Input Andoutput Research Articles

State of-The-Art Multi-Input DC-DC Converter Topologies and their Control Techniques: A Review

Environmental awareness and technological advancement have made renewable energy sources such as fuel cell,solar photovoltaic (PV), wind energy an emerging research area. Power electronics converters are used as interface betweenthe renewable energy sources and the load unit, aimed at matching these sources with the load demand and improving thesteady-state and dynamic characteristics. Traditional method of using multiple single-input single-output dc-dc converter tointegrate several renewable energy sources, comes with challenges in controller design, poor efficiency, low power densityand increase in cost. To address these limitations, the use of an integrated multi-port converter is a preferable solution.Recently, several multi-port dc-dc converter topologies have been reported each having its peculiarity in terms of number ofcomponent count, relationships between input and output, voltage conversion ratio, control algorithm and efficiency. Thisarticle reviewed multi-port dc-dc converters presented by different research groups from the point of view of topology, inputand output relation and structure. Furthermore, control algorithms proposed to solve various control problems are alsoreviewed.

A Relationship Analysis Between the Poverty Rate and the Regional Development Level in Kulon Progo Regency, Indonesia

This study aims to identify the level of poverty and the level of regional development, along with an analysis of the relationship and typology between the two, in Kulon Progo Regency. The analysis unit in this research was 12 subdistricts in Kulon Progo Regency. The method used in the research is descriptive quantitative, with secondary data analysis using factor analysis, natural breaks, Pearson Product Moment correlation test, alignment levels, regional typologies with scatter plot, and spatial analysis. Meanwhile, regional development policy recommendations are also prepared, based on the type of area resulting from the typology using a SWOT analysis. The results showed that there are variations in poverty and regional development levels. The correlation test between the poverty rate and three factors in the regional development rate showed a fairly strong negative relationship with input and output factors and a strong negative relationship with process factors, which means that the higher the poverty level, the lower the level of regional development, and vice versa. Meanwhile, regional development policy recommendations are prepared based on four quadrants of the resulting regions, namely Quadran I (developed regions), Quadran II (developing regions), and Quadran III and IV (underdeveloped regions).

Agricultural Terms of Trade in Pakistan: Issues of Profitability and Standard of Living of the Farmers

In the wake of the liberalised trade regime, Pakistan is undergoing structural adjustments. At the heart of the adjustments is liberalisation of markets and prices, including freeing the currency market, reducing industrial protection, and introducing financial austerity and macroeconomic stability. The agricultural sector is also undergoing these changes, which include eliminating export taxes and other trade restrictions and reducing producer subsidies (input and output subsidies). Such changes in the sector are critical as agriculture is the largest sector of Pakistan economy.

ИНТЕЛЛЕКТУАЛЬНАЯ РЕКОМЕНДАТЕЛЬНАЯ СИСТЕМА ДЛЯ ПРОСТРАНСТВЕННОГО АНАЛИЗА

The work is devoted to the analysis of mechanisms of formation of recommendations andevaluation of the user's reaction to them in the interactive mode of work with the geoinformationsystem. One of the important areas of application of recommender systems is the search and decision-making in spatial situations. A peculiarity of this class of problems is the uncertainty of taskdefinition and ambiguity of decision evaluation. Users are often faced with problems that do nothave a clear formulation. To try to solve them, it is necessary not only to designate the direction ofsolution search, but also to find an adequate sequence of tasks with clearly formulated input andoutput data. Recommendations in such cases are designed in a dialogue with the user-analyst todevelop a strategy for finding solutions. In this paper we study a smart recommendation systemusing the experience of dialog interaction. We propose a model of adaptation to the mental imageof the problem, which builds the user, taking into account the levels of situational awareness andcognitive load. The peculiarity of the model is the use of visual cartographic objects, which areindicators of the state of the mental image. A recommendation is represented by a set of objectsthat are introduced into the field of cartographic analysis. This implicitly induces a certain semanticdirection of increasing situational awareness. A criterion of satisfaction with the recommendationis suggested. A diagram of recommender system states, which describes the selection of context,adequate to the problem being solved, is given. The context is understood as an informationobject, capable of providing program functions and data for solving problems of a limited class.A sequence of contexts in an analysis session is considered as a precedent of experience. Indicatorsof trend, tendency and rhythm are proposed for possible chains of contexts. The degree ofsemantic proximity of precedents to the current course of search for a solution is estimated bythese indicators. Their use will increase the speed of adaptation.

Carbon dioxide emissions reduction efficiency and growth potential: case of China

PurposeUnderstanding China's carbon dioxide (CO2) emission status is crucial for getting Carbon Neutrality status. The purpose of the paper is to calculate two possible scenarios for CO2 emission distribution and calculated input-output flows of CO2 emissions for every 31 China provinces for 2012, 2015 and 2017 years.Design/methodology/approachIn this study using the input and output (IO) table's data for the selected years, the authors found the volume of CO2 emissions per one Yuan of revenue for the industry in 2012 and the coefficient of emission reduction compared to 2012.FindingsResults show that in the industries with a huge volume of CO2 emissions, such as “Mining and washing of coal”, the authors cannot observe the reduction processes for years. Industries where emissions are being reduced are “Processing of petroleum, coking, nuclear fuel”, “Production and distribution of electric power and heat power”, “Agriculture, Forestry, Animal Husbandry and Fishery”. For the “construction” industry the situation with emissions did not change.Originality/value“Transport, storage, and postal services” and “Smelting and processing of metals” industries in China has the second place concerning emissions, but over the past period, emissions have been sufficiently reduced. “Construction” industry produces a lot of emissions, but this industry does not carry products characterized by large emissions from other industries. Authors can observe that Jiangsu produces a lot of CO2 emissions, but they do not take products characterized by significant emissions from other provinces. Shandong produces a lot of emissions and consumes many of products characterized by large emissions from other provinces. However, Shandong showed a reduction in CO2 emissions from 2012 to 2017.

A Two-Step Unsupervised Learning Approach to Diagnose Machine Fault Using Big Data

The modern industrial sector requires an intelligent fault diagnosis system to ensure reliable and safe processingsince traditional methods require expert diagnosis, which consumes time and requires labor. Furthermore, diagnosticresults are influenced by the expert’s expertise and in-depth knowledge of the machine. The objective of this paper isto solve the manual intervention problem and improve the fault diagnosis. We propose a novel two-stage unsupervisedlearning algorithm based on artificial intelligence (AI) that learns fault features efficiently from raw vibration signals.To accomplish the aforementioned goal, we encapsulate the two-stage learning technique such as sparse filtering andRectified Linear Unit (ReLU) regression function. As a first step, we used a two-layer neural network sparse filteringprocedure to extract vibration signals’ features. Based on vibration signals, ReLU regression determines the healthcondition of the machine in the second phase. ReLU is a linear function that improves the performance of neural network training. Here we utilized a sigmoid and softmax regression function to compare the performance of ReLU. Thesigmoid function works well for binary classification, whereas softmax works well for multiclass classification. A database of motor-bearing vibration signals containing signals about four different health conditions of machines, suchas Inner race faults (IF), Outer race faults (OF), and rolling faults (RF). The sparse filter is evaluated on different inputand output dimensions, which significantly increases the learning accuracy. We classified the health condition usingReLU and achieved 93.8% accuracy, which is higher than sigmoid and softmax. Through the two-step learning process, machine fault diagnosis is enhanced, as well as big data is effectively handled.

ScDIOR: single cell RNA-seq data IO software

BackgroundSingle-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression. The rapid development of computational methods promotes the insight of heterogeneous single-cell data. An increasing number of tools have been provided for biological analysts, of which two programming languages- R and Python are widely used among researchers. R and Python are complementary, as many methods are implemented specifically in R or Python. However, the different platforms immediately caused the data sharing and transformation problem, especially for Scanpy, Seurat, and SingleCellExperiemnt. Currently, there is no efficient and user-friendly software to perform data transformation of single-cell omics between platforms, which makes users spend unbearable time on data Input and Output (IO), significantly reducing the efficiency of data analysis.ResultsWe developed scDIOR for single-cell data transformation between platforms of R and Python based on Hierarchical Data Format Version 5 (HDF5). We have created a data IO ecosystem between three R packages (Seurat, SingleCellExperiment, Monocle) and a Python package (Scanpy). Importantly, scDIOR accommodates a variety of data types across programming languages and platforms in an ultrafast way, including single-cell RNA-seq and spatial resolved transcriptomics data, using only a few codes in IDE or command line interface. For large scale datasets, users can partially load the needed information, e.g., cell annotation without the gene expression matrices. scDIOR connects the analytical tasks of different platforms, which makes it easy to compare the performance of algorithms between them.ConclusionsscDIOR contains two modules, dior in R and diopy in Python. scDIOR is a versatile and user-friendly tool that implements single-cell data transformation between R and Python rapidly and stably. The software is freely accessible at https://github.com/JiekaiLab/scDIOR.

Multi-task equilibrium scheduling of Internet of Things: A rough set genetic algorithm

The Internet of Things (IoT) server suffers from numerous business traffic with network bandwidth growth, resulting in downtime. Providing business support without affecting the user experience is the primary problem that IoT companies need to consider and solve in the face of traffic impact. This paper proposes a load balancing scheduling algorithm based on Particle Swarm Optimization Genetic Algorithm (PSO-GA) for IoT clusters. The algorithm uses CPU occupancy rate, memory occupancy rate, network bandwidth occupancy rate, and disk Input and Output (IO) occupancy rate to comprehensively measure the server node load and establish a resource balance model. The fitness function is used to quantify the influence as the basis of weight adjustment. Then, the Particle Swarm Optimization (PSO) algorithm uses the disturbance factor and contraction operator. The optimized algorithm is used to calculate the optimal solution of the fitness function and obtain the optimal weight. Finally, the PSO-GA algorithm is simulated, tested, and compared with the other three load balancing algorithms. As seen from the test results of response delay, throughput, request error rate, and resource utilization, the performance of this algorithm is improved by more than 5% compared with the performance of the traditional method, and the optimization ability is improved obviously. The research content of this paper provides a new way to alleviate the network load, reduce the server overload, congestion, downtime, and other problems, and realize the multi-task balanced scheduling of IoT.

MODIFIED QUADRATIC BUCK BOOST CONVERTER FOR PV APPLICATIONS

Buck-boost DC/DC converters have been extensively employed in stepping up/down the voltage for PV applications. The input and the output port current of the traditional quadratic buck-boost converter are discontinuous, which result in increased input and output current ripples and complicate the design of the input and output filters. Hence these problems can be solved by using a modified quadratic buck boost converter, which provides wider voltage conversion ratio with continuous input and output ports current. Dual output can be derived from this single input converter for multiple output applications. Also a fuzzy logic controller can be designed for this proposed converter for better and faster performance.

NOMINAL BALLISTIC MISSILE ASCENT OPTIMIZATION WITH THERMAL INVESTAGATION

Ballistic Missile Trajectory is one of the big issues to simulate and also to be optimized. The main objective of this paper is to find a method to obtain the Nominal trajectory for the ballistic missile through finding out the optimal parameters for each ballistic trajectory phases. To define the problem; the paper will list all governing equations and describe the parameters of input and output for each phase of the trajectory. Then, a method to reduce the number of input parameters will be discussed. We study each parameter effect to get the most optimal trajectory for the missile. At the final, optimal flight will be discussed in details. Although, we investigate the thermal effect for entry point from space into the earth atmosphere

Unlearning the boundary-crossing constraint: Processing Instruction and the acquisition of motion event construal

This study explores the effects of instruction on the acquisition of motion event construal among learners of English as a second language. The challenge for learners with Verb-framed first languages is that they need to 'unlearn' the boundary-crossing constraint and conflate manner and motion in the main verb, as in "she ran into the bank", however, there is little research on how this domain can be taught. We evaluate performance on story-telling productive tasks using three experimental treatments involving 1) an input-only approach based on the principles of Processing Instruction, 2) combined input and output training and 3) explicit information only about the target construction. The findings show that boundary-crossing constructions expressing manner can be taught and learning effects generalised to non-boundary-crossing structures not included in the training material. The effectiveness of input-only instruction persists over a two-week period, and compares positively with that of an input+output teaching package.

Employee Productivity of Hotel Room Attendants: An Input–Output Perspective

ABSTRACT This study examines the productivity of room attendants in a hotel housekeeping department by using the input and output (IO) approach and explores the influence of customer market segments and room types on employee productivity. Using the qualitative approaches of direct observation and in-depth interviews, time checks for 22 guestrooms were directly observed in terms of hotel room cleaning procedures. Interview results indicated that 18 room attendants had received proper training. However, the room attendants encountered many obstacles during their daily operations, such as long waiting times for linens, thereby negatively affecting their productivity. Customer segmentation and room type also resulted in additional time required for room cleaning. Previous theoretical contributions and future managerial implications are also discussed.

Novel Process Simulation of Biodiesel Production from Crude Castor Oil

A continuous process flowsheet for biodiesel production from crude castor oil and methanol was developed on Aspen HYSYS simulation environment. It consists of an acid-catalysed sub-process for pre-treatment of the crude feedstock and a base-catalysed one for trans-esterification of treated oil. Reactions involved were defined using kinetic parameters obtained from lab-scale experiments for increased realism, and reactor setup was decided based on basic technological and economical assessments. Specifications of other unit operations and flows were obtained from different approximation methods, literature adaptation and trade-off consideration. Input and output quality controls were achieved via simple adjustments with tight tolerances. The use of crude feedstock reduced the cost of raw material while expanding the scale of the process, which managed to produce 8000 tons of biodiesel having purity of over 99 % a year. Specifically, the process could remove up to 90.83 % amount of free fatty acids in the crude oil and convert up to 93.82 % of oil to biodiesel.

GenChem v1.0 – a chemical pre-processing and testing system for atmospheric modelling

Abstract. This paper outlines the structure and usage of the GenChem system, which includes a chemical pre-processor GenChem.py) and a simple box model (boxChem). GenChem provides scripts and input files for converting chemical equations into differential form for use in atmospheric chemical transport models (CTMs) and/or the boxChem system. Although GenChem is primarily intended for users of the Meteorological Synthesizing Centre – West of the European Monitoring and Evaluation Programme (EMEP MSC-W) CTM and related systems, boxChem can be run as a stand-alone chemical solver, enabling for example easy testing of chemical mechanisms against each other. This paper presents an outline of the usage of the GenChem system, explaining input and output files, and presents some examples of usage. The code needed to run GenChem is released as open-source code under the GNU license.

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME.

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis

AbstractReduction of carbon dioxide (CO2) emissions is one of the biggest challenges for global sustainable development, in which economic growth characterized by industrialization plays a formidable role. We innovatively adopted the input and output (I-O) table of 41 countries released by World I-O Database to determine the industrial structure change and analyze its impact on CO2 emission evolution by developing a cross-country panel model. The empirical results show that industrial structure change has a significantly negative effect on CO2 emissions; to be specific, 0.1 unit increase in the linkage of manufacturing sector and service sector will lead to a decrease of 0.94 metric tons per capita CO2 emissions, indicating that upgrading industrial structure contributes to carbon mitigation and sustainable development. Further, urbanization, technology and trade openness have significantly negative impact on CO2 emissions, while economy growth and energy use take positive impacts. In particular, a 1% increase in per capita income will contribute to an increase of 8.6 metric tons per capita CO2 emissions. However, the effect of industrial structure on environment degradation is moderated by technology level. These findings fill the gaps of previous literature and provide valuable references for effective policies to mitigate CO2 emissions and achieve sustainable development.

MicrIO: an open-source autosampler and fraction collector for automated microfluidic input-output.

Microfluidic devices are an enabling technology for many labs, facilitating a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a 'world-to-chip' barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an 'AutoSipper' for input and a 'Fraction Collector' for output. To facilitate widespread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to two microfluidic devices: a simple, valved inlet manifold and a microfluidic droplet generator that produces beads with distinct spectral codes. Analysis of the collected materials in each case establishes the ability of the platform to draw from and output to specific wells of multiwell plates with negligible cross-contamination between samples.

A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming

Regarding the high corrosion resistance of brass in sulfuric acid, its leaching process is the most important step in hydrometallurgical recovery of brass scraps. In this study, the electrochemical dissolution of brass chips in sulfuric acid has been investigated. The electrochemical cell voltage depends on various parameters. Regarding the complexity of electrochemical dissolution, the system voltage could not be easily predicted based on the operational parameters of the cell. So, it is necessary to use modeling techniques to predict cell voltage. In this study, 139 leaching experiments were conducted under different conditions. Using the experimental results and gene expression programming (GEP), parameters such as acid concentration, current density, temperature and anode-cathode distance were entered as the inputs and the voltage of the electrochemical dissolution was predicted as the output. The results showed that GEP-based model was capable of predicting the voltage of electrochemical dissolution of brass alloy with correlation coefficient of 0.929 and root square mean error (RSME) of 0.052. Based on the sensitivity analysis on the input and output parameters, acid concentration and anode-cathode distance were the most and least effective parameters, respectively. The modeling results confirmed that the proposed model is a powerful tool in designing a mathematical equation between the parameters of electrochemical dissolution and the voltage induced by variation of these parameters.

The Spatial Spillover Effect of Input and Output of Scientific Progress on Regional Economic Growth: The Case of Guangdong Province

This paper makes an empirical analysis of the spatial spillover effect of regional economic growth by using Moran’s I and Spatial Durbin Model to study the input and output of technological progress, with the panel data of 21 prefecture-level cities in Guangdong Province from 2008 to 2017. The empirical results show that the spatial autocorrelation exists in the economic development of Guangdong Province, and both the input and output of scientific research innovation have a significant positive effect on the regional economic growth. Under the spatial contiguity weights matrix, the output of scientific research and innovation has a more obvious spillover effect on the economic growth of neighboring cities than the input of scientific research and innovation.

Trajectories of nitrate input and output in three nested catchments along a land use gradient

Abstract. Increased anthropogenic inputs of nitrogen (N) to the biosphere during the last few decades have resulted in increased groundwater and surface water concentrations of N (primarily as nitrate), posing a global problem. Although measures have been implemented to reduce N inputs, they have not always led to decreasing riverine nitrate concentrations and loads. This limited response to the measures can either be caused by the accumulation of organic N in the soils (biogeochemical legacy) – or by long travel times (TTs) of inorganic N to the streams (hydrological legacy). Here, we compare atmospheric and agricultural N inputs with long-term observations (1970–2016) of riverine nitrate concentrations and loads in a central German mesoscale catchment with three nested subcatchments of increasing agricultural land use. Based on a data-driven approach, we assess jointly the N budget and the effective TTs of N through the soil and groundwater compartments. In combination with long-term trajectories of the C–Q relationships, we evaluate the potential for and the characteristics of an N legacy. We show that in the 40-year-long observation period, the catchment (270 km2) with 60 % agricultural area received an N input of 53 437 t, while it exported 6592 t, indicating an overall retention of 88 %. Removal of N by denitrification could not sufficiently explain this imbalance. Log-normal travel time distributions (TTDs) that link the N input history to the riverine export differed seasonally, with modes spanning 7–22 years and the mean TTs being systematically shorter during the high-flow season as compared to low-flow conditions. Systematic shifts in the C–Q relationships were noticed over time that could be attributed to strong changes in N inputs resulting from agricultural intensification before 1989, the break-down of East German agriculture after 1989 and the seasonal differences in TTs. A chemostatic export regime of nitrate was only found after several years of stabilized N inputs. The changes in C–Q relationships suggest a dominance of the hydrological N legacy over the biogeochemical N fixation in the soils, as we expected to observe a stronger and even increasing dampening of the riverine N concentrations after sustained high N inputs. Our analyses reveal an imbalance between N input and output, long time-lags and a lack of significant denitrification in the catchment. All these suggest that catchment management needs to address both a longer-term reduction of N inputs and shorter-term mitigation of today's high N loads. The latter may be covered by interventions triggering denitrification, such as hedgerows around agricultural fields, riparian buffers zones or constructed wetlands. Further joint analyses of N budgets and TTs covering a higher variety of catchments will provide a deeper insight into N trajectories and their controlling parameters.