The paper presents a PyDigitizer application designed to extract numerical coordinates of existing curves from image format. The application is created in the Python programming language and offers the possibility of manual extraction of curve coordinates, intersection of curves with user-specified abscissa or ordinate, generation of polynomial regression equations of curves. All generated information (coordinates, intersection points, regression curves) can be exported to Microsoft Excel. Also, point coordinates of Excel curves can be loaded into PyDigitizer to obtain polynomial regression curves or intersections.

During the operation of each machine, the deformation and stress state of its supporting structure is directly related to the load. The load intensity ranges from the minimum to the maximum value and is a function of nominal force, torque, and kinematic-dynamic values of moving and executive assemblies during the machine operation process. The intensity of deformations is also a function of the shape and type of the supporting structure. However, although the supporting structure is reliable, which refers to its integrity, the production of a quality workpiece (product) does not have to be reliable. Therefore, it is important that the deformation states of the supporting structure be in the appropriate tolerance field, regardless of the intensity of the load. If the intensity of deformations is outside a certain field, it will directly affect the quality of the workpiece that is made on the subject machine. It is known that load-bearing structures can be of open and closed types, load-bearing structures of closed type are used for higher loads and larger workpiece dimensions. By installing sensors on the elements of the supporting structure with accompanying measuring chains in real-time, the deformation state of certain elements or places on the supporting structure can be monitored, based on which the reliable quality of the workpiece can be influenced.

Progress towards effective treatment of epileptic seizures has seen much improvement in the past decade. In particular, the emergence of phenomenological models of epileptic seizures specifically designed to capture the electrical seizure dynamics in the Epileptor model is inspiring new approaches to predicting and controlling seizures. These new models present in various forms and contain important but unmeasurable variables that control the occurrence of seizures. These models have been used mostly as nodes in large networks to study the complex brain behaviour of seizures. In order to use this model for the purposes of seizure forecasting or to control seizures through deep brain stimulation, the states of the model will need to be estimated. Although devices such as EEG electrodes can be related to some of the states of the model, most remain unmeasured and would require an observer (as defined in control theory) for their estimation. Additionally, we would like to consider the case for large nodes of systems where the number of electrodes is far smaller than the number of nodes being estimated. In this paper, we provide methods towards obtaining the full states of these phenomenological models using nonlinear observers. In particular, we explore the effectiveness of the Extended Kalman Filter for small networks of nodes of a smoothed sixth order Epileptor model. We show that observer design is possible for this family of systems and identify the difficulties in doing so.Clinical relevance—The methods presented here can be applied with an individual epileptic patient’s EEG to reveal previously hidden biomarkers of epilepsy for seizure forecasting.

Although miRNA-27a has been identified as a promising candidate for miRNA mimic therapy of obesity, its application is limited due to enzymatic degradation and low membrane permeation. To overcome these problems, we developed cationic nanostructured lipid carriers (cNLCs) using high-pressure homogenization and used them as non-viral carriers for the anti-adipogenic miRNA-27a. Cargo-free octadecylamine-containing NLCs and miRNA/cNLC complexes were characterized regarding particle size, size distributions, zeta potential, pH values, particle topography and morphology, and entrapment efficacy. Furthermore, the cytotoxicity and cellular uptake of the miRNA/cNLC complex in the 3T3-L1 cell line were investigated. The investigation of the biological effect of miRNA-27a on adipocyte development and an estimation of the accumulated Oil-Red-O (ORO) dye in lipid droplets in mature adipocytes were assessed with light microscopy and absorbance measurements. The obtained data show that cNLCs represent a suitable DDS for miRNAs, as miRNA/cNLC particles are rapidly formed through non-covalent complexation due to electrostatic interactions between both components. The miRNA-27a/cNLC complex induced an anti-adipogenic effect on miRNA-27a by reducing lipid droplet accumulation in mature adipocytes, indicating that this approach might be used as a new therapeutic strategy for miRNA mimic replacement therapies in the prevention or treatment of obesity and obesity-related disorders.

Premature babies and those born with a medical condition are cared for within the neonatal intensive care unit (NICU) in hospitals. Monitoring physiological signals and subsequent analysis and interpretation can reveal acute and chronic conditions for these neonates. Several advanced algorithms using physiological signals have been built into existing monitoring systems to allow clinicians to analyse signals in real time and anticipate patient deterioration. However, limited enhancements have been made to interactively visualise and adapt them to neonatal monitoring systems. To bridge this gap, we describe the development of a user-friendly and interactive dashboard for neonatal vital signs analysis written in the Python programming language where the analysis can be performed without prior computing knowledge. To ensure practicality, the dashboard was designed in consultation with a neonatologist to visualise electrocardiogram, heart rate, respiratory rate and oxygen saturation data in a time-series format. The resulting dashboard included interactive visualisations, advanced electrocardiogram analysis and statistical analysis which can be used to extract important information on patients’ conditions.Clinical Relevance— This will support the care of preterm infants by allowing clinicians to visualise and interpret physiological data in greater granularity, aiding in patient monitoring and detection of adverse conditions. The detection of adverse conditions could allow timely and potentially life-saving interventions for conditions such as sepsis and brain injury.

Gravity inversions have contributed greatly to our knowledge of the interior of planetary bodies and the processes that shaped them. However, previous global gravity inversion methods neglect the inference of mantle density anomalies when using techniques to decrease the non‐uniqueness of the inversion. In this work, we present a novel global gravity inversion algorithm, named THeBOOGIe, suited to inferring global‐scale density anomalies within the crust and mantle of planetary bodies. The algorithm embraces the nonuniqueness inherent in gravity inversions by not prescribing at the outset a density interface or depth range of interest. Instead, the method combines a Bayesian approach with a flexible incorporation of prior geological or geophysical information to infer density anomalies at any depth. A validation test using synthetic lunar‐like gravity data shows that THeBOOGIe can constrain the lateral location of crustal density anomalies but tends to overestimate their thicknesses. Importantly, THeBOOGIe can detect deep mantle density anomalies and quantify the level of confidence in the inferred density models. Our results show that THeBOOGIe can provide complementary information to one‐dimensional seismic models of the interior of the terrestrial planets and the Moon by constraining density anomalies that are not spherically symmetric. Additionally, THeBOOGIe is specially suited to constraining the interior of partially differentiated bodies where these large‐scale density anomalies are more likely to exist. Finally, thanks to the flexible use of priors, THeBOOGIe is an essential tool to understand the interior of planetary bodies lacking additional constraints.

: The understanding of the transboundary impact of Climate Change on hydropower is not well-established in the literature, where few studies take a system perspective to understand the relative roles of different technological solutions for coordinated water and energy management. This study contributes to addressing this gap by introducing an open-source, long-term, technologically-detailed water and energy resources cost-minimisation model for the Drin River Basin, built in OSeMOSYS. The analysis shows that climate change results in a 15-52% annual decline in hydro generation from the basin by mid-century. Albania needs to triple its investments in solar and wind to mitigate the risk of climate change. Changing the operational rules of hydropower plants has a minor impact on the electricity supply. However, it can spare significant storage volume for flood control.

Unsupervised learning (UL) is widely used in the wireless resource allocation problems due to its lower computational complexity and better performance compared with traditional optimization algorithms. Since wireless resource allocation problems usually have several constraints, primal-dual learning based UL framework are widely adopted. However, the primal-dual learning approach has the problem of oscillation around the constraint threshold while training and there may be serious constraint violations when deployment. In addition, although the output of the neural network can also be restricted to the feasible region by the penalty function method, the optimality of such training methods cannot be guaranteed. In this article, we combine the primal dual learning method with the penalty function method and propose a regularized unsupervised learning (RUL) framework to enhance the robustness of the primal-dual learning based UL framework. In the proposed RUL framework, we use regularization techniques to improve the robustness of primal-dual learning by reducing the risk of constraint violations while training. A quadratic penalty term is introduced into the Lagrangian function of the wireless optimization problem where the constraints can be equivalent to equality constraints to form its augmented Lagrangian function. In the simulation, we give a simple point to point power optimization problem as an example to show that the proposed RUL can improve the robustness of constraint convergence, and can also accelerate training speed.