Continuous Relaxation Research Articles

Effect of distribution characters of ferrite/bainite on deformation compatibility in dual-phase pipeline steel: Experimental and numerical study

The morphology distribution of microstructure has an important influence on the match of the strength and plasticity of dual-phase pipeline steel. Bainite and PF dual-phase steels with layered and network morphology were obtained by combining the Thermomechanical Control Process (TMCP) and inter-critical isothermal heat treatment. A novel experimental-numerical methodology studied the effect of phase distribution characters on the deformability of PF and bainite dual-phase pipeline steel. The plastic damage features and microscopic strain and stress distribution in both network and layered microstructure were clarified. The obvious differences in the microstructure evolution for both samples were captured by a quasi-situ tensile test. The PF phase in the network morphology sample was believed the main deformation phase, but the deformation of the PF phase in the layered morphology sample was restrained. Two three-dimensional (3D) crystal plasticity finite element models (CPFEMs) were established based on the electron backscatter diffraction (EBSD) data. The simulation results indicated that the strain field in layered microstructure was more uniform than that in network microstructure, and the continuous internal stress relaxation (ISR) in the bainite phase led to plastic damage at low tensile strain level; The strain field in network microstructure mainly existed in the PF phase, the deformation of the PF phase can be fully utilized. When changing the tensile x-direction of the layered microstructure to the z-direction, the strain field showed an obvious anisotropy, and loading in the z-direction increased the risk of interlaminar crack formation. • Quasi-situ tensile test was used to study the deformation behavior • Strain/stress distribution characteristics were clarified using CPFEM method • Uniform strain distribution and ISR in layered microstructure decreased its plasticity • z-axis loading increased the risk of interlaminar crack for layered microstructure

The Effect of Effleurage Massage Techniques on Afterpains Pain in Multigravida Postpartum Mothers at the Tanjung Selamat Health Center, Kec. Padang Tualang, Kab. Leaving in 2022

Afterpains pain in multigravida postpartum women is caused by contractions in continuous relaxation of the uterus. Effleurage is a massage technique that is safe, easy to do, doesn't require a lot of tools, doesn't cost money, doesn't have side effects and can be done alone or with the help of others. The purpose of this study was to determine the effect of the Effleurage Massage Technique on Afterpains Pain in Multigravida Postpartum Mothers at UPT Puskesmas Tanjung Selamat, Kec. Padang Tualang, Kab. Langkat. The design of this research is descriptive with a quasi-experimental study approach. The sampling technique used is purposive sampling, namely the technique of determining the sample with certain considerations as desired by the researcher. Samples on postpartum mothers were taken with a percentage of 25% so that the sample in this study was 30 people. The results of the study prior to the Effleurage Massage Technique the majority experienced moderate pain as many as 20 respondents (67%). Meanwhile, after the Effleurage Massage Technique, the majority experienced a decrease in mild pain as many as 30 respondents (67%). The conclusion of this study is that there is an effect of Effleurage Massage Technique on Afterpains Pain in Multigravida Postpartum Mothers at UPT Puskesmas Tanjung Selamat, Kec. Padang Tualang, Kab. Langkat. The results of the statistical test using the t test showed that the p value (0.000 < (0.05) means Ha is accepted, so there is an effect of the Effleurage Massage Technique on Afterpains Pain in Multigravida Postpartum Mothers at UPT Puskesmas Tanjung Selamat, Padang Tualang District, Langkat Regency

Design and Analysis of Switchback Experiments

Switchback experiments, where a firm sequentially exposes an experimental unit to random treatments, are among the most prevalent designs used in the technology sector, with applications ranging from ride-hailing platforms to online marketplaces. Although practitioners have widely adopted this technique, the derivation of the optimal design has been elusive, hindering practitioners from drawing valid causal conclusions with enough statistical power. We address this limitation by deriving the optimal design of switchback experiments under a range of different assumptions on the order of the carryover effect—the length of time a treatment persists in impacting the outcome. We cast the optimal experimental design problem as a minimax discrete optimization problem, identify the worst-case adversarial strategy, establish structural results, and solve the reduced problem via a continuous relaxation. For switchback experiments conducted under the optimal design, we provide two approaches for performing inference. The first provides exact randomization-based p-values, and the second uses a new finite population central limit theorem to conduct conservative hypothesis tests and build confidence intervals. We further provide theoretical results when the order of the carryover effect is misspecified and provide a data-driven procedure to identify the order of the carryover effect. We conduct extensive simulations to study the numerical performance and empirical properties of our results and conclude with practical suggestions. This paper was accepted by George Shanthikumar, big data analytics. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4583 .

Auto-DenseUNet: Searchable neural network architecture for mass segmentation in 3D automated breast ultrasound.

Accurate segmentation of breast mass in 3D automated breast ultrasound (ABUS) plays an important role in breast cancer analysis. Deep convolutional networks have become a promising approach in segmenting ABUS images. However, designing an effective network architecture is time-consuming, and highly relies on specialist's experience and prior knowledge. To address this issue, we introduce a searchable segmentation network (denoted as Auto-DenseUNet) based on the neural architecture search (NAS) to search the optimal architecture automatically for the ABUS mass segmentation task. Concretely, a novel search space is designed based on a densely connected structure to enhance the gradient and information flows throughout the network. Then, to encourage multiscale information fusion, a set of searchable multiscale aggregation nodes between the down-sampling and up-sampling parts of the network are further designed. Thus, all the operators within the dense connection structure or between any two aggregation nodes can be searched to find the optimal structure. Finally, a novel decoupled search training strategy during architecture search is also introduced to alleviate the memory limitation caused by continuous relaxation in NAS. The proposed Auto-DenseUNet method has been evaluated on our ABUS dataset with 170 volumes (from 107 patients), including 120 training volumes and 50 testing volumes split at patient level. Experimental results on testing volumes show that our searched architecture performed better than several human-designed segmentation models on the 3D ABUS mass segmentation task, indicating the effectiveness of our proposed method.

Discrete Joint Semantic Alignment Hashing for Cross-Modal Image-Text Search

Supervised cross-modal image-text hashing has aroused extensive concentrations in comprehending the correspondence between vision and language for data search tasks. Existing methods learn the compact hash codes by leveraging a given image-text data pairs or supervised information to explore such correspondence. However, they still confront obvious drawbacks. First, there is no engagement between multiple semantic information that yields the suboptimal search performance. Second, most of them adopt continuous relaxation strategy by discarding the discrete constraints, which results in large binary quantization errors. To deal with these problems, we propose a novel supervised hashing method, termed Discrete Joint Semantic Alignment Hashing (DJSAH). Specifically, it builds a connection between semantics (a.k.a. class labels and pairwise similarities) by the joint semantic alignment learning. And thus the high-level discriminative semantics can be preserved into the hash codes. Besides, a well-designed discrete optimization algorithm with linear computation and memory cost is developed to reduce the information loss of the hash codes with no need for relaxation. Extensive experiments and analyses on three benchmark datasets validate the superiority of the proposed DJSAH against several state-of-the-art hashing methods.

Dynamic RAT Selection and Transceiver Optimization for Mobile-Edge Computing Over Multi-RAT Heterogeneous Networks

Mobile-edge computing (MEC) integrated with multiple radio access technologies (multi-RATs) is a promising technique for satisfying the growing low-latency computation demand of intelligent Internet of Things (IoTs) applications. Under the wireless MapReduce framework for executing nomographic functions, this article investigates the joint RAT selection and transceiver design for over-the-air (OTA) aggregation of intermediate values (IVAs) in multi-RAT MEC systems, while taking into account the energy budget constraint for the local computing and IVA transmission per wireless device (WD), so as to adapt to the instantaneous communication opportunities in multiple RATs and the dynamic computational task loads. To provide a complete Pareto optimal solution, we minimize the weighted sum of the computational mean squared error (MSE) of the aggregated IVA at the RAT receivers, the IVA transmission cost of the WDs, and the associated transmission time delay. The joint RAT selection and transceiver design problem for OTA aggregation of the IVAs is a nonconvex mixed-integer problem, which is NP hard. We develop a low-complexity algorithm to solve the challenge by continuous relaxation and alternating optimization. Specifically, the optimal receive beamforming vectors at the gNB/access points (APs) are shown to be the minimum MSE (MMSE) filters. Exploiting the hidden convexity of the remaining subproblem, we obtain an efficient iterative algorithm by alternating between the RAT selection and the transmit coefficient variables for OTA aggregation of IVAs. Extensive numerical results verify the effectiveness of our proposed design as compared to other existing schemes.

BNAS-v2: Memory-Efficient and Performance-Collapse-Prevented Broad Neural Architecture Search

In this article, we propose BNAS-v2 to further improve the efficiency of broad neural architecture search (BNAS), which employs a broad convolutional neural network (BCNN) as the search space. In BNAS, the single-path sampling-updating strategy of an overparameterized BCNN leads to terrible unfair training issue, which restricts the efficiency improvement. To mitigate the unfair training issue, we employ a continuous relaxation strategy to optimize all paths of the overparameterized BCNN simultaneously. However, continuous relaxation leads to a performance collapse issue that leads to the unsatisfactory performance of the learned BCNN. For that, we propose the confident learning rate (CLR) and introduce the combination of partial channel connections and edge normalization. Experimental results show that 1) BNAS-v2 delivers state-of-the-art search efficiency on both CIFAR-10 (0.05 GPU days, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> faster than BNAS) and ImageNet (0.19 GPU days) with better or competitive performance; 2) the above two solutions are effectively alleviating the performance collapse issue; and 3) BNAS-v2 achieves powerful generalization ability on multiple transfer tasks, e.g., MNIST, FashionMNIST, NORB, and SVHN. The code is available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/zixiangding/BNASv2</uri></monospace> .

Inter-conversion of the generalized Kelvin and generalized Maxwell model parameters via a continuous spectrum method

• Constructed the continuous spectra ( H τ and L ρ ) via the direct and indirect methods. • Obtained the optimal ranges of retardation time and relaxation time. • Proposed a spectrum method to establish the inter-conversion between E k and D j Accurate determination of the Prony series parameters of the generalized Maxwell (GM) model and the generalized Kelvin (GK) model is crucial to the characterization of linear viscoelastic (LVE) properties of asphalt mixture and the prediction of in-service performance of asphalt pavement. However, existing methods may not be able to provide satisfactory accuracy due to their weakness of fully complying with the LVE theory or the involvement of unsolvable high-order nonlinear systems. To identify the Prony series parameters more efficiently and accurately, this paper developed a continuous spectrum method to establish the inter-conversion between the GM and GK model parameters. In the newly developed method, the complex modulus mater curves are first constructed through the conventional master curve models. The continuous relaxation and retardation spectra are then established using the proposed direct or indirect methods. The time and strength constants of the GM and GK models are subsequently calculated via an extended search method, based on which the Prony series parameters and the LVE variables of the asphalt mixture can be finally determined. Accuracy of the new method in determining the LVE variables was validated through a comparison of the calculated and measured LVE variables of asphalt mixture, while the equivalence between the GM and GK models established based on the new method was verified in different domains. Besides, the success implementation of the new method to the generalized Sigmodal model demonstrates the universality of the newly developed continuous spectrum method in determining the LVE variables of asphalt mixture.

Hydrogen-Based Networked Microgrids Planning Through Two-Stage Stochastic Programming With Mixed-Integer Conic Recourse

Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners—This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications.

An improved method to establish continuous relaxation spectrum of asphalt materials

The relaxation spectrum plays a vital role in the study of linear viscoelastic (LVE) properties of asphalt materials; however, the current developed using methods are not accurate enough to characterize the LVE properties of asphalt materials. This paper aims to develop an improved method to determine the continuous relaxation spectrum of asphalt materials. A modified Christensen–Anderson–Marasteanu (CAM) model is first selected as the master curve model of the storage modulus, upon which the master curve model of loss modulus is then established using the numerical form of the exact Kramers-Kronig relation between the storage modulus and loss modulus. According to LVE conversions between the relaxation spectrum and storage modulus, the model of the continuous relaxation spectrum is subsequently derived and finally determined based on the same model parameters of the storage modulus and loss modulus models. This study demonstrates that the relaxation spectrum of both mixtures and binders constructed by the proposed method is compliant with LVE theory and physical causality. The generated relaxation spectrum is verified to be accurate enough to compare the storage modulus and loss modulus values recalculated by the relaxation spectrum and the corresponding values predicted by master curve models. The results also show that the approximate Kramers-Kronig relation between the storage modulus and loss modulus cannot be used to establish the relaxation spectrum of asphalt materials, especially for asphalt binders. In addition, the generated relaxation spectrum was utilized to construct the relaxation modulus of mixtures and to develop the stiffness modulus of binders, respectively. Since the relaxation modulus and creep stiffness are determined from the relaxation spectrum, both are consistent with LVE theory and physical causality.

Parallel batching with multi-size jobs and incompatible job families

Parallel batch scheduling has many applications in the industrial sector, like in material and chemical treatments, mold manufacturing and so on. The number of jobs that can be processed on a machine mostly depends on the shape and size of the jobs and of the machine. This work investigates the problem of batching jobs with multiple sizes and multiple incompatible families. A flow formulation of the problem is exploited to solve it through two column generation-based heuristics. First, the column generation finds the optimal solution of the continuous relaxation, then two heuristics are proposed to move from the continuous to the integer solution of the problem: one is based on the price-and-branch heuristic, the other on a variable rounding procedure. Experiments with several combinations of parameters are provided to show the impact of the number of sizes and families on computation times and quality of solutions.

Differentiable Learning of Sequence-Specific Minimizer Schemes with DeepMinimizer.

Minimizers are widely used to sample representative k-mers from biological sequences in many applications, such as read mapping and taxonomy prediction. In most scenarios, having the minimizer scheme select as few k-mer positions as possible (i.e., having a low density) is desirable to reduce computation and memory cost. Despite the growing interest in minimizers, learning an effective scheme with optimal density is still an open question, as it requires solving an apparently challenging discrete optimization problem on the permutation space of k-mer orderings. Most existing schemes are designed to work well in expectation over random sequences, which have limited applicability to many practical tools. On the other hand, several methods have been proposed to construct minimizer schemes for a specific target sequence. These methods, however, only approximate the original objective with likewise discrete surrogate tasks that are not able to significantly improve the density performance. This article introduces the first continuous relaxation of the density minimizing objective, DeepMinimizer, which employs a novel Deep Learning twin architecture to simultaneously ensure both validity and performance of the minimizer scheme. Our surrogate objective is fully differentiable and, therefore, amenable to efficient gradient-based optimization using GPU computing. Finally, we demonstrate that DeepMinimizer discovers minimizer schemes that significantly outperform state-of-the-art constructions on human genomic sequences.

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

We present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software. Program summaryProgram title: PyOECP 0.5CPC Library link to program files:https://doi.org/10.17632/vsh6vb9cbv.1Developer's repository link:https://github.com/tyoon124Code Ocean capsule:https://codeocean.com/capsule/89645681Licensing provisions: BSD-3 Clause LicenseProgramming language: Python3External routines: Numpy, Scipy, and MatplotlibNature of problem: Estimate and interpret the frequency-dependent (complex) permittivity in fluid(s) and their mixtures based on the reflection coefficients obtained from the open-ended coaxial probe (OECP) technique. The obtained dielectric spectra can be useful for understanding the relaxation processes in an arbitrary fluid.Solution method: The reflection coefficient data is obtained as a text format from a Vector Network Analyzer (VNA). This text data is parsed into a Numpy array. Three different modules (Stuchly, Marsland, and Komarov) can be used to transform the parsed reflection coefficient data into complex permittivity as a function of the measurement frequency. The resultant dielectric spectra can be dissected and interpreted by fitting either discrete relaxation model(s) based on the Markov Chain Monte Carlo algorithm or a continuous relaxation model by applying the Zasetsky-Buchner method.

Joint Subcarrier and Transmission Power Allocation in OFDMA-Based WPT System for Mobile-Edge Computing in IoT Environment

Mobile-edge computing (MEC) is expected to play an important role in the next-generation of Internet-of-Things (IoT) services with artificial intelligence (AI) by providing the sustainable computation capability of resource-constrained IoT devices. Since the finite battery lifetime has been a longstanding challenge of the MEC system for IoT services, the wireless power transfer (WPT) technology has been recently developed for the MEC system in order to support the perpetual operation of IoT devices. In this article, we introduce two resource allocation problems for OFDMA-based WPT-MEC systems: 1) a max–min energy fairness (MMEF) problem and 2) a power sum maximization (PSM) problem. These problems ensure high-performance computations for AI-based applications, where the network reliability and the tremendous power consumption may be required. Moreover, we incorporate a logarithmic nonlinear energy harvesting (EH) model into our formulated problems, which lead to nonconvex mixed-integer nonlinear programming (MINLP) problems. In order to resolve these NP-hard problems, we convert the proposed problems into their equivalent convex forms by applying the continuous relaxation method. The near-optimal solutions are thereby obtained in closed-form expressions by leveraging the Lagrangian duality method for each relaxed problem. Numerical results are presented to validate the merits of the proposed algorithms of MMEF&PSM over the alternative benchmark algorithm and provide significant insights on the effects of the key system parameters, including the number of IoT devices and power transmission subcarriers.

Effect of the supersaturation with nitrogen on the structure refinement and magnetic properties of mechanically alloyed and heated Fe14Cr ferritic alloys

Nano-nitride reinforcement of Fe–Cr alloys are under intense investigations, e.g., along with oxide-strengthened, nitride-strengthened reduced activation steels with superior high temperature mechanical properties are developed as materials for nuclear energy applications. Fe14Cr alloy powders were produced by mechanical milling for up to 170 h under a nitrogen atmosphere followed by heating under an Ar5%H2 atmosphere to 600°C–1120 °C. The samples were investigated by means of X-ray diffraction, DTA/TG and magnetic measurements. CrN, detected after milling for 58 h, reached 39 wt % (5.3 wt.% total N) at 170 h milling. The ferrite supersaturated with nitrogen shows a strong decrease in grain size and saturation magnetization, an increase in lattice constant, microstrain and coercivity, Hc. Upon heating, the metastable ultrafine α-ferrite and CrN in the as-milled samples undergo transformations and recrystallization into ferrite grains free of supersaturation, whose microstructure follows a continuous relaxation. The supersaturation with nitrogen was retained upon heating in the relatively stable ferrite crystalized at the milling stage. This microstructure undergoes a discontinuous relaxation in the 800°C–1120 °C region: a pronounced decrease of grain size (down to ∼25 nm), an increase of lattice constant, microstrain and Hc. These improvements were associated with features of discontinuous precipitation reaction of coherent nitride precipitates (involving a new α”-phase with expanded lattice), in connection with the heating in the austenite phase field. Such microstructure refinement or (nitride) strengthening of Fe14Cr alloys can be achieved on powders milled for much less time as compared to previously reported (oxide dispersion) strengthening of Fe14Cr–W–Ti ferritic steels.

Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed-Integer Nonlinear Programming

In this article, we investigate coordinated receding-horizon control of vehicle speed and transmission gearshift for energy-efficient operation of automated battery electric vehicles (BEVs). The introduction of multispeed transmissions in BEVs enables manipulation of electric motor operating point under given vehicle speed and acceleration command, thus creating the opportunity to further improve BEV energy efficiency. However, co-optimizing vehicle speed and transmission gearshift leads to a mixed-integer nonlinear programming (MINLP) problem, and it is well known that solving MINLP problems is computationally very challenging. To address this challenge, we propose a novel continuous relaxation technique that enables the computation of solutions to the speed and gearshift co-optimization problem using off-the-shelf nonlinear programming solvers. After analyzing theoretical properties of the proposed relaxation technique, we demonstrate its effectiveness through simulation-based case studies, where we show that co-optimizing vehicle speed and transmission gearshift can lead to considerably greater energy efficiency than optimizing them separately or sequentially and the proposed relaxation technique can reduce the computational cost of the co-optimization problem to a level that is comparable to the time budget available for onboard implementation.

Continuous spectrum approach to evaluate the effect of binder modification and aging

ABSTRACT The present work proposes a novel mathematical approach to directly obtain the continuous spectrum of asphalt binder from frequency domain data. Using the asymmetric sigmoidal function to obtain continuous spectrum offers advantages of flexibility and improvement in describing the spectrum well. Initially, complex modulus, storage modulus, and storage compliance master curves were constructed using experimental data. In the next stage, the continuous relaxation and retardation spectrum were constructed directly from the storage modulus and storage compliance master curves, respectively. Statistical indicators like mean, variance, and skewness were used to describe the shape of these finalised continuous spectra. To check the efficacy of the proposed approach, asphalt binders altered with modifiers (at different dosages) were aged under laboratory conditions. Critical evaluation of the results indicated that the underlying changes could be captured using the continuous spectra (which were not obvious with respective master curves or black space diagrams). Continuous spectra of binders constructed (with different dosages of modifier and under different aging conditions) were compared using the statistical indicators. Thus, the spectral shape parameters could capture the effect of aging and modifier dosage. These changes can be attributed to molecular changes, chemical reactions occurring with the addition of modifiers and aging.

Asuhan Komplementer Tatalaksana Afterpain pada Ibu Postpartum : Literature Review

The puerperium is a period of recovery of the mother after giving birth which is characterized by the return of the reproductive organs to their pre-pregnancy state with a puerperal period of 6-8 weeks. During the recovery period, postpartum mothers will experience many changes, both physically and psychologically, if there is no assistance from both the family and health workers, it is possible that problems will occur beyond the postpartum period. Afterpain or often called abdominal cramps is pain caused by continuous contraction and relaxation of the uterus, usually lasting 3-4 days after delivery and is common in multiparas. Objective: to determine the complementary care afterpain management in postpartum mothers. The method used in this study uses a literature study or literature review. The selected articles were 4 reviews that matched the criteria with the topic of afterpain management in postpartum mothers without giving pharmacology and medical measures. Discussion: afterpain management for postpartum mothers can be given complementary care by giving effluarge massage therapy, sacral plexus massage, candle therapy, postpartum gymnastics, other care such as emptying the bladder, providing a comfortable position (prone position by propping the stomach with a pillow), providing relaxation techniques, early mobilization, adequate rest and fulfillment of balanced nutrition. The results of this literature study can be a management intervention in the management of afterpain complaints in postpartum mothers.

Does NoL monitoring affect opioid consumption during da Vinci prostatectomy?

HintergrundDie Gabe von Opioiden zur Schmerzunterdrückung spielt eine zentrale Rolle in der modernen Anästhesiologie. Messungen von Hypnosetiefe und Muskelrelaxierung sind im Gegensatz zur Schmerzmessung seit Jahren etabliert. Seit Kurzem ist das PMD200 („Pain Monitoring System“; Fa. Medasense Biometrics™ Ltd., Ramat-Gan, Israel) verfügbar. Dieser Schmerzmonitor misst nichtinvasiv und errechnet einen dimensionslosen Schmerzindex („nociceptor level“, NoL). Die Validität und Zuverlässigkeit des Verfahrens sind Gegenstand von klinischen Studien.FragestellungReduziert die Verwendung des PMD200 die Gabe von Analgetika während einer Da-Vinci-Prostatektomie?Material und MethodenIn die Studie wurden 50 Patienten aufgenommen. Nach gewichtsadaptierter Sufentanilgabe zur Narkoseinduktion und einem 10 µg Bolus vor Hautschnitt erfolgte die intraoperative Analgesie durch subjektive Entscheidung (CONT) oder aufgrund eines erhöhten NoL-Index (INT). Die statistische Auswertung erfolgte durch Mann-Whitney-U-, Kolmogorow-Smirnow-Test und Levene-Statistik.ErgebnisseIn der INT-Gruppe war die Anzahl der Sufentanilboli/h nicht signifikant geringer als in der CONT-Gruppe (p = 0,065). Die Varianz der Sufentanilgaben unterschied sich signifikant (p = 0,033). In der CONT-Gruppe war die Applikation normal verteilt (p = 0,2), in der INT-Gruppe hingegen nicht (p = 0,003).DiskussionEine mögliche Interpretation der Daten ist, dass die Schmerzmittelgabe in der INT-Gruppe individualisierter erfolgte, d. h., es wurden nichterforderliche Schmerzmittelgaben vermieden, und gleichzeitig detektierte das NoL-Monitoring einzelne Patienten mit deutlich erhöhtem Schmerzmittelbedarf. Diese Schlussfolgerung ist nur unter der Voraussetzung zulässig, dass das PMD200 auch tatsächlich die Entität Schmerz misst.

The thermally activated deformation behaviour of single-crystalline microcast aluminium wires

A microcasting process is used to produce high aspect ratio (>30) monocrystalline pure aluminium wires with a diameter between 14 and 115 µm. The role of thermal activation in the plastic deformation of these microwires is measured by means of (single) tensile stress relaxation tests. The microwires deform largely in an intermittent fashion, i.e., through repeated sudden displacement bursts, also during stress relaxation, implying that the bursts can be triggered through thermal activation. By separating the intermittent and the continuous parts of the stress relaxation load vs. time signal, we measure an activation area characteristic of the continuous relaxation mechanism. Haasen plots of the continuous relaxation data suggest the presence of a back-stress on the order of 2.5 MPa, which can be attributed to the thin layer of oxide covering the metal. Smaller (14–25 µm) diameter crystals oriented for single slip show highly scattered activation area values and a steeper rate of increase of data in the Haasen plot than all other samples, which conform, save for the back-stress, with what is observed in bulk aluminium. Present findings are consistent with data from commensurate aluminium crystals in replicated microcellular structures. Data of this work show that, in single slip within aluminium crystals, the coupling between the activation area and the flow stress is altered when crystals are below 30 µm in diameter. The absence of a systematic difference in data between samples produced of 4N or 5N aluminium suggests that this conclusion is characteristic of the pure metal.