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The present case-report empathizes the usefulness of post-mortem computed tomography (PMCT) in the detection of live birth signs in a baby found dead, comparing the results with standard autoptic, histologic and immunohistochemical examinations.The report describes the case of a new-born who was thrown out on the footpath from a window of the fifth floor according to the investigative police, whilst the mother has hidden herself immediately after childbirth.PMCT showed diffusely aerated lung parenchyma with signs of severe lung distress, air in the airways with no fluid inside, and air in the upper gastro-intestinal tract, in absence any resuscitation manoeuvres performed on the baby at delivery. PMCT underlined multiple fractures of the skull with degeneration of cerebral parenchyma.All these findings were confirmed by conventional autopsy and histological and immunohistochemical examinations, clinching the usefulness of PMCT in differentiating live births from stillbirths.(c) 2023 Elsevier B.V. All rights reserved.

The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of the entire platform in terms of accuracy of the recognition process while also highlighting the benefits it might bring in smart environments.

Introduction The motor vehicle crash (MVC) constitutes an important challenge for forensic pathology in order to identify the manner and cause of death. Our study focuses on a fatal accident during a rally race corresponding to MVC sub-category. Materials and method Postmortem computed tomography (PMCT) was performed before the conventional autopsy. Autoptic and PMCT data were compared. Data collection allowed analyzing biomechanical dynamics of the incident and post-traumatic injuries through qualitative-statistics and solicitation quantitative indices. Results Photo and circumstantial evidence analysis showed a wrong installation of double shoulder belt system of head and neck support (HANS) collar. PMTC clearly highlighted multiple and bilateral fractures involving roof and base of skull; a displaced fracture of the right acetabulum was also encountered. Autopsy confirmed PMCT data and revealed a brainstem laceration. AIS (Abbreviated Injury Scale) achieved a maximum score in consideration of fatal injuries. Discussion The injuries analysis resulting from photographic surveys examination, conventional autopsy, and PMCT has led us to confirm a fatal front collision with a tree trunk. Head trauma represents a major injury in the present case. In this case, head injuries, related to whiplash trauma, are a consequence of a double shoulder belt system (HANS collar component) wrong installation. Conclusion MVC and especially high-speed motor racing represent an important death cause. There was, for this reason, a marked development of cars and occupants’ safety systems, such as HANS collar. PMCT improves the diagnostic performance of conventional autopsy and increases forensic medical knowledge related to traumatic injuries.

Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMS's, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks.

Machine learning (ML) algorithms are the basis of many services we rely on in our everyday life. For this reason, a new research line has recently emerged with the aim of investigating how ML can be misled by adversarial examples. In this paper we address an e-health scenario in which an automatic system for prescriptions can be deceived by inputs forged to subvert the model's prediction. In particular, we present an algorithm capable of generating a precise sequence of moves that the adversary has to take in order to elude the automatic prescription service. Experimental analyses performed on a real dataset of patients' clinical records show that a minimal alteration of the clinical records can subvert predictions with high probability.

Objective: To identify the best method among the radiologic, laparoscopic and laparotomic scoring assessment to predict the outcomes of cytoreductive surgery in patients with advanced ovarian cancer (AOC). Methods: Patients with AOC who underwent pre-operative computed tomography (CT) scan, laparoscopic evaluation, and cytoreductive surgery between August 2016 and February 2021 were retrospectively reviewed. Predictive Index (PI) score and Peritoneal Cancer Index (PCI) scores were used to estimate the tumor load and predict the residual disease in the primary debulking surgery (PDS) and interval debulking surgery (IDS) after neoadjuvant chemotherapy (NACT) groups. Concordance percentages were calculated between the two scores. Results: Among 100 eligible patients, 69 underwent PDS, and 31 underwent NACT and IDS. Complete cytoreduction was achieved in 72.5% of patients in the PDS group and 77.4% in the IDS. In patients undergoing PDS, the laparoscopic PI and the laparotomic PCI had the best accuracies for complete cytoreduction (R0) [area under the curve (AUC) = 0.78 and AUC = 0.83, respectively]. In the IDS group, the laparotomic PI (AUC = 0.75) and the laparoscopic PCI (AUC= 0.87) were associated with the best accuracy in R0 prediction. Furthermore, radiological assessment, through PI and PCI, was associated with the worst accuracy in either PDS or IDS group (PI in PDS: AUC = 0.64; PCI in PDS: AUC = 0.64; PI in IDS: AUC = 0.46; PCI in IDS: AUC = 0.47). Conclusion: The laparoscopic score assessment had high accuracy for optimal cytoreduction in AOC patients undergoing PDS or IDS. Integrating diagnostic laparoscopy in the decision-making algorithm to accurately triage AOC patients to different treatment strategies seems necessary.

Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so‐called cold‐start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi‐agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed. A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic, spatial and temporal features; then, a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi‐Armed Bandit policy. Framing the trajectory recommendation as a reinforcement learning problem permits to provide high‐quality suggestions while avoiding both cold‐start and preference elicitation issues. The effectiveness of the approach is demonstrated by some deployments in real‐life scenarios, such as smart campuses and theme parks.

Simulations are indispensable to reduce costs and risks when developing and testing algorithms for unmanned aerial vehicles (UAV) especially for applications in high risk scenarios like search and rescue (SAR) operations and post-disaster damage assessment. Many UAV applications require real-time tasks for which the timeliness of computations is fundamental. However, standard simulation tools are not guaranteed to run in sync with real-time events, leading to unreliable assessments of the ability of the target hardware to perform specific tasks. In this work we present a simulation and test system able to run UAV tasks on resource-constrained target hardware possibly adopted in these applications. The system allows for hardware-in-the-loop simulations in which a virtual UAV provided with virtual sensors is controlled by the software under test (SUT) running on the target hardware, while simulated and real time are kept in sync. We provide experimental results from the execution of several increasingly difficult tasks in the system.

Vehicular Ad-hoc NETworks (VANETs) enable a countless set of next-generation applications thanks to the technological progress of the last decades. These applications rely on the assumption that a simple network of vehicles can be extended with more complex and powerful network infrastructure, in which several Road Side Units (RSUs) are employed to achieve application-specific goals. However, this assumption is not always satisfied as in many real-world scenarios it is unfeasible to have a conspicuous deployment of RSUs, due to both economic and environmental constraints. With the aim to overcome this limitation, in this paper we investigate how the only Vehicle-to-Vehicle (V2V) communications can be effectively exploited to share data among the vehicles about an event of interest, such as vehicular traffic. In this sense, we propose a novel communication schema based on the Population Protocol model that allows vehicles to be efficiently updated about a given event. Experimental analysis aims to evaluate the performance of the proposed schema, while also highlighting the benefits it might bring in VANETs applications.

Background. To compare sensitivity of unenhanced computed tomography (CT) and contrast-enhanced CT for the identification of the etiology of bowel obstruction.Materials and Methods. We retrospectively evaluated abdominal CT scans of patients operated for bowel obstruction from March 2013 to October 2017. Two radiologists evaluated CT scans before and after contrast agent in two reading sessions. Then, we calculated sensitivity of CT in the diagnosis of bowel obstruction and determined in which cases the etiology of bowel obstruction was detected on both unenhanced and enhanced CT or on enhanced CT only. The reference standard was defined as the final diagnosis obtained after surgery.Results. Eighteen patients (mean age72±15years, age range 37-88 years) were included in the study. Sensitivity of unenhanced CT and enhanced CT was not significantly different in either small bowel obstruction (64%, 7/11 patients vs. 73%, 8/11 patients;P=0.6547) or large bowel obstruction (71%, 5/7 patients vs. 100%, 7/7 patients;P=0.1410). Adhesions were identified on unenhanced CT as the etiology of small bowel obstruction in 80% (4/5) of patients. Tumors were identified on unenhanced CT as the etiology of large bowel obstruction in 67% (4/6) of patients.Conclusion. In the diagnosis of small bowel obstruction due to adhesions with normal bowel wall thickening and when a neoplasm is identified as the etiology of large bowel obstruction on unenhanced CT, an intravenous contrast agent may be avoided for the identification of the etiology. In remaining cases, contrast agent is still recommended.

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users' needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users' needs and preferences.

The purpose of this article is to provide an up-to-date overview on imaging of paediatric vascular soft tissue masses, including both neoplastic and non-neoplastic lesions. We describe the-often challenging-imaging diagnosis-mainly performed by ultrasound (and secondarily by MRI) and differential diagnosis of vascular soft tissue lesions in children. We underline how important it is to determine whether a vascular anomaly has a regional vascular origin, or if there are other entities, ranging from benign to malignant lesions, which have flow-signal or blood degradation products. Even though clinical examination and patient's history are the first and indispensable steps in the initial diagnosis, the role of imaging is crucial, not only to determine whether a mass represents a true tumour/pseudo-tumour, but also to achieve a more correct diagnosis and determine the extension of the tumour/pseudo-tumour and its relation with the nearby anatomic structures.

The gap between informal functional specifications and the resulting implementation in the chosen programming language is notably a source of errors in embedded systems design. In this paper, we discuss a methodology and a software platform aimed at coping with this issue in programming resource-constrained wireless sensor network nodes (WSNs). Whereas the typical development model for the WSNs is based on cross compilation, the proposed approach supports high-level symbolic coding of abstract models and distributed applications, as well as their test and their execution, directly on the target hardware. As a working example, we discuss the application of our methodology to specify the functional behavior of a radio transceiver chip. The resulting executable specifications are augmented with automatically generated runtime verification code. Our approach is also compared to code development for two prominent WSN general-purpose operating systems.

The applications of forensic radiology involve both Virtopsy both studies on living people - to demonstrate bone age, search for foreign bodies, such as voluntary injection of drug ovules or surgical sponges accidentally forgotten, to assess gunshot wounds, to evaluate injuries by road accidents, and cases of violence or abuse (both in adults and in children). Computed tomography is the most used imaging tool used in forensic pathology and its indications are mainly focused on cases of unnatural deaths or when a crime is suspected. It is preferred over the standard autopsy in selected cases, such as in putrefied, carbonized or badly damaged bodies; or as a preliminary evaluation in mass disasters.

Due to the growing use of CT, there has been an increase in the frequency of detecting focal liver lesions. Intrinsically hyperattenuating hepatic lesions or pseudolesions are not uncommon at unenhanced CT. Hyperattenuating hepatic lesions can be divided into non-calcified and calcified. Causes of intrinsic hyperattenuation include hemorrhage, thrombosis, and calcifications. Focal liver lesions can show hyperattenuation on unenhanced CT in case of severe liver steatosis. Recognition of etiologies associated with hyperattenuation on unenhanced CT can help the radiologist in characterizing focal liver lesions and pseudolesions. In this paper, we describe the spectrum of intrinsically hyperattenuating focal liver lesions and pseudolesions at unenhanced CT.

Imaging of lymphoma is based on the use of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) and/or contrast-enhanced CT, but concerns have been raised regarding radiation exposure related to imaging scans in patients with cancer, and its association with increased risk of secondary tumors in patients with lymphoma has been established. To date, lymphoproliferative disorders are among the most common indications to perform whole-body magnetic resonance imaging (MRI). Whole-body MRI is superior to contrast-enhanced CT for staging the disease, also being less dependent on histology if compared to 18F-FDG-PET/CT. As well, it does not require exposure to ionizing radiation and could be used for the surveillance of lymphoma. The current role of whole-body MRI in the diagnostic workup in lymphoma is examined in the present review along with the diagnostic performance in staging, response assessment and surveillance of different lymphoma subtypes.

Purpose: The primary objective was to assess the frequency of appropriateness of computed tomography (CT) for acute abdominal pain (AAP) in the emergency department; the secondary aim was to compare the diagnostic accuracy of ultrasound (US) and CT in the diagnosis of the aetiology of AAP for diseases that can be diagnosed by US; and the third objective was to assess extent to which inappropriate CT examinations for AAP result in ionizing radiation exposure. Material and methods: In this retrospective single-centre study, we included patients aged between 15 and 46 years referred to the emergency department for AAP in 2016 and submitted to abdominal CT scans, collecting a total of 586 patients. In 152 patients with the more frequent pathologies, we compared the referral reason and current guidelines of the European Society of Radiology (ESR) IGUIDE®. Then we measured and compared the sensitivity of US and CT for the identification of the aetiology of AAP for diseases whose diagnosis can be reached by US. We also recorded the mean computed tomography dose index (CTDIvol), dose length product (DLP) and its standard deviation, and we calculated the effective dose (ED) using CT-Expo® software. Results: According to IGUIDE and based on the clinical suspicion of CT requests, CT examination was considered crucial in 264 (45.05%) patients. 322 patients had a referral reason for CT scan that could be considered "possibly appropriate" according Iguide criteria (4, 5, 6 scoring). Of these, 135 had an inappropriate CT request according to image findings. Conclusions: A better clinical framing and a correct interpretation of the reference guidelines could reduce unjustified exposure to ionizing radiation.

Vehicular Social Networks (VSNs) is an emerging communication paradigm, derived by merging the concepts of Online Social Networks (OSNs) and Vehicular Ad-hoc Networks (VANETs). Due to the lack of robust authentication mechanisms, social-based vehicular applications are vulnerable to numerous attacks including the generation of sybil entities in the networks. We address this important issue in vehicular crowdsourcing campaigns where sybils are usually employed to increase their influence and worsen the functioning of the system. In particular, we propose a novel User Recruitment Policy (URP) that, after extracting the participants within the event radius of a crowdsourcing campaign, detects and filters out the sybil vehicles by using a novel sybil detection approach, called SybilDriver. This technique combines the advantages of VANETs and OSNs by means of an innovative concept of proximity graph obtained from the physical vehicular network, in conjunction with a community detection and Random Forest techniques adopted in the OSN domain. Detailed experimental evaluations demonstrate the effectiveness of our approach and also show that it outperforms existing state-of-the-art methods typically used in the OSNs.1