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A surging demand for sustainable energy and the urgency to lower greenhouse gas emissions is driving industrial systems towards more eco-friendly and cost-effective models. Biogas from agricultural and municipal organic waste is gaining momentum as a renewable energy source. Concurrently, the European Hydrogen Strategy focuses on green hydrogen for decarbonising the industrial and transportation sectors. This paper presents a multi-objective network design model for urban–industrial symbiosis, incorporating anaerobic digestion, cogeneration, photovoltaic, and hydrogen production technologies. Additionally, a Bayesian best-worst method is used to evaluate the weights of the sustainability aspects by decision-makers, integrating these into the mathematical model. The model optimises industrial plant locations considering economic, environmental, and social parameters, including the net present value, energy consumption, and carbon footprint. The model’s functionalities are demonstrated through a real-world case study based in Emilia Romagna, Italy. It is subject to sensitivity analysis to evaluate how changes in the inputs affect the outcomes and highlights feasible trade-offs through the exploration of the ϵ-constraint. The findings demonstrate that the model substantially boosts energy and hydrogen production. It is not only economically viable but also reduces the carbon footprint associated with fossil fuels and landfilling. Additionally, it contributes to job creation. This research has significant implications, with potential future studies intended to focus on system resilience, plant location optimisation, and sustainability assessment.

Abstract The development and manufacture of self-driving cars (SDCs) have triggered unprecedented challenges among car manufacturers and smart road operators to accelerate awareness and implementation of innovative technologies for cooperative, connected and automated mobility (CCAM), especially those with a high level of automation and safety. Safety improvement is a pre-requisite to justify and unleashing a mass deployment of connected and driverless cars to reach the goal of zero-accident in 2050 set by the European Commission. Behind these motivations a well-justified and widely acceptable high-level safety target for SDCs is mandatory. The aim of this article is to contribute to the derivation of an harmonised high-level safety target for SDCs, starting from the safety requirements and the state of the art achieved by train and airplane operations. The novelty of our approach is to leverage the Common Safety Method-Design Targets (CSM-DT) already adopted and widely accepted by the railway community. According to this approach, the derived, justified and harmonised high-level design safety target for SDCs, defined as the average probability of a dangerous failure PFSDC per 1 h, should be 1 × 10−7/h. An example of PFSDC allocation to individual SDC safety functions, including position determination based on Global Navigation Satellite System (GNSS), is described using a fault tree. The proposed methodology can speed up the validation and certification process needed to authorise the SDCs, by capitalising the know-how and best practices in use since many years for the train management.

Introduction: The timing of contralateral symmetrisation in patients with large and ptotic breasts undergoing a unilateral skin-reducing mastectomy (SRM) is one of the most debated topics in the reconstructive field. There is no evidence to support the advantage of immediate or delayed symmetrisation to help surgeons with this decision. The aim of this study was to investigate the clinical and aesthetic outcomes of immediate symmetrisation. Methods: A randomised observational study was conducted on patients who underwent an SRM for unilateral breast cancer. Based on a simple randomisation list, patients were divided into two groups: a delayed symmetrisation group versus an immediate symmetrisation group. The postoperative complications, BREAST-Q outcomes and reoperations were compared. Results: Out of a total of 84 patients undergoing an SRM between January 2018 and January 2021, 42 patients underwent immediate symmetrisation and 42 patients had delayed symmetrisation. Three implant losses (7.2%) were observed and we reported three wound dehiscences; one of these was in a contralateral breast reconstruction in the immediate symmetrisation group. The BREAST-Q patient-reported outcome measures recorded better aesthetic outcomes and a high patient satisfaction for the immediate symmetrisation group. Conclusions: Simultaneous controlateral symmetrisation is a good alternative to achieve better satisfaction and quality of life for patients; from a surgical point of view, it does not excessively impact on the second time of reconstruction.

The objective of a sound event detector is to recognize anomalies in an audio clip and return their onset and offset. However, detecting sound events in noisy environments is a challenging task. This is due to the fact that in a real audio signal several sound sources co-exist. Moreover, the characteristics of polyphonic audios are different from isolated recordings. It is also necessary to consider the presence of noise (e.g. thermal and environmental). In this contribution, we present a sound anomaly detection system based on a fully convolutional network which exploits image spatial filtering and an Atrous Spatial Pyramid Pooling module. To cope with the lack of datasets specifically designed for sound event detection, a dataset for the specific application of noisy bus environments has been designed. The dataset has been obtained by mixing background audio files, recorded in a real environment, with anomalous events extracted from monophonic collections of labelled audios. The performances of the proposed system have been evaluated through segment-based metrics such as error rate, recall, and F1-Score. Moreover, robustness and precision have been evaluated through four different tests. The analysis of the results shows that the proposed sound event detector outperforms both state-of-the-art methods and general purpose deep learning-solutions.

In a world facing increasing environmental and energy challenges, anaerobic digestion of agrifood by-products and food waste could contribute to the production of green energy while reducing greenhouse gas emissions into the atmosphere. Anaerobic digestion is a biological process capable of breaking down and stabilising organic matter in the absence of oxygen and converting it into a renewable source of energy, known as biogas. Biomethane production also enables the generation of electricity and produces digestate, a by-product of the digestion process that can be used as a soil conditioner or fertiliser. This review aims to highlight how substrate pretreatment, together with the optimisation of operating parameters, application of additives, recirculation of digestate and frequent feeding, can increase biogas production. An overview of the basics of the anaerobic digestion of agrifood by-products and food waste is provided, including feedstock characteristics (nutrient content, particle size and inhibitory compounds) and process parameters (process configuration, pH, temperature, total and volatile solids, total Kjeldahl nitrogen, ammonium, chemical oxygen demand, carbon/nitrogen ratio, retention time, organic loading rate, etc.). In addition, recent studies in the field of processes, equipment and pretreatments that can significantly improve the anaerobic digestion process of agricultural and food wastes were classified and discussed. Finally, the challenges and future perspectives of biogas production from the agrifood sector are addressed.

Nowadays, Global Navigation Satellite Systems are the main source for high accuracy positioning and timing. For this reason, they are essential both for everyday activities and services, and for the industrial and critical infrastructure sectors. Moreover, the spread of increasingly autonomous vehicles results in strict accuracy and integrity requirements. This leads to the need for additional infrastructure to send corrections to the end users and mitigate the measurement errors, the Augmentation Networks. However, due to the increasing exploitation of localization functionalities, the Augmentation Networks could become a primary target for attackers resulting in a high financial and safety cost. Among the possible attacks, spoofing, that is the generation of a fake satellite signal which is seen as genuine by the receiver, is one of the most powerful and tricky. In this contribution, a detection and mitigation strategy for Augmentation Network spoofing attacks is proposed. We introduce two attack models and present a technique based on K-means clustering to counteract them. More in details, our approach is based on the computation of the number of clusters formed by the Augmentation Network corrections. Starting from the hypothesis that under nominal conditions only one cluster is present, the effects of the attacks on the clustering procedure are analyzed, and several attack simulations are performed to evaluate the algorithm performances. The proposed method has been compared both to an Augmentation Network attack detection technique, and to a receiver-level spoofing mitigation approach, showing comparable or better performances. Moreover, to the best of our knowledge, this is the first work addressing mitigation for spoofing attacks which target an Augmentation Network.

Hereditary Breast and Ovarian Cancer (HBOC) syndrome is a condition in which the risk of breast and ovarian cancer is higher than in the general population. The prevalent pathogenesis is attributable to inactivating variants of the BRCA1-2 highly penetrant genes, however, other cancer susceptibility genes may also be involved. By Exome Sequencing (ES) we analyzed a series of 200 individuals selected for genetic testing in BRCA1-2 genes according to the updated National Comprehensive Cancer Network (NCCN) guidelines. Analysis by MLPA was performed to detect large BRCA1-2 deletions/duplications. Focusing on BRCA1-2 genes, data analysis identified 11 cases with pathogenic variants (4 in BRCA1 and 7 in BRCA1-2) and 12 with uncertain variants (7 in BRCA1 and 5 in BRCA2). Only one case was found with a large BRCA1 deletion. Exome analysis allowed to characterize pathogenic variants in 21 additional genes: 10 genes more traditionally associated to breast and ovarian cancer (ATM, BRIP1, CDH1, PALB2, PTEN, RAD51C, and TP53) (5% diagnostic yield) and 11 in candidate cancer susceptibility genes (DPYD, ERBB3, ERCC2, MUTYH, NQO2, NTHL1, PARK2, RAD54L, and RNASEL). In conclusion, this study allowed a personalized risk assessment and clinical surveillance in an increased number of HBOC families and to broaden the spectrum of causative variants also to candidate “non-canonical” genes.

IntroductionLaparoscopic surgery has been adopted in some parts of the world as an innovative approach to the resection of gastric cancers. However, in the modern era of surgical oncology, to overcome intrinsic limitations of the traditional laparoscopy, the robotic approach is advocated as able to facilitate the lymph node dissection and complex reconstruction after gastrectomy, to assure oncologic safety also in advanced gastric cancer patients. Previous meta-analyses highlighted a lower complication rate as well as bleeding in the robotic approach group when compared with the laparoscopic one. This potential benefit must be balanced against an increased time of intervention. The aim of this umbrella review is to provide a comprehensive overview of the literature for surgeons and policymakers in order to evaluate the potential benefits and harms of robotic gastrectomy (RG) compared with the laparoscopic approach for gastric cancer.Methods and analysisWe will perform a comprehensive search of the PubMed, Cochrane and Embase databases for all articles published up to May 2019 and reference list of relevant publications for systematic review and meta-analyses comparing the outcomes of RG and laparoscopic gastrectomy in patients with gastric cancer. Studies will be selected by two independent reviewers based on prespecified eligibility criteria and the quality will be assessed according to AMSTAR (A MeaSurement Tool to Assess systematic Reviews) checklist. All information will be collected using piloted and standardised data-extraction forms in DistillerSR developed following the Joanna Briggs Institute’s recommended extraction items.Ethics and disseminationThis umbrella review will inform clinical and policy decisions regarding the benefits and harms of RG for treating gastric cancer. The results will be disseminated through a peer-reviewed publication, conference presentations and the popular press. Formal ethical approval is not required as primary data will not be collected.PROSPERO registration numberCRD42019139906.

Surgical site infection occurs with high frequency in gastrointestinal surgery, contributing to the high incidence of morbidity and mortality. The accepted practice worldwide for the prevention of surgical site infection is providing single- or multiple-dose antimicrobial prophylaxis. However, most suitable antibiotic and optimal duration of prophylaxis are still debated. The aim of the systematic review is to assess the efficacy of antimicrobial prophylaxis in controlling surgical site infection rate following esophagogastric surgery. PubMed and Cochrane databases were systematically searched until 31 October 2021, for randomized controlled trials comparing different antimicrobial regimens in prevention surgical site infections. Risk of bias of studies was assessed with standard methods. Overall, eight studies concerning gastric surgery and one study about esophageal surgery met inclusion criteria. No significant differences were detected between single- and multiple-dose antibiotic prophylaxis. Most trials assessed the performance of cephalosporins or inhibitor of bacterial beta-lactamase. Antimicrobial prophylaxis (AMP) is effective in reducing the incidence of surgical site infection. Multiple-dose antimicrobial prophylaxis is not recommended for patients undergoing gastric surgery. Further randomized controlled trials are needed to determine the efficacy and safety of antimicrobial prophylaxis in esophageal cancer patients.

A novel method for watermarking and ciphering color images, based on the joint use of a key-dependent wavelet transform with a secure cryptographic scheme, is presented. The system allows to watermark encrypted data without requiring the knowledge of the original data and also to cipher watermarked data without damaging the embedded signal. Since different areas of the proposed transform domain are used for encryption and watermarking, the extraction of the hidden information can be performed without deciphering the cover data and it is also possible to decipher watermarked data without removing the watermark. Experimental results show the effectiveness of the proposed scheme.

Standard edge detectors react to all local luminance changes, irrespective of whether they are due to the contours of the objects represented in a scene or due to natural textures like grass, foliage, water, and so forth. Moreover, edges due to texture are often stronger than edges due to object contours. This implies that further processing is needed to discriminate object contours from texture edges. In this paper, we propose a biologically motivated multiresolution contour detection method using Bayesian denoising and a surround inhibition technique. Specifically, the proposed approach deploys computation of the gradient at different resolutions, followed by Bayesian denoising of the edge image. Then, a biologically motivated surround inhibition step is applied in order to suppress edges that are due to texture. We propose an improvement of the surround suppression used in previous works. Finally, a contour-oriented binarization algorithm is used, relying on the observation that object contours lead to long connected components rather than to short rods obtained from textures. Experimental results show that our contour detection method outperforms standard edge detectors as well as other methods that deploy inhibition.