Storing apples for up to a year is a well-established practice aimed at providing a continuous, locally-produced fruit supply to consumers and adapting to market trends for optimized profits. Temperature control is the cornerstone of postharvest conservation and apples are typically kept at temperatures from 0 to 3 °C. However, the energy-intensive process of the initial cool down and subsequent temperature maintenance poses significant financial challenges and contributes to the carbon footprint. Higher storage temperatures could reduce cooling-related energy usage but also pose the risk of enhanced ripening and quality loss. This work explores different storage technologies aiming to reduce energy consumption such as 1-methylcyclopropene, ultra-low oxygen and dynamic controlled atmosphere together with raised temperatures. The integration of advanced monitoring and control systems, coupled with data analytics and energy management in apple storage is also discussed. These sustainable strategies can be implemented without cost-intensive construction measures in standard storage facilities. Furthermore, beneficial side effects of higher storage temperatures in terms of a reduced occurrence of storage disorder symptoms and higher maintenance of quality attributes are also discussed for this special issue on sustainable horticultural production systems and supply chains.

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

As is well known, the main contribution of the FRP strips to the strength of load-bearing walls is an improvement in the in-plane strength. This paper deals with the possibility of applying the FRP strips in way to modify the strengthening mechanism of the FRP reinforcing system, from an in-plane to an out-of-plane strengthening mechanism. In order to achieve this goal, a second reinforcement system – derived from the CAM system (Active Confinement of Masonry) – provides connections between the FRP strips placed on the opposite sides of the wall. This new strengthening technique – called the straps/strips technique – establishes a stiffness constraint that forces the opposing FRP strips to behave like two flanges of an FRP I-beam embedded in the wall. Consequently, the use of FRP strips also improves the flexural strength of the wall. The present paper briefly summarizes the results obtained in previous works with the straps/strips technique and proposes an improvement of this strengthening technique, based on some weak-points emerged in the early experimentations. The paper also shows the results of a further experimental test, performed with the improved straps/strips technique. Finally, the similarity between FRP strips with transversal connection and concrete wythes of a sandwich panel with flexible connectors leads to interpret the behavior of the ideal I-beam in terms of composite action established between the FRP strips. This paves the way for analytical modeling of the straps/strips technique.

Fire is a common natural disturbance in forest ecosystems and plays an important role in subsequent vegetation patterns. Based on the spatial sequence method instead of the time successional sequence method, this study selected burned areas in different locations in the Anning River Basin, which contains typical dry valleys. Quadrat surveys and quantitative classification were used to identify the vegetation classification, distribution pattern, and environmental interpretation during the natural restoration process after forest fire. The results showed that: (1) the vegetation community in the early stage of natural recovery after forest fire disturbance could be divided into seven community types, and Quercus guyavaefolia H. Leveille (Qg) was the dominant species in the community; (2) vegetation samples could be divided into five ecological types, and the classification and distribution pattern of community types in this region changed most obviously with altitude; and (3) detrended correspondence analysis could clearly classify vegetation community types, and detrended canonical correspondence analysis could well reveal the relationships between species and environmental factors. This study provides a scientific basis guiding the restoration of ecosystem structural stability and biodiversity in burned areas.

The study comprehensively evaluated the prognostic roles of PLR, NLR, MLR, BLR, and ELR in patients with acute exacerbation chronic obstructive pulmonary disease (AECOPD). 619 patients with AECOPD and 300 healthy volunteers were retrospectively included into the study. The clinical characteristics containing laboratory findings of the AECOPD patients and the blood cell counts (CBCs) of the healthy volunteers were collected. Compared with the healthy volunteers, PLR, NLR, and MLR were elevated in COPD patients in stable condition, and were further ele-vated during exacerbation. ELR showed the opposite trend. PLR, NLR, and MLR were all posi-tively correlated with hospital LOS as well as CRP. In contrast, ELR was negatively correlated with hospital LOS as well as CRP. Elevated PLR, NLR, and MLR were all associated with more serious airflow limitation in AECOPD. Elevated PLR, NLR, and MLR were all associated with increased in-hospital mortality while Elevated ELR was associated with decreased in-hospital mortality in AECOPD. A nomogram was construct to predict in-hospital mortality in AECOPD. The nomo-gram had a C-index of 0.850 (95% CI: 0.799 – 0.901) with good predictive value and clinical ap-plicability. In summary, PLR, NLR, MLR, and ELR served as predictors for clinical outcomes in patients with AECOPD.