Salvia officinalis L., also known as the “Salvation Plant”, has been long used and well-documented in traditional medicine around the globe. Its bioactive compounds, and especially its polyphenol profile, have been extensively researched and reviewed. However, sage’s beneficial effects reach much further, and nowadays, with a range of new extraction techniques, we are discovering new components with new therapeutic effects, especially in the context of neurodegenerative diseases and various carcinomas. This review describes the bioactive profile of various sage preparations depending on the extraction techniques and extraction parameters, and this review lists the newest research findings on its health effects.

Analysis of harmonic propagation in transmission network represents an important factor in exploitation of the power system. These analyses are conducted in time and frequency domains. In a case of a robust power system, analyses are usually conducted in frequency domain and usually carried out in the Three-phase system (TPS), so that all effects of interest in the frequency spectrum are taken in consideration. Transmission line (TL) modeling in TPS and frequency domain, with the usage of Kron Matrix Reduction (KMR), can be found in this paper. The model is created for frequency values between 0.05 (Hz) and 10 (kHz), which represent the frequency spectrum of interest. Mathematical procedure for implementing the KMR can also be found in this paper. Usage of KMR for voltage profile analysis on a TL is shown on a plain example. The proposed model is compared with tested mathematical models from EMTP-RV.

Concurrent software based on a shared-memory model is predominant in industrial applications that cannot afford to execute complex message-passing libraries. However, direct access to shared memory creates implicit dependencies between concurrently executing components. Therefore, the development and maintenance of such software is hard. In this paper, we argue the need to manage, at the architectural level, the implicitly high coupling between concurrent components that share memory. We suggest an approach that verifies architectural specifications against the implementation and finds potential mismatches. While static analysis approaches can be complete and verify all possible mismatches, they are often imprecise, leading to a large number of false warnings, especially in concurrent software. Instead, we built our approach, using dynamic analysis, on top of one of the most well-known algorithms for detecting data races, Eraser Lockset, and extended its model to support features required for the verification process. Since Lockset operates on the execution traces, test cases that produce these traces must ensure proper coverage. Therefore, we argue the need to use test cases conforming to the strict modified condi-tion/decision coverage criteria (MC/DC). Our version of Lockset takes advantage of the fact that possible shared memory locations are known in advance. We further improved its precision by considering atomic operations as a synchronization mechanism. The approach was evaluated on industrial AUTOSAR drivers that execute concurrently.

As robots, both individually and in groups, become more prevalent in everyday contexts (e.g., schools, workplaces, educational and caregiving institutions), it is possible that they will be perceived as outgroups, or come into competition for resources with humans. Research indicates that some of the psychological effects of intergroup interaction common in humans translate to human-robot interaction (HRI). In this paper, we examine how intergroup competition, like that among humans, translates to HRI. Specifically, we examined how Number of Humans (1, 3) and Number of Robots (1, 3) affect behavioral competition on dilemma tasks and survey ratings of perceived threat, emotion, and motivation (fear, greed, and outperformance). We also examined the effect of perceived group entitativity (i.e., cohesiveness) on competition motivation. Like in social psychological literature, these results indicate that groups of humans (especially entitative groups) showed more greed-based motivation and competition toward robots than individual humans did. However, we did not find evidence that number of robots had an effect on fear-based motivation or competition against them unless the robot groups were perceived as highly entitative. Our data also show the intriguing finding that participants displayed more fear of and competed slightly more against robots that matched their number. Future research should more deeply examine this novel pattern of results compared to one-on-one HRI and typical group dynamics in social psychology.

Social robots have been designed to engage with older adults and children separately, but their use for intergenerational (IG) interactions, especially in nonfamilial settings, has not been studied. In addition to the challenge of simultaneously meeting the varied needs and preferences of older adults and children, the dynamic nature of these settings makes the use of robots for IG activities difficult. This paper presents a first exploratory study meant to inform the design and use of social robots for IG activities in nonfamilial settings by analyzing interviews and observations conducted at a co-located preschool and assisted living-dementia care center. Interactions occurring with and around robots were analyzed, particularly focusing on whether they fulfill the community's goals of providing children and older adults with engaging opportunities for IG contact. Findings suggest integrating intermittent pauses and breaks in interactions with the robot and unstructured collaborative robot-assisted activities can meet the needs of both generations, and call for greater community involvement in HRI for IG research.

Introduction: Golden retriever muscular dystrophy (GRMD), an X‐linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD. Methods: To assess severity in the GRMD, we analyzed texture features extracted from multi‐parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines. Results: A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan‐time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort. Conclusions: The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380–386, 2019

Introduction: The concept of quality of life (QoL) is becoming an increasingly important criterion in the assessment of treatment outcomes, health outcomes and in the assessment of the benefit-to-load ratio of drugs or therapies that have equivalent mechanisms of action. Aim: The aim of the study was to evaluate the improvement of quality of life, tolerability of therapy and patient compliance in patients with depression and/or anxiety disorder treated with antidepressants. Methods: The study was designed as a clinical, multicenter, prospective, cohort study involving 682 patients of both sexes diagnosed with depression and/or anxiety disorder observed over the 9 months period. The study was conducted from January to December 2017 in six research centers of the PI Health Center of the Canton of Sarajevo. The patients were divided into three groups: depressive, anxious and mixed anxiety-depressive disorder, and the therapy administered was, paroxetine or escitalopram. MOS (Medical Outcomes Study) sleep scale and Q-LES-Q-SF (Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form) scale were used for quality of life evaluation. Patients were observed six times over the course of the study. Results: The results of the MOS questionnaire showed that more than 90 percent of patients with depression and/or anxiety disorder who had taken fluoxetine, paroxetine or escitalopram for 36 weeks experienced an improvement in the sleep problem index. Sleep duration was greatly improved in all patients regardless of the antidepressants used. The results of the Q-LES-Q-SF questionnaire showed a significant improvement in quality of life as well as overall pleasure and satisfaction with life due to the use of antidepressants. Conclusion: Therapy with fluoxetine, paroxetine and escitalopram leads to a significant improvement of all recorded parameters, along with the overall quality of life, which makes them very effective in the treatment of depression and/or anxiety disorders.