scholarly journals Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data

2019 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Big Data ◽  
Forecasting Model ◽  
Short Term ◽  
Port Development ◽  
Forecasting Accuracy ◽  
Port Throughput ◽  
Infrastructure Investments ◽  
The Cost ◽  
Modelling And Forecasting

Modelling and forecasting port throughput enables stakeholders to make efficient decisions ranging from management of port development, to infrastructure investments, operational restructuring and tariffs policy. Accurate forecasting of port throughput is also critical for long-term resource allocation and short-term strategic planning. In turn, efficient decision-making enhances the competitiveness of a port. However, in the era of big data we are faced with the enviable dilemma of having too much information. We pose the question: is more information always better for forecasting? We suggest that more information comes at the cost of more parameters of the forecasting model that need to be estimated. We comparemultiple forecasting models of varying degrees of complexity and quantify the effect of the amount of data on model forecasting accuracy. Our methodology serves as a guideline for practitioners in this field. We also enjoin caution that even in the era of big data more information may not always be better. It would be advisable for analysts to weigh the costs of adding more data: the ultimate decision would depend on the problem, amount of data and the kind of models being used.

scholarly journals Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data

2019 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Big Data ◽  
Forecasting Model ◽  
Short Term ◽  
Port Development ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Port Throughput ◽  
Infrastructure Investments ◽  
The Cost ◽  
Modelling And Forecasting

Modelling and forecasting port throughput enables stakeholders to make efficient decisions ranging from management of port development, to infrastructure investments, operational restructuring and tariffs policy. Accurate forecasting of port throughput is also critical forlong-term resource allocation and short-term strategic planning. In turn, efficient decision making enhances the competitiveness of a port. However, in the era of big data we are faced with the enviable dilemma of having too much information. We pose the question: is more information always better for forecasting? We suggest that more information comes at the cost of more parameters of the forecasting model that need to be estimated. We comparemultiple forecasting models of varying degrees of complexity and quantify the effect of the amount of data on model forecasting accuracy. Our methodology serves as a guideline for practitioners in this field. We also enjoin caution that even in the era of big data more information may not always be better. It would be advisable for analysts to weigh the costsof adding more data: the ultimate decision would depend on the problem, amount of data and the kind of models being used.

scholarly journals Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 10 ◽  
Author(s):  
Rabiya Khalid ◽  
Nadeem Javaid ◽  
Fahad A. Al-zahrani ◽  
Khursheed Aurangzeb ◽  
Emad-ul-Haq Qazi ◽  
...  
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Proposed Model ◽  
Multiple Variables ◽  
Long Short Term Memory

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

scholarly journals Methods to Prevent Future Severe Animal Welfare Problems Caused by COVID-19 in the Pork Industry

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 830
Author(s):  
Temple Grandin
Keyword(s):  
Animal Welfare ◽  
Detrimental Effect ◽  
Third Party ◽  
Electrical Parameters ◽  
Short Term ◽  
Pork Industry ◽  
Third Party Auditing ◽  
The Cost ◽  
High Welfare

In the U.S., the most severe animal welfare problems caused by COViD-19 were in the pork industry. Thousands of pigs had to be destroyed on the farm due to reduced slaughter capacity caused by ill workers. In the future, both short-term and long-term remedies will be needed. In the short-term, a portable electrocution unit that uses scientifically validated electrical parameters for inducing instantaneous unconsciousness, would be preferable to some of the poor killing methods. A second alternative would be converting the slaughter houses to carcass production. This would require fewer people to process the same number of pigs. The pandemic revealed the fragility of large centralized supply chains. A more distributed supply chain with smaller abattoirs would be more robust and less prone to disruption, but the cost of pork would be greater. Small abattoirs can coexist with large slaughter facilities if they process pigs for specialized premium markets such as high welfare pork. The pandemic also had a detrimental effect on animal welfare inspection and third party auditing programs run by large meat buyers. Most in-person audits in the slaughter plants were cancelled and audits were done by video. Video audits should never completely replace in-person audits.

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

What money can’t buy: an argument against paying people to get vaccinated

2021 ◽  
pp. medethics-2021-107235
Author(s):  
Nancy S Jecker
Keyword(s):  
Best Practices ◽  
Economic Conditions ◽  
Vaccine Hesitancy ◽  
Short Term ◽  
Healthy Communities ◽  
Good Citizenship ◽  
Mutant Strains ◽  
The Cost ◽  
Better Than

This paper considers the proposal to pay people to get vaccinated against the SARS-CoV-2 virus. The first section introduces arguments against the proposal, including less intrusive alternatives, unequal effects on populations and economic conditions that render payment more difficult to refuse. The second section considers arguments favouring payment, including arguments appealing to health equity, consistency, being worth the cost, respect for autonomy, good citizenship, the ends justifying the means and the threat of mutant strains. The third section spotlights long-term and short-term best practices that can build trust and reduce ‘vaccine hesitancy’ better than payment. The paper concludes that people who, for a variety of reasons, are reluctant to vaccinate should be treated like adults, not children. Despite the urgency of getting shots into arms, we should set our sights on the long-term goals of strong relationships and healthy communities.

scholarly journals Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 843 ◽  
Author(s):  
Keke Wang ◽  
Dongxiao Niu ◽  
Lijie Sun ◽  
Hao Zhen ◽  
Jian Liu ◽  
...  
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Harmony Search ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Correlation Degree ◽  
Learning Machine ◽  
Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

US corporate tax reform would risk highway fund

2015 ◽  
Keyword(s):  
House Of Representatives ◽  
Tax Reform ◽  
Fuel Efficiency ◽  
Corporate Tax ◽  
Short Term ◽  
Content Type ◽  
Long Duration ◽  
Corporate Tax Reform ◽  
The Cost

Significance On July 15, the House of Representatives passed a short-term funding measure, against the wishes of many in the Senate. US infrastructure is facing a fiscal crunch. Taxes on gasoline have traditionally supported highway appropriations. However, eroding purchasing power and greater fuel efficiency means that about 30% of highway funding must be found from other sources, difficult in the current Congress. The present round of appropriations expires on July 31. Impacts A corporate tax might provide a long-term resolution, but the pursuit of it would come at the cost of seeking more modest solutions. These would provide stability for a year or two, necessary for projects of long duration. If corporate tax reform is not completed before the end of 2015, it will probably not get done in a presidential election year. If Congress were to rely on the prospect of these taxes for the HTF, it might find itself in a similar position in a few months.

Up, Up and Away! The Economics of Vertical Farming

2014 ◽  
Vol 2 (1) ◽  
pp. 40 ◽  
Author(s):  
Chirantan Banerjee ◽  
Lucie Adenaeuer
Keyword(s):  
Concurrent Engineering ◽  
Fruits And Vegetables ◽  
Consumer Preferences ◽  
Economic Feasibility ◽  
Market Potential ◽  
Short Term ◽  
Vertical Farming ◽  
Footprint Area ◽  
The Cost

With rising population and purchasing power, demand for food and changing consumer preferences are building pressure on our resources. Vertical Farming, which means growing food in skyscrapers, might help to solve many of these problems. The purpose of this study was to construct a Vertical Farm and thereof investigate the economic feasibility of it. In a concurrent Engineering Study initiated by DLR Bremen, a farm, 37 floors high, was designed and simulated in Berlin to estimate the cost of production and market potential of this technology. It yields about 3,500 tons of fruits and vegetables and ca. 140 tons of tilapia fillets, 516 times more than expected from a footprint area of 0.25 ha due to stacking and multiple harvests. The investment costs add up to € 200 million, and it requires 80 million litres of water and 3.5 GWh of power per year. The produced food costs between € 3.50 and € 4.00 per kilogram. In view of its feasibility, we estimate a market for about 50 farms in the short term and almost 3000 farms in the long term. To tap the economic, environmental and social benefits of this technology, extensive research is required to optimise the production process.

scholarly journals OPTIMAL CLIMATE POLICY IN 3D: MITIGATION, CARBON REMOVAL, AND SOLAR GEOENGINEERING

2021 ◽  
pp. 2150008
Author(s):  
MARIIA BELAIA ◽  
JUAN B. MORENO-CRUZ ◽  
DAVID W. KEITH
Keyword(s):  
Climate Model ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Short Term ◽  
Trade Offs ◽  
Solar Geoengineering ◽  
Optimal Climate Policy ◽  
The Cost ◽  
Far Future

We introduce solar geoengineering (SG) and carbon dioxide removal (CDR) into an integrated assessment model to analyze the trade-offs between mitigation, SG, and CDR. We propose a novel empirical parameterization of SG that disentangles its efficacy, calibrated with climate model results, from its direct impacts. We use a simple parameterization of CDR that decouples it from the scale of baseline emissions. We find that (a) SG optimally delays mitigation and lowers the use of CDR, which is distinct from moral hazard; (b) SG is deployed prior to CDR while CDR drives the phasing out of SG in the far future; (c) SG deployment in the short term is relatively independent of discounting and of the long-term trade-off between SG and CDR over time; (d) small amounts of SG sharply reduce the cost of meeting a [Formula: see text]C target and the costs of climate change, even with a conservative calibration for the efficacy of SG.

scholarly journals Faktor yang Memengaruhi Pemilihan Metode Kontrasepsi

2017 ◽  
Vol 5 (2) ◽  
pp. 91
Author(s):  
Rendys Septalia ◽  
Nunik Puspitasari
Keyword(s):  
Mental Health ◽  
Population Growth ◽  
Contraceptive Use ◽  
Drop Out ◽  
Contraceptive Methods ◽  
Short Term ◽  
Contraceptive Usage ◽  
Material Costs ◽  
The Cost

Contraception was the most effective way to control the population growth. The most widely favored in Indonesia was a short-term contraceptive methods. High attainment acceptor on short-term contraceptive methods because short-term contraceptive methods was a methods contraception affordable, while the fees for the long-term contraceptive methods was more expensive. The incidence of injectable contraceptives and pills drop-out was higher than the long-term contraceptive methods that contributed to the failure of population growth control program. This study to analyze the factors that affect the selection contraceptive methods. This study was an observational study with cross sectional design. Sampling with systematic random and obtained were 79 acceptors. The independent variables were the cost of contraceptive use, non-material costs (experience side effects), cultural obstacle, social adjustments obstacle, physic and mental health obstacle, and accessibility obstacle. Data collected using the questionnaire and analyse by multiple logistic regression. The results showed that the significant factor were the cost of contraceptive usage (pvalue = 0.002), the cost of non-material (experience side eff ects) (pvalue = 0.007), and factors that didn’t have signifi cant influence were cultural obstacle (pvalue = 0.105), social adjustments obstacle (pvalue = 0.999), physic and mental health obstacle (pvalue = 0.920), and accessibility obstacle (pvalue = 0.438). The conclusion were the cost of contraceptive use and non-material costs (experience side eff ects) aff ected the selection of contraception. It was need the cooperation between religious leaders, community leaders, and health care workers in a common understanding on the cost of contraceptive usage.

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