Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented—two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast.

Achieving sustainability in constant development in every area in today’s modern business has become a challenge on the one hand, and an imperative on the other. If the aspect of business excellence achievement is also added to it, the complexity of the system increases significantly, and it is necessary to model a system considering several parameters and satisfying the multi-criteria function. This paper develops a novel integrated model that involves the application of a subjective-objective model in order to achieve business sustainability and excellence. The model consists of fuzzy PIPRECIA (fuzzy pivot pairwise relative criteria importance Assessment) as a subjective method, CRITIC (criteria importance through intercriteria correlation) and I-distance method as objective methods. The goal is to take the advantages of these approaches and allow for more accurate and balanced (symmetric) decision-making through their integration. The integrated subjective-objective model has been applied in a narrow geographical area to consider and evaluate banks as a significant factor in improving the social aspect of sustainability. An additional contribution of the paper is a critical overview of multi-criteria problems in which the levels of the hierarchical structure contain a different (asymmetric) number of elements. A specific example has also been used to prove that only a hierarchical structure with an equal number of lower-level elements provides precise weights of criteria in accordance with the preferences of decision-makers referring to subjective models. The results obtained are verified throughout the calculation of Spearman and Pearson correlation coefficients, and throughout a sensitivity analysis involving a dynamic reverse rank matrix.

ABSTRACT Background Non-medical use of prescription drugs is a major public health concern in the United States. Prescription opioids and sedatives are among the most widely abused drugs and their combined use can be lethal. Increasingly rigid prescribing guidelines may contribute to the changing context of opioid use and increase drug diversion. Objective To examine gender differences in diversion of prescription opioids and sedatives among non-medical prescription opioid and sedative polysubstance users. We hypothesize that men will be more likely than women to engage in incoming diversion. Methods Data from the Prescription Drug Abuse, Misuse, and Dependence Study, a cross-sectional study focused on prescription drug users, were analyzed. Non-medical use was defined as use of a drug that was not prescribed or use in a way other than prescribed. Individuals who reported past 12-month non-medical opioid and sedative use were included; diversion was defined as incoming (obtaining drugs from a source other than a health professional) and outgoing (giving away/selling/trading prescription drugs). Results Among the 198 polysubstance users, 41.4% were female. Men were 2.85 times as likely as women to report incoming diversion (95% CI: 1.21–6.72). Women were more likely to obtain opioids from a healthcare professional; men were more likely to obtain sedatives from a roommate, coworker, or friend. Over half of men and women reported outgoing diversion opioids or sedatives. Conclusion Drug diversion highlights an important point of intervention. Current prevention efforts that target prescribers should be expanded to include users and diversion activities; these interventions should be gender-specific.

With great interest, we have read the article by Backes et al ,1 on the pre-resection accuracy of the real-time optical diagnosis of T1 colorectal cancer (T1CRC) in large non-pedunculated colorectal polyps. In this multicentre, prospective study, the authors developed and validated the OPTICAL model, in which a sensitivity of 78.7% (95% CI: 64.3 to 89.3) for optical diagnosis of T1CRC was obtained. With the implementation of the Dutch bowel cancer screening programme (BCSP) in 2014, a shift has occurred towards the more frequent diagnoses of early AJCC (American Joint Committee on Cancer) stage I cancers.2 Estimating the risk of a T1CRC is crucial to determine the optimal treatment strategy, and to select cases for more elaborative and expensive endoscopic en bloc resection techniques such as endoscopic submucosal dissection, transanal minimally invasive surgery or endoscopic full-thickness resection. Current studies mainly report on the outcomes of advanced imaging by expert centres with dedicated endoscopists,3 4 whereas …

Background: Sedentary behavior carries the risk of musculoskeletal problems, especially in the lumbosacral region of the spinal column.  According to modern lifestyle, this has begun to be a public health issue. Objective: To point to the health risks of working at the computer and present an ergonomic analysis of the typical and improved position of workers in front of the computer, thereby reducing the chances of emergence occupational diseases. Results:  Changing the position of the subjects led to a change in lumbar pressure from 2,818 N/m2 to 351 N/m2. Software analysis of the changed position indicates that this position is acceptable, both for the lumosacral region of the spine and for the abdominal muscles. Conclusions:  A change in body position will decrease lumbar moment and the load on the lumbosacral region of the spine. Work chair with lumbar support, the right desk height, setting the appropriate position of the monitor, selecting the optimal keyboard and mouse, dividing the workspace into appropriate zones, as well as changing lifestyle and habits should be part of the management of people who spend most of their working time in a sitting position.

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.