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In this study, we chose to conduct a gender-based contextual analysis of research-performing organizations (RPOs) in the higher-education sector in Albania as a first step toward the implementation of gender budgeting (GB). Our rationale for conducting such a contextual analysis is the overarching need to achieve the European Commission's strategic objectives regarding gender equality in research and innovation. To carry out this analysis, we used reports from She Figures to calculate dominant gender indicators; these reports were produced in collaboration with the statistics published by the Institute of Statistics of Albania. Our methodology is based on a mixed-methods approach that aims to better our understanding of the situation in Albania. The quantitative findings provided by our contextual analysis within academia were synthesized with qualitative findings resulting from a comparative analysis of the content of gender equality plans (GEPs) currently being implemented by thirteen universities in Albania. The results of our contextual analysis study show that even though women account for more than half of the total population of researchers at the national level, playing a significant role in research and innovation, we recommend that the government develop its first national GEP to counteract the inequality that persists in the career trajectories of women and men. GB represents an effective strategy for reducing gender inequality in this context. The supporting results of the content analysis indicate that the phenomenon of vertical segregation has been identified in the great majority of RPOs that have carried out gender-based contextual analyses; moreover, we observed the interaction of GB with GEPs within three such organizations' approach to the gender-based allocation of finances.

The role and the importance of mobile electronic service are indisputable and serve as necessary condition for a sustainable development throughout the economy. In this article through an empiric study noted and recorded this role in the Albanian market, and draw valid conclusions for the future and challenges of mobile services market. The structure of this scientific article is structured in the following: short introduction, theoretical review of literature, methodology, and analyze of concrete case, included here and one detailed SWOT analyze. In one special annex are given questionnaires and explanations for them and conclusions and further recommendations. [PUBLICATION ABSTRACT]

The aim of this study was to observe the effect of electroacupuncture (EA) at the ST36 acupoint on the firing rate of gastric-related neurons in the spinal dorsal horn (SDH) and nucleus tractus solitarius (NTS). There were different effects of gastric distention in SDH and NTS in 46 male Sprague-Dawley rats. In 10 excitatory neurons in SDH, most of the neurons were inhibited by homolateral EA. The firing rates decreased significantly (P<0.05) in 10 excitatory gastric-related neurons in NTS; the firing rates of 6 neurons were further excited by homolateral EA, with a significant increase of the firing rates (P<0.05); all inhibitory gastric-related neurons in NTS were excited by EA. The inhibition rate of homolateral EA was significantly increased in comparison with contralateral EA in gastric-related neurons of SDH (P<0.05). There was no significant difference between homolateral and contralateral EA in gastric-related neurons of NTS. EA at ST36 changes the firing rate of gastric-related neurons in SDH and NTS. However, there are some differences in responsive mode in these neurons. The existence of these differences could be one of the physiological foundations of diversity and complexity in EA effects.

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.