Sequential Operations Research Articles

Fat grafting versus implant-based treatment of breast asymmetry, a single surgeon experience over 13 years: a paradigm shift?

Breast asymmetry can result from congenital or traumatic aetiologies. Breast implants, autologous fat grafting, or a combination of both of these techniques are commonly used to achieve symmetry. This study adds critical evaluation of long-term patient outcomes in a large study group, to evaluate pearls and pitfalls of these treatment modalities. A prospectively maintained database of a single surgeon experience in breast asymmetry treatment over a 13-year period (2006-2018) was retrospectively analysed. Breast implant surgery and fat grafting to treat asymmetry were compared in terms of number of operations to achieve symmetry, complications, and overall patient satisfaction. Thirty-five patients underwent breast implant surgery, requiring an average 2.1±1.6 operations to achieve symmetry, with a major complication rate (requiring secondary procedures) of 26% (n=9). Again, 26% (n=9) were converted to lipofilling due to either implant removal or unsatisfactory results. Thirty (86%) patients underwent fat transfer monotherapy to achieve symmetry and no major complications were recorded. Nine percent (n=3) of these patients preceded to have additional implant surgery. Although implant-based reconstruction seemingly offers a quick single stage procedure, it is associated with significantly more revision procedures as a result of complications including capsular contracture, implant rupture and breast distortion. Fat grafting, despite requiring sequential operations to achieve initial symmetry, ultimately offers a more durable result and is associated with significantly fewer and more minor complications, while not increasing the total number of procedures required to achieve symmetry in the long term.

Повышение эффективности работы в пассажирском комплексе Октябрьской железной дороги за счет использования алгоритма принятия решений с помощью аналитических инструментов качества

bjective: Development of an instrument to form measures aimed at improving and monitoring their im- plementation, which will consider deviations from the specified parameters. The formation instrument is regarded as a complete set of elements for performing a function by systematizing a set of units (objects, phenomena), i. e., step-by-step application of tools compiled into a single algorithm. Methods: The main tools for analyzing and managing the passenger complex during ongoing activities are: Pareto chart, cause & effect diagram (Ishikawa), correlation, risk management (3-map method), strategic management (X-matrix), etc. Results: The study has revealed that the formation instrument consists of a sequence of systematic forecasting and evaluation of the algorithm of sequential operations: statistics, analysis, risk calculation, verification, control, etc. It has been established that it is necessary to strive for the ability to achieve a given parameter of the process stability in the passenger complex avoiding critical devia- tions from the desired result. Practical importance: Using the example of the 2019/2020 Oktyabrskaya Railway passenger complex winterization, the apparently universal principle of using the formation in- strument has been demonstrated. This principle can be not only applied to other passenger complex pro- cesses but also used in the processes of other facilities in the Russian Railways network.

MLAF: Multi-Level Attention Fused Model for Sentence Similarity Measurement

Semantic textual similarity measurement is an essential task in text mining and natural language processing, which has profound research value. Recent LSTM-based methods have achieved attractive success by modelling sentences as fixed-length vectors. However, since the LSTM network can only extract the semantic information of sentences sequentially, the longterm dependency of sentences cannot be efficiently captured when the sequence length is long. Moreover, current models treat each syntactic component in the sentence equally, while the effects of different syntactic elements to the sentence meaning expression are different. Generally, nouns, verbs, and adjectives contribute more than prepositions and conjunctions to the semantic meaning of the sentence. In this paper, we propose a multi-level attention fused (MLAF) model, which correlates self-attention and word attention with sentence representations, extracting the semantic information of sentences from different levels. The self-attention supplements sequential operations to model long-term dependency of sentences. The word attention built upon raw sentence highlights the vital syntactic components of sentences. Experiments on different datasets demonstrate that the proposed MLAF model has achieved significant improvements over baselines.

A Review of Multilevel Converters With Parallel Connectivity

Cascaded-bridge converters (CBCs) and modular multilevel converters (MMCs) enjoy growing popularity mostly due to modularity and scalability. Conventionally, their submodules allow only serial and bypass operation so that the use of low-voltage components for high-voltage output becomes possible. Dually, submodule parallelization adds the switched-capacitor behavior to CBCs/MMCs and has witnessed an upward trend in recent years. The salient advantages of parallel operation comprise sensorless voltage balancing, capacitance saving, current sharing, and system efficiency optimization. To capture the advancement in the field, this article reviews the state-of-the-art multilevel converters with parallel connectivity, covering various submodules, macrolevel circuit topologies, implementation challenges, and solutions, as well as control and optimization schemes. In particular, this article derives and classifies submodules as well as macro-level topologies according to basic H-bridge, asymmetrical half-bridge, and symmetrical half-bridge submodules. On top of that, this article introduces strategies for the simplification of submodules and the creation of novel topologies yet maintaining parallel connectivity. We highlight the role of graph theory in creating new analytic and synthetic methodologies for multilevel converters. In addition, this article discloses the relationship between multilevel converters with parallel connectivity and switched-capacitor converters.

Hybrid approaches for accelerated convergence of block-Jacobi iterative methods for solution of the neutron transport equation

The Parallel Block Jacobi (PBJ) [1,2] scheme is an iterative method with application in neutron transport solution which requires asynchronous sub-domain interfaces, where asynchronous here means flux continuity is not imposed across sub-domain interfaces. In this work, we present the development, analysis, and testing of hybrid methods which utilize both PBJ-type methods and synchronous sweep-based methods in a single iterative step. These methods are developed to reduce the penalty to iterative performance of the former incurred in problems containing optically thin cells, while maintaining the PBJ method's applicability to massively parallel solution on unstructured grids. As a prelude to this ultimate goal, this study is conducted in serial operation on 2-D Cartesian grids to facilitate implementation of the various tested iterative methods, to enable the reported spectral analysis, and for shorter computational execution times allowing the large amounts of performance data to be collected. The analysis consists of two Fourier analyses, demonstrating the sequential execution of a PBJ method and SI to be iteratively robust with respect to optical thickness. The results of these analyses and preliminary computational experimentation demonstrate the effectiveness of methods that execute PBJ and SI only in optically thick and thin regions of a problem, respectively. Further experimentation, including a parametric study, then demonstrates this approach to be effective when the thin region is solved using Inexact Parallel Block Jacobi (IPBJ), with sub-domains of a size such that the applicability to massively parallel solution on unstructured grids is not diminished. Finally, testing on realistic test problems containing large void regions confirms the viability of this approach. Performance comparisons include the traditional iterative acceleration methods Diffusion Synthetic Acceleration, Partial-current Nonlinear Diffusion Acceleration, and Adjacent-cell Preconditioner. The results show that while the hybrid PBJ methods generally consume more iterations than these traditional methods, our hybrid approach maintains an iteration count on the same order of magnitude as these acceleration methods, which are not fully developed for massively parallel solution on unstructured grids.

Titanium alloy components fabrication by laser depositing TA15 powders on TC17 forged plate: Microstructure and mechanical properties

The manufacturing of titanium alloy components is of great significance for many industrial applications. High-performance titanium alloy parts are generally produced by a serial operation of hot forging process and extensive machining. Recently, additive manufacturing (AM) technologies such as direct laser deposition (DLD) has been developed to produce net-shape components. In this study, the parts were fabricated by depositing TA15 titanium alloy powders via DLD process on forged TC17 titanium alloy plates. Microstructure, microhardness and tensile properties of as-fabricated sample were examined. By using digital image correlation (DIC), the strain fields as well as the global strain behavior of the tensile specimen have been analyzed. The sample consists of three typical zones: the laser deposited zone (LDZ), the forged substrate zone (SZ), and the heat affected zone (HAZ) with good metallurgical bonding. Superfine basketweave microstructure forms within β grains in LDZ and HAZ because of fast cooling rate. The as-fabricated specimens have good tensile properties with an average ultimate tensile strength of 1027 MPa and average elongation of 4.57%. Non-uniform local strain concentration occurs in LDZ, while no strain concentration is found in HAZ and SZ. The remarkable non-uniformity of plastic strain is ascribed to different microstructures.

Study of the influence of treatment on the strength of undoped indium antimonide single-crystal plates

The method of plane-transverse bending was used to measure the strength of thin single-crystal plates of undoped InSb with a crystallographic orientation of (100). It was found that the strength of the plates (thickness ≤ 800 μm) depends on their processing. Using a full processing cycle (grinding and chemical polishing) allows to increase the strength of InSb plates by 2 times (from 3.0 to 6.4 kg/mm2). It is shown that the dependence of strength on processing for wafers with (100) orientation is similar to this dependence for wafers (111), while the strength of wafers (111) is 2 times higher. The contact profilometry method was used to measure the roughness of thin plates, which also passed successive processing steps. It was found that during a full cycle of processing, the roughness of InSb plates decreases (Ra from 0.6 to 0.04 μm), leading to a general smoothing of the surface roughness. The strength and roughness of the (100) InSb and GaAs wafers are compared. It was found that the strength of GaAs cut wafers is 2 times higher than the strength of InSb cut wafers and slightly increases after a full cycle of their processing. It was shown that the roughness of GaAs and InSb plates after a full cycle of surface treatment is significantly reduced: 10 times for InSb due to overall surface leveling and 3 times for GaAs (Rz from 2.4 to 0.8 μm) due to a decrease in the peak component. Conducting a full cycle of processing InSb plates can increase their strength by removing broken layers by sequential operations and reducing the risk of mechanical damage.

X-ray tomographic reconstruction and segmentation pipeline for the long-wavelength macromolecular crystallography beamline at Diamond Light Source.

In this paper a practical solution for the reconstruction and segmentation of low-contrast X-ray tomographic data of protein crystals from the long-wavelength macromolecular crystallography beamline I23 at Diamond Light Source is provided. The resulting segmented data will provide the path lengths through both diffracting and non-diffracting materials as basis for analytical absorption corrections for X-ray diffraction data taken in the same sample environment ahead of the tomography experiment. X-ray tomography data from protein crystals can be difficult to analyse due to very low or absent contrast between the different materials: the crystal, the sample holder and the surrounding mother liquor. The proposed data processing pipeline consists of two major sequential operations: model-based iterative reconstruction to improve contrast and minimize the influence of noise and artefacts, followed by segmentation. The segmentation aims to partition the reconstructed data into four phases: the crystal, mother liquor, loop and vacuum. In this study three different semi-automated segmentation methods are experimented with by using Gaussian mixture models, geodesic distance thresholding and a novel morphological method, RegionGrow, implemented specifically for the task. The complete reconstruction-segmentation pipeline is integrated into the MPI-based data analysis and reconstruction framework Savu, which is used to reduce computation time through parallelization across a computing cluster and makes the developed methods easily accessible.

Dilated causal convolution with multi-head self attention for sensor human activity recognition

Systems of sensor human activity recognition are becoming increasingly popular in diverse fields such as healthcare and security. Yet, developing such systems poses inherent challenges due to the variations and complexity of human behaviors during the performance of physical activities. Recurrent neural networks, particularly long short-term memory have achieved promising results on numerous sequential learning problems, including sensor human activity recognition. However, parallelization is inhibited in recurrent networks due to sequential operation and computation that lead to slow training, occupying more memory and hard convergence. One-dimensional convolutional neural network processes input temporal sequential batches independently that lead to effectively executed operations in parallel. Despite that, a one-dimensional Convolutional Neural Network is not sensitive to the order of the time steps which is crucial for accurate and robust systems of sensor human activity recognition. To address this problem, we propose a network architecture based on dilated causal convolution and multi-head self-attention mechanisms that entirely dispense recurrent architectures to make efficient computation and maintain the ordering of the time steps. The proposed method is evaluated for human activities using smart home binary sensors data and wearable sensor data. Results of conducted extensive experiments on eight public and benchmark HAR data sets show that the proposed network outperforms the state-of-the-art models based on recurrent settings and temporal models.

Hybrid constructed wetlands system for domestic wastewater treatment under tropical climate: Effect of recirculation strategies on nitrogen removal

The objective of the present study was to evaluate the performance of a domestic wastewater treatment system based on hybrid constructed wetlands (CWs) under tropical climate. Moreover, the effect of different recirculation strategies particularly on nitrogen removal was also evaluated, including an analysis of nitrogen balance. The system was composed of a septic tank (pre-treatment), a horizontal subsurface flow and a vertical subsurface flow CWs. Three systems were analyzed: with the ornamental species Sagittaria lancifolia, the cattail Typha dominguensis and without vegetation as control. Evapotranspiration in the CWs was monitored during the warmest period of the year. First, the three systems were operated in serial mode evaluating two contact times of 2 d and 4 d. The three hybrid CWs were able to remove more than 92% of organic matter (COD, BOD5 and TOC), 88% of TSS and 99% (equivalent to 4.5 log10 units) of pathogens (Total Coliforms and Escherichia coli), regardless of plant selection and contact time. However, applying a contact time of 4 d resulted in higher removal of pathogens. Concerning nutrients removal, the combination of a contact time of 4 d and vegetation was required to achieve removal efficiencies over 66% for nitrogen and 90% for phosphorus. Finally, two recirculation strategies (RS) using a contact time of 4 d were evaluated in order to enhance nitrogen removal: RS1 (to the horizontal units) and RS2 (to the septic tank). The results yielded removal efficiencies similar to those obtained during serial operation in the case of organic matter (COD and BOD5) and phosphorus regardless of the RS applied. However, the removal of TSS decreased with RS2 as expected, due to the new load of solids coming from the septic tank. Nevertheless, nitrogen removal was increased over 85% in the hybrid CWs with vegetation, satisfactorily achieving system optimization. The highest nitrogen removal efficiency (≈ 97%) was obtained with the combination of RS2 and the plant species T. dominguensis. However, the use of the ornamental species S. lancifolia was not discarded since it provides an additional aesthetic benefit.

ВНУТРЕННИЙ ЭКСПРЕСС КОНТРОЛЬ ПЕРСПЕКТИВНЫХ ПОКАЗАТЕЛЕЙ БУХГАЛТЕРСКОЙ (ФИНАНСОВОЙ) ОТЧЕТНОСТИ ОРГАНИЗАЦИИ

The article substantiates some prospective indicators of the financial reporting: profitable investments in tangible assets and financial investments, the appearance of which in an organization is a prerequisite for logical sequential operations for recognizing income. The interest of well-known user groups is revealed: the owners of the organization (founders, shareholders); the management, those charged with governance and the personnel of the organization; partners of the organization (buyers and customers, suppliers and contractors, tenants, borrowers and lenders, agents and principals, banks, insurance organizations, debtors and creditors, investors; the Federal Tax Service of Russia, the Pension Fund and other state bodies; customs authorities, arbitration courts, publishing houses, advertising companies, in the prompt receipt of accounting and economic information on prospective indicators of financial reporting to achieve various management goals. The essence of internal express control of prospective indicators of financial reporting is substantiated as a quick follow-up control, involving the implementation of actions according to a specific algorithm : substantiation of prospective indicators; consistent implementation of control measures and procedures; confirmation of the completeness and reliability of legal and primary documents on the movement of prospective indicators of financial reporting. The authors argue that, due to the internal express control of prospective indicators of financial reporting, accounting and economic information will become more understandable and accessible to the concerned users,

A dual population multi-operator genetic algorithm for flight deck operations scheduling problem

It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations. In this paper, the precedence constraints and resource constraints in flight deck operations are analyzed, then the model of the multi-aircraft integrated scheduling problem with transfer times (MAISPTT) is established. A dual population multi-operator genetic algorithm (DPMOGA) is proposed for solving the problem. In the algorithm, the dual population structure and random-key encoding modified by starting/ending time of operations are adopted, and multiple genetic operators are self-adaptively used to obtain better encodings. In order to conduct the mapping from encodings to feasible schedules, serial and parallel scheduling generation scheme-based decoding operators, each of which adopts different justified mechanisms in two separated populations, are introduced. The superiority of the DPMOGA is verified by simulation experiments.

A Practical Transferring Method from Batch to Flow Synthesis of Dipeptides via Acid Chloride Assisted by Simulation of the Reaction Rate

This paper proposes a technical approach for seamlessly setting the conditions of continuous flow synthesis from batch data. We directly detected the acid chloride formation from Fmoc-l-Phe-OH in batch using the in-line monitoring technology ReactIR and logically calculated the residence time under continuous flow conditions based on kinetic study. Assisted by this simulation method, the sequential flow operations, which consist of acid chloride formation from Fmoc-l-Phe-OH, cooling and coupling with Fmoc-l-Phe-Cl and H2N-l-Phe-OMe, were completed in approximately 1 min.

Micro-electromechanical-system-tuned resonant filters spanning the 8-12 µm band.

The spectral band covering ∼8-12µm is atmospherically transparent and therefore important for terrestrial imaging, day/night situational awareness systems, and spectroscopic applications. There is a dearth of tunable filters spanning the band. Here, we propose and demonstrate a new, to the best of our knowledge, tunable-filter method engaging the fundamental physics of the guided-mode resonance (GMR) effect realized with a non-periodic lattice. The polarization-dependent filter is fashioned with a one-dimensional Ge grating on a ZnSe substrate and interrogated with a ∼1.5mm Gaussian beam to show clear transmittance nulls. To expand the tuning range, the device parameters are optimized for sequential operation in TM and TE polarization states. The theoretical model exhibits a tunable range exceeding 4 µm, thus covering the band fully. In the experiment, a prototype device exhibits a spectral range of 8.6-10.0 µm in TM and 9.9-11.7 µm in TE polarization or >3µm total. With additional efforts in fabrication, we expect to achieve the full range.

An Intelligent Human-Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves.

The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.

Study on Optimal Operation Mode of One-key Sequence Control in Substation

At present, some substations have carried out sequential control pilot applications, but there are generally problems such as inconsistent operation methods, narrow operation coverage, incomplete typical functions, and complicated manual intervention links. The article constructs a multi-objective comprehensive evaluation model of operation mode based on the one-key sequential control operation mode of different application scenarios. This article establishes the weights of various indicators including operation efficiency, economic benefits and other factors, conducts a comprehensive evaluation and proposes an optimal operation mode. Through actual cases, the article also analyzes the functional transformation scheme of one-key sequence control technology of intelligent substation.

The Technique for Zoning by the Soil Strata Mechanical Stability Degree

The safety of buildings and structures largely depends on the bearing capacity of soil foundations. Structurally unstable dispersed soils with unstable water-colloidal bonds are widespread in Russia, therefore, the issue of evaluating the mechanical stability of dispersed soils is relevant. The authors have developed a technique for zoning by the soil strata mechanical stability degree, the approbation of which is exemplified by a large construction site being designed. The deformation modulus has been used as the main criterion. The technique involves a series of sequential logical operations outlined herein. On the experimental site, areas with a low, medium, and high stability degree of the soil stratum and its engineering-geologic elements (EGE) have been identified. For sites with a low stability degree, recommendations for their development are given.

Concretization as a Mechanism of Change in Psychodrama: Procedures and Benefits.

Concretization is a concept that has different meanings in different psychological theories and varying manifestations in different psychotherapies. In psychodrama, much of the available information on concretization draws on J. L. Moreno’s initial conceptualization, descriptive case studies, and interpretations in the various approaches. However, concretization has not been empirically studied as a concept or as a therapeutic mechanism of change. Therefore, the purpose of this qualitative study was to generate an empirically based conceptualization and operationalization of concretization as well as to identify its clinical benefits in psychodrama. To this end, semistructured in-depth interviews were conducted with seven experienced psychodrama therapists. Using a grounded theory approach for the data analysis, the model that emerged consists of three pathways toward concretization: realistic concretization, symbolic concretization, and integrated concretization. The findings suggest a sequential multistep operation that can be linear or nonlinear, depending on the protagonist’s need. The findings also underscore four benefits of concretization as a mechanism of change in psychodrama: reducing the ambiguity of the problem, externalizing the protagonist’s problem, enhancing the therapist-protagonist therapeutic bond, and bypassing the protagonist’s defense mechanisms. The model is discussed in light of the findings and the literature, and future directions are suggested.

Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit

To overcome the shortcomings of traditional roller bearing remaining useful life prediction methods, which mainly focus on prediction accuracy improvement and ignore labor cost and time, the present work proposed a novel prediction method by combining an improved one-dimensional convolution neural network (1D-CNN) and a simple recurrent unit (SRU) network. For feature extraction, the proposed method uses the ability of the 1D-CNN to extract signal features. Moreover, use the global maximum pooling layer to replace the fully connected layer. In the prediction part, a parallel-input SRU network was established by reconstructing the serial operation mode of a traditional recurring neural network (RNN). Finally, experiments were carried out using the XJTU-SY dataset to verify. Results revealed that on the premise of ensuring prediction accuracy, the 1D-CNN-SRU method could reduce manual intervention and time cost to a certain extent and provide an intelligent method for roller bearing remaining useful life prediction.

КАЗАХСТАНСКИЕ ЦЕОЛИТЫ КАК ПЕРСПЕКТИВНЫЙ МАТЕРИАЛ В ПРОЦЕССАХ ВОДОПОДГОТОВКИ МОЛОЧНОЙ ОТРАСЛИ

In the modern world, there are practically no sources of clean water suitable for human consumption. As a result, at any enterprise in the food industry a number of sequential operations are carried out related to the purification of water necessary for the production of products. In all processes of processing dairy raw materials, water supply to enterprises is carried out from a public water supply network and / or from an artesian well. The article discusses the feasibility of using natural zeolites to ensure water safety. Research data of Kazakhstan zeolites of various deposits and their application in practice were studied.. As shown by the results of experimental studies, zeolites of the Taizhuzgen deposit significantly improve water quality. As a result of activation, the sorption ability of zeolite rises to 85-90%. Production tests of zeolites indicate the significant potential of this mineral raw material. Natural zeolites are cheap, resistant to mechanical wear and can be reused after regeneration. Studies have shown that Taizuzgen zeolite is a good sorption material and can be recommended in further studies with the aim of developing filters for water purification used in water treatment processes in the dairy industry.