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Most decision-making models, including the pairwise comparison method, assume the honesty of the decision-maker. However, it is easy to imagine a situation where the decision-maker tries to manipulate the ranking results. This problem applies to many decision-making methods, including the pairwise comparison method. This article proposes three simple algorithmic methods for manipulating data using the pairwise comparison method. The proposed solutions try to mimic the behavior of a dishonest decision-maker who, acting under time pressure, chooses a simple strategy that leads to pushing through a given alternative. We also test the susceptibility to detection of the proposed manipulation strategies. To this end, we propose a convolutional neural network architecture, which we train based on generated data consisting of the original random pairwise comparison matrices and their manipulated counterparts. Our approach treats the pairwise comparison matrices as two- or three-dimensional images specific to the decision situation. In the latter case, the matrices are initially transformed into a three-dimensional map of local inconsistencies, and only data processed in this way are subjected to analysis using neural networks. The experiments indicate a significant level of detection of the proposed manipulations. In numerical tests, the effectiveness of the presented solution ranges from 88% to 100% effectiveness, depending on the tested algorithm and test parameters. The measured average computation time for the single case analyzed oscillated below one millisecond, which is a more than satisfactory result of the performance of the built implementation. We can successfully use the neural networks trained on synthetic data to detect manipulation attempts carried out by real experts. Preliminary tests with respondents also indicated high effectiveness in detecting manipulation. At the same time, they signaled the difficulty of distinguishing actual manipulation from a situation in which an expert strongly prefers one or more selected alternatives.

The µ opioid receptor (MOR) is the key target for analgesia, but the application of opioids is accompanied by several issues. There is a wide range of opioid analgesics, differing in their chemical structure and their properties of receptor activation and subsequent effects. A better understanding of ligand-receptor interactions and the resulting effects is important. Here, we calculated the respective binding poses for several opioids and analyzed interaction fingerprints between ligand and receptor. We further corroborated the interactions experimentally by cellular assays. As MOR was observed to display ligand-induced modulation of activity due to changes in membrane potential, we further analyzed the effects of voltage sensitivity on this receptor. Combining in silico and in vitro approaches, we defined discriminating interaction patterns responsible for ligand-specific voltage sensitivity and present new insights into their specific effects on activation of the MOR.

Computational methods play a significant role in reducing energy consumption in cities. Many different sensor networks (e.g., traffic intensity sensors, intelligent cameras, air quality monitoring systems) generate data that can be useful for both efficient management (including planning) and reducing energy usage. Street lighting is one of the most significant contributors to urban power consumption. This paper presents a summary of recent attempts to use computational methods to reduce energy usage by lighting systems, with special focus on graph-based methods. Such algorithms require all the necessary data to be integrated, in order to function properly: this task is not trivial, and is very time-consuming; therefore, the second part of the paper proposes a novel approach to integrating urban datasets and automating the optimisation process. In two practical examples, we show how spatially triggered graph transformations (STGT) can be used to build a model based on the road network map, sensor locations and street lighting data, and to introduce semantic relations between the objects, including utilisation of existing infrastructure, and planning of development to maximise efficiency.

To precisely characterize CD146 in adipose stromal/progenitor cells (ASCs) we sorted the stromal vascular faction (SVF) of human abdominal subcutaneous white adipose tissue (sWAT) according to cell surface (cs) expression of CD146, DLK1 and CD34. This test identified three main SVF cell populations: ~50% cs-DLK1−/cs-CD34+/cs-CD146− ASCs, ~7.5% cs-DLK1+/cs-CD34dim/+/cs-CD146+ and ~7.5% cs-DLK1+/cs-CD34dim/+/cs-CD146− cells. All cells contained intracellular CD146. Whole mount fluorescent IHC staining of small vessels detected CD146+ endothelial cells (CD31+/CD34+/CD146+) and pericytes (CD31−/CD34−/CD146+ ASCs). The cells in the outer adventitial layer showed the typical ASC morphology, were strongly CD34+ and contained low amounts of intracellular CD146 protein (CD31−/CD34+/CD146+). Additionally, we detected wavy CD34−/CD146+ and CD34dim/CD146+ cells. CD34dim/CD146+ cells were slightly more bulky than CD34−/CD146+ cells. Both CD34−/CD146+ and CD34dim/CD146+ cells were detached from the inner pericyte layer and protruded into the outer adventitial layer. Cultured early passage ASCs contained low levels of CD146 mRNA, which was expressed in two different splicing variants, at a relatively high amount of the CD146-long form and at a relatively low amount of the CD146-short form. ASCs contained low levels of CD146 protein, which consisted predominantly long form and a small amount of short form. The CD146 protein was highly stable, and the majority of the protein was localized in the Golgi apparatus. In conclusion, the present study contributes to a better understanding of the spatial localization of CD34+/CD146+ and CD34−/CD146+ cells in the adipose niche of sWAT and identifies CD146 as intracellular protein in cs-DLK1−/cs-CD34+/cs-CD146− ASCs.

Concentrated solar power plants belong to the category of clean sources of renewable energy. The paper discusses the possibilities for the use of molten salts as storage in modern CSP plants. Besides increasing efficiency, it may also shift their area of application: thanks to increased controllability, they may now be used not only to cover baseload but also as more agile, dispatchable generators. Both technological and economic aspects are presented, with focus on the European energy sector and EU legislation. General characteristics for CSP plants, especially with molten salt storage, are discussed. Perspectives for their development, first of all in economic aspects, are considered.

Automated planning is a well-established field of artificial intelligence (AI), with applications in route finding, robotics and operational research, among others. The task of developing a plan is often solved by finding a path in a graph representing the search domain; a robust plan consists of numerous paths that can be chosen if the execution of the best (optimal) one fails. While robust planning for a single entity is rather simple, development of a robust plan for multiple entities in a common environment can lead to combinatorial explosion. This paper proposes a novel hybrid approach, joining heuristic search and the wavefront algorithm to provide a plan featuring robustness in areas where it is needed, while maintaining a low level of computational complexity.

This paper presents and compares the possible energy savings in various approaches to outdoor lighting modernization. Several solutions implementable using currently-available systems are presented and discussed. An innovative approach using real-time sensor data is also presented in detail, along with its formal background, based on Artificial Intelligence methods (rule-based systems) and graph transformations. The efficiency of all approaches has been estimated and compared using real-life data recorded at an urban setting. The article also presents other aspects which influence the efficiency and feasibility of intelligent lighting projects, including design quality, design workload and conformance to standards.