Development and evaluation of the focused assessment of sonographic pathologies in the intensive care unit (FASP-ICU) protocol

Background The use of ultrasonography in the intensive care unit (ICU) is steadily increasing but is usually restricted to examinations of single organs or organ systems. In this study, we combine the ultrasound approaches the most relevant to ICU to design a whole-body ultrasound (WBU) protocol. Recommendations and training schemes for WBU are sparse and lack conclusive evidence. Our aim was therefore to define the range and prevalence of abnormalities detectable by WBU to develop a simple and fast bedside examination protocol, and to evaluate the value of routine surveillance WBU in ICU patients. Methods A protocol for focused assessments of sonographic abnormalities of the ocular, vascular, pulmonary, cardiac and abdominal systems was developed to evaluate 99 predefined sonographic entities on the day of admission and on days 3, 6, 10 and 15 of the ICU admission. The study was a clinical prospective single-center trial in 111 consecutive patients admitted to the surgical ICUs of a tertiary university hospital. Results A total of 3003 abnormalities demonstrable by sonography were detected in 1275 individual scans of organ systems and 4395 individual single-organ examinations. The rate of previously undetected abnormalities ranged from 6.4 ± 4.2 on the day of admission to 2.9 ± 1.8 on day 15. Based on the sonographic findings, intensive care therapy was altered following 45.1% of examinations. Mean examination time was 18.7 ± 3.2 min, or 1.6 invested minutes per detected abnormality. Conclusions Performing the WBU protocol led to therapy changes in 45.1% of the time. Detected sonographic abnormalities showed a high rate of change in the course of the serial assessments, underlining the value of routine ultrasound examinations in the ICU. Trial registration The study was registered in the German Clinical Trials Register (DRKS, 7 April 2017; retrospectively registered) under the identifier DRKS00010428.

Reflecting on over a year of remote training, and where businesses must go from here

Purpose The study aims to explore how businesses across the UK have adapted to over a year of remote training, and where there is room for improvement as long-term hybrid working plans are put in place. The study also uncovers what digital tools businesses have relied on to deliver learning and development initiatives during remote working, and their effects on employee engagement and experience. Design/methodology/approach An independent body of research was commissioned among 750 UK business leaders and 1,235 UK adults in full-time employment. Findings The research found that while the majority of businesses were able to leverage digital solutions during extended periods of remote work, significant numbers found it difficult to train and develop employees remotely, with many employees dissatisfied with the outcomes. Originality/value The research offers some valuable insights for business leaders looking to improve their training schemes as workplaces settle into new patterns of working.

Strength training in professional soccer: effects on short-sprint and jump performance

Several studies have confirmed the efficacy of strength training to maximize soccer player performance during competition. The aim of this meta-analysis was to determine the effects of different strength training protocols on short-sprint and vertical jump performance of professional soccer players from the first division of their countries. The following inclusion criteria were employed for the analysis: (a) randomized studies; (b) high validity and reliability instruments; (c) studies published in a high-quality peer-reviewed journal; (d) studies involving professional soccer players from the first division; (e) studies with descriptions of strength training programs; and (f) studies where countermovement jump and 10-m sprint time were measured pre and post training. Overall, the different strength-oriented training schemes produced similar performance improvements, which seem not to depend on the training strategy. Strength training appears to have a lower effect when applied during in-season than when applied in pre-season periods in first division soccer players. In this meta-analysis it is not possible to confirm that strength training in isolation is capable of improving the short-sprint and jump performance of elite soccer players. The congested fixture schedule and, thus, the limited time to perform complementary (non-specific) training sessions, may contribute to these reduced effects.

Surgical Perspective: UWI Graduates Path to a Certificate of Completion of Training (CCT) & Fellowship in the UK

Surgical post graduate training is a rapidly evolving field that has seen major technological shifts in its delivery of care. Our aim in this article is to deliver a viewpoint of a contemporary roadmap for the University of the West Indies (UWI) graduate. The overall path and length of training schemes in the United Kingdom (UK), in respect to general surgery and urology is presented. It is important for the reader to understand that the UWI graduate has many different avenues they can pursue to gain entry onto a UK surgical training programme. The Caribbean should benefit from the connectivity and collaboration with our international colleagues. Keywords: surgery, postgraduate, training, general surgery, urology, fellowship

Desertification and Degradation Risks vs Poverty: A Key Topic in Mediterranean Europe

Land degradation and, subsequently, desertification processes are conditioned by biophysical factors and human impacts. Nowadays, there is an increasing interest by social scientists to assess its implications. Especially, it is relevant to the potential changes and landscape deterioration on population, economic systems and feedbacks of local societies to such adjustments. Assessing social facets should also be related to desertification risks, integrated socio-economic inputs and environmentally sustainable development perspectives. However, investigations about the effects of land degradation conditioned by global socioeconomic-factors from a holistic point of view are scarce. In this review, we pretend to discuss past and recent findings on land degradation risks related to poverty, especially based on Mediterranean Europe. To achieve this goal, we focused on key socioeconomic forces such as developmental policy, production and market structure, social change and population mobility. Our review showed that regional disparities based on complex dynamics of demographic forces (e.g. migration, fertility and ageing) and economic drivers of change (e.g. industrial concentration, urbanization, crop intensification, tourism pressure, coastalization) are keys to understand Mediterranean regions such as Southern Italy, a region exposed to high desertification risk in Europe. We concluded that the overexploitation of territories, soil and water degradation urban expansion, tourism and unplanned industrialization are some sectors and activities which can be highly affected by political and socioeconomic forces leading to unsustainable forms of land management and types of development. Special attention should be paid to social policies, education and training schemes to reduce rural migration and potentiate territorial knowledge to avoid land degradation, considering other social issues such as poverty or centralization. The potential role of win-win policies abating poverty and reducing desertification risk is evident in Mediterranean Europe and achieving land degradation neutrality necessary.

Training Program for The Making of Educative Game Tools for Traveling Traffic Games for

This community services activity in the field of science aims to improve the ability of kindergarten teachers and PAUD tutors in making educational Game Tools for traffic signs for learning knowledge about traffic discipline on the highway so that learning activities are fun and useful. This activity is divided into several training schemes with the aim of (a) Making educational traffic aids in kindergartens and PAUD, (b) Designing and implementing quality learning and (c) evaluating the learning of basic knowledge about traffic discipline oriented to safety and security . Training in the form of workshops and assistance in making learning systems and teaching prototypes. The output of PKM is the compilation of training modules and scientific articles on integration in the field of engineering and basic education studies which become a reference for the development of learning systems in kindergarten and early childhood education

Self-Interference Channel Training for Full-Duplex Massive MIMO Systems

Full-duplex (FD) is a promising technology for increasing the spectral efficiency of next-generation wireless communication systems. A major technical challenge in enabling FD in a real network is to remove the self-interference (SI) caused by simultaneous transmission and reception at the transceiver, and the SI cancellation performance depends significantly on the estimation accuracy of the SI channel. In this study, we proposed a novel partial SI channel training method for minimizing the residual SI power for FD massive multiple-input multiple-output (MIMO) systems. Based on an SI channel training framework under a limited training overhead, using the proposed scheme, the BS estimates only a part of the SI channel vectors, while skipping the channel training for the other remaining SI channel vectors by using their last estimates. With this partial training framework, the proposed scheme finds the optimal partial SI channel training strategy for pilot allocation to minimize the expected residual SI power, considering the time-varying Rician fading channel model for the SI channel. Therefore, the proposed scheme can improve the sum-rate performance compared with other simple partial training schemes for FD massive MIMO systems under a limited training overhead. Numerical results confirm the effectiveness of the proposed scheme for FD massive MIMO systems compared with the full training scheme, as well as other partial training schemes.

Multi-agent deep reinforcement learning: a survey

The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario. To survey the works that constitute the contemporary landscape, the main contents are divided into three parts. First, we analyze the structure of training schemes that are applied to train multiple agents. Second, we consider the emergent patterns of agent behavior in cooperative, competitive and mixed scenarios. Third, we systematically enumerate challenges that exclusively arise in the multi-agent domain and review methods that are leveraged to cope with these challenges. To conclude this survey, we discuss advances, identify trends, and outline possible directions for future work in this research area.

Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning

Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches.