Current Step Research Articles

Topology and Formation of Current Source Step Down Resonant Switched Inductor Converters

This paper presents a current converter that uses an inductor based approach for interim energy storage. A family of the circuits for step down conversion is examined for both non-inverting and inverting operations. The paper has disclosed the method of the generation, so that any order of 1/n conversion ratio can be made. One of the features is to use two transistors only in the common half-bridge style. The main contribution is its special current conversion capability and soft-switching, because it eliminates switching loss and the spike in the devices using a resonant capacitor with the switched-inductor. The performance has been proved to work well for current bucking. This is a new concept for the power converter and is an advanced development of the conventional switched-inductor converter, switched-capacitor, and resonant converter; it is a duality of the switched-capacitor converter. The paper provides a theoretical approach for the current source topology and its formation. It prepares for vast applications in the current source photovoltaic system and current mode system. Experiment verification and loss analysis have proven the preferable characteristics. Benchmarking comparison with similar converters has been made and advanced features have been described. The proposed converter presents current mode research for increasing application in the coming decade.

Modeling the Dynamics of Collective Behavior in a Reflexive Game with an Arbitrary Number of Leaders

An oligopoly with an arbitrary number of Stackelberg leaders under incomplete, asymmetrical agents' awareness and inadequacy of their predictions of competitors' actions is considered. Models of individual decision-making processes by agents are studied. The reflexive games theory and collective behavior theory are the theoretical basis for construction and analytical study process models. They complement each other in that reflexive games allow using the collective behavior procedures and the results of agents' reflections, leading to a Nash equilibrium. The dynamic decision-making process considered repeated static games on a range of agents' feasible responses to the expected actions of the environment, considering current economic restrictions and competitiveness in each game. Each reflexive agent in each game calculates its current goal position and changes its state, taking steps towards the current position of the goal to obtain positive profit or minimize losses. Sufficient conditions for the convergence of processes in discrete time for the case of linear costs of agents and linear demand is the main result of this work. New analytical expressions for the agents' current steps' ranges guarantee the convergence of the collective behavior models to static Nash equilibrium is obtained. That allows each agent to maximize their profit, assuming common knowledge among the agents. The processes when the agent chooses their best response are also analyzed. The latter may not give converging trajectories. The case of the duopoly in comparison with modern results is discussed in detail. Necessary mathematical lemmas, statements, and their proofs are presented.

Adaptive momentum with discriminative weight for neural network stochastic optimization

Optimization algorithms with momentum have been widely used for building deep learning models because of the fast convergence rate. Momentum helps accelerate Stochastic gradient descent in relevant directions in parameter updating, minifying the oscillations of the parameters update route. The gradient of each step in optimization algorithms with momentum is calculated by a part of the training samples, so there exists stochasticity, which may bring errors to parameter updates. In this case, momentum placing the influence of the last step to the current step with a fixed weight is obviously inaccurate, which propagates the error and hinders the correction of the current step. Besides, such a hyperparameter can be extremely hard to tune in applications as well. In this paper, we introduce a novel optimization algorithm, namely, Discriminative wEight on Adaptive Momentum (DEAM). Instead of assigning the momentum term weight with a fixed hyperparameter, DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value that configures momentum in the current step. In this way, DEAM involves fewer hyperparameters. DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training the deep learning models of both convex and nonconvex situations.

Modeling neighborhood-scale shallow geothermal energy utilization: a case study in Berlin

Nowadays, utilizing shallow geothermal energy for heating and cooling buildings has received increased interest in the building sector. Among different technologies, large borehole heat exchanger arrays are widely employed to supply heat to various types of buildings. Recently, a 16-borehole array was constructed to extract shallow geothermal energy to provide heat to a newly-developed public building in Berlin. To guarantee the quality of the numerical model and reveal its sensitivity to different subsurface conditions, model simulations were conducted for 25 years with two finite element simulators, namely the open-source code OpenGeoSys and the widely applied commercial software FEFLOW. Given proper numerical settings, the simulation results from OpenGeoSys and FEFLOW are in good agreement. However, further analysis reveals differences with respect to borehole inflow temperature calculation implemented in the two software. It is found that FEFLOW intrinsically uses the outflow temperature from the previous time step to determine the current inflow temperature, which makes it capable of much faster simulation by avoiding iterations within a single time step. In comparison, OpenGeoSys always updates the inflow and outflow temperature based on their current time step values. Because the updates are performed after each iteration, it delivers more accurate results with the expense of longer simulation time. Based on this case study, OpenGeoSys is a valid alternative to FEFLOW for modeling ground source heat pump systems. For modellers working in this field, it is thus recommended to adopt small enough time step size, so that potential numerical error can be avoided.

State Estimation With a Destination Constraint Imposed by Proportional Navigation Guidance Law

Proportional navigation (PN) guidance laws have been commonly applied to homing missile systems guiding the missile to its target. When the target is stationary and its location is known a priori, the state of the missile is subjected to a destination constraint, which contains extra information on missile state that can help increase tracking performance. This article concerns with the constraint modeling and the state estimation with a destination constraint imposed by PN guidance law in 3-D space. Constraint models corresponding to three representative PN guidance laws: pure PN, true PN, and generalized PN, are presented using the state augmentation method, which stacks the states at the previous and current time steps in one state vector to express the constraint relationships. For cases where the destination information is noisy and the parameters of the guidance law, i.e., the navigation constant and the bias angle, are not known a priori, the unknown parameters are estimated along with the missile state in the state vector in an augmentation way. Based on the formulations of constraint models, the construction of pseudomeasurements is carried out. A constrained state estimation method is proposed by incorporating the pseudomeasurements into the nonlinear filtering process. In this method, a sequential process framework is employed and the cubature Kalman filter is used to handle the high-dimensional filtering problem and the strong nonlinearity involved in pseudomeasurements. Numerical experiments in three scenarios are performed to demonstrate the validity of the proposed models along with the estimation method.

Abstract WP66: Acute Stroke Screening For Cognitive Disorders And Depression

Introduction: As stroke survivors transition from acute to post-acute care, and finally to community settings, the Centers for Disease Control reports ~65% receive NO rehabilitation. Even more receive rehabilitation too late, after critical brain changes for recovery are complete. Stroke survivors with invisible disabilities of cognition and depression are especially vulnerable to experience poor recovery. We launched a dedicated process to identify invisible disabilities. Our long-term objective is to make acute and post-acute, evidence-based intervention accessible. Hypothesis: >50% of acute stroke patients have cognitive deficits, or depression. Methods: Our comprehensive stroke center completes bedside psychometric assessment with standardized instruments for aphasia (Language Screening Test, LAST), spatial neglect (Catherine Bergego Scale, CBS), memory/global cognition (Montreal Cognitive Assessment, MoCA), delirium (3-Minute Diagnostic Interview for the Confusion Assessment Method, 3D-CAM) and depression (Patient health questionnaire, PHQ-8). Patients unable to respond to questions are assessed for spatial neglect and delirium (standardized observations). Results: 105 ischemic stroke survivors were assessed in the first quarter of program launch (April-July, 2021). Of that group, patients met screening criteria for spatial neglect (47%), aphasia (40%), delirium (19%) and depression (31%). Over 90% had memory / global cognitive impairment (MoCA<26/30). Conclusions: Our initiative, which includes systematic acute stroke unit spatial neglect screening, confirmed the previously reported high rate of cognitive disorders and depression (Champod, Eskes, Barrett, 2020). Our current step implements uniform recommendations for patients with deficits, and will examine post-acute outcomes, number receiving rehabilitation and medical follow-up, and treatment disparities (right/left stroke, under-represented groups).

Services-oriented intelligent milling for thin-walled parts based on time-varying information model of machining system

Experience-based process optimization cannot accurately and automatically adapt to the time-varying cutting condition of thin-walled parts because of the large amount of removed material. Thus, machining chatter and error are easily generated if the process parameters are not adjusted in time. To address this problem, a novel intelligent milling methodology is proposed for thin-walled parts, which has three innovations: data-driven time-varying information model construction, adaptive process optimization, and service-oriented automatic process execution. Using this methodology, a time-varying information model of a machining system was first constructed to characterize the actual cutting condition, and this model can be used as the digital twin model for machining thin-walled parts. In addition, the in-process physical and geometric data in the current process step were collected to construct the information model. Subsequently, the process parameters of the next process step were adaptively optimized based on the information model. Finally, a service-oriented process execution stream was built to automate the entire milling process, including data collection, process optimization, and process execution. To verify the proposed method, intelligent milling was performed on thin-walled parts. The experimental results demonstrated that this method can effectively and automatically control the machining chatter and thickness error according to the actual cutting condition. Compared with nominal milling, the machining surface roughness improved from Ra 2.4 to Ra 1.6, and the thickness accuracy improved from ±0.03 to ±0.02 mm.

NOVATIONS OF LEGISLATION IN ACCOUNTING FIXED ASSET

Innovations and changes of approaches in accounting of fixed assets in accordance with the new Federal Accounting Standards of the Russian Federation (FSB) are considered. Fixed assets (OS) represent a non-current assets including the material form of modern technologies and innovations used in the production process and directly affects the cost of manufactured products. Adequate reporting of fixed assets is important for users. The innovations of legislative are contained in two new standards - FSB 6/2020 «Fixed Assets» and FSB 26/2020 «Capital Investments», and represent current steps to harmonize the domestic accounting of fixed assets with international financial reporting standards (IFRS). The article analyzes in detail the current PBU 6/01 "Accounting of fixed assets", legislative innovations and the main approaches of IFRS in terms of accounting and reporting of fixed assets. Legislative changes are structured and analyzed. Special emphasis is placed on the elements of harmonization of accounting and financial reporting for determining the limit of the cost of fixed assets, revaluation of fixed assets, the procedure for calculating depreciation of fixed assets. The main conclusion based on the results of the study allows us to assert that the financial statements formed according to the new rules will be significantly closer to the financial data of the organization's financial statements presented under IFRS.

Numerical prediction and experimental investigation of residual stresses in sequential milling of GH4169 considering initial stress effect

Residual stresses affect the service life and cause the deformation of machined parts. Therefore, the study on the development of residual stresses during machining operation is very important. For this, analytical and numerical models have been developed to predict the residual stresses for single-step machining operation. However, the parts are usually machined with a serial of production processes. A numerical model for predicting residual stress in sequential side milling GH4169, considering initial stress, was proposed in this research. The initial residual stress distribution due to previous-step milling was considered in the proposed model. It was assumed that this initial residual stress value changed with the depth from the machined surface, while the residual stress on the identical horizontal plane was assumed with uniform distribution. The proposed numerical simulation model could predict the stress value of the machined surface, the stress value of the compression valley, and the residual stress distribution in the depth direction beneath the machined surface with high accuracy. Experimental investigations of sequential side milling GH4169 were conducted and the generated residual stresses were measured. The distribution trend and influence rule of residual stress between two sequential milling steps were analyzed. The residual stress distribution beneath the surface showed a spoon-shaped pattern. The thickness of the residual stress influence layer (RSIL) after the current milling step was affected by the depth of cut and the RSIL thickness of the previous milling step. The numerical model could predict the thickness of RSIL and optimize the depth of cut in sequential side milling.

Comprehensive study on unipolar RRAM charge conduction and stochastic features: a simulation approach

An in-depth analysis of resistive switching (RS) in unipolar devices is performed by means of a new simulator based on resistive circuit breakers of different features. The forming, set and reset processes are described in terms of the stochastic formation and rupture of conductive filaments (CFs) of several branches in the dielectric. Both, the electric field and temperature dependencies are incorporated in the simulation. The simulation tool was tuned with experimental data of devices fabricated making use of the Ni/HfO2/Si stack. The variability and the stochastic behavior are characterized and reproduced correctly by simulation to understand the physics behind RS. Reset curves with several current steps are explained considering the rupture of different branches of the CF. The simulation approach allows to connect in a natural manner to compact modeling solutions for the devices under study.

Comparative Study of Different Activation Procedures of High Temperature Proton Exchange Membrane Fuel Cells

Herein, the objective is to investigate the activation mechanism and activation process in high‐temperature proton exchange membrane fuel cell under different activation procedures from multiple perspectives. The effects of galvanostatic, temperature cycling, current step cycling, and increasing back pressure on cell performance were investigated by current density distribution test, electrochemical impedance spectroscopy, and distribution of relaxation times analysis. The results show that galvanostatic activation was the optimal activation mode. Current step cycling and elevating back pressure activation increase the voltage growth rate, but make the difference in the optimization of each parameter become greater, and do harm to cell performance stability and long‐term durability. Temperature cycling activation increases the charge transfer resistance and makes the uniformity of current density distribution deteriorate due to the thermal and mechanical stresses. It emphasizes the fact that different activation modes had great influence on the uniformity of current density distribution, and the optimization speed and change trend of each key parameter are not synchronized during the activation process. It is insufficient to focus only on voltage reaching steady state, and factors such as voltage, impedance, and uniformity of current density distribution should be considered comprehensively.

Neural Network Algorithm for Adjusting the PI Controller in the Shearer Control System

The control system of a shearer is considered, which is designed for destroying rock and loading it onto a scraper conveyor. When working out a coal seam by a shearer, external disturbances — the coal resistance to cutting, solid inclu- sions of rock, changes in the width of the screw, which vary indefinitely, lead to a deterioration in the quality of transients. The paper focuses on the shearer control system, the key elements of which are: a movement drive, a cutting drive, a coal face and a standard regulator that provides the system with the desired control quality indicators. A typical cutting current controller in the form of a PI controller with parameters configured for a specific mode of operation of the shearer cannot ensure the optimal functioning of the control system in all modes due to the non-linearity of the control object and the random of changes in the coal resistance to cutting. To improve the control quality indicators, it is necessary to choose the parameters of the PI controller so as to minimize the amplitudes of the current steps of the cutting motor, and therefore reduce the amplitudes of the moment in the transmission of the cutting drive and minimize the system quieting time. In this paper, we propose a tuning algorithm based on obtaining the values of the controller parameters for each of the possible modes of operation of the shearer, identifying the type of disturbing effect by the response curves of the system available to observation. At the same time, the use of an artificial feed forward neural network, acting as an operational means of recognizing a multidimensional response curve in the control loop, is proposed. A neural network of two architectures was used: with a scalar and a vector output function. The curve recognition algorithm satisfies the limitations on the speed of solving the problem of controlling an electromechanical system, since the recognition of a disturbance occurs in a time that does not exceed the output of the process to the maximum of the current step. The correctness of the obtained results was confirmed by the results of computer modeling.

Krylov subspace recycling for evolving structures

Krylov subspace recycling is a powerful tool when solving a long series of large, sparse linear systems that change only slowly over time. In PDE constrained shape optimization, these series appear naturally, as typically hundreds or thousands of optimization steps are needed with only small changes in the geometry. In this setting, however, applying Krylov subspace recycling can be a difficult task. As the geometry evolves, in general, so does the finite element mesh defined on or representing this geometry, including the numbers of nodes and elements and element connectivity. This is especially the case if re-meshing techniques are used. As a result, the number of algebraic degrees of freedom in the system changes, and in general the linear system matrices resulting from the finite element discretization change size from one optimization step to the next. Changes in the mesh connectivity also lead to structural changes in the matrices. In the case of re-meshing, even if the geometry changes only a little, the corresponding mesh might differ substantially from the previous one. Obviously, this prevents any straightforward mapping of the approximate invariant subspace of the linear system matrix (the focus of recycling in this paper) from one optimization step to the next; similar problems arise for other selected subspaces. In this paper, we present an algorithm to map an approximate invariant subspace of the linear system matrix for the previous optimization step to an approximate invariant subspace of the linear system matrix for the current optimization step, for general meshes. This is achieved by exploiting the map from coefficient vectors to finite element functions on the mesh, combined with interpolation or approximation of functions on the finite element mesh. We demonstrate the effectiveness of our approach numerically with several proof of concept studies for a specific meshing technique.

Talking to children about their HIV diagnosis: a discussion rooted in different global perspectives.

An online meeting was arranged with four professionals representing four countries to debate current practices and future steps in naming HIV to children (disclosing HIV status). This article considers the evidence and reports on the commentary and debate from the meeting. Naming HIV to children remains a challenge. Although studies identify some of the facilitators and barriers to informing children of their HIV diagnosis, further review of practice is required. This article presents a global perspective of naming practices from different settings. The article comprises commentary and a report of the online debate, along with supporting evidence. The four participating authors concluded that health professionals must work in collaboration with families to support early naming of HIV to children or having an open discussion about HIV in clinics. Naming when a child is younger reduces self-stigma and empowers children and young people to adhere to their medication, make informed decisions and share their own diagnosis appropriately. The authors concluded that health professionals play a key role in educating colleagues and the public to reduce stigma and discrimination. Professionals working with children and families living with HIV require support and resources to instil confidence in naming and facilitate naming of HIV status to a child.

Comprehensive and person-centred approach in research: what is missing?

The concept of a comprehensive and person-centred approach in healthcare is not new and it is the basic principle that is embedded in the International Classification of Functioning, Disability and Health (ICF) framework. However, the implementation of a comprehensive and person-centred approach has not been fully translated into research development in people living with spinal cord injuries (SCI). This approach in research is important as the perspectives of persons living with SCI should be equally valued drivers in any research intended to provide a direct or indirect outcome to people living with a SCI. This perspective paper will discuss some of the limiting factors and provide some examples of previous and current successful steps being taken towards the worldwide implementation of this approach. Finally, this paper will suggest some of the steps needed to implement this person-centred model in research in people with SCI.

Расчет ступенчатого стержня при продольно-поперечном изгибе с дискретной осевой нагрузкой

This paper aims at obtaining formulas for bending moments and shear forces in a rectilinear elastic stepped rod under plane longitudinal-transverse bending. Each step of the rod (segment) can consist of different materials and have its own shape and cross- sectional dimensions. The rod can be loaded by an axial longitudinal force at the beginning of each step. The eccentricity of longitudinal forces at the beginning of each step (segment) is taken into account, which occurs due to the mismatch of longitudinal axes at the current and previous steps. Each segment of the rod can be exposed to a transverse action represented as concentrated bending moments, concentrated forces, and uniformly distributed loading. The resulting algebraic equations of the bending moments and shear forces are obtained for the stepped rod under longitudinal-transverse bending. The numerical model has been considered. The study results show that allowance for longitudinal action on the stepped rod bending with discrete axial loading leads to an increase in the ordinates of epures and bending moments, as well as in shear forces as compared to transverse bending caused by transverse loading only. Moreover, the internal transverse forces do not remain constant on the rod segments which are free from uniformly-distributed transverse loading. The obtained formulas for bending moments and transverse forces can be applied in calculations of elastic stepped rods under longitudinal-transverse bending.

Processing edible insects into powders: a review of available processes and potential microbial inactivation methods

In Europe, people are increasingly interested in edible insects to meet the growing demand for food diversity and protein supply, and bring expected nutritional and environmental benefits. Among all insect-based foods, insect powder is one of the most promising choices, which presents added advantages in the ability of mixing with versatile ingredients, long shelf-life and higher consumer acceptance (non-recognisable form). As a novel protein source, insects require the well-designed manufacturing process to produce high quality insect powder, while guaranteeing food safety of the final product. This review provides a brief overview of the current consumption patterns and general processing steps of insect powders. With regard to inactivating potential microbial hazards, traditional thermal treatment (e.g. boiling, blanching,) is still the predominant method in insect sector, which shows a desirable microbial reduction but may affects nutritional properties and organoleptic attributes. The use of food preservatives and fermentation have also been incorporated as means for eliminating pathogenic bacteria in some insect products. Recent years, a set of advanced or innovative technologies have been developed for inactivation of microorganisms in various foods while maintaining food quality, such as infrared heating, instant control pressure drop technology, high hydrostatic pressure, and cold plasma. However, except for the initial exploration of very few techniques in the laboratory, most novel technologies have not been attached much importance in insect sector. Eventually, this review highlights the lack of data on the impacts of different processing methods (both conventional and innovative, individual or in combination) for edible insects, encompassing the control of microbial activities, nutritional value, and sensory qualities.

Progress, pitfalls, and path forward of drug repurposing for COVID-19 treatment.

On 30 January 2020, the World Health Organization (WHO) declared the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic a public health emergency of international concern. The viral outbreak led in turn to an exponential growth of coronavirus disease 2019 (COVID-19) cases, that is, a multiorgan disease that has led to more than 6.3 million deaths worldwide, as of June 2022. There are currently few effective drugs approved for treatment of SARS-CoV-2/COVID-19 patients. Many of the compounds tested so far have been selected through a drug repurposing approach, that is, by identifying novel indications for drugs already approved for other conditions. We here present an up-to-date review of the main Food and Drug Administration (FDA)–approved drugs repurposed against SARS-CoV-2 infection, discussing their mechanism of action and their most important preclinical and clinical results. Reviewed compounds were chosen to privilege those that have been approved for use in SARS-CoV-2 patients or that have completed phase III clinical trials. Moreover, we also summarize the evidence on some novel and promising repurposed drugs in the pipeline. Finally, we discuss the current stage and possible steps toward the development of broadly effective drug combinations to suppress the onset or progression of COVID-19.

Generalized Input-Output Hidden-Markov-Models for Supervising Industrial Processes

Often in industrial processes, batches of material are processed sequentially and repeatedly through a deterministic sequence of process steps. The possibly large number of sensor measurements collected throughout such industrial processes require supervisory modeling techniques that allow for characterizing the operating conditions (or states) of these process steps. This would allow for inference and predictions over the operating conditions of the current and upcoming process steps (e.g., abnormal behavior, failures, etc.) which may significantly assist the operators of the process. Traditionally, such inference questions in repeated processes can be handled in a rather straightforward manner via Input-Output Hidden Markov Models (IOHMM). However, standard IOHMM are limited to repeated and identical processes, while industrial processes may comprise multiple non-identical process steps and possibly with non-standard interdependencies between process steps. For this reason, in this paper we propose a generalization of standard IOHMM that is more appropriate for modeling industrial processes. Furthermore, we derive the update recursions for training such models and we show analytically that such update recursions guarantee an increase in the likelihood of the observation sequences.

Using System Identification and Central Pattern Generators to Create Synthetic Gait Data

One way to accelerate fall prevention research is through the generation of synthetic gait data. The objective of this paper is to explore a proof-of-concept method using a central pattern generator (CPG) and system identification techniques to generate synthetic human gait data. The two neuron CPG model used in this paper actuated a single point mass that represented the height of the human hip. This continuous model was converted into a discrete system with four state variables – hip height, descending drive, and two CPG inhibition variables. To capture the step-to-step variation in the experimental dataset, we performed linear and nonlinear system identification on the discrete system. Compared to the experimental dataset, the linear model yielded a 12% error, while the nonlinear method yielded an 11% error, indicating that this system with the data used was predominantly linear. Using the identified linear model, we generated synthetic data by repeatedly applying the linear model to the current step's state to generate the next step's state. The resulting synthetic data had some similarity to the experimental data but clearly did not fully replicate the experimental signal properties, indicating that the methods used here have promise but are currently insufficient.