Potentially inappropriate prescribing (PIP) of drugs is defined as the use of drugs whose potential damage can overcome benefits. Elderly patients (65 years and older) with renal insufficiency are at particular risk, because they take a lot of drugs, and for the usage of the same, the patients need to have great knowledge and skills. To identify the risk factors contributing to potentially inappropriate prescribing of drugs in elderly patients with renal insufficiency. The study was designed as an observational case-control study. The research was carried out at the Clinic of Nephrology, Clinical Center Nis, Serbia. The data were collected from the medical files of elderly patients undergoing chronic hemodialysis treatment, as well as by interviewing. The cases were patients in whom the potentially inappropriate prescribing of drugs was determined by Beers criteria, and the controls were patients who used properly prescribed medications. The risk factors for PIP were established by univariant and subsequent ly by multivariate logistic regression. The study included 68 patients older than 65 years who were on chronic hemo-dialysis treatment, 41 (60.3%) of them were men and 27 (39.7%) women. The average age of the studied population was 72.71 ± 5.92 years, among which the youngest patient was 65 and the oldest 85 years old. According to Beers criteria, PIP was found in 14 (21%) patients. A significantly higher number of drugs was given to the patients in whom the potentially inadequate prescription of medication was established (Z = 2.650; p = 0.008). The patients to whom the drugs were potentially inappropriately prescribed had a significantly higher number of comorbidities compared to other patients (χ2 = 2.636; p = 0.008). This study showed that patients who have multiple comorbidities and take multiple drugs are at a substantially greater risk of having at least one drug potentially inadequately prescribed. PIP often results in the occurrence of toxic or side effects, and ultimately damage to the body. Acta Medica Medianae 2018;57(1):12-18.

Ensuring costs reduction and increasing competitiveness and satisfaction of end users are the goals of each participant in the supply chain. Taking into account these goals, the paper proposes methodology for defining the most important criteria for suppliers’ evaluation. From a set of twenty established criteria, i.e. four sets of criteria: finances, logistics, quality and communication and business including its sub-criteria, we have allocated the most important ones for supplier selection. Analytic Hierarchy Process (AHP) based on rough numbers is presented to determine the weight of each evaluation criterion. For the criteria evaluation we have used knowledge from the expert in this field. The efficacy of the proposed evaluation methodology is demonstrated through its application to the company producing metal washers for the automotive industry. Next a sensitivity analysis is carried out in order to show the stability of the model. For checking stability the AHP method in conventional form is used in combination with fuzzy logic.

Previous research has suggested that videos can be used to experimentally manipulate social stimuli. In the present study, we used the California scrub-jays’ cache protection strategies to assess whether video playback can be used to simulate conspecifics in a social context. In both the lab and the field, scrub-jays are known to exhibit a range of behaviours to protect their caches from potential pilferage by a conspecific, for example by hiding food in locations out of the observer’s view or by re-caching previously made caches once the observer has left. Here, we presented scrub-jays with videos of a conspecific observer as well as two non-social conditions during a caching period and assessed whether they would cache out of the observer’s “view” (Experiment 1) or would re-cache their caches once the observer was no longer present (Experiment 2). In contrast to previous studies using live observers, the scrub-jays’ caching and re-caching behaviour was not influenced by whether the observer was present or absent. These findings suggest that there might be limitations in using video playback of social agents to mimic real-life situations when investigating corvid decision making.

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.

Wirelessly powered implantable medical devices require efficient power transfer through biological tissue within safety constraints on energy absorption, often in the presence of environmental variability. Accurate modeling of the tissue medium is essential to evaluate the performance and sensitivity of transcutaneous powering systems. Here, we investigate loop and dipole antenna topologies in proximity to simulated tissue models and experimental phantoms, with emphasis on representing heterogeneous tissue with functionally equivalent simplified models, and modeling variability in tissue properties for sensitivity analyses. We first present a modified phantom formulation that provides greater control over frequency-dependent properties. We then show that homogeneous phantoms have limited use at representing input impedance and energy absorption at ultrahigh operating frequency by analyzing each antenna topology in proximity to layered or homogeneous tissue across frequency. We compare loop and dipole antenna topologies in terms of specific absorption rate and impedance, and show that frequency-dependent tissue behavior must be considered even at fixed operating frequencies. Finally, we discuss the dual utility of a transmitting antenna as a resonator to detect changes in tissue properties in addition to powering an implanted device.

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.

This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadway features in streetlevel panoramas, a slow and costly process. We propose to automate this process using a deep convolutional neural network that directly estimates the star rating from a street-level panorama, requiring milliseconds per image at test time. Our network also estimates many other roadlevel attributes, including curvature, roadside hazards, and the type of median. To support this, we incorporate taskspecific attention layers so the network can focus on the panorama regions that are most useful for a particular task. We evaluated our approach on a large dataset of real-world images from two US states. We found that incorporating additional tasks, and using a semi-supervised training approach, significantly reduced overfitting problems, allowed us to optimize more layers of the network, and resulted in higher accuracy.