 to October 2019) were produced with as lower as possible addition of ferromanganese. The additional sulfur addition in all 12 cases was not needed. Also, the melt processing time, surface quality of rolled material and sulfur cored wire consumption did not change statistically significantly after reduction of ferromanganese addition. The steel cleanliness was statistically significantly better. [ABSTRACT FROM AUTHOR]

The market of batteries for electric mobility is expected to greatly expand in Europe during the next decades. The most sustainable batteries to be produced in the future depend on the primary and secondary resources available and on the capacity to produce and recycle battery cells. This paper proposes an optimization model based on Material Flow Analysis and prospective Life Cycle Assessment which allows us to suggest the types of batteries which should be produced and recycled, the primary and secondary resources to be used, and in which quantities. Several scenarios are proposed to address the uncertainty of the future batteries market. In the Reference Scenario, designed to reflect the European policies, the results show that Europe would achieve high self-sufficiency from external producers (72%), but this percentage could decrease drastically in the case of alternative scenarios (up to 35%). Furthermore, in the Reference Scenario, lithium-iron-phosphates (40%) and lithium-air (23%) batteries result to be the best options. To cut the consumption of lithium and cobalt, sodium-ion cells (3%) could also be considered for manufacturing or even nickel-cobalt-manganese (6%) type to exploit secondary cobalt. In alternative scenarios instead, the proposed algorithm would suggest different choices as a function of the boundary conditions. For instance, in case the demand and the re-use rate of the cells are higher than in the Reference Scenario, the environmental impact of batteries for mobility turns out to increase because of the lower self-sufficiency of the European industry. • Material flow analysis and prospective LCA are integrated into an optimization model. • Several scenarios are set to address the uncertainty of the future batteries industry. • The European batteries' production and recycling to cut GHG emissions are assessed. • The optimal management of primary and secondary resources is evaluated in time. • The model evaluates the batteries industry's relevant GHG mitigation (up to −45%). [ABSTRACT FROM AUTHOR]

This paper proposes an optimal method for estimating time-dependent climate change signals from general circulation models. The basic idea is to identify vectors that maximize the mean-square detection statistic derived from optimal fingerprinting techniques. The method also provides an objective and systematic procedure for identifying the limit to which a signal can be restricted in space and time without losing detectability. As an illustration, the method is applied to the Coupled Model Intercomparison Project, phase 3 multimodel dataset to determine the continental seasonal-mean anomaly in surface air temperature and precipitation that is most detectable, on average, in these models. Anomalies in seasonal-mean surface air temperature are detectable in all seasons by almost all models on all continents but Europe; seasonal-mean anomalies over Europe are undetectable for some models, though this does not preclude other expressions of the signal, such as those that include longer time averages or time-lag information, from being detectable. Detectability in seasonal-mean temperature is found not only for multidecadal warming trends but also for cooling after major volcanic eruptions. In contrast, seasonal-mean precipitation anomalies are detectable in only a few models for averages over 5 yr or more, suggesting that the signal should include more spatiotemporal detail to be detectable across more models. Nevertheless, of the precipitation anomalies that are detectable, the signal appears to be of two characters: a systematic trend and enhanced frequency of extreme values. These results derived from twentieth-century simulations appear to be consistent with previous studies based on twenty-first-century simulations with larger signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Perturbed physics ensembles (PPEs) have been widely used to assess climate model uncertainties and have provided new estimates of climate sensitivity and parametric uncertainty in state-of-the-art climate models. So far, mainly global climate models were used to generate PPEs, and little work has been conducted with regional climate models. This paper discusses the parameter uncertainty in two PPEs of a regional climate model driven by reanalysis data for the present climate over Europe. The uncertainty is evaluated for the variables of 2-m temperature, precipitation, and total cloud cover, with a focus on the annual cycle, interannual variability, and selected extremes. The authors show that the simulated spread of the PPEs encompasses the observations at a regional scale in terms of the annual cycle and the interannual variability, provided observational uncertainty is taken into account. To rank the PPEs a new skill metric is proposed, which takes into account observational uncertainty and natural variability. The metric is a generalization of the climate prediction index (CPI) and is compared to metrics used in other studies. The consideration of observational uncertainty is particularly important for total cloud cover and reveals that current observations do not allow for a systematic evaluation of high precipitation intensities over the entire European domain. The skill framework is additionally used to identify important model parameters, which are of interest for an objective model calibration. [ABSTRACT FROM AUTHOR]

The European biodiesel industry is currently facing several challenges affecting the profitability of investment projects in the industry. Among these challenges are higher prices for oilseeds, which are the main input for biodiesel production, lower fiscal support by national governments for biodiesel producers and high price volatility of oil markets. Thus identifying all opportunities for optimizing the value chain and lower the production cost of biodiesel is a main requirement for an efficient project management in the biodiesel industry. The paper addresses this topic by developing a decision support system tailored to the needs of Romanian investors in biodiesel production. The system optimizes the main activities of the biodiesel value chain and supports the decision making process at management level. In addition the DSS enables the user to perform sensitivity analysis based on varying various input parameters. [ABSTRACT FROM AUTHOR]

This paper describes the algorithms used in a planning and scheduling software tool developed for the European Space Agency in the framework of the Mars Express mission. The planning and scheduling algorithm computes a feasible schedule for the transmission of telecommands (TCs) from the ground segment to the space segment, complying with a number of technical constraints. Owing to the distance between Mars and Earth, it is important that the robustness of the schedule is taken into account because repair operations may be very time consuming or even impossible. For this reason, besides the maximization of the number of TCs transmitted from Earth to Mars, the scheduler is also designed to maximize the number of full confirmations and secondary time windows, which are two special characteristics of the Mars Express schedule explicitly designed for the sake of robustness. Besides the maximization of robustness, the scheduling algorithm that can run with different settings can be used to optimize some secondary figures of merit, such as the average saturation of the memory devices of the space segment and the usage of the time windows available for communication. Computational results on real instances are presented. [ABSTRACT FROM PUBLISHER]

This paper builds a dynamic programming model to optimize the collections process in consumer credit. It determines which collections actions should be undertaken and how long they should be performed, including theoretical results about the form of the optimal policy under certain conditions. Finally, a case study is described based on data from the collections department of a European bank. [ABSTRACT FROM AUTHOR]

The challenge in vehicle and crew scheduling is to arrange a given set of bus trips into running boards while minimizing certain objectives and complying with a given set of constraints. This scheduling was solved several tens years ago manually by a dispatcher who used his skill, experience, knowledge of history, and intuition. This attitude worked quite well in small instances but could not be applied in larger ones. Development of computers brought opportunities to build computerized vehicle and crew optimization systems. This paper describes a computer system KASTOR developed for vehicle and crew optimization, which complies with the special requirements of Czech and Slovak bus providers, and is significantly different from those in some west European countries. [ABSTRACT FROM AUTHOR]

Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves in-sample relative errors of 22.3% and 15.9% for the PTDFs and RAMs respectively, while the out-of-sample ones are 25.4% and 18.1% respectively. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure. [ABSTRACT FROM AUTHOR]

Background Agri-food is one of the most important sectors of the industry in Europe and potentially a major contributor to the global warming. Sustainability issues in this context pose a huge challenge for several reasons: the variety of considered scales, the number of disciplines involved, the uncertainties, the out-of-equilibrium states, the complex quantitative and qualitative factors, the normative issues and the availability of data. Although important insight and breakthroughs have been attained in different scientific domains, an overarching and integrated analysis of these complex problems have yet to be realized. Scope and Approach This context creates huge opportunities for research in interaction with mathematical programming, integrative models and decision-support tools. The paper propose a computational viewpoint including questions of holistic approach, multiscale reconstruction and optimization. Some directions are discussed. Key Findings and Conclusions Several research questions based on a mathematical programming framework are emerging: how can such a framework manage uncertainty, cope with complex qualitative and quantitative information essential for social and environmental considerations, encompass diverse scales in space and time, cope with a multivariable dynamic environment and with scarcity of data. Moreover, how can it deal with different perspectives, types of models, research goals and data produced by conceptually disjoint scientific disciplines, ranging from physics and physiology to sociology and ethics? Building models is essential, but highly difficult; it will need a strong iterative interaction combining computational intensive methods, formal reasoning and the experts of the different fields. Some future research directions are proposed, involving all those dimensions: mathematical resilience, human-machine interactive learning and optimization techniques. [ABSTRACT FROM AUTHOR]

Numerous Life Cycle Analysis of bread pointed out that wheat production and transportation have important impacts on the environment. But wheat is grown all around the world and it is not always consumed where it is produced. In this paper an analysis of the wheat movements around Europe is done, taking special attention on the Spanish situation. An optimization of wheat exchanges is done eliminating the import–export overlapping. This optimization entails a reduction of the number of trips by 34% and the CO 2 emissions reduction due to transportation is calculated to be 121.175 tones. Consequently less trips, fewer trucks, less traffic and lower emissions that brings economic, social and environmental benefits. [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. The main motive for this study was to study the influences of non-metallic inclusions on mechanical properties obtained by tensile testing. From January 2016 to December 2021, all available tensile strength data (472 cases–472 test pieces) of 17 low alloy steel grades, which were ordered and used by the final user in rolled condition, were gathered. Based on the geometry of rolled bars, selected chemical composition, and average size of worst fields non-metallic inclusions (sulfur, silicate, aluminium and globular oxides), determined based on ASTM E45, several models for tensile strength, yield strength, percentage elongation, and percentage reduction area were obtained using linear regression and genetic programming. Based on modeling results in the period from January 2022 to April 2022, five successively cast batches of 30MnVS6 were produced with a statistically significant reduction of content of silicon (t-test, p < 0.05). The content of silicate type of inclusions, yield, and tensile strength also changed statistically significantly (t-test, p < 0.05). The average yield and tensile strength increased from 458.5 MPa to 525.4 MPa and from 672.7 MPa to 754.0 MPa, respectively. It is necessary to emphasize that there were no statistically significant changes in other monitored parameters. [ABSTRACT FROM AUTHOR]

A novel experimental approach of inter- and intramodel comparisons is conducted with five power market models to give recommendations for modelers working on decarbonization pathways of Europe until 2050. The experiments investigate the impact of model type (optimization vs. simulation), planning horizon (intertemporal vs. myopic), temporal resolution (8760 vs. 384 h), and spatial resolution (28 countries vs. 12 mega-regions). The model type fundamentally determines the evolution of capacity expansion. Planning horizon (assumed foresight of firms) plays a minor role for scenarios with high carbon prices. For low carbon prices in turn, results from myopic models deviate considerably from those of intertemporal models. Lower temporal and spatial resolutions foster wind power via storage and via neglected transmission boundaries, respectively. Using simulation instead of optimization frameworks, a shorter planning horizon of firms, or lower temporal and spatial resolutions might be necessary to reduce the computational complexity. This paper delivers recommendations on how to limit the discrepancies in such cases. • Comparison of five power market models with harmonized input data and model features. • Evaluating the role of model type, planning horizon, spatial and temporal resolution. • Model type decisive for investment decisions. • Myopic modeling hardly changes variable renewable energy expansion but might favor conventional power plants. • Spatial and temporal resolution crucial for ease of variable renewable energy integration. [ABSTRACT FROM AUTHOR]

Tunnels are significant in shaping our future infrastructure. Increasing limitations of the available ground require underground solutions for future infrastructure projects. Due to various reasons, boundary conditions for project realization are becoming more and more substantial, and the parties involved have to tackle a wide range of stakeholders' interests. Digitization may be able to handle some of these challenges and is one of the most relevant topics in the near future of the construction industry. First approaches of digital data capturing in different activity fields of project execution, such as geological and geotechnical conditions, survey, and reporting, are launched within Implenia's initiative. This article discusses essential aspects of data capturing, particularly in the light of the vast data volumes generated throughout tunnel construction in Europe and gives an insight into the various activities of Implenia on how to tackle these problems. The various TBM and drill‐and‐blast excavated tunnel projects illustrate the variability of data management and implemented solutions to work with the tremendous amount of data. In each case, one can clearly state that construction teams involved in digital data management highly appreciated the merits for their daily business: saving time, gaining transparent information, controlling processes, and more. The beneficial capability of appropriate data management in project execution is evident and will be carried out within Implenia's projects. [ABSTRACT FROM AUTHOR]

With the current European tendency to gradually increase the contribution of renewable energy to almost the entire production, the rate of wind farm constructions is at an all-time high. As the locations for the wind turbines are dictated by factors usually independent of soil conditions (average wind speeds, possibility of connecting to existing electrical networks, approval of authorities and local population), it is not uncommon for the geotechnical conditions to be unfavorable. In these circumstances, the choice of the optimal foundation system is an important aspect in the design phase of a wind turbine. The aim of this paper is to analyze the suitability of the available foundation solutions (shallow foundation on natural or improved ground or piled foundation - classical or piled raft) for a number of idealized soil profiles and varying loads. The study is highly parametric, as all foundation solutions are analyzed for a range of realistic tower loads and different (including extremely difficult) soil conditions. The expected conclusions are general statements about favorable conditions for each foundation system and the expected failure criteria. [ABSTRACT FROM AUTHOR]

Abstract: In many European countries, municipalities offer their inhabitants a wide variety of social services. In this paper we will focus on efficiently scheduling home care, transportation of the elderly, and home meal delivery. These so-called municipal or communal routing problems can be modeled as different variants of the vehicle routing problem, a well-known optimization problem from the literature. We present a focused literature review and report on case studies using Finnish data. The computational results show that there is a significant potential for cost savings for all applications considered. [Copyright &y& Elsevier]

Abstract: This paper addresses a hub-and-spoke network problem for railroad freight, where a central planner is to find transport routes, frequency of service, length of trains to be used, and transportation volume. Hub-and-spoke networks, often found in air freight, have not been favoured by railways in the past. Such a structure could be profitable, however, if there exist concentrated freight flows on some service links. We formulate a linear integer programming model whose objective function includes not only the typical operational cost, but also cost due to the transit time spent by freight in the network. We then develop heuristic algorithms to solve large scale instances occurring in rail freight systems in France plus Italy; Germany; and a 10-country European network. By assuming that every node is equipped with consolidation capability, we let the final solution naturally reveal potential hub locations, the impact of several of which is studied by sensitivity analysis. [Copyright &y& Elsevier]

Intensively managed agricultural areas in North-Western Europe have been undergoing a shift from solely production oriented use to provision of multiple services and functions. Design and assessment of multifunctional agricultural landscapes could be supported by exploration of trade-offs between financial returns from agriculture, landscape quality, nature conservation and environmental quality. This paper presents the Landscape IMAGES methodology for spatially explicit exploration of options for multifunctional agriculture in landscapes at a scale of a few km2. The framework has been developed to support discussions and inform decision making by local and regional policy makers, land owners and land managers. Other relevant stakeholders could include non-governmental organizations representing nature conservation and environmental protection objectives. The structure of the Landscape IMAGES framework prototype is elaborated and its functioning is illustrated with a near-real example of a grassland-dominated landscape with hedgerows bordering the fields. In this landscape, four objectives are being pursued by adjusting land-use intensity and hedgerow presence: (1) acceptable agronomic yields for farms, (2) diversification of the botanical composition of fields and hedgerows, (3) variation in plant communities in the fields and half-openness of the landscape, and (4) reduction of nutrient losses to the environment. For exploration of the trade-offs between multiple objectives a heuristic search method (i. E. , differential evolution) is employed, which yields a large range of alternative, acceptable configurations of the landscape. The framework provides explicit insight in the trade-off between the objectives and is implemented in a visual application that enables the comparison of alternative options. The method can be applied to a range of spatially explicit land-use and nature allocation problems and will further evolve as a result of anticipated interactions with stakeholders. [Copyright &y& Elsevier]

In order to adequately handle the huge increase in traffic over the past three decades, most North American and European countries invested enormous funds in building highway networks. Nowadays, most of these networks are complete or close to completion. The biggest challenge highway agencies and departments of transportation face is the maintenance of these networks, keeping them safe and serviceable, with limited funds. The use of consistent measures of safety is fundamental for the development of optimum strategies for bridge maintenance. The analysis of safety on a component basis is a gross approximation of the real system performance of a bridge. In this paper, a model using lifetime functions to evaluate the overall system probability of survival of existing bridges, under maintenance or no maintenance, is proposed. In this model, bridges are modeled as systems of independent and/or correlated components. The proposed model is applied to an existing bridge located in Denver, Colorado, and the optimal maintenance strategy of this bridge is obtained in terms of service life extension and cumulative maintenance cost. [Copyright &y& Elsevier]

Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. For hot rolling also continuous rolling line (10 stands) is used. The rolling plan contains more than 400 different shapes (flat, square and round bars) and dimensions which are rolled monthly. When changing from one dimension to another, the adjustment of individual stands occurs so that the desired dimension is achieved with the new setting. While changing the rolling dimensions the following could be taken into account: changing of the rolling stand, changing of guides or changing of position of corresponding guides. The aim of the study was reducing the changing of the guides (entry and exit). This aim was accomplished by changing the pass design and by simultaneously mounting of additional guides. In order to change the pass design, the genetic algorithm was used. The optimization has shown that based on existing rolling schedules of round bars the changes can be reduced in average by 36.3%. [ABSTRACT FROM AUTHOR]

The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bi-level optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources. [ABSTRACT FROM AUTHOR]

Virtual powertrain analysis is widely applied in the automotive industry to cope with the increasing complexity and variance of future vehicle propulsion technologies. Since the vehicle-usage behavior has a strong impact on component loads, realistic computation results require accurate assumptions about these boundaries. In this context, driving cycles (DCs) are used to represent the system boundaries in vehicle operation. The aim of this article is to identify multiple characteristic driving cycles (CDCs) from extensive vehicle measurement data which represent the full variety of possible real-driving scenarios. Vehicle measurements are segmented and consumption-relevant features are extracted from each segment. These features are then used to apply clustering and classification techniques to identify characteristic groups that are consequently assigned to different driving environments and driving styles. In order to obtain even more realistic driving scenarios, a data-fusion approach is used to incorporate a road slope signal from a NASA digital elevation model for each segment. Lastly, a genetic mixed-integer optimization algorithm is proposed to efficiently generate representative DCs for each characteristic group of driving segments. The main contribution of this article is a data-driven identification of the parameter space of real-driving scenarios from extensive vehicle measurements including the implementation of road slope information. The scenarios are represented via a constrained number of compact CDCs which enables comprehensive investigations of new powertrain technologies under average as well as extreme real-driving conditions to develop efficiency-robust powertrain systems. [ABSTRACT FROM AUTHOR]

Short abstract: The authors implemented two integer programming models to monitor student achievements at the Universidad Politécnica de Madrid by periodically assessing (i.e., testing) these students. The Bologna Declaration became the guiding document of the Bologna Process, which led to the creation of the European Higher Education Area (EHEA) to coordinate higher education across Europe. The Escuela Técnica Superior de Ingenieros Industriales (ETSII) of the Universidad Politécnica de Madrid adopted one of its pillars: a student-centered learning model. This new model requires the adaptation of existing docent methodologies and the development of tools to monitor student achievements by means of continuous assessment. The implementation of this approach in bachelor's degree programs was especially complex because of the considerable number of students and subjects involved. To meet EHEA requirements, ETSII's board of directors and the school's academic committee designed and implemented a method to implement periodic assessment activities for all degree programs. This method entailed a large number of activities across all courses that had to be scheduled to ensure that all requirements would be met. The schedule was generated manually until the volume of activities increased such that manual scheduling was no longer viable. To address this problem, we implemented two integer programming models, which we have successfully applied since the 2013–2014 academic year. [ABSTRACT FROM AUTHOR]

An important source of model uncertainty in climate models arises from unconfined model parameters in physical parameterizations. These parameters are commonly estimated on the basis of manual adjustments (expert tuning), which carries the risk of overtuning the parameters for a specific climate region or time period. This issue is particularly germane in the case of regional climate models (RCMs), which are often developed and used in one or a few geographical regions only. This study addresses the role of objective parameter calibration in this context. Using a previously developed objective calibration methodology, an RCM is calibrated over two regions (Europe and North America) and is used to investigate the transferability of the results. A total of eight different model parameters are calibrated, using a metamodel to account for parameter interactions. The study demonstrates that the calibration is effective in reducing model biases in both domains. For Europe, this concerns in particular a pronounced reduction of the summer warm bias and the associated overestimation of interannual temperature variability that have persisted through previous expert tuning efforts and are common in many global and regional climate models. The key process responsible for this improvement is an increased hydraulic conductivity. Higher hydraulic conductivity increases the water availability at the land surface and leads to increased evaporative cooling, stronger low cloud formation, and associated reduced incoming shortwave radiation. The calibrated parameter values are found to be almost identical for both domains; that is, the parameter calibration is transferable between the two regions. This is a promising result and indicates that models may be more universal than previously considered. [ABSTRACT FROM AUTHOR]

Europe stated the ambitious target of becoming carbon neutral by 2050 to combat climate change and meet the requirements imposed by the Paris Agreement, and renewable energy has proved to be a promising solution for the decarbonization of many sectors. Nonetheless, their aleatory nature leads to grid unbalances due to the difference between supply and demand. Storage solutions are needed, and electrofuels become a key factor in this context: they are fuels produced from electricity, which leads to carbon-neutral fuels if it originates from renewable sources. These can constitute a key solution to store the surplus energy and to decarbonize the so-called hard-to-abate sectors. Electrofuel production technologies have not yet been fully developed, and, in this context, extensive study of the state-of-the-art of existing projects can be very useful for researchers and developers. This work researches the European projects funded by the Horizon 2020 Programme regarding electrofuel production. The projects were analyzed in-depth using specific features, and the results were presented. [ABSTRACT FROM AUTHOR]

A 1.2-meter reflectarray antenna has been designed to accomplish the requirements of a Direct Broadcast Satellite (DBS) mission, which provides a South America transmit-receive coverage in Ku band. The antenna has been designed by applying first of all a pattern synthesis technique to obtain the required phase distribution on the reflectarray at several frequencies in transmit and receive bands. Then, the patch dimensions have been optimized in a configuration made of three stacked layers of varying-sized patches in order to provide the required phase distribution at transmit and receive frequencies. An antenna demonstrator has been manufactured and tested. The measured patterns are in good agreement with the simulations and they are close to fulfill the coverage requirements in both transmit and receive bands. [ABSTRACT FROM PUBLISHER]

Economic ideas and processes are becoming increasingly integrated with more traditional engineering and hydrologic models of water management problems. Combining economic management concepts and performance indicators with an engineering-level of understanding of a hydrologic system can provide results and insights more directly relevant for water management decisions and policies. When such models are developed and used with involvement of stakeholders, they can become a basis for shared understanding of water problems as a foundation for negotiated management and policy solutions. When implemented with optimization software, integrated hydro-economic models also can suggest promising innovative solutions for policy-makers to consider. Their applications to river basin management problems are reviewed. Economic and integrated economic-engineering-hydrologic modeling is then discussed in the context of the evolving European Water Framework Directive. Relevant items are cost recovery and water pricing, cost-effectiveness of water management measures, and public participation in decision processes. [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (t-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model's analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same. [ABSTRACT FROM AUTHOR]