Parallel Stages Research Articles

A wide-range reconfigurable RF energy harvesting system with integrated storage and load regulation controller

This paper presents an integrated RF energy harvesting system that uses a rectifier with 30 reconfigurable parallel stages to cover a wide input power range extending from -14dBm to 4dBm. The system mitigates the conduction losses associated with the reconfigurable switches via employing gate switching. Additionally, a voltage feedback loop is implemented to regulate the average output voltage level at the main load at 1V, while delivering the excess power to a secondary storage unit (supercapacitor) at 3V. Circuit simulations, using a 130 nm CMOS technology, show that the proposed design is capable of achieving a peak Power Conversion Efficiency (PCE) of 45% at 0dBm using an external matching network, and a maximum of 36% at 0dBm with internal matching. Additionally, the system achieves high PCE (>20%) for an input power range of more than 14dB, along with an excellent load regulation performance of 27mV, eliminating the need for extra voltage regulators at the output.

Day‐ahead unit commitment for hydro‐thermal coordination with high participation of wind power

AbstractThe variability and uncertainty of renewable resources impose new challenges in the operational planning related to the unit commitment of generation units. The development of day‐ahead multi‐period optimal power flow, under integration of wind power, requires modelling of multiple scenarios in order to ensure an optimal power flow minimising the generation cost. A progressive hedging approach has been proposed and developed to solve efficiently the unit commitment problem as a two‐stage stochastic programming problem to update each stage in parallel. The performance of progressive hedging is compared with a standard mixed‐integer linear programming problem. The results indicate that the computation time is 50 times faster than standard mixed‐integer linear programming. The test case system is based on a reduced version of the interconnected Colombian system. The comparative results indicate an important reduction in computational time.

Eco-efficiency evaluation of Chinese provincial industrial system: A dynamic hybrid two-stage DEA approach.

In China, industrial pollution has become an urgent problem for policy makers and enterprise managers. To better support industrial development, we need to determine the effectiveness of policies through efficiency evaluation. China’s provincial industrial system consists of two stages: production and emission reduction. The emission reduction stage is composed of three parallel sub stages: solid waste treatment, waste gas treatment and wastewater treatment. In this process, the treatment capacity of industrial wastewater treatment facilities can be used as carry forward variable, which is not only the desirable output of the previous emission reduction stage, but also the input of the current emission reduction stage. Therefore, this paper proposes a dynamic hybrid two-stage data envelopment analysis (DEA) model for eco-efficiency evaluation of industrial systems, and applies it to a case study of Chinese regional industry. Applying the data collected from 2011 to 2015 to the model, the following conclusions can be drawn: (1) During the whole survey period, the average eco-efficiency was 0.9027. The overall eco-inefficiency of China’s provincial industrial system during the study period is mainly due to low efficiency of solid waste treatment and waste gas treatment. (2) The average eco-efficiency of provincial industrial system increased steadily from 2011 (0.6448) to 2014 (0.6777), but decreased slightly in 2015 (0.5908). (3) The carry forward treatment capacity of industrial wastewater treatment facilities has a remarkable impact on provincial industrial system efficiency scores, especially at the wastewater treatment stage (0.6002 vs 0.3691). (4) Provincial industrial system exists distinct geographical characteristics of low efficiency. This study has important guiding significance for policy makers and enterprise managers who are concerned about industrial pollution control.

Global attractivity of the equilibria of the diffusive SIR and SEIR epidemic models with multiple parallel infectious stages and nonlinear incidence mechanism

In this paper, we are concerned with the diffusive SIR and SEIR epidemic models with multiple parallel infectious stages and nonlinear incidence mechanism. We first establish a priori L∞-norm estimates for the solutions to the epidemic models, and then derive the global attractivity of disease-free equilibrium and endemic equilibrium for the two models in terms of the basic reproduction number. Our results show that the diversity of infectious stages, the infection mechanism and the diffusion do not affect the global dynamics of the infectious disease.

Capturing CO2 from the atmosphere: Design and analysis of a large-scale DAC facility

Direct air carbon capture (DAC) technologies can substantially lower the CO2 concentrations in the atmosphere by enabling negative emissions. This study shows in detail the design of the DAC plant at industrial scale and provides insights on its performance in terms of process economic and CO2 emissions count. The proposed DAC plant is optimized to capture CO2 directly from the atmospheric air, employing a sensitivity analysis to find the influence of operation and design parameters on the total cost. Absorption using sodium hydroxide as chemisorbent is utilized with a capture rate of 0.7. Industrially mature common process units are considered to achieve a design that is relevant in the near future. An initial base case design indicates a carbon cost of 244 $/ton-CO2 with the operating expenses comprising 84% of the total cost. Then, two scenarios are proposed to enhance the process performance: heat integration and use of renewable energy. Through the heat integration, the carbon ratio (CO2 captured / CO2 emitted) improves from a value of 2.7 for the base case to 3.73, meaning less CO2 is emitted per captured amount due to lower fuel consumption. The resulting cost goes down to 125 $/ton-CO2, with two additional heat exchangers added to the network. Furthermore, renewable scenario is considered where a parallel electrolysis stage feeds the process hydrogen fuel and oxygen required for combustion in the calciner. This scenario indicates that higher operating costs are incurred due to the expensive green fuel. Finally, a profitability analysis is performed to establish the feasibility for further processing to methanol in a Power-to-X facility. The estimations indicate that the hydrogen price has to go down by 46.3% in order to break-even.

Design and control of an x-y-θz parallel piezoelectric nanopositioning stage with a large moving platform

This paper introduces the design and control of a new type of x- y-θ z parallel piezoelectric nanopositioning stage. Using the spatial cross-distributed double parallel four-bar linkage (DPFL), the mirror symmetrically distributed driving unit and auxiliary unit are designed. Then, a parallel piezoelectric nanopositioning stage with a large moving platform and good guidance and decoupling performance are designed. The workspace and frequency response characteristics of the stage are analyzed based on the stiffness and dynamic models. To improve the static and dynamic characteristics of the stage, the structural size is optimized, and the optimization results are verified by Finite Element Analysis (FEA). To reduce the response time, hysteresis nonlinearity, and external disturbance of the stage, a compound controller combining a rate-dependent Prandtl–Ishlinskii (PI) hysteresis compensator and a proportional-integral controller is designed. Finally, the prototype of the stage is made by Wire Electro-Discharge Machining (WEDM) technology. The open-loop performance of the stage is tested experimentally. The results show that the maximum relative coupling error between x and y directions is less than 0.5%. Also, the experimental control results show that the designed compound controller has a fast response speed, good positioning, and tracking performance.

Launching to an orbit with a chemical propellant staged rocket systems

There is one way to explore space by using the space launch vehicles, which is known as rockets, and it can carry useful load named simply as payload of satelite from Earth into Space. In this study, performance predictions of the multi rocket motors are discussed and compared with single rocket motor with the same amount of propellant used for space travel. In this article in serial or tandem staging schemes, the boosting stage is usually the largest, the second stage and subsequent upper stages are above it, usually decreasing in size are used. In boosting stage parallel staging schemes solid or liquid rocket boosters are used to assist with launch. At low level starting to high altitude higher density fuel solid fuels, kerogen and cryogenic hydrogen(-250°C) are used as fuel. In solid propellants oxidizer is generally ammonium per chloride is used but in cryogenic liquid propellants oxygen(183.3°C) are used. In the first stage, both liquid propellant in a booster and five solid rocket propellent are used to reach about a certain altitude and velocity. In second stage, after reducing the weight by ejecting the five solid rocket propellent and only liquid propellant is used only to reach the an extra altitudes and velocities at low earth orbit (LEO). Drag and gravity effects are successfully used in all of the calculations. The added total result of velocities and altidudes found by these staged rockets are higher than the first single staged case. The advantage of multistage rockets, having same amount of propellant in staged rockets where total velocity will be increased by separating and removing waste from the system weight out of the system. Use of staged rocket system are usefull for increasing the amount of payload and decreasing the cost per unit weight as well.

SISTEM INFORMASI BERKAH BERSAMA BERBASIS WEB

Berkah Bersama Company is a core partnership company engaged in broiler farming. Within the company, there are three divisions, namely general and finance, logistics, production, and marketing. The production and marketing division plays a role in the growth of chickens to the sale of chickens. After the chicken is sold, the data on chicken growth and chicken sales will be recapitulated using Microsoft Excel. The process of data collection is still often an error in data input and at the time of data recapitulation often occurs calculation errors and losses in the final process. From these problems, the authors conduct research by building a web-based mutual blessing information system in the production and marketing division that will solve the above problems. The research method used is an incremental model that applies the stages in parallel where the first stage has not been completed, so it can go to the next stage. And the system design uses entity relationship diagrams and an integrated modeling language. The result of this research is a blessing information system with web-based production and marketing divisions that can assist and simplify data input and data recording in the production and marketing divisions.

Robust and optimum color image watermarking method based on a combination of DWT and DCT

A hybrid and robust digital image watermarking is suggested by using advantages of both frequency domain and wavelet domain. In this method, the most suitable color component of the host image and the most efficient wavelet sub-band are chosen, concluding from the comprehensive experiments. In this paper, three parallel stages are used to embed and extract the watermark message. After transforming color component of the host image into the wavelet domain, some preprocessing with Discrete Cosines Transformers are applied, and then the watermark message is embedded into the best range by using a secret key. By detecting the rank of constructed detection matrices and voting among three output bits, the watermark bit is extracted. Simulation results demonstrate that the proposed method, besides the high robustness against Gaussian noise-based attacks especially AWGN, has performed well in imperceptibility and embedding capacity factors.

A Fusion Transformer for Multivariable Time Series Forecasting: The Mooney Viscosity Prediction Case.

Multivariable time series forecasting is an important topic of machine learning, and it frequently involves a complex mix of inputs, including static covariates and exogenous time series input. A targeted investigation of this input data is critical for improving prediction performance. In this paper, we propose the fusion transformer (FusFormer), a transformer-based model for forecasting time series data, whose framework fuses various computation modules for time series input and static covariates. To be more precise, the model calculation consists of two parallel stages. First, it employs a temporal encoder–decoder framework for extracting dynamic temporal features from time series data input, which analyzes and integrates the relative position information of sequence elements into the attention mechanism. Simultaneously, the static covariates are fed to the static enrichment module, which is inspired by gated linear units, to suppress irrelevant information and control the extent of nonlinear processing. Finally, the prediction results are calculated by fusing the outputs of the above two stages. Using Mooney viscosity forecasting as a case study, we demonstrate considerable forecasting performance improvements over existing methodologies and verify the effectiveness of each component of FusFormer via ablation analysis, and an interpretability use case is conducted to visualize temporal patterns of time series. The experimental results prove that FusFormer can achieve accurate Mooney viscosity prediction and improve the efficiency of the tire production process.

A parallel 3-DOF micro-nano motion stage for vibration-assisted milling

In this paper, the design, optimization, and application of a parallel three-degree-of-freedom micro-nano motion stage for vibration-assisted milling are reported. This stage is driven by the piezoelectric effect to achieve precise position adjustment. A compound differential branch chain is designed to solve the poor stiffness of traditional branch chains and large deflection errors. Mechanical properties of the stage are theoretically, numerically, and experimentally investigated. The output displacement and natural frequency are weighed by orthogonal optimization. The open-loop tests on the prototype showed the stage performance such as hysteresis, tracking, and decoupling. Milling of typical microstructure surfaces is employed to demonstrate the effectiveness of the stage.

Application of the LDA Model for Obtaining Topics from the WIKICORPUS

A fundamental problem in text analysis of great amount of information is to discover the topics described in the documents. One of the most useful application involves the extraction of topics from documents corpus. Such is the case of Wikicorpus that consists of approximately 250,000 documents totaling in 250 millions of words. In this work, a system based on the Latent Dirichlet Allocation (LDA) model has been developed to carry out the task of automatically selecting the words of the corpus and, based on their frequency in the documents, it would indicate that they may or not belong to certain topic, classifying words without human intervention. Due to the large amount of information of the corpus, a Serial-Parallel Algorithm (SPA) in C/C++ and OpenMP have been used to perform parallel programming, since in parallel stages all threads must share certain variables, so the design architecture was shared memory

Extending a fuzzy network data envelopment analysis model to measure maturity levels of a performance based-budgeting system: A case study

Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice.

A new parallel algorithm for computing formal concepts based on two parallel stages

The fixpoints of Galois connections induced by binary relational data are called formal concepts in formal concept analysis (FCA). Computing formal concepts is one of the most important issues in FCA, while Close-by-One (CbO) and its variants are usually considered as the most efficient serial algorithms for this task. Current approaches to parallelization of CbO-type algorithms such as PCbO (Parallel CbO) usually enter the parallel stage after a serial stage which could be a possible bottleneck. In this paper, we propose a new parallel algorithm for computing formal concepts, which is composed of two parallel phases. The new algorithm parallelizes both the computations of the top L recursion levels and the workload distribution, which decouples worker threads from the main thread. We describe the algorithm and present an experimental evaluation of its performance and comparison with PCbO on various datasets. Results indicate that our algorithm performs better, especially when a dataset is dense or has a large size.

Gasification of Densified Biomass (DB) and Municipal Solid Wastes (MSW) Using HTA/SG Technology

The necessity of economical and rational use of natural energy sources caused a rapid development of research on the possibilities of using non-conventional energy resources. Taking the above into account, a new technological process of thermochemical conversion of biomass and communal waste, commonly known as High Temperature Air/Steam Gasification (HTA/SG) and Multi-Staged Enthalpy Extraction Technology (HTAG-MEET), was developed. In relation to traditional techniques of gasification or combustion of hydrocarbon fuels, the presented concept is characterized by higher thermal efficiency of the process, low emission of harmful compounds of carbon, sulfur, nitrogen, dioxins, furans and heavy metals. The use of a high-temperature gasification factor causes an increased thermochemical decomposition of solid fuels, biomass and municipal waste into gaseous fuel (syngas), also with increased hydrogen content and Lower Calorific Value (LCV). In this study, the possibility of using a batch type reactor (countercurrent gasifier) was analyzed for gasification of biomass and municipal waste in terms of energy recovery and environmental protection. The proposed research topic was aimed at examining the possibility of using the thermal utilization of biomass and municipal waste through their high-temperature decomposition in the presence of air, a mixture of air and steam. The main goals of the research were achieved during the implementation of several parallel stages of the schedule, which included, primarily: (a) study of the possibility of using thermal utilization of biomass and municipal waste through their high-temperature gasification in the presence of air or a mixture of air and steam and, secondary (b) analytical and numerical modeling of high-temperature gasification of biomass and municipal waste with the use of ANSYS CFD Fluent 6.3 software. Selected results of the experimental and numerical studies are properly presented. The higher temperature gasification concept shows the capability of this technology for maximizing the gaseous product yield in an up-draft fixed bed gasifier. It was also observed that at a high temperature, steam addition contributed to the thermal conversion of biofuels to gas with higher production of hydrogen.

Parallel extreme gradient boosting classifier for lung cancer detection

Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.

Development of a model of medication review for use in clinical practice: Bristol medication review model

BackgroundMedication review is a core aspect of medicine optimisation, yet existing models of review vary substantially in structure and content and are not necessarily easy to implement in clinical practice. This study aimed to use evidence from the existing literature to identify key medication review components and use this to inform the design of an improved review model.MethodsA systematic review was conducted (PROSPERO: CRD42018109788) to identify randomised control trials of stand-alone medication review in adults (18+ years). The review updated that by Huiskes et al. (BMC Fam Pract. 18:5, 2017), using the same search strategy implemented in MEDLINE and Embase. Studies were assessed using the Cochrane risk of bias tool. Key review components were identified, alongside relevant clinical and health service outcomes. A working group (patients, doctors and pharmacists) developed the model through an iterative consensus process (appraisal of documents plus group discussions), working from the systematic review findings, brief evidence summaries for core review components and examples of previous models, to agree on the main purpose of the review model, overarching model structure, review components and supporting material.ResultsWe identified 28 unique studies, with moderate bias overall. Consistent medication review components included reconciliation (26 studies), safety assessment (22), suboptimal treatment (19), patient knowledge/preferences (18), adherence (14), over-the-counter therapy (13) and drug monitoring (10). There was limited evidence from studies for improvement in key clinical outcomes. The review structure was underpinned by patient values and preferences, with parallel information gathering and evaluation stages, feeding into the final decision-making and implementation. Most key components identified in the literature were included. The final model was considered to benefit from a patient-centred, holistic approach, which captured both patient-orientated and medication-focused problems, and aligned with traditional consultation methods thus facilitating implementation in practice.ConclusionsThe Bristol Medication Review Model provides a framework for standardised delivery of structured reviews. The model has the potential for use by all healthcare professionals with relevant clinical experience and is designed to offer flexibility of implementation not limited to a particular healthcare setting.

Research on dynamic load sharing characteristics of double input face gear split-parallel transmission system

The face gear power-split system has huge superiorities over the traditional transmission form in the application of modern rotorcraft, and it has become the research trend of the industry in recent years. Thus this paper took the double input face gear split-parallel transmission system used in the rotorcraft as the research target, and established its dynamics model through the lumped parameter theory. Based on the Newtonian second law, the dynamics equations were built and solved to gain the meshing forces and load sharing coefficients of the transmission system. Simultaneously, the impacts of the eccentric errors, support stiffness, and torsional stiffness on the load sharing characteristics were studied. The results show that the meshing forces and load sharing coefficients of each gear pair have periodic changes; the eccentric errors of each drive stage gear have only a significant effect on the corresponding drive stage. Moreover, the changes in the support stiffness of the split-torque shafts and double gear shafts mainly affect the load distribution of the parallel stage, and the shaft torsional stiffness is less sensitively to maintain load balance. In addition, the increment of the shaft stiffness increases the load sharing coefficients of the corresponding gear pairs.

An iterated greedy matheuristic for scheduling in steelmaking-continuous casting process

Steelmaking-Continuous Casting (SCC) is a bottleneck in the steel production process and its scheduling has become more challenging over time. In this paper, we provide an extensive literature review that highlights challenges in the SCC scheduling and compares existing solution methods. From the literature review, we collect the essential features of an SCC process, such as unrelated parallel machine environments, stage skipping, and maximum waiting time limits in between successive stages. We consider an SCC scheduling problem with as objective the minimisation of the weighted sum of cast break penalties, total waiting time, total earliness, and total tardiness. We formulate the problem as a mixed-integer linear programming model and develop an iterated greedy matheuristic that solves its subproblems to find a near-optimal solution. Through numerical experiments, we show that our algorithm outperforms two types of genetic algorithms when applied to test instances.

A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages

In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency.