Management Scenarios Research Articles

A bioeconomic model for a multispecies small-scale fishery system

A fishery encompasses various interconnected systems, including ecological, socioeconomic, and governing systems. Managing fisheries requires the simultaneous consideration of all these systems, making it a challenging endeavor. To address these challenges, fisheries bioeconomic models have emerged as a crucial tool. They are particularly valuable in the context of small-scale fisheries, which are often complex, overlooked and poorly understood. Thus, this paper presents a dynamic multispecies and multigear bioeconomic model that can illuminate the ecological, economic, and social dimensions of small-scale fisheries under different management scenarios. The model was applied to a small-scale fisheries system in Southern Brazil that has as a notable feature a cooperative fishing behavior between dolphins and fishers. Three scenarios were explored: the base scenario (status quo), the optimal management scenario, and the constrained optimal management scenario. The model outputs demonstrated a clear tradeoff between labour effort, species conservation, and economic rent. Shifting from the base to an optimal management scenario would result in a labour employment reduction within the system but concurrently yield higher stock levels, economic rent, and wages. These results illustrate how our model can explore critical management scenarios across the multiple dimensions of fisheries systems. In essence, this research offers a novel contribution in the form of a bioeconomic model tailored for small-scale fisheries involving multiple species.

Levodopa Equivalent Daily Dosage: Geographical Variations and Real-Life Modules in Parkinson's Disease.

The Levodopa Equivalent Daily Dosage (LEDD) calculation algorithms help in capturing and harmonization of Parkinson's Disease (PD) therapies. Analyzing these updates is essential for validating their effectiveness. To assess updated LEDD conversion factors in capturing the newer therapies in PD and therapy modules in different geographical cohorts. Data were sourced from 10 Centers from 6 countries representing 2 different continents. The study compared the LEDD conversion factors proposed by Tomlinson et al and Jost et al, alongside investigating demographic disparities. The analysis involved 2943 subjects; 87% (n = 2577) met the UK Brain Bank criteria for PD. The LEDD differed significantly across methodologies (Tomlinson vs. Jost, 598 mg vs 610 mg, P < 0.0001). Geographical disparities highlighted variations in PD onset age (P < 0.0001). Jost and Tomlinson's calculations demonstrated consistency within but significant differences across countries (P < 0.0001).Age at onset revealed statistically significant differences in LEDD requirements (P < 0.0001), which were particularly higher in 21-50 years (718 mg vs 566 mg). This subgroup also demonstrated increased usage of non-Levodopa therapies (P < 0.0001). Men exhibited higher total LEDD (P = 0.001). 34% reported dyskinesia, associated with higher LEDD (756 mg, P < 0.0001). Surgically treated patients also had higher LEDD (P < 0.0001) and a significant difference between Jost and Tomlinson dosages (761 mg vs716mg) reflecting the incorporation of newer therapeutic molecules. This analysis delineates the importance of updated LEDD algorithms and intricacies in the landscape of PD treatment, underscored by geographical, age-related, and gender-specific variations, in real-life management scenarios.

Balancing livestock production and environmental outcomes in northern Australia’s tropical savanna under global change

Abstract Livestock production is an integral part of the global food system and the livelihoods of local people, but it also raises questions of environmental sustainability due to issues such as greenhouse gas (GHG) emissions, biodiversity decline, land degradation, and water use. Further challenges to extensive livestock systems may arise from changes in climate and the global economy (particularly variation in prices for livestock and carbon). However, significant potential exists for both mitigating these impacts and adapting to change via altering stocking rates, managing fire, and supplementing cattle diets to reduce methane emissions. We developed an integrated, spatio-temporal modelling approach to assess the effectiveness of these options for land management in northern Australia’s tropical savanna under different global change scenarios. Performance was measured against a range of sustainability indicators, including environmental (GHG emissions, biodiversity, water intake, and land condition) and agricultural (profit, beef production) outcomes. Our model shows that maintaining historical stocking rates is not environmentally sustainable due to the accelerated land degradation exacerbated by a changing climate. However, planned early dry season burning substantially reduced emissions, and in our simulations was profitable under all global change scenarios that included a carbon price. Overall, the balance between production and environmental outcomes could be improved by stocking below modelled carrying capacity and implementing fire management. This management scenario was the most profitable (more than double the profit from maintaining historical stocking rates), prevented land degradation, and reduced GHG emissions by 23%. By integrating the cumulative impacts of climate change, external economic drivers, and management actions across a range of sustainability indicators, we show that the future of rangelands in Australia’s savannas has the potential to balance livestock production and environmental outcomes.

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Enhancing Efficiency of the Minipe Left Bank Canal Irrigation System: A Strategic Approach

Minipe left bank canal faces a head-tail water imbalance due to insufficient water availability and inefficiency in water distribution. To address this issue, Minipe anicut is raised by 3.5m. Though ample water could be released, irrigation water would not be dispersed adequately along the canal due to mis-usage and mismanagement. The study aims to establish an operational pattern for the canal to deliver adequate water to the entire command area during both the Yala and Maha seasons. Simulation models were used to model the canal's operational pattern, including inflows and irrigation demands. Catchment areas, which bring water to the Minipe Left Bank canal, were delineated using ArcGIS software, and inflows were estimated using HEC-HMS software. Crop water requirements were estimated using CROPWAT program. A system simulation model developed usingWEAP software was used to simulate irrigation system operation. Nine management scenarios were evaluated, including introducing new cropping patterns, shifting sowing dates, increasing Minipe anicut discharges, and reducing the command area. The study found that introducing a 70% paddy crop pattern, advancing the sowing date by two weeks, and raising the Minipe anicut discharge by 15% significantly reduce unmet demands. Implementing these improvements could result in a 75% reduction in unmet demand.

Limited cougar recolonization of eastern North America predicted by an individual-based model

As carnivores recolonize parts of their historical range, and such recolonization is increasingly recognized for its ability to reconfigure and revitalize degraded ecosystems, understanding where and when range expansion may continue is useful for facilitating scenario planning and identifying roles for conservation measures.We developed an individual-based model to predict carnivore range expansion and applied it to cougars (Puma concolor) in North America between 2023 and 2100. We parameterized our model with empirical movement, demographic, and survival data, and validated its performance by hindcasting nine observed recolonization events from the 1990s – 2023.Our model accurately recreated historical recolonization events and forecasted cougars reclaiming 2.1 % of unoccupied range by 2100, mostly in boreal Canada. Of currently unoccupied states/provinces (“jurisdictions”), only Manitoba received universal support across model runs for hosting a breeding population by 2100. Oklahoma, Minnesota, Kansas, and Iowa, requiring dispersal across nonhabitat, had 30 %, 30 %, 11 %, and 2 % probability of recolonization, respectively. No other jurisdictions were forecast to be recolonized.Mortality from harvest in Midwestern source populations and vehicle collisions dominated outcomes for eastward-moving females. Simulated management scenarios eliminating such hunting and adding nine wildlife crossing structures, however, did not significantly change recolonization probability.In areas where current cougar range abuts unoccupied breeding habitat, we estimated slower rates of expansion, varying from 2 to 3 km·yr−1, yielding roughly 150–230 km of linear expansion in those regions by 2100.Our mechanistic model and underlying empirical data provide a credible and transferable approach to forecast carnivore range expansion.

Modeling Ecosystem Regulation Services and Performing Cost–Benefit Analysis for Climate Change Mitigation through Nature-Based Solutions Using InVEST Models

Climate change and land degradation menace ecosystem sustainability. This study assessed the effectiveness of integrating nature-based solutions (NBSs); soil and water conservation techniques, agroforestry, and reforestation, to mitigate these impacts. Focusing on carbon storage and sediment retention at the watershed level (Sidi Barrak), the InVEST model quantified changes from 1990 to 2050 under the Business as Usual (BAU) and management scenarios. The results showed a significant decrease in sediment retention and carbon storage from 19.25 to 15.5 t ha−1year−1 and from 1.72 to 1.61 t ha−1year−1, respectively, between 1990 and 2021. By 2050, BAU scenario projections demonstrate a 28% decrease in sediment retention and a 16% drop in carbon storage under Representative Concentration Pathways (RCP) 4.5. The Management scenarios indicate substantial improvements, with carbon storage increasing by 77% and sediment retention by 87% when all strategies were combined. The economic valuation, performed through the application of the cost–benefit analysis, shows positive net benefit values (NPVs) for the different NBS management scenarios. The combined management scenario, which includes soil and water conservation techniques, agroforestry, and reforestation under the same scenario, presents the highest total NPV with 11.4 M€ (2%, 2050), an average of 130 €/ha (2%, 2050), and an opportunity cost of 1.7 M€ compared to BAU. Such results may orient decision-making by providing solid arguments toward ecosystem resilience and climate change mitigation.

O PLANEJAMENTO TRIBUTÁRIO DO PRODUTOR RURAL PESSOA FÍSICA: UMA ANÁLISE SOBRE A EVASÃO FISCAL

The agribusiness is one of the most relevant sectors in the economy. In 2022, it represented 24.3% of the Brazilian GDP. Due to its complex production chain, the stage within the gate is essential, as it represents rural activities focused on agriculture and livestock farming. In these activities, rural producers make crucial decisions related to plant and animal production and the allocation of resources for these activities. The general objective of this study is to present tax planning for individual rural producers within the gate as a strategy to avoid the hypothesis of tax evasion. To achieve this, the specific objectives are to conceptualize tax planning applied to individual rural producers, present the strategic use of tax planning for individual rural producers within the gate, and analyze tax evasion by individual rural producers. The methodological approach is based on logical-semantic constructivism, being a qualitative study with bibliographic, legal, and documentary research as instruments. As results, tax planning, or fiscal avoidance, was conceptualized as a strategic tool that uses legal means to reduce the tax burden. As a tool for individual rural producers, it requires continuous effort for its full implementation, as it is embedded in the complex scenario of sector management and taxation. The study also presents tax evasion considering the incidence of Income Tax (IR), Rural Territorial Property Tax (ITR), Circulation of Goods and Services Tax (ICMS), Social Integration Program (PIS), Contribution to Social Security Financing (COFINS), and Rural Worker Assistance Fund (FUNRURAL) on individual rural producers and their ancillary obligations, highlighting the Digital Cash Book of the Rural Producer (LCDPR), eSocial, and EFD-Reinf.

Research on the impact and improvement path of artificial intelligence on government regulation efficiency

With the deep integration of intelligent technology and government management scenarios, the management functions of modern government have also undergone changes, and artificial intelligence has gradually penetrated into the modernization process of government management. Artificial intelligence not only accelerates the transformation and innovation of government management models, but also impacts government regulation, triggers information security risks, and brings legal and ethical issues to the government. The article will analyze from four aspects: achieving intelligent control, strengthening joint governance, conducting credit supervision, and accelerating regulatory mechanism reform, providing a basis for the government to choose a path to improve regulatory efficiency in the intelligent era.

Evaluation of Waste Management in XYZ University Area in Yogyakarta

The increasing volume of waste is driven by several factors, including population growth, urbanization, and changes in lifestyle. This rise in waste generation has outpaced the capacity of available landfill space, leading to the improper accumulation of unmanaged waste. This study aims to evaluate the current waste management practices, analyze the waste generation and composition, and propose an effective waste management scenario. A quantitative descriptive analysis approach was employed, utilizing waste generation and composition data measured in accordance with the Indonesian National Standard (SNI) 19-3964-1994. The proposed waste management scenario is aligned with the guidelines outlined in Sleman Regency Regulation No. 4 of 2015. The operational techniques for managing waste include stages such as storage, collection, transportation, and treatment, which are outsourced to a third party. The findings of this study indicate that the waste at eight sampling points averaged 359,250 kg/day and 4,058 m³/day. The waste composition is predominantly organic (2.48%), paper and cardboard (12.4%), and other types of waste (11.07%). The proposed processing methods focus on utilizing technologies for organic waste and residue treatment, such as Masaro technology.

Senecio inaequidens DC. will thrive in future climate: A case study in a Mediterranean biodiversity hotspot

Monitoring the expansion of invasive non-native plants under current and future climatic conditions is crucial for understanding biodiversity threats, addressing the ecological impact, and developing effective management strategies. This study focuses on modelling the expansion and distribution of Senecio inaequidens DC. on the island of Sardinia (Italy) to address these environmental challenges. The objectives were to identify bio-climatically suitable areas under current conditions, project potential future distribution, and evaluate invasion dynamics on the island to localize suitable areas for effective management strategies.Species data were collected from 1991 to the present, supplemented by global databases and analyzed using an ensemble species distribution model approach. This approach utilized presence data, high-resolution current bioclimatic variables (40 m2), developed explicitly for our study area, and two future scenarios derived from the newly Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socio-economic Pathways (SSP2–4.5 and SSP5–8.5 for 2040 and 2060).The ensemble model's findings suggest a close alignment between the currently documented occurrences of S. inaequidens and its bio-climatically suitable habitats in Sardinia. Moreover, predictions indicate high bioclimatic suitability for S. inaequidens in the western and southwestern coastal regions, contrasting with its known occurrences at higher altitudes. Notably, the model also forecasts high bio-climatic suitability across most small islands surrounding the region and in central-east Sardinia, potentially indicating habitats at lower altitudes compared to current records.Under the SSP2–4.5 scenario, suitable areas are expected to nearly double by 2040 and more than double by 2060, compared to current conditions. Under the SSP5–8.5 scenario, the increase in suitable habitats is projected to be about 83.31% by 2040 and more than double by 2060. These results highlight the species' ability to thrive under climate change, with a more pronounced range expansion under the pessimistic management regime (SSP5–8.5) than under the intermediate one (SSP2–4.5), particularly in the central region of the island. This expansion under the more severe management scenario is particularly alarming as it reflects limited implementation of environmental management policies.The study underscores the potential ecological risk posed by S. inaequidens due to its potential range expansion and ability to invade different habitat types, from coastal regions to mountainous areas, under current and future scenarios. Based on these findings, we propose targeted management actions for monitoring and eradicating the species, leveraging prior information and local experiences to mitigate its impact.

Prospects of the Application of Data Science in the Financial Field

The application of data science in the financial field has become an important trend in the financial industry. This article reviews the current application status of data science in the financial field, including application scenarios in risk management, credit assessment, market forecasting, etc. Through the analysis of existing research, this article summarizes the benefits brought by data science in the financial field, such as improving decision-making efficiency, reducing risks, optimizing customer experience, etc. In addition, this article also looks forward to the future development trends of data science in the financial field, including the application prospects of new technologies such as machine learning and deep neural networks in the financial field. Finally, this article calls on financial institutions to strengthen the training and technical investment in data science talents to adapt to the new trends in the development of financial technology and achieve digital transformation and innovative development in the financial industry. This article provides a relatively comprehensive introduction to the application of data science in multiple financial fields, reflecting the integration of mathematics, finance, computer science, and other disciplines. Most of the literature on academic websites starts from a specific aspect and introduces a certain field in depth. Although it is quite specific, it lacks an overall perspective.

A knowledge-based directional multicriteria framework with defuzzified subset of preferences for sustainable banking strategy analysis

PurposeThis article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.Design/methodology/approachThe approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.FindingsThe outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.Research limitations/implicationsThe research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.Practical implicationsDecisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.Originality/valueBy integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.

Evaluating crown scorch predictions from a computational fluid dynamics wildland fire simulator

BackgroundCrown scorch—the heating of live leaves, needles, and buds in the vegetative canopy to lethal temperatures without widespread combustion—is one of the most common fire effects shaping post-fire canopies. Despite the ability of computational fluid dynamic models to finely resolve fire activity and buoyant plume dynamics including heterogenous 3D distributions of forest canopy heating, these models have had only limited use in simulating fire effects and have not been used to evaluate crown scorch. Here, we demonstrate a method of evaluating crown scorch using a computational fluid dynamics model, FIRETEC, and validate this approach by simulating the experiments that were used to develop Van Wagner’s 1973 crown scorch model.ResultsThe average scorch height prediction from FIRETEC compares well with the empirical model derived by Van Wagner, which is the most widely used empirical model for crown scorch. We further find that the 3D buoyant plume dynamics from a steady and homogeneous idealized heat source on the ground results in a spatially heterogenous crown scorch pattern reflecting complex heating dynamics that are best represented by percent scorch rather than height of scorch.ConclusionsThe ability of the computational fluid dynamics model to capture variation in crown scorch due to 3D buoyant plume dynamics provides direct links between forest structure, fire behavior, and fire effects that can be used by forest managers and researchers to better understand how fires result in crown damage under various environmental and management scenarios.

Deep learning model for the deformation prediction of concrete dams under multistep and multifeature inputs based on an improved autoformer

The long-term prediction of deformation in concrete dams is a critical requirement for maintaining their structural integrity over time in practical management scenarios. While most existing models predominantly address short-term and medium-term predictions, there remains a significant challenge in establishing reliable correlations within complex long-term temporal patterns and varying environmental factors. Consequently, there is limited research on multistep prediction. To solve these problems, an Autoformer-based model for multistep and multifeature dam deformation prediction is proposed. In the proposed model, the sequence decomposition of dam monitoring data is used as the basic construction module inside the depth model. Then, Deep Automatic Correlation (Deep-AutoCorrelation) mechanism is employed to obtain the long-term dependence between dam deformation and environmental load. The encoder-decoder structure integrates time series decomposition and the Deep-AutoCorrelation to predict the multistep deformation of the dam. In the multistep deformation prediction of the dam, the improved Autoformer model demonstrates state-of-the-art performance, resulting in a 48% increase in prediction accuracy across five monitoring point datasets when compared to six benchmark models. Experimental results indicate that this method effectively addresses the challenge of predicting long-term deformation of dams subjected to complex environmental loads. It demonstrates robust capabilities in extracting long-term temporal features and avoids the increase of computational complexity caused by deeper time extraction.

Cost Benefit Analysis for Feasibility of Waste Management Scenario (Case Study of Ganet Landfill, Tanjungpinang City)

The increase in the number and activities of the population will be in line with the increase in waste generation, resulting in an increase in the burden on the Ganet landfill. The current controlled landfill method can no longer manage the increasing waste load. A more efficient waste management design system is needed to minimize and prevent potential environmental damage. Waste management scenarios are designed based on the potential for waste processing at the Ganet landfill. In selecting waste management scenarios, an economic analysis is required, which is key in designing an effective waste management system. Cost Benefit Analysis (CBA) is used as an economic assessment of the 4 scenarios designed, namely scenario 0 (controlled landfill), scenario 1 (utilization by scavengers, composting and controlled landfill), scenario 2 (utilization by scavengers, composting, pyrolysis and controlled landfill) and scenario 3 (utilization by scavengers, composting, pyrolysis, paving block making and controlled landfill). The cost and benefit components were identified for each waste treatment process. The waste processing that costs the most is controlled landfill in scenario 0 amounting to IDR 8,326,123,157.87, while the waste processing process that produces the greatest benefit value is the manufacture of paving blocks amounting to IDR 158,392,662.33. The results of the Cost Benefit Analysis calculation show a Benefit Cost Rasio (BCR) value of 1.91, Net Present Value (NPV) of 8,448,737,857.40 and the fastest Payback Period (PP) of 5 months is scenario 3. This shows that the scenario is economically feasible to do. The results of this study are expected to be a consideration in the policy formulation related to waste management at the Ganet landfill.

Assessing the strategic role of urban green spaces for habitat connectivity in multi-family residential plots

Urban green spaces are key for maintaining and restoring habitat connectivity within cities. Despite substantial investigations on the contribution to connectivity of different types of urban green spaces, the specific contribution of green spaces located in multifamily residential (MFR) plots has yet to be assessed. Estimating this contribution is crucial given the multiple roles fulfilled by these green spaces in urban planning. To achieve this goal, we focused on four taxa: the European hedgehog (Erinaceus europaeus), the Red squirrel (Sciurus vulgaris), forest passerine birds and moths. We assessed the potential habitat connectivity for each taxon across the metropolitan urban area of Lyon (France), with landscape graphs built using 1-metre resolution land cover maps. Considering the current state as a reference, we then designed five land cover change scenarios affecting habitat distribution in the MFR plots: (i) maximum and (ii) minimum theoretical connectivity (references), (iii) improve quality of existing green spaces, (iv) improve quantity of green spaces with sub-optimal quality and, (v) maximum restoration (management scenarios). The taxon- and scenario-specific impacts highlight the crucial contribution of private spaces to habitat connectivity. For all taxa, while the MFR only cover 8 % of the study area, connectivity would increase by up to 60 % if these plots were greened. In contrast, sealing these plots could reduce overall connectivity by up to 40 %. The interpolation of local connectivity metrics for each habitat patch and each MFR plot allowed us to identify the most relevant plots for urban greening. Because our approach can improve habitat connectivity at different scales, from neighbourhoods to entire urban areas, it could prove useful for landscape planning in most urban settings.

Plastic waste management in urban areas: Key takeaways from the “Send Plastic Home” project in Bangkok, Thailand

This study evaluates the plastic waste recycling program, named “Send Plastic Home,” tailored for urban areas in Thailand. Thirteen strategically located plastic drop-off points were implemented, and their effectiveness was assessed using the Productivity Index (PI). In-depth interviews and group discussions were conducted to identify key stakeholders and estimate the Social Return on Investment (SROI) associated with the project. Moreover, the environmental impacts of various plastic waste management scenarios were investigated and compared. To improve the PI of drop-off points, pass-through points for smaller amounts of plastic and drive-through points for bulk returns are recommended. Increasing drop-off points in highly populated areas, coupled with effective multi-channel public relations, would be beneficial. The current plastic waste management approach, combining recycling and waste-to-energy methods, yields commendable environmental benefits, particularly in mitigating marine aquatic ecotoxicity. With a collected plastic waste volume of 22,135 kg, greenhouse gas emissions and waste management costs are minimized by 27 tonCO2eq and 1803 USD, respectively. The program operates effectively, as evidenced by an impressive SROI ratio of 8.45. These findings provide a data-driven foundation and actionable insights for the design and implementation of more efficient plastic waste collection infrastructures in urban environments, aligning with the circular economy concept.

Effective grease separator management is the key to enhancing bioenergy recovery of fat, oil, and grease (FOG) and contributing to a circular bio-economy

Management of fat, oil and grease (FOG) is crucial for the recovery of renewable resources and the protection of sewer systems. This study aims to identify the potential quantities and qualities of FOG that can be acquired through optimised grease separator (GS) management approaches in hotels and restaurants during seasonal tourism. A technical survey of 20 GS from hotels and restaurants in the federal state of Tyrol, Austria was conducted. The findings revealed that 55 % of the GS were in poor condition, often due to infrequent maintenance and limited operator's knowledge. The FOG layer quality and quantity was monitored over three years and physicochemical parameters including total residue, volatile solids, total organic carbon, lipid content, and biomethane yield, were analysed. An optimised management approach, which involved up to 4 GS emptying per season, revealed a significant increase in FOG quantity for the majority of the inspected establishments, with an overall doubling of the acquired FOG volume. Based on these results, the energy potential of GS is presented in three potential management scenarios. The energy recovered from GS increased by 246 %. This highlights the importance of proper GS management in the hospitality sector, which can play a critical role in promoting environmental sustainability and renewable energy production.

Clean4TSDB: A Data Cleaning Tool for Time Series Databases

Billions of data points are generated by devices equipped with thousands of sensors, leading to significant data quality issues in time series data. These errors not only complicate time series data management but also compromise the accuracy and reliability of analysis based on such data. Given the noteworthy characteristics of time series data, existing cleaning methods struggle to provide adequate repairs, and tools supporting expressive constraints for time series remain scarce. To address this, we develop Clean4TSDB, a specialized data cleaning system for time series databases. This system integrates three key modules: expressive data quality constraint discovery, violation detection, and multivariate time series repairing, forming a comprehensive "profiling-detection-repair" workflow. Technically, we introduce TSDD, a data quality constraint that effectively captures contextual relationships within multivariate time series, and implement an efficient algorithm for its automated mining. Leveraging both row- and column-based constraints, we propose an effective time series cleaning algorithm. From a system standpoint, Clean4TSDB is pre-configured for seamless integration with time series databases like Apache IoTDB. Using user-provided and algorithmically-mined constraints, it effectively identifies various error patterns and offers reliable cleaning solutions. Furthermore, we establish a comprehensive library of state-of-the-art time series repair algorithms to meet the diverse needs of different management scenarios.