Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna
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Optimizing plant biomass from constructed wetlands for biogas production within the water-energy-food nexus
Full text linkAs the global demand for sustainable energy solutions grows, Constructed Wetlands (CWs) are gaining recognition not only for their effectiveness in wastewater treatment but also for their untapped potential as bioenergy sources. This study explores the viability of CW-derived plant biomass for biogas production, evaluating how plant species, maturity stages, and storage durations can influence methane yield. Using biomass from a free water surface wetland in Italy, four plant species, e.g., Phragmites australis, Typha latifolia, Carex spp., and Iris pseudacorus, were analyzed through Biomethane Potential (BMP) tests at three storage intervals: i) immediate – t (0), ii) three months after harvesting – t(1), and iii) six months – t(2) after harvesting, respectively. Results indicate that biogas yield peaked at t(1) for all species, with Iris pseudacorus showing consistent performance over time, and low carbon-to-nitrogen (C/N) ratios correlating with higher methane output. While plant maturity and storage significantly affected volatile solids and gas production, not all decreases in solids translated to higher methane yields. These findings indicate that CW biomass holds potential as a renewable feedstock for biogas production, though further optimization and scale-up studies are needed to confirm its practical applicability. By aligning with the Water-Energy-Food Nexus and Nature-based Solutions (NbS), the research promotes integrated approaches to enhance resource recovery, reduce waste, and support climate resilience
Unobserved component models, approximate filters and dynamic adaptive mixture models
Full text linkState estimation in unobserved component models with parameter uncertainty is traditionally performed through approximate filters, where Gaussian distributions with given moments are employed to replace otherwise intractable conditional densities. This paper re-examines signalplus-noise models where parameter uncertainty is induced by a latent variable that may assume a f ixed number of states. First, it is shown that, for these models, the approximate filters commonly adopted in the literature can be obtained as linear combinations of minimum variance linear unbiased estimators. Second, it is observed that they coincide with filters implied by a novel class of dynamic adaptive mixture models, where the parameters of a mixture of distributions evolve over time following a recursion that is based on the score of the one-step-ahead predictive distribution. Focusing on a robust specification, where the mixture components are Student’s t distributions, we prove existence, stationarity, and ergodicity of the data generating process as well as invertibility of the filter, and consistency and asymptotic normality of the maximum likelihood estimator of the static parameters. An application to energy spot prices is discussed, where the novel specification is compared with, and shown to outperform, robust score-driven f ilters and the related class of mixture autoregressive models
Molecular and structural basis for nitrosoglutathione-dependent redox regulation of triosephosphate isomerase from Chlamydomonas reinhardtii
No full textProtein S-nitrosylation is a reversible redox-based post-translational modification that plays an important role in cell signaling by modulating protein function and stability. At the molecular level, S-nitrosylation consists of the formation of a nitrosothiol (-SNO) and is primarily induced by the trans-nitrosylating agent nitrosoglutathione (GSNO). Triosephosphate isomerase (TPI), which catalyzes the interconversion of dihydroxyacetone phosphate and glyceraldehyde-3-phosphate, has been identified as a putative target of S-nitrosylation in both plant and non-plant systems. Here we investigate the molecular basis for GSNO-dependent regulation of chloroplast TPI from the model green alga Chlamydomonas reinhardtii (CrTPI). Molecular modelling identified Cys14 and Cys219 as potential sites for interaction with GSNO, though crystallography of GSNO-treated CrTPI revealed S-nitrosylation only at Cys14. To disclose GSNO target sites, we generated and characterized Cys-to-Ser variants for Cys14 and Cys219, identifying Cys219 as a key residue mediating the GSNO-dependent modulation of CrTPI activity. Molecular dynamics simulations further revealed the stabilizing interactions of S-nitrosylated cysteines with their local environments. Overall, our results indicate that CrTPI catalysis is modulated by GSNO through a redox-based mechanism involving Cys219, which highlights a conserved regulatory strategy shared with human TPI
A fuzzy-and-fair framework for solar irradiance modeling and derivative pricing: Bridging photovoltaic production risk and climate-linked finance
No full textThe variability of solar irradiance is a critical source of risk for photovoltaic (PV) producers, directly affecting energy output, project valuation, and financial stability. As solar penetration grows, existing hedging tools that primarily address price risk are insufficient, since they do not protect against the volumetric risk arising from fluctuations in solar production. This paper introduces a novel fuzzy-and-fair modeling framework that simultaneously addresses the physical characterization of solar radiation and its market valuation through derivative pricing. Our approach leverages a bounded transformation and a bimodal representation (clear and cloudy conditions) model calibrated on extraterrestrial horizontal radiation to produce smooth, seasonal, and realistic clear-sky profiles, accounting for daily and inter-annual variability. The innovation lies in directly integrating these irradiance estimates into the fair pricing of solar radiation derivatives, financial instruments designed to hedge volumetric production risk in PV projects. By translating irradiance uncertainty into market-consistent risk premiums, the model enables more robust PV project valuations, providing forward-looking signals for PV plant design, investment appraisal, and energy portfolio management by structuring hedging contracts in incomplete markets where solar radiation is a non-tradable underlying. In this work, we propose a two-step pricing methodology: first, fair values are obtained via replication-based strategies adapted to incomplete markets; second, a fuzzy extension accounts for ambiguity in data quality and heterogeneous expectations, generating bid–ask corridors for solar radiation derivatives. An empirical analysis using data from eight European locations demonstrates the model’s accuracy, flexibility, and applicability across different climatic regimes. The proposed approach offers both methodological innovation in stochastic modeling and practical tools for integrating renewable resource variability into financial decision-making, thereby supporting a more resilient and stable renewable energy transition
Bridging biodiversity gaps: Assessing R tools for harmonising vascular plant records
No full textBiodiversity databases provide unprecedented opportunities for the use of species occurrence data for the
development of large scale biodiversity analyses. However, these records often contain taxonomic uncertainties
that can ultimately affect the outcomes of downstream analyses. Although several tools have been developed to
address these issues, there is limited guidance on how to efficiently use and integrate them.
Here, we present a reproducible workflow for handling vascular plant occurrence data, and provide the first
comparative analysis of R packages for the taxonomic harmonisation of vascular plant names. Our goal is to
assess the differences in performance across the tested tools and to highlight best practices for leveraging large
biodiversity databases.
We first downloaded occurrence data for vascular plants in Italy from the Botanical Information and Ecology
Network (BIEN) and Global Biodiversity Information Facility (GBIF). We then compared seven R packages for
taxonomic harmonisation, evaluating their ability to resolve names to accepted taxa and their overall
performance.
Our results highlight heterogeneity in the number of names resolved by the different tools, with packages
relying on plant-specific databases and implementing fuzzy matching outperforming those based on generalist
databases and with no possibility of fuzzy matching. These findings underscore that the choice of both packages
and taxonomic authorities can have a strong influence on data cleaning outcomes
Shoreline evolution in a low-lying coastal region under anthropogenic influence
No full textThis study aims to reconstruct and analyze shoreline evolution along the highly urbanized Emilia-Romagna
coast (Italy) over the period 1984–2023 using satellite-derived shorelines. Landsat and Sentinel-2 imagery were
processed with the CoastSat toolbox, and shoreline positions were corrected for tide and wave setup before
deriving yearly averages from 2,200 transects. While the spatial resolution of yearly-averaged SDS is lower than
that of conventional techniques, this approach enables, for the first time in this region, the reconstruction of
shoreline dynamics as a continuous time series spanning interannual to multi-decadal scales. Results highlight
substantial spatial and temporal variability across the shoreline, driven by the interplay of natural processes
and anthropogenic interventions such as coastal defenses, repeated nourishments, and sediment extraction.
Despite ongoing sea-level rise and subsidence, most of the coast exhibits stability or net advancement, largely
maintained through human interventions. The resulting dataset provides one of the most temporally extensive
records of shoreline variability for the Emilia-Romagna coast and represents a valid basis for future monitoring
and coastal management
Robot-aided electric vehicle routing problem with lockers and prime customers prioritization
No full textSatisfactory and fast customer service is one of the critical parts of last-mile delivery. Companies like Amazon prioritize Prime members with same-day delivery while offering lockers for customer convenience. Additionally, robot-aided Electric Vehicle (EV) delivery is recognized for its cost efficiency and fast service in densely populated areas. Integrating EVs, delivery robots, and lockers, and prioritizing Prime customers can improve efficiency and service responsiveness. This integrated approach offers home delivery by EVs and robots and self-pickup from lockers. Every customer is assigned a prize (profit), with a higher profit associated with the Prime membership. Each EV dispatches robots, with a “dispatch-wait-collect” tactic, to serve the customers, while some customers are allocated to the lockers. This study introduces the Robot-Aided Electric Vehicle Routing Problem with Lockers and Prime Customer Prioritization (REVRP-LPCP), which aims to determine the least-cost routes for EVs and robots, assign customers to lockers, and prioritize prime customers by serving them within a single-period planning horizon. The REVRP-LPCP is formulated using a mixed-integer linear programming model, improving the EV-only-based delivery system by 52.94% and 21.95% in EV route and utilization costs on average. A metaheuristic is introduced, incorporating problem-specific repair and improvement operators to efficiently address large instances of the problem, outperforming Gurobi in 36 large instances by an average of 2.79% in terms of solution quality. Also, our method has identified 44 new best solutions in the related benchmarks. A comprehensive sensitivity analysis is conducted, assessing various scenarios and providing managerial insights
„Obwohl ich das jetzt auch nicht soo schlecht finde“ vs. „Obwohl, ne. Dann bleibt die Tür zu.“ Konzessiv- und Korrektivsätze auf WhatsApp im DaF-Unterricht
No full textMittels einer quantitativen Analyse von Textnachrichten im Instant-Messaging-Dienst WhatsApp (Kap. 2) soll zuerst das Datenmaterial in Bezug auf obwohl numerisch erhoben werden. Die gewonnenen Daten werden im Anschluss daran statistisch verarbeitet, um das Distributionsverhältnis möglicher verschiedener Verwendungsweisen von obwohl zu erfassen (Kap. 3). Der Einblick in den empirischen Sachverhalt erlaubt dann mittels einer qualitativen Analyse, die „polyfunktionalen Eigenschaften“ (Freywald 2018) von obwohl im jeweiligen Kontext zu definieren. Die Schwierigkeit bei der Erhebung des Datenmaterials besteht in diesem Falle allerdings darin, dass der Instant-Messaging-Dienst WhatsApp – im Unterschied etwa zu X (früher Twitter) – keine öffentlich zugängliche Kommunikationsplattform ist, auf der man ungehindert (nicht nur) auf Textnachrichten zugreifen könnte. Vielmehr findet der Austausch u. a. von Mitteilungen, Kontaktdaten, verlinkten Querverweisen, Bild-, Video- und Audio-Dateien zwischen zwei oder mehr Personen im geschlossenen Raum statt. Um daher valide und verwertbare Daten erheben zu können, erfolgt die Recherche und das Sammeln relevanter Informationen zu obwohl über die Datenbank Mobile Communication Database 2 (MoCoDa 2). Datenbanken wie MoCoDa 2 haben sowohl ein „didaktisch-pädagogisches“ als auch „unterrichtsmethodisches Potenzial“, denn sie bieten u.a. „interessante und neue Zugänge für die Sprachbeschreibung“ und die Entwicklung „von Lehr- und Unterrichtsmaterialien sowie für die Anwendung und Unterrichtspraxis“ (Flinz et al. 2021). Im Zuge der Kultur der Digitalität (Stadler 2016), „die althergebrachte Kommunikationspraktiken regelrecht revolutioniert“ hat (Moraldo 2004), will die Arbeit nicht zuletzt bei der Gestaltung von Lehr- und Lernprozessen das Bewusstsein für einen nachhaltigen Einsatz neumedialer Kommunikationsformen im DaF-Unterricht schärfen. In Kap. 4 wird dann für eine Parallelisierung der Behandlung von Konzessiv- und Korrektivsätzen (vgl. zum Be¬griff Moraldo 2012b) im DaF-Unterricht plädiert, die anhand von ‚getippten Dialogen‘ (Dürscheid/Brommer 2009) aus MoCoDa 2 durchgeführt werden kann. Abgeschlossen wird mit einem Fazit (Kap. 5)
Duration-Informed Workload Scheduler
No full textHigh-performance computing systems are complex machines whose behaviour is governed by the correct functioning of its many subsystems. Among these, the workload scheduler has a crucial impact on the timely execution of the jobs continuously submitted to the computing resources. Making high-quality scheduling decisions is contingent on knowing the duration of submitted jobs before their execution–a non-trivial task for users that can be tackled with Machine Learning. In this work, we devise a workload scheduler enhanced with a duration prediction module built via Machine Learning. We evaluate its effectiveness and show its performance using workload traces from a Tier-0 supercomputer, demonstrating a decrease in mean waiting time across all jobs of around 11%. Lower waiting times are directly connected to better quality of service from the users’ point of view and higher turnaround from the system’s perspective
Grading and staging for pituitary neuroendocrine tumors
Full text linkPituitary adenoma/pituitary neuroendocrine tumors (PitNETs) are the second most common primary intracranial tumor and the most frequent neuroendocrine tumors/neoplasms of the human body. Thus, they are one of the most frequent diagnoses in neuropathologist's practise. 2022 5th edition WHO Classification of Endocrine and Neuroendocrine Tumors does not support a grading and/or staging system for PitNETs and argues that histological typing and subtyping are more robust than proliferation rate and invasiveness to stratify tumors. Numerous studies suggest the existence of clinically relevant molecular subgroups encouraging an integrated histo-molecular approach to the diagnosis of PitNETs to deepen the understanding of their biology and overcome the unresolved problem of grading system. The present review illustrates the main issues involved in establishing a grading and a staging system, as well as alternative systems validated by independent series to date. The state of art of the current histological and molecular markers is detailed, demonstrating that a standardized and reproducible clinico-pathological approach, combined with the integration of molecular data may help build a workflow to refine the definition of PitNETs with ‘malignant potential’ and most importantly, avoid delay in patient treatment. Next molecular studied are needed to validate an integrated histo-molecular grading for PitNETs