On-demand Phase Research Articles

Impact of cyclization and dendronization on multi-tunable thermoresponsive behaviors of polyacrylamide copolymers

Two pairs of linear and cyclic polyacrylamide-type thermoresponsive heterofunctional-dendron-bearing polymers (THDPs) are designed to achieve on-demand phase transitions and nanostructures.

Ultrawideband Schiffman Phase Shifter Designed With Deep Neural Networks

This article presents a novel method for the forward modeling and inverse design of a class of Schiffman phase shifters using deep neural networks (DNNs). Since DNNs are capable of mapping the highly nonlinear correlations between inputs and outputs, we constructed a fully connected DNN to predict the electromagnetic (EM) responses of Schiffman phase shifters given their physical dimensions. Based on this fast and accurate modeling tool, a cascaded inverse design DNN was then built and trained to achieve instant on-demand phase shifter designs. This approach is versatile and can be easily modified to accomplish different design goals. To demonstrate its efficacy, we trained two DNNs to realize Schiffman phase shifter designs with different bandwidths (40% and 60%) and arbitrary phase shift targets (0°–180°). Simulation results and experimental verifications substantiate that their performances are comparable with the state-of-the-art designs. Moreover, we discussed the proposed methods’ potential in dealing with design tasks that are nonintuitive and beyond the scope of the existing approaches. We envision that this DNN approach can be extended to the design of various EM components including but not limited to antennas, filters, power dividers, and frequency selective surfaces (FSS).

On-Demand Phase Control of a 7-Fiber Amplifiers Array with Neural Network and Quasi-Reinforcement Learning

We report a coherent beam combining technique using a specific quasi-reinforcement learning scheme. A neural network learned by this method enables the tailoring and locking of a tiled beam array on any phase map. We present the experimental implementation of on-demand phase control by a neural network in a seven-fiber laser array. This servo loop needs only six phase corrections to converge to the desired phase set at any profile, with a bandwidth higher than 1 kHz. Moreover, we demonstrate the dynamical feature of adaptive phase control, performing sequences of controlled phase sets. It is the first time, to the best of our knowledge, that an actual array of seven-fiber amplifiers has been successfully phase-locked and controlled by machine learning.

Optical Modulation of Electron Beams in Free Space.

We exploit free-space interactions between electron beams and tailored light fields to imprint on-demand phase profiles on the electron wave functions. Through rigorous semiclassical theory involving a quantum description of the electrons, we show that monochromatic optical fields focused in vacuum can be used to correct electron beam aberrations and produce selected focal shapes. Stimulated elastic Compton scattering is exploited to imprint the required electron phase, which is proportional to the integral of the optical field intensity along the electron path and depends on the transverse beam position. The required light intensities are attainable in currently available ultrafast electron microscope setups, thus opening the field of free-space optical manipulation of electron beams.

Neural network enabled metasurface design for phase manipulation.

The phase of electromagnetic waves can be manipulated and tailored by artificial metasurfaces, which can lead to ultra-compact, high-performance metalens, holographic and imaging devices etc. Usually, nanostructured metasurfaces are associated with a large number of geometric parameters, and the multi-parameter optimization for phase design cannot be possibly achieved by conventional time-consuming simulations. Deep learning tools capable of acquiring the relationship between complex nanostructure geometry and electromagnetic responses are best suited for such challenging task. In this work, by innovations in the training methods, we demonstrate that deep neural network can handle six geometric parameters for accurately predicting the phase value, and for the first time, perform direct inverse design of metasurfaces for on-demand phase requirement. In order to satisfy the achromatic metalens design requirements, we also demonstrate simultaneous phase and group delay prediction for near-zero group delay dispersion. Our results suggest significantly improved design capability of complex metasurfaces with the aid of deep learning tools.

Efficient resource scaling based on load fluctuation in edge-cloud computing environment

With the rapid development of information technology, edge computing has grown rapidly by pushing large amounts of computing to the edge of the network. However, due to the rapid growth of edge access devices and limited edge storage space, the edge cloud faces many challenges in addressing the workloads. In this paper, a cost-optimized resource scaling strategy is proposed based on load fluctuation. Firstly, the load prediction model is built based on DBN with supervised learning to predict the workloads of edge cloud. Then, a cost-optimized resource scaling strategy is presented, which comprehensively considers reservation planning and on-demand planning. In the reservation phase, the long-term resource reservation problem is planned as a two-stage stochastic programming problem, which is transformed into a deterministic integer programming problem. In the on-demand phase, the on-demand resource scaling problem planning is solved as an integer programming problem. Finally, extensive experiments are conducted to evaluate the performance of the proposed cost-optimized resource scaling strategy based on load fluctuation.

Phase and amplitude beam shaping with two deformable mirrors implementing input plane and Fourier plane phase modifications.

We find that ideas in optical image encryption can be very useful for adaptive optics in achieving simultaneous phase and amplitude shaping of a laser beam. An adaptive optics system with simultaneous phase and amplitude shaping ability is very desirable for atmospheric turbulence compensation. Atmospheric turbulence-induced beam distortions can jeopardize the effectiveness of optical power delivery for directed-energy systems and optical information delivery for free-space optical communication systems. In this paper, a prototype adaptive optics system is proposed based on a famous image encryption structure. The major change is to replace the two random phase plates at the input plane and Fourier plane of the encryption system, respectively, with two deformable mirrors that perform on-demand phase modulations. A Gaussian beam is used as an input to replace the conventional image input. We show through theory, simulation, and experiments that the slightly modified image encryption system can be used to achieve arbitrary phase and amplitude beam shaping within the limits of stroke range and influence function of the deformable mirrors. In application, the proposed technique can be used to perform mode conversion between optical beams, generate structured light signals for imaging and scanning, and compensate atmospheric turbulence-induced phase and amplitude beam distortions.

Efficacy of standard prophylaxis versus on-demand treatment with bayer's sucrose-formulated recombinant FVIII (rFVIII-FS) in Chinese children with severe hemophilia A

ABSTRACTIn China, care of patients with severe hemophilia primarily involves insufficient dosing of on-demand treatment and secondary low-dose prophylaxis (10 IU/kg 2× /wk). We sought to evaluate 3× /wk, standard-dose prophylaxis with sucrose-formulated recombinant factor VIII (rFVIII-FS; Bayer) compared with on-demand treatment in Chinese children with severe hemophilia A. Children and adolescents aged 2–16 years with severe hemophilia A, no inhibitors, and no prophylaxis for >6 consecutive months before study entry were eligible for this 24-week, interventional, sequential-treatment study. Patients received rFVIII-FS on demand for 12 weeks followed by a 12-week prophylaxis period (25 IU/kg 3× /wk). The primary efficacy endpoint was comparison of the annualized bleeding rate (ABR) of all bleeds in the prophylaxis versus on-demand phase. Additional variables included ABR of joint bleeds, school attendance/activity, daily activity, and hemophilia Joint Health Score (HJHS). Thirty patients (median age, 12 years) were treated and analyzed. Compared with on-demand treatment, prophylaxis reduced median (quartile [Q1; Q3]) ABR of all bleeds (57.5 [44.5; 73.9] vs 0 [0; 4.0]) and joint bleeds (34.5 [26.1; 56.5] vs 0 [0; 4.0]). Median (range) total HJHS improved after both the prophylaxis and on-demand phases (8.0 [0–48.0] and 11.0 [0–55.0], respectively) compared with baseline (16.0 [0–56.0]). School attendance/activity and daily activity improved with prophylaxis versus on demand. No inhibitors or treatment-related adverse events were reported. In this first prospective, standard-dose, secondary prophylaxis study in China, rFVIII-FS prophylaxis reduced bleeding and improved health outcomes versus on-demand treatment in children with severe hemophilia A.

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

This paper proposes an N-modular redundancy (NMR) technique with low energy-overhead for hard real-time multi-core systems. NMR is well-suited for multi-core platforms as they provide multiple processing units and low-overhead communication for voting. However, it can impose considerable energy overhead and hence its energy overhead must be controlled, which is the primary consideration of this paper. For this purpose the system operation can be divided into two phases: indispensable phase and on-demand phase. In the indispensable phase only half-plus-one copies for each task are executed. When no fault occurs during this phase, the results must be identical and hence the remaining copies are not required. Otherwise, the remaining copies must be executed in the on-demand phase to perform a complete majority voting. In this paper, for such a two-phase NMR, an energy-management technique is developed where two new concepts have been considered: i ) Block-partitioned scheduling that enables parallel task execution during on-demand phase, thereby leaving more slack for energy saving, ii ) Pseudo-dynamic slack, that results when a task has no faulty execution during the indispensable phase and hence the time which is reserved for its copies in the on-demand phase is reclaimed for energy saving. The energy-management technique has an off-line part that manages static and pseudo-dynamic slacks at design time and an online part that mainly manages dynamic slacks at run-time. Experimental results show that the proposed NMR technique provides up to 29 percent energy saving and is 6 orders of magnitude higher reliable as compared to a recent previous work.

Pantoprazole On-Demand Effectively Treats Symptoms in Patients with Gastro-Oesophageal Reflux Disease

The efficacy of pantoprazole as on-demand therapy for the long-term management of patients with mild gastro-oesophageal reflux disease (GORD) has been demonstrated in clinical studies. In this study, the efficacy of pantoprazole 20mg and esomeprazole 20mg as on-demand therapy for relief of symptoms of mild GORD was compared. Patients with reflux oesophagitis grade A or B (Los Angeles classification) or endoscopy-negative reflux disease (enGORD) were treated with pantoprazole 20mg once daily for 28 days during the acute phase (AP, n = 236). Patients without heartburn during the final 3 days of the AP entered the long-term phase (LTP, n = 199) and were randomised to either pantoprazole 20mg or esomeprazole 20mg as on-demand treatment for 6 months. Antacids were provided as rescue medication during this phase. The mean intensities of the symptoms of heartburn, acid eructation and pain on swallowing, both separately and as a combined symptom score, together with the mean duration of these symptoms during on-demand treatment, were compared between the two treatment groups. The number of tablets taken was also compared. After 4 weeks of treatment with pantoprazole, 87.3% of patients had relief from heartburn, 74.1% from epigastric pain and 80.8% from acid eructation, according to the investigator assessment. A total of 236 patients were eligible for the on-demand phase. Based on patient diary data, on-demand treatment with pantoprazole resulted in significantly lower mean intensity of heartburn compared with that in the esomeprazole group (1.12 for pantoprazole and 1.32 for esomeprazole, respectively [p = 0.012], in the intention-to-treat [ITT] population). The mean symptom intensities of acid eructation and pain on swallowing, together with the duration of these symptoms, were comparable in the two treatment groups. The combined symptom score of the three symptoms heartburn, acid eructation and pain on swallowing was numerically lower in the pantoprazole group compared with the esomeprazole group (1.72 vs 1.99, respectively, in the ITT population). Tablet intake was comparable in both groups. Relief of symptoms in Helicobacter pylori-positive and -negative patients was also similar in both treatment groups. Both treatments were well tolerated with a good safety profile. On-demand therapy with either pantoprazole 20mg or esomeprazole 20mg is a comparably effective treatment strategy for the long-term treatment of non-erosive and mild GORD. However, the mean intensity of heartburn was significantly lower with pantoprazole treatment.

Pilot Study of On-Demand Therapy with Pantoprazole 20mg for Long-Term Treatment in Patients with??Mild Gastro-Oesophageal Reflux Disease

Few clinical studies have demonstrated the efficacy of proton pump inhibitors (PPIs) as on-demand therapy for the long-term management of patients with mild gastro-oesophageal reflux disease (GORD). On-demand therapy with pantoprazole 20mg may offer an effective strategy for the management of mild GORD. Patients with mild GORD grade 0 or 1 and heartburn of at least moderate intensity at the start of the study were treated with pantoprazole 20mg once daily for 28 days (n = 254). Patients with no heartburn during the final 3 days of the acute treatment phase entered the long-term treatment phase and took pantoprazole 20mg on demand for 6 months. Antacids were provided as rescue medication during this phase. The primary endpoint was the percentage of patients unwilling to continue the study because of insufficient control of symptoms. In addition, the intensities and the relapse rates for heartburn, acid eructation and epigastric pain were determined. At the end of the acute treatment phase, 234 of 254 patients (92.1%) were free from heartburn and entered the long-term treatment phase (intention-to-treat [ITT]). The time until study discontinuation because of inadequate control of heartburn during the on-demand treatment phase ranged from 9 to 147 days. In the ITT population, only 2.8% of patients were unwilling to continue the study because of insufficient heartburn control. Relapse rates were 4.3% for heartburn, 2.3% for acid eructation and 1.0% for epigastric pain. After 6 months of on-demand treatment, 82% of patients stated that improvements in their symptoms were maintained. For symptom control during the on-demand phase, patients took one tablet of pantoprazole 20mg every 2.3 days. Additionally, patients took one magaldrate tablet every 2.4 days. Results of this pilot study show that on-demand therapy with pantoprazole 20mg appears to be an effective treatment strategy for the long-term treatment of mild GORD. Treatment acceptance by patients was high.

Service curve based routing subject to deterministic QoS constraints

The problem of traffic engineered routing subject to deterministic QoS constraints with a minimum cost objective is considered. A novel blend of generic service curves as abstraction of network links of the associated network calculus under min-plus algebra and of existing heuristics for the restricted shortest path problem, is presented as a generic approach applicable in arbitrary network infrastructures. In particular, we define load-dependent link cost and present in pseudo-code notation the details of the different steps of the developed algorithm, which comprises of a pre-computation and an on-demand phase. A tabu-search is deployed as a local search heuristic to exploit the neighborhood of a pre-computed minimum weight path. The presented approach provides a generic technical solution for minimum-cost routing subject to deterministic QoS constraints, which includes only a reasonably limited number of tuning knobs and thus facilitates the network operator to concentrate on the rather economical task of link cost definition.