Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

Accurate motion estimation and super-resolution techniques for digital images and video

The object of this thesis concerns high accuracy motion estimation and the enhancement of resolution and definition (super-resolution) in digital images and video. Following an overview of the underlying theory and some basic concepts, two new sub-pixel motion estimation methods are presented, aiming at maximum accuracy. The first of those follows the classic approach of dividing one image into blocks, which are then paired with blocks in another image, arriving at an analytical polynomial expression that defines the motion vectors which minimize the mean squared error. The second motion estimation method presented in this thesis follows a completely different and novel approach, constructing an implied, in-between, image which is then used to calculate motion more accurately, reducing the interpolation error that is inherent in the more classic approaches. In the field of super-resolution the thesis examines three different approaches: kernel regression, dictionary-based, and multi-frame, the latter requiring accurate motion estimation. Selected published methods are reviewed, and a number of alterations and improvements are proposed for each. A new dictionary-based method is also proposed, using a dictionary constructed from the input image itself and focusing on normalization and sorting of the dictionary to facilitate fast and effective search. The presented and proposed methods undergo experimental testing and are compared to each other and with state-of-the art methods. Experimental results on qualitative and quantitative evaluations and comparisons are presented, using both image examples and graphs and tables of the relevant metrics.Το αντικείμενο της παρούσας διατριβής στρέφεται γύρω από την εκτίμηση κίνησης μεγάλης ακρίβειας και την αύξηση της ανάλυσης και της ευκρίνειας (super-resolution) σε ψηφιακές εικόνες και video. Μετά από μια επισκόπηση της θεωρίας και κάποιων βασικών εννοιών, παρουσιάζονται δύο νέες μέθοδοι sub-pixel εκτίμησης κίνησης, οι οποίες αποσκοπούν στον προσδιορισμό της κίνησης με μέγιστη ακρίβεια. Η πρώτη από αυτές ακολουθεί την κλασική προσέγγιση όπου η μία εικόνα χωρίζεται σε blocks, τα οποία αντιστοιχίζονται σε blocks της άλλης εικόνας, καταλήγοντας όμως σε μια αναλυτική πολυωνυμική σχέση, η οποία προσδιορίζει εκείνα τα διανύσματα κίνησης τα οποία ελαχιστοποιούν το μέσο τετραγωνικό σφάλμα. Η δεύτερη μέθοδος ακολουθεί μια εντελώς διαφορετική και πρωτότυπη προσέγγιση, κατασκευάζοντας μία ενδιάμεση υποθετική εικόνα, η οποία χρησιμοποιείται για τον ακριβέστερο υπολογισμό της κίνησης, μειώνοντας έτσι τα σφάλματα παρεμβολής που είναι εγγενή στις πιο κλασικές προσεγγίσεις. Στον τομέα του super-resolution η διατριβή μελετά τρεις διαφορετικές προσεγγίσεις: με χρήση συναρτήσεων πυρήνα, με χρήση λεξικού, και με χρήση πολλαπλών εικόνων χαμηλής ανάλυσης, η οποία και απαιτεί ακριβή εκτίμηση κίνησης. Παρουσιάζονται επιλεγμένες μέθοδοι της βιβλιογραφίας, πάνω στις οποίες προτείνονται μετατροπές και βελτιώσεις. Προτείνεται μια νέα μέθοδος super-resolution με χρήση λεξικού που κατασκευάζεται από την εικόνα προς ανακατασκευή, η οποία εστιάζει στην κανονικοποίηση και ταξινόμηση του λεξικού για την ταχεία και αποτελεσματική αναζήτηση μέσα του. Οι παρουσιαζόμενες και προτεινόμενες μέθοδοι ελέγχονται πειραματικά και συγκρίνονται μεταξύ τους και με μεθόδους στην αιχμή της τεχνολογίας. Παρατίθενται αποτελέσματα ποιοτικής και ποσοτικής αξιολόγησης και σύγκρισης, τόσο μέσω της απεικόνισης χαρακτηριστικών παραδειγμάτων όσο και με τη βοήθεια πινάκων και γραφημάτων κατάλληλων μετρικών

An Enhanced Temporal Feature Integration Method for Environmental Sound Recognition

Temporal feature integration refers to a set of strategies attempting to capture the information conveyed in the temporal evolution of the signal. It has been extensively applied in the context of semantic audio showing performance improvements against the standard frame-based audio classification methods. This paper investigates the potential of an enhanced temporal feature integration method to classify environmental sounds. The proposed method utilizes newly introduced integration functions that capture the texture window shape in combination with standard functions like mean and standard deviation in a classification scheme of 10 environmental sound classes. The results obtained from three classification algorithms exhibit an increase in recognition accuracy against a standard temporal integration with simple statistics, which reveals the discriminative ability of the new metricsPeer reviewe

MC-hands-1M: A glove-wearing hand dataset for pose estimation

We introduce MC-hands-1M, a synthetic glove-wearing hand dataset for pose estimation. In the zip folder, there exist two subfolders: one containing roughly 750K images (Big set) and another with 250K images (Small set) along with the 2D camera plane and 3D world ground truth data of the corresponding poses. Each set is organized in folders named as Rendered View X, representing a specific camera in the 3D space with a fixed rotation and location. In each of those folders, there exist a json file containing corresponding data for the camera (location, rotation, intrinsics' matrix and images' relative paths) along with the aforementioned ground truth per image (pose). For each of those views, there exist other subfolders named as Scene 's Collection 's Objects' States' Combination Y. Each of those folders contains images of the different poses from the set camera view, given a different combination of background, lighting, glove- and cloth-like materials, and hand's a priori scaling state

A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze

This survey article is concerned with the emergence of vision augmentation AI tools for enhancing the situational awareness of first responders (FRs) in rescue operations. More specifically, the article surveys three families of image restoration methods serving the purpose of vision augmentation under adverse weather conditions. These image restoration methods are: (a) deraining; (b) desnowing; (c) dehazing ones. The contribution of this article is a survey of the recent literature on these three problem families, focusing on the utilization of deep learning (DL) models and meeting the requirements of their application in rescue operations. A faceted taxonomy is introduced in past and recent literature including various DL architectures, loss functions and datasets. Although there are multiple surveys on recovering images degraded by natural phenomena, the literature lacks a comprehensive survey focused explicitly on assisting FRs. This paper aims to fill this gap by presenting existing methods in the literature, assessing their suitability for FR applications, and providing insights for future research directions

Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Traditional drone handheld remote controllers, although well-established and widely used, are not a particularly intuitive control method. At the same time, drone pilots normally watch the drone video feed on a smartphone or another small screen attached to the remote. This forces them to constantly shift their visual focus from the drone to the screen and vice-versa. This can be an eye-and-mind-tiring and stressful experience, as the eyes constantly change focus and the mind struggles to merge two different points of view. This paper presents a solution based on Microsoft’s HoloLens 2 headset that leverages augmented reality and gesture recognition to make drone piloting easier, more comfortable, and more intuitive. It describes a system for single-handed gesture control that can achieve all maneuvers possible with a traditional remote, including complex motions; a method for tracking a real drone in AR to improve flying beyond line of sight or at distances where the physical drone is hard to see; and the option to display the drone’s live video feed in AR, either in first-person-view mode or in context with the environment

Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Traditional drone handheld remote controllers, although well-established and widely used, are not a particularly intuitive control method. At the same time, drone pilots normally watch the drone video feed on a smartphone or another small screen attached to the remote. This forces them to constantly shift their visual focus from the drone to the screen and vice-versa. This can be an eye-and-mind-tiring and stressful experience, as the eyes constantly change focus and the mind struggles to merge two different points of view. This paper presents a solution based on Microsoft’s HoloLens 2 headset that leverages augmented reality and gesture recognition to make drone piloting easier, more comfortable, and more intuitive. It describes a system for single-handed gesture control that can achieve all maneuvers possible with a traditional remote, including complex motions; a method for tracking a real drone in AR to improve flying beyond line of sight or at distances where the physical drone is hard to see; and the option to display the drone’s live video feed in AR, either in first-person-view mode or in context with the environment

CDK4/6 and aromatase inhibitors as first‐line treatment in metastatic high‐grade neuroendocrine carcinoma of the breast: A case report

Key Clinical Message There is no consensus regarding the therapeutic approach of breast neuroendocrine carcinomas (NECs). As most NECs are hormone receptor positive and HER‐2 negative, we suggest that endocrine‐based strategies may play a leading role. Here, we report a new treatment strategy by incorporating CDK4/6 inhibitors in the therapeutic armamentarium. Abstract Primary neuroendocrine neoplasms of the breast constitute a rare entity. They are characterized by predominant neuroendocrine differentiation and are further divided into well‐differentiated neuroendocrine tumors and poorly differentiated (high‐grade) neuroendocrine carcinomas (NECs). Regarding their therapeutic approach, there are no standardized guidelines. Herein, we present the first case ever reported, concerning a female patient with de novo metastatic breast NEC who received hormonal therapy, a combination of a CDK4/6 inhibitor palbociclib with letrozole and triptorelin, as first‐line treatment with significant clinical and radiological response. As most NECs are estrogen receptor and/or progesterone receptor positive and HER‐2 negative, we suggest that hormonal therapy may play a leading role even in the first‐line setting. The present report provides a new treatment strategy by incorporating CDK4/6 inhibitors in the therapeutic armamentarium of breast NECs