Short Step Research Articles

On the Design of Broadband Rectangular Waveguide Pressure Windows

In this article, the design of full-band rectangular waveguide pressure windows for high power applications is presented. The proposed design features a compact size, low loss, and a simplified fabrication process. A short impedance step is deployed to achieve a deep matching level over the full band of the standard waveguide. An approximate circuit model is proposed to select the initial dimensions for the proposed design to reduce the design time frame. Despite the wide use of pressure windows in many applications, the need for an accurate model still exists. The proposed model has been validated over different rectangular waveguide standards through numerical simulations with a maximum mean square error of 3%. Based on the proposed model, standard WR90 and WR28 waveguide Teflon-based windows are fabricated and measured. The measured results show a VSWR below 1.15 with an average low insertion loss of 0.08 dB. In addition, the pressure effect on the electrical performance of the pressure window is assessed. A pressure test is carried out, showing that the proposed pressure window designs are suitable for pressures up to 45 and 75 Ib/in 2 without any significant deterioration for WR90 and WR28 waveguide windows, respectively. The proposed pressure windows show superior electrical and mechanical specifications compared with both the state-of-the-art in the literature and the industrial products.

Spatio-temporal parameters and body of gait and body deformations in patients with osteogenesis imperfecta

The inability to adjust step length can lead to falls in older people when navigating everyday terrain. Precisely targeted forward placement of the leading foot, constituting step length adjustment, is required for adaptive gait control, but this ability may reduce with ageing. The objective of this study was to investigate ageing effects on step length adaptation using real-time biofeedback.Does ageing affect the ability to adapt step length to match a target using real-time biofeedback?Fifteen older adults (67 ± 3 years; 8 females) and 27 young adults (24 ± 4 years; 13 females) completed a step length adaptation test while walking at preferred speed on a treadmill. The test involved walking while viewing a monitor at the front of the treadmill that showed a real-time signal of absolute left-right foot displacement. The task was to match the local maxima of the signal (i.e. step length) to two target conditions, at 10 % longer or 10 % shorter than mean baseline step length. When the target was displayed, it remained unchanged for a set of 10 consecutive step attempts. Three sets of 10 attempts for each target condition were allocated in random order, for a total of 30 step attempts per target. Average absolute error and average error (bias) of step length accuracy was computed for each target condition and compared between groups.: The step adaptation test identified that older adults had greater mean absolute error for both short and long step targets and showed a step length-dependent bias significantly different to the young.Real-time foot position feedback could be a useful tool to train and evaluate step adaptation in older people.

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system.

An improved quantum particle swarm photovoltaic multi‐peak mPPT method combined with Lévy flight

AbstractInertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi‐peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Lévy flight strategy with DCWQPSO algorithm. Lévy flight is a non‐Gaussian random process. The algorithm introduces Lévy flight strategy to change the mutation formula of particles and uses the characteristics of Lévy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast‐tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness.

Accelerated spin dynamics using deep learning corrections

Theoretical models capture very precisely the behaviour of magnetic materials at the microscopic level. This makes computer simulations of magnetic materials, such as spin dynamics simulations, accurately mimic experimental results. New approaches to efficient spin dynamics simulations are limited by integration time step barrier to solving the equations-of-motions of many-body problems. Using a short time step leads to an accurate but inefficient simulation regime whereas using a large time step leads to accumulation of numerical errors that render the whole simulation useless. In this paper, we use a Deep Learning method to compute the numerical errors of each large time step and use these computed errors to make corrections to achieve higher accuracy in our spin dynamics. We validate our method on the 3D Ferromagnetic Heisenberg cubic lattice over a range of temperatures. Here we show that the Deep Learning method can accelerate the simulation speed by 10 times while maintaining simulation accuracy and overcome the limitations of requiring small time steps in spin dynamic simulations.

Rapid generation of regionally specified CNS neurons by sequential patterning and conversion of human induced pluripotent stem cells.

The differentiation of patient-specific induced pluripotent stem cells (iPSCs) into specific neuronal subtypes has been exploited as an approach for modeling a variety of neurological disorders. However, achieving a highly pure population of neurons is challenging when using directed differentiation methods, especially for neuronal subtypes generated by complex and protracted protocols. In this study, we efficiently produced highly pure populations of regionally specified CNS neurons by using a modified NGN2-Puromycin direct conversion protocol. The protocol is amenable across a range of iPSC lines, with more than 95% of cells at day 21 positive for the neuronal marker MAP2. We found that conversion from pluripotent stem cells resulted in neurons from the central and peripheral nervous system; however, by incorporating a short CNS patterning step, we eliminated these peripheral neurons. Furthermore, we used the patterning step to control the rostral-caudal identity. This approach of sequential patterning and conversion produced pure populations of forebrain neurons, when patterned with SMAD inhibitors. Additionally, when SMAD inhibitors and WNT agonists were applied, the approach produced anterior hindbrain excitatory neurons and resulted in a neuronal population containing VSX2/SHOX2 V2a interneurons. Overall, this sequential patterning and conversion protocol can be used for the production of a variety of CNS excitatory neurons from patient-derived iPSCs, and is a highly versatile system for investigating early disease events for a range of neurological disorders including Alzheimer's disease, motor neurons disease and spinal cord injury.

The importance of short lag-time in the runoff forecasting model based on long short-term memory

It is still very challenging to enhance the accuracy and stability of daily runoff forecasts, especially several days ahead, owing to the non-linearity of the forecasted processes. Here, we hypothesize that short lag-time has a significant impact on forecasting results. Thus, we incorporate short previous time steps into long short-term memory (LSTM) and develop the Self-Attentive Long Short-Term Memory (SA-LSTM). In SA-LSTM, the self-attention mechanism is used to model interdependencies within short previous time steps. SA-LSTM is evaluated at eight runoff datasets. The experimental results demonstrate that, compared with state-of-art benchmark models, SA-LSTM achieves the best performance. The RMSEs of SA-LSTM are at least 2.3% smaller than that of the second best model at the seventh day. The NSEs and NSE_In of SA-LSTM are at least 4.6% and 6.4% higher than those of the second best model at the seventh day. Furthermore, SA-LSTM also surpasses the baseline methods for base, mean and peak flows. The superiority of SA-LSTM can be attributed to its exploitation of information in short lag-time.

Rapid and sensitive detection of Salmonella spp. in raw minced meat samples using droplet digital PCR

With its ability to easily infect each step of food production chain, Salmonella spp. stands as one of the most important foodborne pathogens which impose threats against economy and human health by creating high morbidity and mortality, worldwide. Although sensitive screening and detection methodologies based on common conventional techniques have been developed for Salmonella spp., their long turnaround time and strict legislative demands directs the researchers to develop more sensitive and rapid methods. Digital PCR has many potential advantages over its closest competitor, Real-Time PCR. It gives more accurate, sensitive, and precise results, especially for samples that contain low pathogen concentrations and shows more resistance against PCR inhibitors. In this study, we developed a droplet digital PCR screening methodology with a short pre-enrichment step. For this purpose, 25 g raw minced meat was subjected to Salmonella contamination, and following a very short 3 h enrichment step, bacterial isolation from 5 mL of sample was achieved. For the specific detection of the Salmonella spp., bipA gene region was selected as a better candidate. Subsequently, droplet digital PCR setup was performed and detection sensitivity of the test was determined as 1.39 cfu/mL. Furthermore, parallel experiments were also conducted using Real-Time PCR, for comparative and validative purposes. Conclusively, data gathered in this study have shown that droplet digital PCR with our proposed setup can sensitively detect Salmonella spp. in raw minced meat samples while generating the same-day results.

Efficient coupling to slow light in a silicon slot waveguide with internal corrugations

We modeled, simulated and experimentally observed efficient slow light coupling to internally corrugated slot Bragg grating (ICSBG) waveguide on a silicon-on-insulator platform. A short step taper is utilized to evanescently couple to the slow light mode near the stopband edge, resulting in minimization of Fabry–Perot reflections due to group index mismatch and a minimum slow light coupling enhancement of ∼1 dB. This is important as it results in a lower insertion loss ICSBG waveguide, which is capable of low loss slow light propagation and therefore is an attractive option as a phase shifter structure for an on-chip slow light enhanced electro-optic modulator or integrated optic sensor applications.

Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach.

Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel approach where the quality of data acquisition could help improve the accuracy of cell tracking algorithms and vice-versa. Generally speaking, when tracking cell movement, the more frequent the images are taken, the more accurate cells are tracked and, yet, issues such as damage to cells due to light intensity, overheating in equipment, as well as the size of the data prevent a constant data streaming. Hence, a trade-off between the frequency at which data images are collected and the accuracy of the cell tracking algorithms needs to be studied. In this paper, we look at the effects of different choices of the time step interval (i.e., the frequency of data acquisition) within the microscope to our existing cell tracking algorithms. We generate several experimental data sets where the true outcomes are known (i.e., the direction of cell migration) by either using an effective chemoattractant or employing no-chemoattractant. We specify a relatively short time step interval (i.e., 30 s) between pictures that are taken at the data generational stage, so that, later on, we may choose some portion of the images to produce datasets with different time step intervals, such as 1 min, 2 min, and so on. We evaluate the accuracy of our cell tracking algorithms to illustrate the effects of these different time step intervals. We establish that there exist certain relationships between the tracking accuracy and the time step interval associated with experimental microscope data acquisition. We perform fully-automatic adaptive cell tracking on multiple datasets, to identify optimal time step intervals for data acquisition, while at the same time demonstrating the performance of the computer cell tracking algorithms.

Hierarchical multiscale numerical modelling of internal erosion with discrete and finite elements

This paper presents a coupled finite and discrete-element model (FEM and DEM) to simulate internal erosion. The model is based on ICY, an interface between COMSOL, an FEM engine, and YADE, a DEM code. With this model, smaller DEM subdomains are generated to simulate particle displacements at the grain scale. Particles in these small subdomains are subjected to buoyancy, gravity, drag and contact forces for short time steps (0.1 s). The DEM subdomains provide the macroscale (continuum) model with a particle flux distribution. Through a mass conservation equation, the flux distribution allows changes in porosity, hydraulic conductivity and hydraulic gradient to be evaluated for the same time steps at a larger, continuum scale. The updated hydraulic gradients from the continuum model provide the DEM subdomains with updated hydrodynamic forces based on a coarse-grid method. The number of particles in the DEM subdomains is also updated based on the new porosity distribution. The hierarchical multiscale model (HMM) was validated with the simulation of suffusion. Results for the proposed HMM algorithm are consistent with results based on a DEM model incorporating the full sample and simulation duration. The proposed HMM algorithm could enable the modelling of internal erosion for soil volumes that are too large to be modelled with a single DEM subdomain.

On the performance of energy walls by monitoring assessment and numerical modelling: a case in Italy

Thermoactive diaphragm walls have proved their efficiency for near-surface geothermal energy use. To get insights into the heat transfer process occurring between the heat exchanger pipes and the surrounding boundaries, an instrumented real case located in northern Italy was taken as reference. Combining on-site monitoring data with computational simulations, the role of the basement space in governing the heat fluxes in the different seasons, and the energy performance of the diaphragm wall and of the ground source heat pump system, is highlighted. The numerical analysis represents an effective predictive tool, but is also highly sensitive with respect to parameters of uncertain or complex definition, such as the boundary thermal conditions and the thermal input at the pipe inlet. Intermittent operation and idle periods require a refined simulation, discretising the time dependency of the input variables in appropriate short time steps.

A new approach toward gait training in patients with Parkinson’s Disease

BackgroundTypically, people with Parkinson’s Disease (PD) progress to develop a gait pattern that is characterized by quick, short and shuffling steps. Gait cycle is altered and lacks definition and fluidity. Gait training combined with a variety of feedback modalities for PD are usually based on non-immediate and externally-based cues but none of these provide real-time feedback on gait quality and acquired gains tend to abate shortly after rehabilitation. Based on principals of motor learning, our team has developed the Heel2Toe sensor to provide real-time auditory feedback during gait training. Research questionIs a short-term training using the Heel2Toe sensor feasible and efficient to improve gait in people with PD? Our objectives are to identify the extent of the immediate response to the feedback within the same session and the carry-over response to training and; 2) to identify patients’ perceived effects, pleasures and challenges of using the Heel2Toe. MethodsSingle-arm, proof-of-concept study. Six people received five sessions of gait training over a 2–3-week period using the Heel2Toe augmented with mobility exercises as an adjunct to gait training. The main outcomes were technically assessed gait parameters collected over a 2-minute walk test, without and with feedback. Heel2Toe signals were analyzed to extract angular velocity(AV), percentage of good steps, average cadence, and AV coefficient of variation(CV). ResultsAn immediate response to the Heel2Toe use and a carry-over response to the short-term training with the sensor were observed: an increase in AV with a reduction in CV (better heel strike and gait regularity); an increase in %good steps; and a near-optimal and homogeneous cadence (∼100 steps/min), which is equivalent to a moderate-intensity walking. SignificanceGait training using the Heel2Toe sensor is feasible and potentially effective for improving gait quality in people with PD. A definitive trial is a logical next step.

Direct synthesis of p-type bulk BiCuSeO oxyselenides by reactive spark plasma sintering and related thermoelectric properties

Herein, we demonstrate that BiCuSeO compound can be formed in bulk directly from the raw materials through reactive spark plasma sintering (RSPS) followed by ball milling and a second short spark plasma sintering step. Compared to BiCuSeO samples obtained by a conventional solid-state reaction, the electrical transport properties of the RSPS bulk were moderately affected by the sintering technique, while the lattice thermal conductivity was almost unaffected, and the figure of merit zT attained a value at 773 K comparable to state-of-the-art BiCuSeO. The results indicate a new scalable method for the preparation of oxyselenides.

Experimental investigation on water removal and gas flow during drainage process in tight rocks

High performance water removal plays a significant role in restoring the gas production when water invasion happens to tight gas reservoirs. In this paper, core samples with the air permeability in range of 0.01–1 mD were selected to investigate water removal behavior in tight gas reservoirs. Meanwhile, the gas flow capability during the process of water removal was discussed. The water desaturation curves generally experienced two stages of water removal in a drainage process: a fast water decline stage induced by immiscible displacement, followed by a long period of slow water saturation reduction stage via the gas flow-through drying. It pointed out that the immiscible displacement mainly affects water removal during the drainage process in which period continuous gas flow channel was built. Once the generation of continuous gas flow channel was accomplished during the immiscible displacement stage, a proper increase of displacement pressure difference can efficiently enhance the gas flow-through drying rate. Normally, it was easy for high permeable tight core sample to experience a short immiscible displacement dominant stage and step into gas flow-through drying dominant stage afterwards. Besides, it was found that the enlargement in water invasion depth will weaken gas flow capacity from water removal. These findings help in understanding the physical process of water removal in tight gas reservoirs, as well as designing a better performance of water removal in a tight gas flied.

PC-based JSI research reactor simulator

A real-time research reactor simulator was developed at the Jožef Stefan Institute for education and training of students and future reactor operators, especially in countries with limited or no access to a research reactor. The research reactor simulator (RRS) simulates time behaviour of reactor power, fuel temperature and reactivity by using the 6-group point kinetics equation with feedback. It features temperature feedback mechanisms and xenon poisoning. Using graphics acceleration to render simulation results and a simple integration scheme with a short simulation step to propagate physics achieves low latency between a user’s input and display of simulation results, which is important for research reactors that can perform fast transients. The simulator targets TRIGA-type reactors, but all physical parameters can be changed on the fly. Thus, the simulator can be easily adapted to simulate other reactors.

Anticipatory control of human gait following simulated slip exposure

A cautious gait (CG), marked by wider and shorter steps, is typically employed to mitigate expected perturbations proactively. However, it is not well understood if and how CG is informed by the task requirements. Therefore, we assessed how CG is adjusted to these requirements. Three groups of ten healthy young adults were exposed to a single uninterrupted protocol of treadmill walking that consisted of three distinct phases. Spatiotemporal step characteristics and margins of stability of the unperturbed strides were compared when participants were (i) only warned of a perturbation, (ii) exposed to fifty unilateral (right) slip-like perturbations and (iii) kept unaware of perturbation removal. Only the perturbation intensity predictability differed between groups. This was either kept consistent or pseudo-randomly or randomly varied. Participants walked with wider and shorter steps following the perturbation warning. However, this extinguished in continuing perturbation absence. Next, during perturbation exposure, participants shortened the step of the perturbed but increased the step of the unperturbed leg. This did not differ between groups. Finally, participants persisted in displaying CG on perturbation removal, but this extinguished over time. Collectively, we show that CG is functionally adjusted to the task requirements. These findings may have practical implications for fall-prevention training.

EDNC: Evolving Differentiable Neural Computers

One recent study in the field of deep learning is extending Artificial Neural Networks (ANNs) by coupling them to external memory resources. Neural Turing Machine (NTM) and Differentiable Neural Computer (DNC) are two counterparts in this field. Research activities fall into two categories of either interacting with memory or choosing controller components. The first approach uses a try and error fashion to provide the controller’s structure that would be a problem when there is no prior knowledge on the controller’s structure for particular tasks. The second approach includes methods for choosing the controller’s structure and weight optimization. NeuroEvolution falls in the second category that automatically and without prior knowledge obtains an appropriate network structure. This research presents Evolutionary Differentiable Neural Computer (EDNC), which uses a novel NeuroEvolution algorithm that is introduced in two types of nested object-oriented encoding called Adaptive Layer NeuroEvolution (ALNE) and Matrix-based one called M_ALNE. These NeuroEvolution algorithms use the following sub-algorithms: Self-Adaptivity in the Initial Population (SAIP), Self-Adaptivity in Evolutionary Structures (SAES), Layer Recombination (LR), Layer Mutation (LM), and Self-Adaptivity in Mutation (SAM). The evolution process starts with SAIP, and then it takes short and long steps to explore a variety of neural structures by SAES algorithm that uses both LR and LM algorithms to provide a variety of structures. Finally, the SAM algorithm guides the selected structures to a target structure. In this research, experiments applied EDNC on well-known tasks, including Facebook bAbI, copy, graph (shortest path), and 8-puzzle. The experiments show that the proposed method in both encodings automatically and without prior knowledge, produces an appropriate controller structure in the shortest time. It though shows that both proposed encodings reduce the evolution time by at least 73% in comparison with the baseline methods.

CLINICAL GAIT ANALYSIS OF SUBJECTS WITH TRANS-FEMORAL AMPUTATION USING POLYCENTRIC FOUR-BAR LINKAGE PROSTHETIC KNEE JOINT

Background: Adequate research is not reported so far to underline the influence of commonly used polycentric knee joints on gait performance of subjects with trans-femoral amputation. Objective: The intent of this investigation is to analyze prosthetic gait of unilateral traumatic trans-femoral amputees with polycentric four-bar linkage knee and compare it with normal subjects for evaluating any asymmetry in gait performance. Methods: Objective three-dimensional gait analysis of 15 subjects [mean (age): 36.4 (10.7) years] were performed in gait lab through force plate and optoelectronic devices to measure temporal-spatial parameters, kinematic and kinetic performances. Gait patterns of amputees were compared with those of 15 individuals with normal gait to analyze distinct functionalities of existing polycentric knee. Results: Asymmetry in gait was observed between amputees and normal subjects for all variables concerned ([Formula: see text]). Amputee gait was with significantly lesser velocity, cadence with shorter step and stride length. There was significantly less hip, knee and pelvic motions, however, pelvic obliquity and rotation did not show significant difference from the normal subjects. The vertical component of the ground reaction force differed significantly between prosthetic and intact limb [49.7 (8.5)% and 90.4 (7.4)% body weight] and also from normal subjects [107.5 (2.4)% body weight] during stance ([Formula: see text]). Interpretation and Conclusion: This difference may be attributed to nonproportionate loading of limbs and mechanical adaptations for counteracting deficiencies of prosthetic side. This study will help to explain gait asymmetry in trans-femoral amputees and to identify underlying mechanisms to enhance the quality of the existing design of prosthetic knee through optimizing design parameters and utilizing appropriate materials.

Lab-on-Chip platform and protocol for rapid foodborne pathogen detection comprising on-chip cell capture, lysis, DNA amplification and surface-acoustic-wave detection

We present an integrated, compact, lab on a chip (LoC) platform and protocol for rapid pathogen analysis in food samples. The proposed LoC is based on an oxygen plasma nanotextured polymeric chip combining in one microfluidic chamber bacteria immunoaffinity capturing on the chip surface, chemical lysis and DNA isothermal amplification, followed by label-free detection with a Surface Acoustic Wave (SAW) biosensor. The proposed operation protocol includes: 1) a short (3 h) off-chip pre-culturing step, starting from 1 to 5 CFUs in only 25 ml of spiked fresh or pasteurized milk, followed by a brief centrifugation step for concentration of the 25 ml down to 100 μl, and, 2) injection of the preconcentrated sample on chip for automated bacterial capture, lysis, DNA amplification and acoustic detection. The sample-to-answer analysis time was less than 4.5 h, which is at least 5-times faster compared to conventional methods. Label-free detection of four different bacteria Salmonella, B. cereus, Listeria and E. coli in milk is demonstrated with minimal off chip handling and record fast analysis time.