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Interest on the potential CO2 sequestration of marine bivalve aquaculture has increased during the last decade. However, there is still some controversy about which biological processes are involved and how to estimate their contribution to the carbon footprint of bivalve aquaculture. This work considers the dissolved inorganic carbon, CO2 and alkalinity fluxes linked to flesh and shell growth, calcification, respiration, faeces egestion, and ammonia excretion, accounting also for the RDOC production associated to these processes. We have developed an algorithm for a dynamic estimation of these fluxes based on a net production DEB growth model for mussels. The resulting model has been implemented in Python to create a toolbox with a graphical user interface. This toolbox allows the selection of different culture strategies, in terms of seeding date, seed size and culture length, and consequently analyzes the carbon footprint and impact on the carbonate chemistry of seawater of aquaculture management

[Abstract] This study presents the results of SARS-CoV-2 surveillance in sewage water of 11 municipalities and marine bioindicators in Galicia (NW of Spain) from May 2020 to May 2021. An integrated pipeline was developed including sampling, pre-treatment and biomarker quantification, RNA detection, SARS-CoV-2 sequencing, mechanistic mathematical modeling and forecasting. The viral load in the inlet stream to the wastewater treatment plants (WWTP) was used to detect new outbreaks of COVID-19, and the data of viral load in the wastewater in combination with data provided by the health system was used to predict the evolution of the pandemic in the municipalities under study within a time horizon of 7 days. Moreover, the study shows that the viral load was eliminated from the treated sewage water in the WWTP, mainly in the biological reactors and the disinfection system. As a result, we detected a minor impact of the virus in the marine environment through the analysis of seawater, marine sediments and, wild and aquacultured mussels in the final discharge point of the WWTP.

The complexity and diversity of the technologies involved in the Internet of Things (IoT) challenge the generalization of security solutions based on anomaly detection, which should fit the particularities of each context and deployment and allow for performance comparison. In this work, we provide a flexible architecture based on building blocks suited for detecting anomalies in the network traffic and the application-layer data exchanged by IoT devices in the context of Smart Home. Following this architecture, we have defined a particular Intrusion Detector System (IDS) for a case study that uses a public dataset with the electrical consumption of 21 home devices over one year. In particular, we have defined ten Indicators of Compromise (IoC) to detect network attacks and two anomaly detectors to detect false command or data injection attacks. We have also included a signature-based IDS (Snort) to extend the detection range to known attacks. We have reproduced eight network attacks (e.g., DoS, scanning) and four False Command or Data Injection attacks to test our IDS performance. The results show that all attacks were successfully detected by our IoCs and anomaly detectors with a false positive rate lower than 0.3%. Signature detection was able to detect only 4 out of 12 attacks. Our architecture and the IDS developed can be a reference for developing future IDS suited to different contexts or use cases. Given that we use a public dataset, our contribution can also serve as a baseline for comparison with new techniques that improve detection performance.

Voice over IP (VoIP) requires a Call Admission Control (CAC) mechanism in WiFi networks to preserve VoIP packet flows from excessive network delay or packet loss. Ideally, this mechanism should be integrated with the operational scenario, guarantee the quality of service of active calls, and maximize the number of concurrent calls. This paper presents a novel CAC scheme for VoIP in the context of a WiFi access network deployed with Unmanned Aerial Vehicles (UAVs) that relay to a backhaul 5G network. Our system, named Codec-Optimization CAC (CO-CAC), is integrated into each drone. It intercepts VoIP call control messages and decides on the admission of every new call based on a prediction of the WiFi network’s congestion level and the minimum quality of service desired for VoIP calls. To maximize the number of concurrent calls, CO-CAC proactively optimizes the codec settings of active calls by exchanging signaling with VoIP users. We have simulated CO-CAC in a 50 m 50 m scenario with four UAVs providing VoIP service to up to 200 ground users with IEEE 802.11ac WiFi terminals. Our results show that without CAC, the number of calls that did not meet a minimum quality level during the simulation was 10% and 90%, for 50 and 200 users, respectively. However, when CO-CAC was in place, all calls achieved minimum quality for up to 90 users without rejecting any call. For 200 users, only 25% of call attempts were rejected by the admission control scheme. These results were narrowly worse when the ground users moved randomly in the scenario.

Unmanned Aerial Vehicle (UAV) networks have emerged as a promising means to provide wireless coverage in open geographical areas. Nevertheless, in wireless networks such as WiFi, signal coverage alone is insufficient to guarantee that network performance meets the quality of service (QoS) requirements of real-time communication services, as it also depends on the traffic load produced by ground users sharing the medium access. We formulate a new problem for UAVs optimal deployment in which the QoS level is guaranteed for real-time voice over WiFi (VoWiFi) communications. More specifically, our goal is to dispatch the minimum number of UAVs possible to provide VoWiFi service to a set of ground users subject to coverage, call-blocking probability, and QoS constraints. Optimal solutions are found using well-known heuristics that include K-means clusterization and genetic algorithms. Via numerical results, we show that the WiFi standard revision (e.g. IEEE 802.11a/b/g/n/ac) in use plays an important role in both coverage and QoS performance and hence, in the number of UAVs required to provide the service.

[Abstract]: The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modeled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

[Abstract]: In the context of phenotype switching and cell fate determination, numerousexperimental studies report hysteresis, despite the fact that the (forward) Chemical Master Equation governing the inherently stochastic underlying gene regulatory networks has a unique steady state (precluding memory effects and hysteresis). In previous works, we demonstrate thathysteresis is a transient phenomenon in systems far from the thermodynamic limit, using the convergence rates of the partial integro-differential equation associated to the forward master equation governing the stochastic process. Here, we make use of the backward master equationto quantify hysteresis and irreversibility based on First Passage Times. First, we derive the backward master equation for a gene regulatory network with protein production in bursts. Solving this equation, we obtain the probability distributions of the first times to reach some fixed final state from one starting state. The mean first passage time provides a measure to quantify how hysteresis and irreversibility in gene regulation at the singlecell level are transient effects that vanish at steady state. In addition, we provide a theoretical basis that reconciles phenotype coexistence and prevalence far from the thermodynamic limit. In fact, we substitute the notion of pseudo-potential (the so-called Waddington landscape) by a time evolving landscape built upon the Chemical Master Equation (CME) in which phenotypes,rather than prevail, persist with different intensities.

The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modeled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

Assessing the carbon footprint of marine bivalve aquaculture demands an accurate estimation of the CO2 release associated to capital goods and aquaculture operations but also to the metabolic CO2 budget of the cultured species. Nowadays, there are discrepancies on the processes to include in that budget, how to estimate them, and which scale should be applied, from individual to ecosystem. Site-specific environmental conditions and culture methods also affect significantly the estimates. Here, we have gathered environmental, biochemical and metabolic data from published scientific articles, reports and existing databases to present the metabolic CO2 budget for mussel aquaculture in the coastal inlets of the Northwest Iberian upwelling. We analyse the contribution of mussel flesh and shell production jointly and separately. At the individual scale, the shell CO2 budget is estimated from CO2 removal by shell matrix protein synthesis and CO2 release during calcification and respiration to support shell maintenance. Organic carbon in mussel flesh and CO2 released by respiration to support flesh maintenance contribute to the flesh CO2 budget. Only calcification and respiration processes are considered when estimating the metabolic carbon footprint of individual mussels because organic carbon in mussel flesh and shell returns to the atmosphere as CO2 in a relatively short period. While the metabolic carbon footprint associated to mussel shell remains constant at 365 kg CO2 per ton of shell, it varies from 92 to 578 kg CO2 per ton of mussel flesh. This large variability depends on mussel seeding time and harvesting size, due to the differential seasonal growth patterns of flesh and shell. Inclusion of the CO2 potentially immobilised in mussel faeces buried in the sediments would lead to a reduction of the metabolic carbon footprint estimates by up to 6 % compared with the individual estimates

This study presents the results of SARS-CoV-2 surveillance in sewage water of 11 municipalities and marine bioindicators in Galicia (NW of Spain) from May 2020 to May 2021. An integrated pipeline was developed including sampling, pre-treatment and biomarker quantification, RNA detection, SARS-CoV-2 sequencing, mechanistic mathematical modeling and forecasting. The viral load in the inlet stream to the wastewater treatment plants (WWTP) was used to detect new outbreaks of COVID-19, and the data of viral load in the wastewater in combination with data provided by the health system was used to predict the evolution of the pandemic in the municipalities under study within a time horizon of 7 days. Moreover, the study shows that the viral load was eliminated from the treated sewage water in the WWTP, mainly in the biological reactors and the disinfection system. As a result, we detected a minor impact of the virus in the marine environment through the analysis of seawater, marine sediments and, wild and aquacultured mussels in the final discharge point of the WWTP.

Thermal processing of canned products is subject to variability among the different items in a batch, which results in differences in terms of food quality and safety. Such variability is mainly caused by heterogeneity in the thermal processing unit as well as differences in the quantity of food, its compaction, and the quantity of packing liquid from item to item. Besides, for a proper design of the process operation strategies, different conflicting objectives, such as maximization of product quality and uniformity or minimization of processing time and energy consumption, must be considered. In this paper, we present a suitable formulation to efficiently address the multi-objective optimization problem of thermal processing of canned food under the presence of product variability. Such variability has been characterized from experimental data provided by an industrial cannery. Our formulation exploits the monotonic and convex behavior of the quality/safety indicators and the product kinetics with respect to the uncertain thermal coefficients, avoiding thus the computationally-expensive stochastic approach via Monte-Carlo simulations. In particular, customizable retort temperature profiles are considered as a beneficial alternative to the standard constant retort temperature practice

Industrial canneries are subject to perturbations that may compromise food safety requirements. In such cases, plant operators typically increase the processing time, leading to undesirable large processing cycles and excessive quality degradation. In addition, differences among the items in a batch lead to variability in terms of quality and safety which, if not explicitly considered in the processing strategy, forces the use of conservative operation policies. In this work, we present an event-based dynamic optimization approach that combines available plant measurements and mathematical model predictions to anticipate the effect of plant perturbations on food safety. A safety software sensor is build upon an on-line predictive simulation and a previous food-variability characterization such that, if any perturbation during the sterilization compromises food safety, a new processing strategy that optimizes a trade off among quality, uniformity and processing time is recomputed and implemented. Such multi-objective dynamic optimization problem under food product variability is efficiently addressed by taking advantage of the monotonicity and convexity properties of the food quality/safety dynamics

Global production of shell calcium carbonate (CaCO3) from bivalve aquaculture amounts about 13.6 million metric tons per year. Shells, traditionally considered a waste of aquaculture activities, have recently acquired an interest under the current framework of zero waste circular economy. Shell CaCO3 is a sustainable biomaterial that could partly replace the presently dominating non-renewable mineral sources in some applications. Although the carbon footprint of powdered CaCO3 production from biological or mineral sources are about the same, the environmental impact is notably different. Furthermore, bivalve CaCO3 contributes to sequester anthropogenic carbon dioxide (CO2) during shell formation, although this is still controversial. Anyway, the integration of bivalve aquaculture in the carbon trading market would demand that shell CaCO3 is employed in activities that sequester their CO2 for prolonged time periods. Ideally, these activities should combine 1) providing the maximum added value to the biomaterial; 2) producing the lowest carbon footprint during their life cycle; and 3) preserving the CaCO3 integrity as long as possible. In this review, we analyse the effect of bivalve consume habits, particularly mussel, on the availability of shells and their possible agricultural, industrial, and environmental engineering applications from these multiple perspectives. Furthermore, global, continental and regional demands of CaCO3 are matched with their respective mussel aquaculture productions in an effort to scale up offer and demand

This paper addresses the problem of closed-loop operation of scheduling, together with the interaction of control and scheduling, for a class of processes that appear very often in industry: those that combine continuous production lines with parallel batch units that share some resources. The paper presents a novel approach to this problem, including batching and a new type of precedence in the assignment problem. It also considers the effect of shared resources on the duration of the cycle time of the batch units. The approach is illustrated with a real-life example of a canned tuna factory

Monitoring plays a key role in all aspects of fisheries management, including those related to sustainable management of resources, the economic performance of the fishery, and the distribution of benefits from the exploitation of the fishery and environment. In this manuscript, an electronic device (the iObserver) is described, which aims to improve fisheries monitoring by identifying and quantifying fishing catches on board commercial vessels. This device is located over the conveyor belt in the fishing sorting area to automatically take pictures of the entire catch during fish separation. Each picture is analyzed using open source image recognition software to identify the number of individuals, the species and length of each individual based on skin descriptors (color, texture), and shape. The iObserver is equipped with a graphical and user-friendly interface. The information provided by the iObserver is sent to the RedBox software, where it is aggregated and augmented with vessel instrumentation data, such as location, velocity, and course. Then, the data are sent to a shore-based center to be used for different purposes, including the following: feeding mathematical models describing stock evolution; identifying those regions with a large presence of individuals below a Minimum Conservation Reference Size (MCRS); and supporting administrative decisions about a given fishing region

Several compounds with enhanced functional properties of interest in the nutraceutical and medical sectors can be recovered by using the biomass currently wasted in fishing extractive and processing activities, promoting the sustainability of this sector and leading to its development under a bio-economical framework. In particular, it has been observed that crustaceans are an important fraction of the total biomass discarded by fisheries, mainly in those metiers involving coastal waters. Crustacean biomass can be destined to the production of chitin/chitosan (in combination with food use of muscle or protein hydrolysates production) since their exoskeletons are one of the most important sources of this polysaccharide available for commercial use. In this work, the sustainability of both the chemical and enzymatic process to obtain chitin at pilot scale was analysed. The three sustainability dimensions were evaluated and integrated by hierarchical methods to provide a consistent comparison baseline between processes. The results indicated that the enzymatic process could be an adequate alternative that should be considered for chitin extraction, especially if water recovery is employed

In this work we develop a model describing the evolution of the adenosine triphosphate (ATP) degradation products, typically used as early indicators of fish quality loss, during storage and transport conditions. The model is constructed following a modular approach that includes essentially three mechanisms: (1) enzymatic transformation of inosine 5′-monophosphate (IMP), inosine (Ino) and hypoxanthine (Hx) with some reactions catalyzed by bacteria; (2) bacterial growth and (3) nucleotide diffusion through the food matrix. This approach allows us to combine the different underlying mechanisms to account for other fish species and conditions. We compare alternative mechanisms explaining the catalytic effect of Pseudomonas and Shewanella populations on the reaction linking IMP, Ino and Hx. The selection is carried out in terms of the Akaike Information Criteria. The predictive capabilities of the selected model are demonstrated with experiments

In this work a robust nonlinear model predictive controller for nonlinear convection-diusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on POD (Proper Orthogonal Decomposition) basis functions. The model selection and model update step is based on a sucient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.

This work presents a methodology which exploits the underlying biochemical structure of bioprocesses to estimate concentrations in aerobic fermenters from oxygen measurements. Although a number of estimators have been proposed over the years in the literature, the methodology proposed in this work is able to operate in transient conditions while does not require the knowledge of the growth kinetics. In addition, it can be also applied to fermenters where the spatial distribution of the concentrations is relevant. In this case, we propose a systematic approach to optimally locate the sensors based on the use of reduced order models. This method allows the reconstruction of the oxygen concentrations from a limited number of sensors. Finally, the methodology proposed will be illustrated on a horizontal tubular reactor for the production of gluconic acid by free-growth of Aspergillus niger.

Gene expression is inherently stochastic. Advanced single-cell microscopy techniques together with mathematical models for single gene expression led to important insights in elucidating the sources of intrinsic noise in prokaryotic and eukaryotic cells. In addition to the finite size effects due to low copy numbers, translational bursting is a dominant source of stochasticity in cell scenarios involving few short lived mRNA transcripts with high translational efficiency (as is typically the case for prokaryotes), causing protein synthesis to occur in random bursts. In the context of gene regulation cascades, the Chemical Master Equation (CME) governing gene expression has in general no closed form solution, and the accurate stochastic simulation of the dynamics of complex gene regulatory networks is a major computational challenge. The CME associated to a single gene self regulatory motif has been previously approximated by a one dimensional time dependent partial integral differential equation (PIDE). However, to the best of our knowledge, multidimensional versions for such PIDE have not been developed yet. Here we propose a multidimensional PIDE model for regulatory networks involving multiple genes with self and cross regulations (in which genes can be regulated by different transcription factors) derived as the continuous counterpart of a CME with jump process. The model offers a reliable description of systems with translational bursting. In order to provide an efficient numerical solution, we develop a semilagrangian method to discretize the differential part of the PIDE, combined with a composed trapezoidal quadrature formula to approximate the integral term. We apply the model and numerical method to study sustained stochastic oscillations and the development of competence, a particular case of transient differentiation attained by certain bacterial cells under stress conditions. We found that the resulting probability distributions are distinguishable from

ATP-derived products are typically used as early indicators of fish quality loss during storage. In this work, we explore different biochemical routes that are potentially relevant in contributing to nucleotide degradation in hake (Merluccius merluccius). A major motivation of this study is to get more insight on the biochemical degradation mechanisms of nucleotide catabolites in hake muscle at fish storage and transport conditions. This requires the identification of its relevant pathways. To that purpose, different degradation routes proposed in the literature are considered and a mathematical model for the degradation process is derived. First order kinetics are assumed for all the reactions and temperature dependence is taken into account through the Arrhenius equation. Unknown model parameters, namely activation energies and pre-exponential Arrhenius coefficients, are estimated via fitting to experimental data. From the estimation results, relevant routes are identified. The kinetic study is performed on sterile fish juice to avoid coupling with microbial degradation mechanisms or possible interferences of the food matrix that might hide biochemical interactions. The proposed scheme adequately describes biochemical changes in nucleotide catabolites under variable temperature profiles. It also reveals a pathway which at least seems relevant for nucleotide degradation in hake

This paper introduces the potential for reusing UI elements in the context of Model-Based UI Development (MBUID) and provides guidance for future MBUID systems with enhanced reutilization capabilities. Our study is based upon the development of six inter-related projects with a specific MBUID environment which supports standard techniques for reuse such as parametrization and sub-specification, inclusion or shared repositories. We analyze our experience and discuss the benefits and limitations of each technique supported by our MBUID environment. The system architecture, the structure and composition of UI elements and the models specification languages have a decisive impact on reusability. In our case, more than 40% of the elements defined in the UI specifications were reused, resulting in a reduction of 55% of the specification size. Inclusion, parametrization and sub-specification have facilitated modularity and internal reuse of UI specifications at development time, whereas the reuse of UI elements between applications has greatly benefited from sharing repositories of UI elements at run time.

[EN] Objective: To identify preoperative parameters of anatomo-electrical atrial remodelling in order to identify the subgroup of patients more favourable to long-term sinus rhythm (SR) re-establishment after concomitant ablation of persistent-permanent atrial fibrillation (AF). Methods: The study included 50 consecutive patients undergoing concomitant surgical ablation of persistent-permanent AF with cryoablation and bipolar radiofrequency Maze IV pattern. The variables collected before the operation were: demographic variables, morbidity, AF evolution time, transthoracic echocardiographic study, and digital electrocardiogram register for f-wave signal organisation analysis (sample entropy [SampEn]). An assessment was also made of the individual and combined association of atrial remodelling parameters with SR restoration by area under ROC curve (AUC). Results: Follow-up 22.32 +/- 3.19 months. Mean AF onset time 4.00 +/- 4.28 years. Mean left atrium diameter 49.90 +/- 8.18 mm (range = 32 - 81 mm), and SR restoration of 62% at the end of follow-up. Parameters best linked to postoperative SR restoration were left atrium diameter (AUC = 0.848)and SampEn (AUC = 0.845). Cut-off points were 50 mm and 0.0857, respectively, obtaining a model with a predictive accuracy when both parameters combined with an AUC = 0.893. Conclusions: Preoperative anatomo-electrical remodelling analysis through indirect non-nvasive variables could be useful to select patients more favourable to SR restoration after concomitant ablation of AF., [ES] Objetivo: Identificación de parámetros de remodelado auricular anatomoeléctrico preoperatorios que permitan seleccionar un subgrupo de pacientes favorable al restablecimiento del ritmo sinusal (RS) a largo plazo tras ablación quirúrgica concomitante de fibrilación auricular (FA) persistente-permanente. Métodos: Cincuenta pacientes consecutivos sometidos a ablación quirúrgica concomitante de FA persistente-permanente por patrón Maze IV mediante crioblación y radiofrecuencia bipolar. Preoperatorio: se consideraron variables demográficas, morbilidad, tiempo de evolución de FA, estudio de ecocardiografía transtorácica y registro de electrocadiograma digital para análisis de organización de señal de ondas f (entropía muestral [SampEn]). Valoración de la asociación individual y conjunta de los parámetros de remodelado auricular con la restauración de RS mediante área bajo la curva ROC (ABC). Resultados: Seguimiento medio 22,32 ± 3,19 meses. Tiempo medio de evolución de FA 4,00 ± 4,28 años. Diámetro auricular izquierdo medio 49,90 ± 8,18 mm (rango = 32–81 mm). Restauración RS 62% al cierre del seguimiento. Los parámetros que mejor se asociaron con la restauración de RS postoperatorio fueron el diámetro auricular izquierdo (ABC = 0,848) y SampEn (ABC = 0,845). Hallados puntos de corte para ambos en 50 mm y 0,0857, respectivamente; se obtuvo un modelo con capacidad predictiva ABC = 0,893. Conclusiones: El análisis del grado de remodelado auricular anatomoeléctrico preoperatorio mediante variables indirectas incruentas podría ser útil para seleccionar los pacientes más favorables para el restablecimiento de RS tras ablación concomitante de FA.

Organic and inorganic pollutant levels were determined for the most discarded species from trawlers operating in Great Sole and Spanish coastal fishing grounds. Results for heavy metals indicated that Cd can reach values higher than legal limits for some species and tissues, while Hg and Pb concentrations are below established values. No significant variation was noticed with fishing grounds, but both season influences in the case of Pb and interspecies variation for Hg and Cd have been detected. Valorization recommendations could be therefore established according to the levels found in the different species

This paper studies the relations among system parameters, uniqueness, and stability of equilibria, for kinetic systems given in the form of polynomial ODEs. Such models are commonly used to describe the dynamics of nonnegative systems, with a wide range of application fields such as chemistry, systems biology, process modeling or even transportation systems. Using a flux-based description of kinetic models, a canonical representation of the set of all possible feasible equilibria is developed. The characterization is made in terms of strictly stable compartmental matrices to define the so-called family of solutions. Feasibility is imposed by a set of constraints, which are linear on a log-transformed space of complexes, and relate to the kernel of a matrix, the columns of which span the stoichiometric subspace. One particularly interesting representation of these constraints can be expressed in terms of a class of monotonous decreasing functions. This allows connections to be established with classical results in CRNT that relate to the existence and uniqueness of equilibria along positive stoichiometric compatibility classes. In particular, monotonicity can be employed to identify regions in the set of possible reaction rate coefficients leading to complex balancing, and to conclude uniqueness of equilibria for a class of positive deficiency networks. The latter result might support constructing an alternative proof of the well-known deficiency one theorem. The developed notions and results are illustrated through examples

Reuse and valorisation of fish by-products is a key process for marine resources conservation. Usually, fishmeal and oil processing factories collect the by-products generated by fishing port and industry processing activities, producing an economical benefit to both parts. In the same way, different added-value products can be recovered by the valorisation industries whereas fishing companies save the costs associated with the management of those wastes. However, it is important to estimate the advantages of valorisation processes not only in terms of economic income, but also considering the environmental impacts. This would help to know if the valorisation of a residue provokes higher impact than other waste management options, which means that its advantages are probably not enough for guarantying a sustainable waste reuse. To that purpose, there are several methodologies to evaluate the environmental impacts of processes, including those of waste management, providing different indicators which give information on relevant environmental aspects. In the current study, a comparative environmental assessment between a valorisation process (fishmeal and oil production) and different waste management scenarios (composting, incineration and landfilling) was developed. This comparison is a necessary step for the development and industrial implementation of these processes as the best alternative treatment for fish by-products. The obtained results showed that both valorisation process and waste management treatments presented similar impacts. However, a significant benefit can be achieved through valorisation of fish by-products. Additionally, the implications of the possible presence of pollutants were discussed.

Current fishing practices result in the waste of 20 million tonnes of valuable resources every year. However, from now on, vessels must keep on board and land both target and those non-target species subject to quota regulations, as regulated by recent EU legislation, in the reform of the Common Fisheries Policy (CFP). Therefore, an important quantity of low-value marine biomass has to be managed in an efficient manner to avoid its waste. Several added value products apart from fishmeal and oil (like enzymes or nutraceuticals) can be obtained from the wide variety of discarded species trough different valorisation processes. The challenge arises when these species can be handled by more than one processing route. The selection of the best alternatives has to fulfil often-opposite sustainability criteria, considering also the constraints associated to each resource and process. This was achieved by a multiobjective framework using a suitable and efficient optimization approach based on scatter-search. The results from the obtained Pareto fronts show that, in general, the valorisation of specific fish parts rather than the use of the whole specimen is more optimal from both points of view. It is also demonstrated that the most suitable products to be obtained are biopeptides, chondroitin sulphate and fish enzymes, due to their high sales price and relative low environmental impact. On the other hand, alternative technologies to present state-of-the-art ones should be considered for the production of chitin, gelatine and fishmeal due to their high environmental cost. Furthermore, a high number of the most optimal valorisation pathways leave biomass unprocessed and therefore, its treatment as solid waste must be included in the economic and environmental costs

Fish quality has a direct impact on market price and its accurate assessment and prediction are of main importance to set prices, increase competitiveness, resolve conflicts of interest and prevent food wastage due to conservative product shelf-life estimations. In this work we present a general methodology to derive predictive models of fish freshness under different storage conditions. The approach makes use of the theory of optimal experimental design, to maximize data information and in this way reduce the number of experiments. The resulting growth model for specific spoilage microorganisms in hake (Merluccius merluccius) is sufficiently informative to estimate quality sensory indexes under time-varying temperature profiles. In addition it incorporates quantitative information of the uncertainty induced by fish variability. The model has been employed to test the effect of factors such as fishing gear or evisceration, on fish spoilage and therefore fish quality. Results show no significant differences in terms of microbial growth between hake fished by long-line or bottom-set nets, within the implicit uncertainty of the model. Similar conclusions can be drawn for gutted and un-gutted hake along the experiment horizon. In addition, whenever there is the possibility to carry out the necessary experiments, this approach is sufficiently general to be used in other fish species and under different stress variables