Global Scheme Research Articles

An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system

Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.

Trophic state assessment of optically diverse lakes using Sentinel-3-derived trophic level index

• The TLI retrieval algorithm with a global OWT classification scheme can achieve higher accuracy. • Using S owt > 0.9 to group training data for each OWT can help build more robust algorithms. • The eutrophic of lakes in Wuhan haven’t decreased during the COVID-19 lockdown period. An accurate estimation of trophic state of lakes with satellite remote sensing is a challenge due to the optical complexity and variability associated with inland waters. Match-up data from 393 sampling stations that has concurrent Sentinel-3 OLCI images were acquired across Wuhan lakes. Trophic Level Index (TLI) algorithms were developed within a global Optical Water Type (OWT) classification system. The performance of algorithms with limited training data gathered by using spectral similarity of highest S owt was not improved compared with that on basis of no classification. In contrast, using spectral similarity of S owt > 0.9 rather than the highest S owt to group more training data with similar traits for each OWT can help build more robust algorithms, which performance is better than that on basis of no classification. Algorithm performance statistics of the test dataset for the stepwise multiple linear regression (SMLR) method were the following: Mean Absolute Error (MAE) = 5.56; Mean Absolute Percentage Error (MAPE) = 11.02 %; Root Mean Square Error (RMSE) = 7.24 and for the back propagation neural network on the basis of the Levenberg-Marquardt-Bayesian regularization algorithm (LMBR-BPNN) method MAE = 4.56; MAPE = 8.33 %; RMSE = 5.98. We detected 8 different OWTs (2,3,4,5,9,10,11,12) in Wuhan lakes and clear spatio-temporal patterns of the trophic state between 2018 and 2020.Our results revealed that the trophic state of Wuhan lakes did not decrease as expected during the COVID-19 lockdown period.

An Overview of Environment-Friendly Refrigerants for Domestic Air Conditioning Applications

Low global warming potential (GWP) refrigerants for the next-generation air conditioning systems have been investigated with target domestic applications. High-GWP refrigerants are mostly used in climate control applications such as heating, ventilation and air conditioning (HVAC) and refrigeration systems. The majority of refrigerants are responsible for significant environmental issues such as ozone layer depletion and global warming. The Montreal Protocol and the Kyoto Protocol have been implemented to address such issues. In the meantime, authorities in many countries have taken the initiative to phase out the usage of environmentally harmful refrigerants in vapor compression refrigeration systems. Following the global warming mitigation scheme by many signatory countries, research interest has been focused on finding alternative refrigerants with low or ultra-low GWP. This study considered the research trend and development of low-GWP refrigerants while examining system performance, safety issues, and the equivalent environmental impact as the critical assessment parameters. Here, the focus is primarily set on the potential of refrigerant blends (HFCs + HFOs) where the GWP value of 300 is set as the threshold value. Targeted for domestic heat pump systems, the performance of such systems using various refrigerant blends is collated and discussed. Many blends offer innovative drop-in replacements for R410A-conforming F-gas regulations. The technical difficulties and realistic remedies for the existing refrigerants are also discussed.

Modeling and Active Learning for Experiments with Quantitative-Sequence Factors

A new type of experiment that aims to determine the optimal quantities of a sequence of factors is eliciting considerable attention in medical science, bioengineering, and many other disciplines. Such studies require the simultaneous optimization of both quantities and sequence orders of several components which are called quantitative-sequence (QS) factors. Given the large and semi-discrete solution spaces in such experiments, efficiently identifying optimal or near-optimal solutions by using a small number of experimental trials is a nontrivial task. To address this challenge, we propose a novel active learning approach, called QS-learning, to enable effective modeling and efficient optimization for experiments with QS factors. QS-learning consists of three parts: a novel mapping-based additive Gaussian process (MaGP) model, an efficient global optimization scheme (QS-EGO), and a new class of optimal designs (QS-design). The theoretical properties of the proposed method are investigated, and optimization techniques using analytical gradients are developed. The performance of the proposed method is demonstrated via a real drug experiment on lymphoma treatment and several simulation studies. Supplementary materials for this article are available online.

A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method

In order to improve the continuity and smoothness of transpolar flight and optimize autonomous navigation performance, a SINS/SRS/CNS (strapdown inertial navigation system/spectral redshift navigation system/celestial navigation system) multi-information fusion global autonomous navigation method based on parameter conversion was studied for this article. The global autonomous navigation scheme based on multi-information fusion was designed. The principle of spectral redshift navigation was studied. On this basis, the system equations of the SINS/SRS/CNS multi-information fusion global autonomous navigation system were established in the middle–low latitudes and high latitudes. Furthermore, the navigation and filter parameter conversion relationships between the geographic navigation coordinate frame and the grid navigation coordinate frame were derived. The simulation and experiment verified that the SINS/SRS/CNS multi-information fusion global autonomous navigation method with parameter conversion can effectively improve the accuracy and smoothness and realize non-oscillation switching in transpolar navigation. In the vehicle experiment, the proposed algorithm improved the horizontal position accuracy by more than 29% compared with the multi-information fusion global autonomous navigation method without filter parameter conversion.

On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses

Background: Time series analyses on the relationship between nurse staffing and inpatient care quality are rare due to inconsistent frequencies of data between common observations of nurse-staffing (e.g., monthly) and inpatient care quality indicators (e.g., quarterly). Methods: In order to deal with the issue of mixed frequency data, this research adopted the MF-VAR model to explore causal relationships among nurse staffing, inpatient care quality, and hospital competition under the global budget payment scheme of Taiwan’s healthcare system. Results: Our results identified bi-directional causation between nurse staffing and patient outcomes and one-way Granger causality running between nurse staffing and reimbursement payments for inpatient care services. Impulse-response analyses found positive (negative) effects of the patient-to-nurse ratio on adverse patient outcomes (reimbursement payments) in all types of hospitals and detrimental effects of adverse patient outcomes on the patient-to-nurse ratio in medical centers and regional hospitals across a 12-month period. Conclusions: These findings suggest that nurse staffing is an essential determinant of both patient outcomes and reimbursement payments. Strategic policies such as direct subsidy and hospital accreditation for appropriate nurse staffing levels should be implemented for medical centers and regional hospitals to mitigate the harmful effects of adverse patient outcomes on nurse staffing.

Large-Scale Transit Network Optimization by Minimizing User Cost and Transfers

This article proposes a methodology for developing optimal transit networks (route structures and headways) that minimizes transit transfers and total user cost while maximizing service coverage, given information on transit demand, transit fleet size, and the street network of the transit service area. The research provides an effective mathematical computational tool with minimal reliance on heuristics. The methodology includes representation of transit route networks and solution search spaces, objective functions representing total user cost and unwillingness of users to make transfers, and a global search scheme based on simulated annealing. The methodology has been tested with published solutions to benchmark problems and has been applied to a large-scale realistic network optimization problem in Miami-Dade County, Florida.

Uncovering Molecular Structure – Redox Potential Relationships for Organic Electrode Materials: A Hybrid DFT – Machine Learning Approach

Density functional theory and machine learning are used to investigate the structure-electrochemical performance relationships of organic moieties for use in Li-ion batteries. Namely, DFT calculations are performed to predict the redox potential of several novel organic molecules with an accuracy within ~0.1 V of experimental measurements. However, despite its ability to provide valuable insight regarding the electrochemical properties of novel organic molecules, our high efficacy DFT modeling protocol demands significant computational time and is therefore impractical for the vast screening of novel material candidate. As a result, we explore machine learning as a strategy for the accelerated discovery of novel organic materials. More critically, we use machine learning as a method for assessing the various structure-electrochemical relationships which can provide a more general guideline for the design of organic electrode materials. We are employing different learning models, including artificial neural networks, gradient-boosting regression, and kernel methods (such as kernel ridge regression and Gaussian process regression), via three different pipelines with varying sophistication with the aim of generating an advanced ML scheme for the accurate prediction and analysis of electrochemical activity. In addition to incorporating structural fingerprints, we are exploring an active learning framework, namely using the efficient global optimization scheme, to explore the materials space strategically using publicly available datasets. Through this approach, we can discover new electrode materials that could 1) have a higher probability for achieving enhanced electrochemical properties and 2) increase our learning model’s performance by increasing our dataset’s representation of the material space. Additionally, we are implementing a high-throughput virtual screening (HTVS) pipeline which consists of several surrogate learning models with increasing levels of fidelity. The material candidates are pruned at each stage to discard samples that are unlikely to possess a desirable redox potential for cathodic application, and the remaining samples move on to the next model. In this way, only a subset of the original dataset which has a higher probability for lying within the desired redox potential range is pursued using the most computationally expensive model.

Sharing network features analysis and dispatching strategy design

PurposeA vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct a highly efficient dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network characteristics.Design/methodology/approachTo that end, in this paper, the profit-changing process of single sharing points is modeled and analyzed first. And then, the characteristics of the whole sharing network are investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named the Two-step random forest reinforcement learning algorithm) is proposed.FindingsThe authors discover that the sharing points of a common sharing network could be categorized into 6 types according to their profit dynamics; a sharing network that is made up of various combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the characteristics, a specific method for guiding the dynamic dispatch of shared products is developed and validated.Originality/valueBecause the suggested method considers the interaction features between sharing points in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.

A WAVEWATCH III® model approach to investigating ocean wave source terms for West Africa: Non-linear wave-wave interaction source terms

• Investigation of ocean wave model parameterization schemes for West Africa. • Selection of appropriate non-linear source terms for ocean wave projections. • DIA term best estimates wave heights for sub-sections of West Africa. • Application of no non-linear term best estimates wave heights for the entire region. Ocean wave forecasting for West Africa has mostly relied on global parameterization schemes and data outputs. Investigating non-linear ocean wave-wave interaction source terms ( S nl ) for the region, that forms part of the wave model parameterization schemes, is a contribution of this study towards developing a regional wave modelling scheme for West Africa. The study evaluates five non-linear source term configurations with the WAVEWATCH III® (WW3) numerical ocean wave model version 5.16; (i) No source term applied - NL0, (ii) Discrete Interaction Approximation (DIA) – NL1, (iii) Exact Interaction Approximation – NL2, (iv) Generalized Multiple DIA – NL3, and (v) Two-Scale Approximation – NL4, in order to determine best performing S nl for projecting significant wave heights and directions for the region. The wave simulations were run on three separate grids comprising a low resolution West Africa grid (latitudes 10 o S – 30 o N; longitudes 35 o W – 15 o E), high resolution south-eastern (latitudes 2 o S – 8 o N; longitudes 10 o W – 10 o E) and north-western (latitudes 10 o N – 25 o N; longitudes 30 o W – 10 o W) sub-grids. Simulations for the entire West Africa grid produced higher accuracies for the wave parameters compared to the higher resolution sub-grids. NL0 best estimates significant wave height whereas NL3 best estimates wave directions for the West Africa grid. For combined stations of the south-eastern sub-grid, while NL1 best estimates wave heights, NL3 best estimates the wave directions with possible alternation with NL1 due to insignificant differences. Similarly, for the north-western sub-grid, while NL1 best estimates significant wave heights, NL0 best estimates wave directions with similar ability to alternate with NL2 in projecting wave directions. Generally, insignificant differences exist between the source terms in projecting wave directions, especially for the north-western sub-region, implying that any of the source terms may be used in projecting wave directions without significantly compromising on accuracy. Thus, NL1 which best estimates significant wave heights for both south-eastern and north-western sub-regions can also be used in estimating the wave directions. Similarly, NL0 would be appropriate for the entire West Africa region.

Protecting local and global symmetries in simulating (1+1)D non-Abelian gauge theories

Efficient quantum simulation protocols for any quantum theories demand efficient protection protocols for its underlying symmetries. This task is nontrivial for gauge theories as it is involves local symmetry/invariance. For non-Abelian gauge theories, protecting all the symmetries generated by a set of mutually non-commuting generators, is particularly difficult. In this letter, a global symmetry-protection protocol is proposed. Using the novel loop-string-hadron formalism of non-Abelian lattice gauge theory, we numerically demonstrate that all of the local symmetries get protected even for large time by this global symmetry protection scheme. With suitable protection strength, the dynamics of a (1+1)-dimensional SU(2) lattice gauge theory remains confined in the physical Hilbert space of the theory even in presence of explicit local symmetry violating terms in the Hamiltonian that may occur in both analog and digital simulation schemes as an error. The whole scheme holds for SU(3) gauge theory as well.

Adaptive optics in the Arctic? A commentary on Fosbury and Jeffery.

Adaptive optics in the Arctic? A commentary on Fosbury and Jeffery.

Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling

The application of robots is growing in most countries, occupying a relevant place in everyday environments. Robots are still affected by errors due to their limitations, which may compromise the final performance. Accurate trajectories and positionings are strict requirements that robots have to satisfy and may be studied by the inverse kinematic (IK) formulation. The IK conventional numerical techniques are computationally intensive procedures, focusing on the robot joint values simultaneously and increasing the complexity of the solution identification. In this scenario, Machine Learning strategies may be adopted to achieve effective and robust manipulator’s IK formulation due to their computational efficiency and learning ability. This work proposes a machine learning (ML) sequential methodology for robot inverse kinematics modeling, iterating the model prediction at each joint. The method implements an automatic Denavit-Hartenberg (D-H) parameters formulation code to obtain the forward kinematic (FK) equations required to produce the robot dataset. Moreover, the artificial neural network (ANN) architecture is selected as a structure and the number of layers in combination with the hidden neurons per layer is defined by an offline optimization phase based on the genetic algorithm (GA) technique for each joint. The ANN is implemented with the following settings: scaled conjugate gradient as training function and the mean squared error as the loss function. Different network architectures are examined to validate the IK procedure, ranging from global to sequential and considering the computation direction (from end-effector or from basement). The method is validated in the simulated and experimental laboratory environment, considering articulated robots. The sequential method exhibits a reduction of the mean squared error index of 42.7–56.7%, compared to the global scheme. Results show the outstanding performance of the IK model in robot joint space prediction, with a residual mean absolute error of 0.370–0.699 mm in trajectory following 150.0–200.0 mm paths applied to a real robot.

Experimental evidences of flexural to shear to crushing failure mode transition in reinforced concrete beams without stirrups

The results of a series of experimental bending tests carried out on reinforced concrete members without transverse reinforcement are reported and illustrated as a contribution to the study of the flexural to shear to crushing failure mode transition. A total of 45 beams were tested to investigate the effects of a wide range of longitudinal reinforcement amounts – 0.25% to 3.39% –, beam depths – 5 to 80 cm – and beam slendernesses – 3 to 24. The analysis of the experimental data encompasses the identification of the overall mechanical response, the assessment of the ultimate load and the detection of the critical crack that leads to the final collapse of the beam. The results are interpreted with reference to a global failure mode transition scheme, which allows to analyze all the failure modes within the same framework. As a result, an effective and synthetic prediction of the failure mode transition is achieved on the basis of dimensionless numbers, namely NP, NC and λl, defined in the framework of linear elastic fracture mechanics. Besides, strengths and weaknesses of the provisions of some codes of practice are highlighted on the basis of the experimental results. In this context, some drawbacks are reported for what concerns the inclusion of the size effect in the models provided to assess the shear capacity of reinforced concrete beams.

Land Suitability Analysis for Potential Vineyards Extension in Afghanistan at Regional Scale Using Remote Sensing Datasets

Grapes are one of the world’s most widely distributed crops and are cultivated in more than 100 countries in the global scheme. Due to climate change and improper vine growth variable selection, production has significantly decreased across countries. Therefore, the primary purpose of this study was to develop a land suitability analysis method using a fuzzy expert system at a regional scale. The fuzzy membership function was used in the ArcGIS® environment to perform the spatial analysis, and the overlay function was used to generate the final suitability map for Afghanistan considering policy planning. The results indicated that 23% (15,760,144 ha) of the areas were potential and located in the highly suitable region for grape production; however, 11% (7,370,025 ha) of the regions were not suitable for vineyards throughout the country of Afghanistan. In the present study, it was observed that most of the vineyards were in highly suitable areas (90%, 80,466 ha), while 0.01% (5 ha) of the vineyards were in less suitable areas. The present analysis demonstrated that the significant extension of grape vines can be possible in highly suitable areas. The results of this research can support decision-makers, farm managers and land developers to find more prospective acreage for expanding vineyards in Afghanistan.

Artificial-Neural-Network-Assisted Sensor Clustering for Robust Communication Network in IoT-Based Electricity Transmission Line Monitoring

As an Internet of Things (IoT) application in the smart grid, dynamic thermal rating (DTR) requires real-time measurement of conductor temperature from field sensors that are installed at remote transmission lines. For such IoT-based transmission line monitoring, we need a robust communication network. This article proposes to group sensors with similar measurement characteristics into a cluster such that each member can accurately represent the entire cluster. Then, communication robustness can be achieved by establishing multiple routes to the control center, where each route may originate from a different member in the cluster. First, we formulate a global constrained optimization for sensor clustering. We use the optimization solutions to train a set of artificial neural networks (ANNs), where each ANN is deployed to make a local decision at one sensor. Specifically, we propose the novel usage of ANN as a tool to transfer knowledge from a global decision maker to a set of local decision makers. Evaluation results show that there remains a noticeable knowledge gap between global and local decision makers, where in average, the global decision maker and an ANN make an identical decision in only about 70% of the times. Despite this less than ideal success probability, the number of clusters produced by the ANN-assisted local scheme is only 1.9 more than that by the global scheme.

Optimal Resource Allocation of 5G Machine-Type Communications for Situation Awareness in Active Distribution Networks

Advanced metering infrastructure targets the real-time collection of massive multisource data with smart meters (SMs), forming a key component of active distribution networks (ADNs). To achieve comprehensive situation awareness for ADNs, the use of high-speed and reliable two-way communications, such as 5G communications, is the basis. However, it is challenging to optimally allocate wireless resources for massive data, especially when emergency events occur randomly. This study discusses an energy-efficient and optimal wireless resource allocation method employing 5G uplink-based machine-type communications for achieving ADN situation awareness. We propose a 5G-based framework that can reasonably reserve resource blocks for emergency data and model the resource allocation scheme as a problem of maximizing energy efficiency (EE). Then, by using the sample average approximation theory, we transform the stochastic constraint and summarize the scheme as a mixed-integer linear programming problem. Finally, we construct an iterative algorithm based on the Lagrange dual algorithm for searching the global optimal allocation scheme. The results of simulations and experiments illustrate that even if emergency events occur randomly, our algorithm can optimally support the cooperative transmission of all sampled data while providing the best EE for large-scale SM communications.

Coordinated optimal design of school building envelope and energy system

In the design of school buildings, it is necessary to trade off the effects of energy, thermal comfort, daylighting, and life cycle cost. In this paper, a multi-objective coordinated design optimization method of school buildings is proposed to realize the efficient global design optimization of school building envelopes and energy systems. The method is mainly divided into three modules: coordinated design variables, envelope optimization and energy system optimization. In the process of coordinated design optimization, through the iterative method based on multi-objective optimization of envelope, multi-attribute decision-making for Pareto scheme set and single-objective optimization of energy system, the error between the optimal values of the coordinated design variable of the envelop and the energy system is reduced, and finally the global optimal design scheme is obtained. The effectiveness of the proposed optimization method is verified by a case study. The results show that the proposed coordinated design optimization method is stable and reliable. Under the policy of selling electricity, the life cycle cost of the building energy system is -$665,459.50 and the payback period is 6.12 years. Under the policy of not selling electricity, the life cycle cost of the building energy system is $75,292.50, which is 2.75% lower than the traditional multi-stage optimization.

Multiple price bubbles in global major emission trading schemes: Evidence from European Union, New Zealand, South Korea and China

Emission trading scheme (ETS) is a flagship climate governance tool to achieve social minimum cost emission reduction target through market mechanism. If price bubble exists in the ETS, the achievement of emission reduction target and the resolution of market risks will be negatively impacted. This paper is one of the few, if not the first, to examine whether price bubble exists in fluctuating carbon prices in the global ETSs. In this study, supremum augmented Dickey–Fuller (SADF) and generalised SADF (GSADF) are adopted to statistically test the existence of price bubbles from the opening date to August 31, 2019 in the four ETSs in European Union, New Zealand, South Korea and Shenzhen of China. The time, frequency, severity and reasons of the emergence and termination of price bubbles in each carbon market are also analysed in detail. Results show that, 1) From the perspective of duration, significant differences exist in the duration of price bubbles amongst the four carbon markets. The price bubbles of European Union (EU), New Zealand and South Korea ETSs last for a long time, and most of the price bubbles last for >5 days. The price bubbles in Shenzhen ETS last for a short time, and the duration of all price bubbles does not exceed 3 days. 2) In terms of quantity, 8, 9, 3 and 4 price bubbles are detected in the four carbon markets in EU, New Zealand, South Korea and Shenzhen of China, respectively. 3) With regard to severity, price bubbles in EU ETS are the most serious. The main reason is that the industries being covered are the widest and the participating countries are the most in EU ETS, so EU ETS is easily impacted by industrial changes and policy adjustments in various countries. 4) The problems on price bubbles in the four ETSs are mainly closely related to macro economy, new and renewable energy, energy price and carbon market-related policies.

Effects of a global budget payment scheme on medical specialty workforces

Containing rising health care costs by a global budget offers promise, but might have unintended effects on the composition of medical specialty workforces. When a budget is given to a group of medical providers and the physician/population ratio is growing, the ex-post total sum of fee units would exceed the ex-ante budget, thereby reducing the reimbursement for all services for all providers. Furthermore, the profit of high revenue services drops more than the profit of low revenue services. Thus, medical school graduates are less likely to choose a specialty with high revenue in response to the global budget scheme and physicians may leave labor force earlier because of lowered profit. The author provides empirical evidence by a synthetic control method that the number of surgeons in Taiwan has exhibited a decrease of 25% after 11 years of Taiwan's global budget scheme. During that same period of time, the total number of practicing physicians decreased only 13%.