Luis Ruiz-Garcia

Introducing vegetation is an effective strategy for improving air quality and mitigating the heat island effect. Green modules, which consist of modules that support substrates and various plant species, integrate these elements. This study analyzes the acoustic absorption properties of a specific green wall module using an impedance gun and the Scan and Paint method for laboratory and on-site measurements. The impedance gun method is effective for in situ analysis, offering advantages over standardized techniques for inhomogeneous samples. The sound absorption coefficient of the substrate and the effects of different plant species were measured. Key findings reveal that the substrate primarily influences sound absorption, with its coefficient increasing with frequency, similar to porous materials. Vegetation enhances the acoustic absorption of the substrate, depending on coverage and thickness, with 80–90% of absorption attributed to the substrate and 4–20% to vegetation. However, not all dense plant species improve absorption; some configurations may decrease it. Improvement correlates with substrate coverage and vegetation layer thickness, while the impact of plant morphology remains unclear. These findings confirm vegetation’s potential as an acoustic absorption tool in urban settings. Additionally, green walls can enhance acoustic comfort in indoor environments such as offices and schools by reducing reverberation. They also improve air quality and provide aesthetic appeal, making them a multifunctional solution for modern architecture.

Introducing vegetation is an effective strategy for improving air quality and mitigating the heat island effect. Green façades, which consist of modules that support substrates and various plant species, integrate these elements. This study analyzes the acoustic absorption properties of a specific green wall module using an impedance gun and the Scan and Paint method for laboratory and on-site measurements. The impedance gun method is effective for in-situ analysis, offering advantages over standardized techniques for inhomogeneous samples. We measured the sound absorption coefficient of the substrate and the effects of different plant species. Key findings reveal that the substrate primarily influences sound absorption, with its coefficient increasing with frequency, similar to porous materials Vegetation enhances acoustic absorption of the substrate, depending on coverage and thickness, with 80-90% of absorption attributed to the substrate and 4-20% to vegetation. However, not all dense plant species improve absorption; some configurations may decrease it. Improvement correlates with substrate coverage and vegetation layer thickness, while the impact of plant morphology remains unclear. These findings confirm vegetation's potential as an acoustic absorption tool in urban settings. Additionally, green walls can enhance acoustic comfort in indoor environments such as offices and schools by reducing reverberation. They also improve air quality and provide aesthetic appeal, making them a multifunctional solution for modern architecture.

This study assesses the adoption and operational effectiveness of Environmental Management Systems (EMSs) in Italian wineries, focusing on ISO 14001:2015. It evaluates commitment, planning, communication strategies, emergency preparedness, and employee training practices. Using a comprehensive survey-based methodology, the research elucidates the dynamics of EMS implementation across various scales of winery operations. The research reveals a strong commitment among wineries to environmental objectives such as waste reduction and efficient electricity and water use. However, significant deficiencies were identified in EMS policy implementation, emergency preparedness, and the uptake of ISO 14001:2015 certification, with larger wineries showing more robust engagement in environmental training than smaller ones. The study incorporates five key performance indicators (KPIs) and a predictive model using logistic regression and Random Forest to analyze the likelihood of ISO 14001 certification based on the analyzed variables. The model highlights established processes, environmental policies, and frequent reviews as significant predictors of certification. These findings contribute original value by identifying critical leverage points and barriers affecting EMS effectiveness within the wine sector. The research uncovers nuanced interactions between the scale of operations and management engagement influencing EMSs’ success. It proposes novel, survey-based KPIs essential for assessing EMS performance in wineries, demonstrating their practical utility in pinpointing areas for improvement. The research limitations include potential biases from varying participation rates among surveyed wineries, affecting extrapolation to the broader Italian wine industry. Despite these limitations, the study provides substantive practical implications, suggesting that wineries can enhance both environmental sustainability and a competitive edge by addressing gaps in EMS implementation.

Determining the residual value of tractors is imperative for comprehensive cost analyses within the agricultural machinery sector. Despite numerous studies offering various models and independent variables, the double square root regression approach, originally developed by Cross and Perry and adapted by ASABE for North American contexts, has been widely utilized. However, factors such as the complexity of OEM portfolios, steep price increases due to compliance with diesel emission regulations, and limited data availability in Europe and its market specificities necessitate improvements in predictive accuracy. This study evaluates different tractor cohort alternatives beyond engine horsepower to enhance predictive robustness. Incorporating brand and powertrain type alongside engine power significantly improved model performance and exhibited the strongest robustness, as evidenced by reduced the root mean square error (RMSE) and increased R-squared values. These findings contribute to the refinement of tractor residual value estimation models, offering valuable insights for stakeholders in the agricultural machinery industry.

Monitoring perishable goods transports is fundamental in order to guarantee their quality for consumers. Fruits & vegetables suffer different risks along their transport along the supply chain, so that material and quality losses occur. This risk is related to lack of appropriate monitoring and control of time, temperature and humidity, and causes senescence

Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R²) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overall R-squared (R²) of 0.99951 and a Root Mean Square Error (RMSE) of 2321. While the parametric polynomial model offers a solution with minimal mathematical and computational complexity, the non-parametric GPR model delivers highly robust outcomes, presenting stakeholders involved in new agriculture tractor transactions with superior data-driven decision-making capabilities.

The residual value of agricultural tractors plays a pivotal role in the financial viability of agribusiness enterprises. Nevertheless, there is a dearth of comprehensive studies concerning the prognostication of both retail and wholesale residual values specific to agricultural tractors within the context of Western Europe. This research introduces an innovative methodology for assessing the residual worth of agricultural tractors, with particular consideration given to the substantial pricing discrepancies between retail and wholesale transactions. Leveraging publicly available auction data, we develop a polynomial regression model aimed at forecasting the intricate relationship between retail and wholesale residual values. Notably, the model demonstrates an exceptional robustness, surpassing previous research endeavors, as evidenced by a remarkably low root-mean-square error (RMSE) of 0.0159 and a combined adjusted coefficient of determination (RSqAdj) of 0.9997. The findings of this study offer invaluable insights into a diverse array of stakeholders, empowering them to make well-informed decisions regarding machinery specifications, investment strategies, and asset disposal choices, thereby facilitating optimal financial performance.

Cold chain disruption and refrigeration failures are common issues in the logistics of perishable food products. In these cases, the use of pallet covers should be very useful, delaying the increase of product temperatures inside the pallets until cooling conditions are restored. However, there are no studies about the performance of pallet covers in these types of situations, which could persist for hours. This paper evaluates the performance of three different types of cold chain covers versus having no cover for three different types of vegetables (chard, cucumbers, and carrots). A refrigeration failure during the cold chain was simulated. The three covers presented an improvement in temperature loss compared to the no-cover situation, with the average time for the temperature to increase from 4 to 10 °C with a cover being a range of 214 to 506 min, while for no cover, from 162 to 211 min. Relative humidity (RH) always presented improved preservation with a cover than with no cover, except for one case. The correlation between the thermal images and sensor temperatures was also studied.

Harvesting is an integral component of the agricultural cycle, necessitating the use of high-performance grain harvester combines, which are utilized for a short period each year. Given the seasonality and significant cost involved, list prices ranging from a quarter to almost a million euros, a fact-based investment assessment decision-making process is essential. However, there is a paucity of research studies forecasting the remaining value of grain harvester combines in recent years. This study proposes a straightforward methodology based on public information that employs various parametric and non-parametric models to develop a robust and user-friendly model that can assist decision makers, such as farmers, contractors, sellers, and finance and insurance entities, in optimizing their harvesting operations. The model employs a power regression mode, with RMSE of 1.574 and RSqAdj of 0.8457 results, to provide accurate and reliable insights for informed decision-making. The robust model transparency enables us to easily create a mainstreamed spreadsheet-based dashboard tool.