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This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to implement flexible and reconfigurable smart production systems using the information system integration in order to optimize material flow in a 4.0 full-service approach. The proposed methodology aims to reduce the occurring stock-out events through a link among the wear-out items rate and the downstream logistic demand. The failure rate items trend is obtained through life-cycle state detection by a curve fitting technique. Therefore, the optimal safety stock size is calculated and then validated by an auto-tuning iterative modified algorithm. In this study, the reorder time has been optimized. The case study refers to the material management of a very high-speed train.

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance.

This study investigated the comparative cultivation of six medicinal plant species (sage, oregano, rosemary, lavender, thyme, and mint) in a dynamic agrivoltaic (AV) system and a neighboring control plot exposed to full sun (referred to as “T”). Specifically, within the dynamic AV system, two distinct plot areas on the ground were identified due to the rotation of the panels: one consistently in the shade of the solar panels (UP), and another alternately in shade and sunlight (BP). The study involved the measurement of solar radiation, air temperature, and infrared leaf temperature during crop growth in these designated plots. Additionally, a weed survey was conducted at harvest time. The findings revealed that solar radiation, air temperature, infrared leaf temperature, and weed coverage were notably lower in the UP plot compared to both the BP and T plots. Furthermore, the yield of essential oils in sage, thyme, mint, and rosemary plants was higher in both the UP and BP plots than in the T plot. Hence, these factors seemingly positively impacted the performance of specific medicinal crops within the dynamic AV system. This information holds significance for producers and processors concerning crop quality.

High Concentrator Photovoltaic Thermal (HCPV/T) systems produce both electrical and thermal energy and they are efficient in areas with high Direct Normal Irradiance (DNI). This paper estimates the lifecycle environmental impact of the HCPV/T 2000x system for both electrical and thermal functionalities. Process-based attributional method following the guidelines and framework of ISO 14044/40 was used to conduct the Life Cycle Assessment (LCA). The midpoint and endpoint impact categories were studied. It was found that the main hotspots are the production of the thermal energy system contributing with 50% and 55%, respectively, followed by the production of the tracking system with 29% and 32% and the operation and maintenance with 13% and 7%. The main contributor to the lifecycle environmental impact category indicators was found to be the raw materials acquisition/production and manufacturing of the thermal energy and tracking systems. The results indicate that the lifecycle environmental impact of the HCPV/T 2000x system is lower compared to fuel-based Combined Heat and Power (CHP) and non-Renewable Energy Sources (non-RES) systems.

The added value coming from involvement of stakeholders in changing attitudes and cultures towards a more sustainable-oriented society has been repeatedly emphasized in documents of the European Union. Those documents emphasize the advisability of creating a more inclusive system from the early planning stages and for the whole process with regard to the development of EU policies, involving the stakeholder as a referring partnership both at the national and regional levels. This paper focuses on a case study related to an Italian region where the local partnership has been involved during the setting up of the Rural Development Program 2014–2020 and where a participatory approach has been applied. In order to create an effective output coming from these open consultations, a participatory approach has been carried out using a dynamized Strengths and Weaknesses, Opportunities and Threats analysis (SWOT Analysis). The results presented here come from a specific thematic table where experts and stakeholders discussed a specific priority focused on innovation, training and advisory in rural areas. As expected, sustainable innovations and services as well as training courses need to be improved, while financial resources addressing those issues need to be increased.

The COVID-19 pandemic has caused an abrupt break in economic, demographic and social dynamics, both in developing countries and advanced economies, perhaps with a more significant impact in the latter, though further evidence is needed to support this assumption. Unfortunately, earlier research on medium- and long-term impacts of the pandemic on urban and regional systems—with particular reference to the demographic dimension—have not yet reached a consensus on methodological and operational approaches. In the present study, we have applied an interpretative framework to the analysis of the demographic balance in Italy, one of the most affected countries in the world, before and during the COVID-19 pandemic. Specifically, we have compared a wide set of demographic indicators at two time periods of equal duration (2002–2010 and 2011–2019) and in two subsequent years (2020 and 2021), controlling for the regional context. These periods were chosen as sufficiently long to be representative of differentiated economic dynamics (2002–2010: economic expansion and demographic recovery; 2011–2019: recession and consequent demographic decline). Years 2020 and 2021 were assumed to reflect the short- and medium-term impact of the COVID-19 pandemic. The results of the statistical analysis highlight how the COVID-19 pandemic has exerted considerable pressure on population dynamics, determining short-term (mortality increase), medium-term (more volatile migration flows) and long-term (fertility decline) effects. Future studies should clarify the aggregate role of pandemics in population dynamics as a possible proxy of the decline of demographically fragile regions in advanced economies.

This study reports the quantification and analysis of the ecosystem services (ESs) value of Posidonia oceanica (L.) Delile (1813) meadows in Italy (Liguria, Tuscany, Latium, Campania, Calabria, Apulia, Sardinia, and Sicily regions). The ES evaluation method of P. oceanica meadows applied in this study was obtained from a previous study in which the site-specific approach for the definition of the benefits and services was applied. The distribution of P. oceanica and the ESs economic value have been managed through an open-source geographic information system (QGIS), focusing on five essential ESs: carbon sequestration, bioremediation, oxygen production, erosion protection, and food production. The average value of the ESs obtained on the Italian national scale is €21,660.5 ha−1 yr−1, which is comparable with the values reported in the international literature concerning P. oceanica ESs. The results of this study confirm that the economic evaluation of ESs is an essential tool for the management of the coastal marine environment, especially considering the modularity of the applied approach. The value of the total benefits, considering the entire extension of the national P. oceanica meadows, represents significant value with respect to the Italian gross domestic product, and the individual budgets of the considered Italian regions.