Abstract
Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, as high levels of inbreeding could lead into whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies but discovering highly variable regions across species' genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify, and design PCR primers for amplification of SSR loci from a multi-individual microsatellite library. By combining commonly used programs for data cleaning and microsatellite mining, this pipeline easily generates, from a fastq file produced by high-throughput sequencing, standard information about the selected microsatellite loci, including the number of alleles in the population subset, and the melting temperature and respective PCR product of each primer set. Additionally, potential polymorphic loci can be identified based on the allele ranges observed in the population, to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers significantly reduces processing time in comparison to manual analysis while keeping the same quality of the results. The elapsed times at each step can be longer depending on the number of sequences to analyze and, if not assisted, the selection of polymorphic loci from multiple individuals can represent a major bottleneck in population studies.

Abstract
This paper presents an algorithm for optimal resilient allocation of Mobile Energy Storage Systems (MESSs) for an active distribution system considering the microgrids coordinated bidding process. The main contribution of this paper is that the impacts of coordinated biddings of microgrids on the allocation of MESSs in the day-ahead and real-time markets are investigated. The proposed optimization framework is another contribution of this paper that decomposes the optimization process into multiple procedures for the day-ahead and real-time optimization horizons. The active distribution system can transact active power, reactive power, spinning reserve, and regulating reserve with the microgrids in the day-ahead horizon. Further, the distribution system can transact active power, reactive power, and ramp services with microgrids on the real-time horizon. The self -healing index and coordinated gain index are introduced to assess the resiliency level and coordination gain of microgrids, respectively. The proposed algorithm was simulated for the 123-bus test system. The method reduced the average value of aggregated operating and interruption costs of the system by about 60.16% considering the coordinated bidding of microgrids for the worst-case external shock. The proposed algorithm successfully increased the self-healing index by about 49.88% for the test system.

Abstract
A production system constrained by workers and machines, where machines are not fully staffed and workers can be transferred between machines, is here considered. Previous simulation research on this type of dual resource constrained production systems assumes that machines are fully reliable. However, this is questionable in most practical situations. Discrete event simulation is used as research method to assess the impact of machine failures on where to transfer workers. Experimentation was carried out for different levels of machine availability, worker utilization and worker assignment rules. Results show that the modified operation due date rule for worker assignment improves tardiness related performance for all production situations considered. This rule shifts between a focus on completing jobs on dune and a focus on speeding up jobs with short processing time. Results further show that ignoring the machine state at worker assignment may lead to significant performance deterioration. For a machine availability of 97% and a worker utilization of 90%, a deterioration between 101% and 416% on the percentage of tardy jobs was observed, compared to scenarios where machines are always available. However, if the machine state is considered, deterioration ranges only between 11% and 30% under the same conditions. 'This highlights the need to considered machine availability at worker assigment.

Abstract
Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R-2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R-2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.

Abstract
Blue Growth, a term first coined by the European Commission as an initiative to harness the untapped potential of Europe's oceans, seas and coasts, identified rich marine resources as an unique asset for economic development in coastal regions and on islands. The European Commission has through the Blue Growth objectives for the first time highlighted marine sectors as unique market opportunities with high growth potential which carry socio-economic importance to the development of coastal regions. Particularly marine sectors such as aquaculture, marine robotics, and marine renewable energy which fulfil global needs in food safety and security, enable monitoring and exploration in harsh and remote conditions, and globally growing energy needs were recognized as catalysts to achieve sustainable development. Marine start-ups and small and medium-sized enterprises (SME) were identified as potential drivers in emerging marine sectors. However, they require support mechanisms tailored to their needs as they are competing for the same business and financial support as land-based SMEs, yet the research and development infrastructure is more difficult to access. ProtoAtlantic, an Interreg Atlantic Area funded project, provided marine-specific support mechanisms to marine start-ups and SMEs in emerging sectors, including business support through the accelerator and mentorship programs, enabling companies to fast track their product development through access to prototyping and testing facilities in all partner regions. The Interreg Atlantic Area encompasses partner regions in France, Ireland, Portugal, Scotland, and Spain. The consortium partners consist of Technopole Brest Iroise (Brest, France), University College Cork - UCC (Cork, Ireland), County Council Cork (Cork, Ireland), INESC TEC (Porto, Portugal), the European Marine Energy Centre - EMEC (Orkney, Scotland), EMERGE (Canary Islands, Spain), and the lead partner, Innovalia Association (Canary Islands, Spain). The strategic collaboration between the partners provided marine start-ups access to testing facilities in the Atlantic Ocean. The extreme living laboratories provided by EMEC, the LiR National Ocean Testing Facilities at UCC's Centre of Marine and Renewable Energy (MaREI centre), and INESC TEC promise harsh real-life conditions which test the suitability of marine technologies to the limit thereby providing start-ups and SMEs with an extra layer of confidence in developing their technologies. This cross-regional collaboration puts the ProtoAltantic program in a unique position, as it is the first of its kind to dedicate marine-specific support to marine start-ups and SMEs which have benefited from the opportunities that ProtoAtlantic has provided. ProtoAtlantic developed a holistic model for the prototyping and exploitation of innovative ideas in emerging maritime sectors. After the identification of ideas from the research community, start-ups, and SMEs with product innovation capacity in the maritime sector, an acceleration program with a normed and structured process was implemented, thus creating a unique ecosystem in the Atlantic that is addressing a co-creation paradigm with the local European start-ups communities and all the stakeholders.

Abstract
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (baseline) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.