Both sustress and distress might impair normal physiological functions and even lead to pathological conditions, while eustress might benefit health through hormesis-induced optimization of homeostasis. Therefore, an optimal stress level is essential for building biological shields to guarantee normal life processes.Finding an optimal set of nodes, called key players, whose activation (or removal) would maximally enhance (or degrade) certain network functionality, is a fundamental class of problems in network science1,2. Potential applications include network immunization3, epidemic control4, drug design5, and viral marketing6. Due to their general NP-hard nature, those problems typically cannot be solved by exact algorithms with polynomial time complexity7. Many approximate and heuristic strategies have been proposed to deal with specific application scenarios1,2,8-12. Yet, we still lack a unified framework to efficiently solve this class of problems. Here we introduce a deep reinforcement learning framework FINDER, which can be trained purely on small synthetic networks generated by toy models and then applied to a wide spectrum of influencer finding problems. Extensive experiments under various problem settings demonstrate that FINDER significantly outperforms existing methods in terms of solution quality. Moreover, it is several orders of magnitude faster than existing methods for large networks. The presented framework opens up a new direction of using deep learning techniques to understand the organizing principle of complex networks, which enables us to design more robust networks against both attacks and failures.

Evaluation of X-ray reject analysis is an important quality parameter in diagnostic facility. The aim of this study was to find out the radiograph rejection and its causes during the coronavirus disease 2019 (COVID-19) pandemics as there was fear of coronavirus disease infection among the technical staff from the incoming patients in a busy, high volume public sector tertiary care hospital.

This descriptive study was conducted at Radiology Department, Lady Reading Hospital, Peshawar from August to November, 2020. The rejected radiographs and their causes were analyzed.

A total of 15,000 X-ray procedures were conducted during study period out of which 2550 cases were repeated making the total rejection 17%. Rejection in male and female were 74.3 and 25.7%, respectively, while rejection in adults was (80.1%) and (19.9%) in pediatric age group of the total rejection. The main cause of rejection was positioning (30.5%) followed by artifacts (22.4%), motion (12.1%), improper collimation (10%), wrong labeling (8.4%), exposure errors (6.9%), detector errors (3.7%), machine faults (2.8%), re-request from referring physician (1.7%), and PACS issues (1.5%). In terms of body anatomical parts, the highest rejection was observed in extremities (44.1%), followed by chest radiography (23.3%), spine (11.4%), abdomen (6.4%), skull (5.9%), pelvis (4.7%), KUB (3.7%), and neck (0.6%), respectively.

Radiograph rejection is common problem in every diagnostic facility but significant reduction can be achieved by implementing rejection analysis as basic quality indicator, and conducting technologist/s specific training programs for their knowledge and skill enhancement.

Radiograph rejection is common problem in every diagnostic facility but significant reduction can be achieved by implementing rejection analysis as basic quality indicator, and conducting technologist/s specific training programs for their knowledge and skill enhancement.In this work, we apply an amine-assisted silica pillaring method to create the first example of a porous Mo2TiC2 MXene with nanoengineered interlayer distances. The pillared Mo2TiC2 has a surface area of 202 m2 g-1, which is among the highest reported for any MXene, and has a variable gallery height between 0.7 and 3 nm. The expanded interlayer distance leads to significantly enhanced cycling performance for Li-ion storage, with superior capacity, rate capably and cycling stability in comparison to the non-pillared analogue. The pillared Mo2TiC2 achieved a capacity over 1.7 times greater than multilayered MXene at 20 mA g-1 (≈320 mA h g-1) and 2.5 times higher at 1 A g-1 (≈150 mA h g-1). The fast-charging properties of pillared Mo2TiC2 are further demonstrated by outstanding stability even at 1 A g-1 (under 8 min charge time), retaining 80% of the initial capacity after 500 cycles. Furthermore, we use a combination of spectroscopic techniques (i.e. XPS, NMR and Raman) to show unambiguously that the charge storage mechanism of this MXene occurs by a conversion reaction through the formation of Li2O. This reaction increases by 2-fold the capacity beyond intercalation, and therefore, its understanding is crucial for further development of this family of materials. In addition, we also investigate for the first time the sodium storage properties of the pillared and non-pillared Mo2TiC2.Selective unidirectional transport of barium ions between droplets in a water-in-chloroform emulsion is demonstrated. Gold nanoparticles (GNPs) modified with a thiolated crown ether act as barium ion complexing shuttles that carry the ions from one population of droplets (source) to another (target). This process is driven by a steep barium ion concentration gradient between source and target droplets. The concentration of barium ions in the target droplets is kept low at all times by the precipitation of insoluble barium sulfate. Apoptozole A potential role of electrostatically coupled secondary processes that maintain the electroneutrality of the emulsion droplets is discussed. Charging of the GNP metal cores by electron transfer in the presence of the Fe(ii)/Fe(iii) redox couple appears to affect the partitioning of the GNPs between the water droplets and the chloroform phase. Processes have been monitored and studied by optical microscopy, Raman spectroscopy, cryogenic scanning electron microscopy (cryo-SEM) and zeta potential. The shuttle action of the GNPs has further been demonstrated electrochemically in a model system.