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Integrating a grid-connected battery into a renewable energy community amplifies the collective self-consumption of photovoltaic energy and facilitates energy arbitrage in the electricity markets. However, how much can energy independence really increase? Is it a cost-effective investment? The answer to these questions represents a novelty in the literature due to the innovative nature of the asset under consideration and the market and regulatory framework in which it is evaluated. Employing a net present value assessment, our analysis incorporated aging effects and conducts sensitivity analyses across various parameters: the number of community customers, electricity market prices, battery cost and size, and the decision to engage in energy arbitrage. Each scenario underwent a 20-year hourly simulation using an aging-aware rolling-horizon 24 h-looking-ahead scheduling, optimized with mixed-integer linear programming. Simulations conducted on the Italian market indicate that dedicating a battery solely to collective self-consumption is the most efficient solution for promoting a community's energy independence, but it lacks economic appeal. However, integrating energy arbitrage, despite slight compromises in self-sufficiency and battery longevity, halves the payback period and enhances the attractiveness of larger battery investments. The net present value is contingent upon the battery size, customer number, and market prices. Nevertheless, if the battery cost does not exceed 200 EUR/kWh, the investment becomes cost-effective across all scenarios. [ABSTRACT FROM AUTHOR]

The development of spectral sensors (SSs) capable of retrieving spectral information have opened new opportunities to improve several environmental and agricultural practices, e.g., crop breeding, plant phenotyping, land use monitoring, and crop classification. The SSs are classified as multispectral and hyperspectral (HS) based on the number of the spectral bands resolved and sampled during data acquisition. Large-scale applications of the HS remain limited due to the cost of this type of technology and the technical difficulties in hyperspectral data processing. Low-cost portable hyperspectral cameras (PHCs) have been progressively developed; however, critical aspects associated with data acquisition and processing, such as the presence of spectral discontinuities, signal jumps, and a high level of background noise, were reported. The aim of this work was to analyze and improve the hyperspectral output of a PHC Senop HSC-2 device by developing a general use methodology. Several signal gaps were identified as falls and jumps across the spectral signatures near 513, 650, and 930 nm, while the dark current signal magnitude and variability associated with instrumental noise showed an increasing trend over time. A data correction pipeline was successfully developed and tested, leading to 99% and 74% reductions in radiance signal jumps identified at 650 and 830 nm, respectively, while the impact of noise on the acquired signal was assessed to be in the range of 10% to 15%. The developed methodology can be effectively applied to other low-cost hyperspectral cameras. [ABSTRACT FROM AUTHOR]

This paper presents an extensive review of the most effective and modern monitoring methods for electrical power lines, with particular attention to high-voltage (HV) and medium-voltage (MV) systems. From a general point of view, the main objective of these techniques is to prevent catastrophic failures by detecting the partial damage or deterioration of components and allowing maintenance operations to be organized. In fact, the protection devices commonly used in transmission and distribution networks guarantee the location of faults, such as short-circuits, putting the non-functioning branch of the network out of service. Nowadays, alongside these devices, it is possible to introduce new intelligent algorithms capable of avoiding the total loss of functionality, thus improving the reliability of the entire network. This is one of the main challenges in modern smart grids, which are characterized by the massive integration of renewable energy sources and a high level of complexity. Therefore, in the first part of this paper, a general overview of the most common protection devices is proposed, followed by an analysis of the most modern prevention algorithms. In the first case, the coordination of the relays plays a fundamental role in obtaining the fault location with a high level of selectivity, while in the field of preventive analysis, it is necessary to address the implementation of artificial intelligence methods. The techniques presented in this paper provide a comprehensive description of the different monitoring approaches currently used in distribution and transmission lines, highlighting the coordination of protection relays, the computational algorithms capable of preventing failures, and the influence of the distributed generation in their management. Therefore, this paper offers an overview of the main diagnostic techniques and protection devices, highlights the critical issues that can be overcome through the introduction of artificial intelligence, and describes the main prognostic methods, focusing on their invasive level and the possibility of operating directly online. This work also highlights the main guidelines for the classification and choice between the different approaches. [ABSTRACT FROM AUTHOR]

Aim of the study is to highlight difficulties faced by an inexperienced surgeon in approaching endorectal-ultrasound, trying to define when learning curve can be considered complete.

A well-designed learning curve is essential for the acquisition of laparoscopic skills: but, are there risk factors that can derail the surgical method? From a review of the current literature on the learning curve in laparoscopic surgery, we identified learning curve components in video laparoscopic cholecystectomy; we suggest a learning curve model that can be applied to assess the progress of general surgical residents as they learn and master the stages of video laparoscopic cholecystectomy regardless of type of patient.

Laparoscopic transabdominal preperitoneal inguinal hernia repair is a safe and effective technique. In this study we tested the hypothesis that self-gripping mesh used with the laparoscopic approach is comparable to polypropylene mesh in terms of perioperative complications, against a lower overall cost of the procedure.

The main objective of this paper is to propose two innovative monitoring methods for electrical disturbances in low-voltage networks. The two approaches present a focus on the classification of voltage signals in the frequency domain using machine learning techniques. The first technique proposed here uses the Fourier transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a multilayered neural network with multivalued neurons (MLMVN). In this case, the classifier structure has three layers and a small number of neurons in the hidden layer. This allows complex-valued inputs to be processed without the need for pre-coding, thus reducing computational cost and keeping training time short. The second technique involves the use of the short-time Fourier transform (STFT) and a convolutional neural network (CNN) with 2D convolutions in each layer for feature extraction and dimensionality reduction. The voltage waveform perturbations taken into consideration are: voltage sag, voltage swell, harmonic pollution, voltage notch, and interruption. The comparison between the two proposed techniques is developed in two phases: initially, the simulated data used during the training phase are considered and, subsequently, various experimental measurements are processed, obtained both through an artificial disturbance generator and through a variable load. The two techniques represent an innovative approach to this problem and guarantee excellent classification results. [ABSTRACT FROM AUTHOR]

Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA) applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes. [ABSTRACT FROM AUTHOR]

This document is an editorial titled "Methods in climate-smart agronomy" published in Frontiers in Agronomy. The editorial discusses the impact of changing climatic conditions on crop production and the need for sustainable farming practices. It highlights the gap between actual and potential production levels and the limitations of heavy use of cultivation inputs. The editorial emphasizes the importance of adopting new technologies, tools, and methodologies, such as precision agriculture and sustainable farming practices, to enhance agricultural productivity, strengthen resilience to climate change, and promote food security and sustainable development. The document also mentions four research articles that explore different approaches to climate-smart agronomy, including improved equipment for crop development studies, geospatial approaches for estimating wind damage, cost-effective devices for measuring crop evapotranspiration, and the effectiveness of ground mulching in water conservation. The overarching objective of these approaches is to enhance agricultural productivity and resilience to climate change. [Extracted from the article]

An intact hepatic artery is the gateway to successful hepato-biliary surgery. Introduction of laproscopic cholecystectomy (LC) has stimulated a renewed interest in the anatomy of hepatic artery. In this case report we have highlighted importance of variations of right hepatic artery in terms of origin and course We present a rare asymptomatic case of liver atrophy due to an intraoperative lesion of right hepatic artery. We also performed a literature review about surgical vascular lesions and tried to confirm the right concept behind “non trivial procedure” of the LC.

Klippel-Trenaunay syndrome (KTS) is a rare congenital disorder. KTS can be diagnosed on the basis of any 2 of 3 features: cutaneous capillary malformations, soft tissue or bony hypertrophy and varicose veins. We present an unusual case of KTS complicated by an infection of venous ulcers of the lower limb by larvae. The treatment of infection was a complete debridement; however baseline treatment of KTS is still in evaluation.

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine. [ABSTRACT FROM AUTHOR]

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs' intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution. [ABSTRACT FROM AUTHOR]

An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days' time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits. [ABSTRACT FROM AUTHOR]

DC–DC converter fault diagnosis, executed via neural networks built by exploiting the information deriving from testability analysis, is the subject of this paper. The networks under consideration are complex valued neural networks (CVNNs), whose fundamental feature is the proper treatment of the phase and the information contained in it. In particular, a multilayer neural network based on multi-valued neurons (MLMVN) is considered. In order to effectively design the network, testability analysis is exploited. Two possible ways for executing this analysis on DC–DC converters are proposed, taking into account the single-fault hypothesis. The theoretical foundations and some applicative examples are presented. Computer programs, based on symbolic analysis techniques, are used for both the testability analysis and the neural network training phase. The obtained results are very satisfactory and demonstrate the optimal performances of the method. [ABSTRACT FROM AUTHOR]

The ongoing climate change, which is threatening grassland agroecosystems throughout Europe, is also evident in the Italian grasslands. These systems, often located in marginal areas, are species-rich ecosystems characterized by variable, and often unreliable, grass and forage production and strongly dependent on interactions between climate, soil and agricultural management practices (e.g. land abandonment, lack of investments on innovation, stocking rates reduction, etc.), making them very sensitive and vulnerable to climate change. This review draws from the scientific literature the impacts of current and expected climatic changes on grassland and forage crop systems framed in three different bio-climatic zones of Italy, namely the Alpine, Continental and Mediterranean, and focussing on: i) grassland biodiversity and vegetation; and ii) forage production and quality. The main aims of this review are to: i) revise the existing literature in the domain, highlighting different or common trends among different Italian biogeographical regions; ii) provide information on the main climatic impacts analysed and drivers involved in the studied evolutions; and iii) point out the knowledge gaps currently pending in order to hypothesize the future scenarios of research in this sector. Even if this review has pointed out differences in approaches, adopted methodologies and purposes of conducted researches, some common trends can be highlighted, though located in three different environments. Expected warming and modification on rainfall pattern will produce deep changes in vegetation of grassland types, with reduction or the disappearance of cold tolerant species and a spread of xeric/thermophilus ones and shrubby vegetation and with a general upward shift of vegetation types in mountain areas. Moreover, a general reduction of aboveground biomass is expected, as summer droughts is considered a main driver able to force grassland productivity. Finally, warming and rainfall reduction are considered the main factors able to reduce forage quality and palatability of grasslands, as a consequence of reduction of nitrogen content in the available biomass and of the higher spread of less unpalatable species and shrubs. The hypothesis is that the information gained from this review can provide insights on the current level of knowledge on the expected impacts of climate change on Italian grassland systems, and support the development of policy strategies for adaptation at national level. [ABSTRACT FROM AUTHOR]

A potential strategy for mitigating nitrous oxide (N2O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (Nfert) with symbiotically fixed nitrogen (Nsymb) from legumes. The input of Nsymb reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N2O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N2O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N2O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N2O emissions compared to the baseline scenario (200 kg N ha−1 yr−1; no legumes), they predicted different levels of absolute N2O emissions and thus also of absolute N2O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha−1 yr−1 input as these were identified as best‐bet climate smart agricultural practices. Key Points: A partial substitution of fertilizer nitrogen with symbiotically fixed nitrogen could mitigate nitrous oxide (N2O) emissions in grasslands by around 130 Gg yr−1Experimentally testing this mitigation option is challenging so modeling offers means to identify the optimum legume/fertilizer combinationThe models showed that net benefits to N2O mitigation and yield can be achieved across a wide range of legume/fertilizer combinations [ABSTRACT FROM AUTHOR]

Budbreak date in grapevine is strictly dependent on temperature, and the correct simulation of its occurrence is of great interest since it may have major consequences on the final yield and quality. In this study, we evaluated the reliability for budbreak simulation of two modeling approaches, the chilling-forcing (CF), which describes the entire dormancy period (endo- and eco-dormancy) and the forcing approach (F), which only describes the eco-dormancy. For this, we selected six phenological models that apply CF and F in different ways, which were tested on budbreak simulation of eight grapevine varieties cultivated at different latitudes in Europe. Although none of the compared models showed a clear supremacy over the others, models based on CF showed a generally higher estimation accuracy than F where fixed starting dates were adopted. In the latter models, the accurate simulation of budbreak was dependent on the selection of the starting date for forcing accumulation that changes according to the latitude, whereas CF models were independent. Indeed, distinct thermal requirements were found for the grapevine varieties cultivated in Northern and Southern Europe. This implies the need to improve modeling of the dormancy period to avoid under- or over-estimations of budbreak date under different environmental conditions. [ABSTRACT FROM AUTHOR]