An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a ...lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets and three real datasets to derive the criteria for sample size. We systematically increase the sample size, starting from 16, by randomly sampling and examine the impact of sample size on classifiers' performance and both effect sizes. Tenfold cross-validation was used to quantify the accuracy.
The results demonstrate that the effect sizes and the classification accuracies increase while the variances in effect sizes shrink with the increment of samples when the datasets have a good discriminative power between two classes. By contrast, indeterminate datasets had poor effect sizes and classification accuracies, which did not improve by increasing sample size in both simulated and real datasets. A good dataset exhibited a significant difference in average and grand effect sizes. We derived two criteria based on the above findings to assess a decided sample size by combining the effect size and the ML accuracy. The sample size is considered suitable when it has appropriate effect sizes (≥ 0.5) and ML accuracy (≥ 80%). After an appropriate sample size, the increment in samples will not benefit as it will not significantly change the effect size and accuracy, thereby resulting in a good cost-benefit ratio.
We believe that these practical criteria can be used as a reference for both the authors and editors to evaluate whether the selected sample size is adequate for a study.
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This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting ...model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States.
Aim
Reef fishes are commonly recognized as sentinels of the ongoing tropicalization in biogeographic transition zones between temperate and tropical areas. Despite the reliance of these marine ...ectotherms on the benthos, the importance of benthic habitat has rarely been considered as a factor constraining fish distribution. Therefore, our study aims at examining the consequences of both temperature and benthic variations on the fish fauna and diagnosing potential sentinels of these environmental changes.
Location
Taiwan, West Pacific.
Taxon
Teleostei (184 species).
Methods
We examined how the partitioning of habitats can influence the specialization of fish fauna along a latitudinal gradient. We diagnosed ‘specialist’ and ‘generalist’ fishes in this partitioning. For each specialist, we further evaluated whether its distribution is constrained by temperature, benthic habitat or both factors combined. The change in sea surface temperature over the last three decades was also monitored.
Results
Fish fauna showed the highest specialization when tropical and subtropical partitions of habitat were considered. Fifty‐one tropical specialists, 7 subtropical specialists and 21 possible generalists were identified. Among specialists, 13 species were associated with temperature, 19 with habitat and 26 with both factors. Steady warming occurred across latitudes, but was accentuated in the winter of subtropical habitat.
Main Conclusions
Our results suggested that the distribution of some specialist fishes was constrained only by temperature while the distribution of some others also depended on the availability of benthic habitats. Consequently, under global warming, the distribution of some specialists might shift in a manner that follows the movement of isotherms, while the distribution of others might also be conditioned by the poleward shifts of benthos. A temporal mismatch between the emergence of suitable thermal environments and the arrival of some specialists may exist. Therefore, the tropicalization of high‐latitude areas may be characterized by different waves of colonization.
目的
岩礁魚類常被認為是熱至溫帶這個生物地理過渡帶發生熱帶化時的指標類群。儘管這些魚類對底棲生物存在著依賴性，底棲棲地卻鮮少被當作是限制魚類分佈的重要因子之一。因此，本研究旨在探討溫度及底棲棲地的變化對該區魚類相的影響，並找出能作為對應這些環境變化的潛在指標性魚種。
地點
臺灣，西太平洋。
分類群
真骨魚 (184種)。
方法
沿著過渡帶的緯度梯度，我們檢試了底棲棲地的分群如何影響岩礁魚類群聚的專一性。我們試圖找出對此分群的專化與廣適魚種。針對這些專化魚種，我們進一步地評估其分佈和溫度、底棲棲地、或是兩者同時的關聯性。我們亦監測過去30年間海水表面溫度的變化。
結果
魚類群聚在底棲棲地被分成熱帶與亞熱帶兩群時有最高的專一性。我們一共找出了51種熱帶、7種亞熱帶專化魚類及21種潛在的廣適魚類。在這些專化種中，13種和溫度有關連， 19種和底棲棲地有關聯，而另外的26種則同時和此兩因子有關聯。穩定的暖化現象在各緯度都有出現，且在亞熱帶棲地的冬天尤其明顯。
主要結論
我們的結果顯示某些專化魚種的分佈僅受溫度限制，而另一些專化魚種的分布亦同時取決於底棲棲地的可得性。由此可知，在全球暖化下，某些專化魚種的分佈有可能會隨著等溫線的移動而發生改變，而其他專化魚種的分佈變化則可能同時受到底棲棲地極向移動的制約。適宜溫度環境之出現以及某些專化魚種的到來可能存在著時間上的不同。因此，高緯度地區的熱帶化有可能會出現魚種分批到來的特徵。
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Plug-in hybrid electric vehicle (PHEV) charging station is playing a critical role in the rapid development of PHEVs. The unique characteristics of charging demands provide flexibility for the ...resource planning of PHEV charging station, while its internal generation resources and procurement decisions from utility grid offer various options to meet the charging demand. To achieve the maximum benefits while managing the associated risk, the operator of PHEV charging station should optimally schedule those resources on both supply and demand sides. In this paper, a stochastic resource-planning scheme for PHEV charging stations is proposed, while two types of PHEV charging loads, including price-responsive commercial charging customers and the contracted controllable charging fleets, are taken into the account. Energy supply decisions on energy procurement in multiple markets and internal generation scheduling are co-optimized with demand-side decisions on charging service pricing and controllable demands allocation. The uncertainties from spot market price and availability of renewable generations are considered in the proposed model. A numerical case study is also provided to illustrate the effectiveness of the proposed scheme.
In recent years, techniques of using system disturbance data to validate generator models have been widely discussed. Dynamic model validation and calibration is becoming one of the important ...applications to smart grid initiative. As this kind of technique is utilized to validate generator model, procedures of data screening and reprocessing are essential because raw data obtained from measurement is not always satisfactory. Regarding model parameter calibration, system model is usually quite complicated with various parameters interacting with each other. Artificial intelligent tool is the prior option to save the laborious tuning process and enhance the parameter accuracy. This paper presents a guideline to validate and calibrate parameters of generating units using the record data from phasor measurement unit. Associated procedures for signal filtering on the measurement data, key parameters screening, intelligent search of model parameters, and cross check of legitimate parameters will be discussed in detail. Finally, two historical disturbance cases that happened in the Taiwan power (Taipower) system are applied in accordance with the proposed guideline to demonstrate its effectiveness on generator parameter validation and calibration.
Load aggregators (LAs) gather regional demand response (DR) resources as a representative of regional users to transact with the grid. They achieve this by facilitating residential customers to ...flexibly participate in DR programs. This article proposes a residential DR strategic bidding approach for LAs to improve system reliability while maximizing their financial advantages. It is based on the deep reinforcement learning (DRL) algorithm and is capable of overcoming the computational limitations of conventional model-based strategy design. A probabilistic model considering the electricity usage habits and response willingness was developed to reliably quantify the uncertainty responsive behaviors. A DR bidding model considering bilateral interaction with declaration risk was developed to balance the contradictory relationship between subsidy prices with uncertain DR quantities. In addition, the soft actor-critic algorithm was implemented to optimize the decision-making process, and thereby, optimize the continuous-action-space problem of maximizing the profits of LAs by DR scheduling. The proposed approach demonstrated its effectiveness and superiority in diverse DR situations, compared with a conventional method and another DRL approach.
The combined effects of anthropogenic and biological CO
inputs may lead to more rapid acidification in coastal waters compared to the open ocean. It is less clear, however, how redox reactions would ...contribute to acidification. Here we report estuarine acidification dynamics based on oxygen, hydrogen sulfide (H
S), pH, dissolved inorganic carbon and total alkalinity data from the Chesapeake Bay, where anthropogenic nutrient inputs have led to eutrophication, hypoxia and anoxia, and low pH. We show that a pH minimum occurs in mid-depths where acids are generated as a result of H
S oxidation in waters mixed upward from the anoxic depths. Our analyses also suggest a large synergistic effect from river-ocean mixing, global and local atmospheric CO
uptake, and CO
and acid production from respiration and other redox reactions. Together they lead to a poor acid buffering capacity, severe acidification and increased carbonate mineral dissolution in the USA's largest estuary.The potential contribution of redox reactions to acidification in coastal waters is unclear. Here, using measurements from the Chesapeake Bay, the authors show that pH minimum occurs at mid-depths where acids are produced via hydrogen sulfide oxidation in waters mixed upward from anoxic depths.
Tetrahymena are ciliated protists that have been used to study the effects of toxic chemicals, including anticancer drugs. In this study, we tested the inhibitory effects of six pyrimidine analogs ...(5-fluorouracil, floxuridine, 5'-deoxy-5-fluorouridine, 5-fluorouridine, gemcitabine, and cytarabine) on wild-type CU428 and conditional mutant NP1 Tetrahymena thermophila at room temperature and the restrictive temperature (37°C) where NP1 does not form the oral apparatus. We found that phagocytosis was not required for pyrimidine analog entry and that all tested pyrimidine analogs inhibited growth except for cytarabine. IC50 values did not significantly differ between CU428 and NP1 for the same analog at either room temperature or 37°C. To investigate the mechanism of inhibition, we used two pyrimidine bases (uracil and thymine) and three nucleosides (uridine, thymidine, and 5-methyluridine) to determine whether the inhibitory effects from the pyrimidine analogs were reversible. We found that the inhibitory effects from 5-fluorouracil could be reversed by uracil and thymine, from floxuridine could be reversed by thymidine, and from 5'-deoxy-5-fluorouridine could be reversed by uracil. None of the tested nucleobases or nucleosides could reverse the inhibitory effects of gemcitabine or 5-fluorouridine. Our results suggest that the five pyrimidine analogs act on different sites to inhibit T. thermophila growth and that nucleobases and nucleosides are metabolized differently in Tetrahymena.
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To systematically review and meta-analyze the associations between sleep disturbances and suicidal ideation, plans, and attempts in adolescents and explore potential moderators of these associations.
...Embase, PubMed, ProQuest, and the China Knowledge Resource Integrated Database were searched from their inception dates to October 19, 2018. We selected cross-sectional, prospective, or retrospective studies without time or language restrictions.
Nine cross-sectional studies, four prospective studies, and one retrospective report that, respectively, involved 37 536, 9295, and 80 adolescents were included in the meta-analysis. Cross-sectional analyses revealed that adolescents with sleep disturbances were at higher risks of suicidal ideation, plans, and attempts (pooled odds ratios ORs = 2.35, 1.58, and 1.92) than those without sleep disturbances. Prospective reports indicated that sleep disturbances in adolescents significantly predicted the risk of suicidal ideation but not suicide attempts (pooled ORs = 1.79 and 1.98, 95% confidence intervals = 1.36-2.36 and 0.62-6.29, respectively). The retrospective study did not support the association between sleep disturbances and suicide attempts. Depression did not moderate the associations between sleep disturbances and suicidal ideation or attempts in adolescents. Adolescents with insomnia complaints had a higher risk of suicidal ideation than those with other sleep complaints. Age, the female percentage, and reliable sleep measures were significant moderators (all p < .05).
Sleep disturbances, particularly insomnia, should be considered an influencing factor when developing preventive strategies against adolescent suicidal ideation. Additional prospective studies are warranted to establish causality of sleep disturbances in youth suicide plans and attempts.
Incentive-based demand response (IBDR) was considered as a feasible option to solve the sudden shortage of distributed energy resource in the microgrid. However, the current IBDR policies and ...strategies are not directly applicable to residents due to many defects. To this end, a versatile hierarchical IBDR strategy is proposed in this article. To ensure the effectiveness of IBDR in load reduction on critical period, clustering with demand response (DR) levels decision-making algorithm is proposed to obtain the reliable estimation of DR quantity. To enhance system reliability, a reliability constraint-based DR contingency management scheme is also proposed. Moreover, to avoid the additional costs cause by DR rebound effect, a systematic costs minimization algorithm is developed. To evaluate the performance of the proposed method, the actual case-based series testing was conducted. The proposed strategy is proved that is able to effectively reduce the overall cost of microgrid while the deficiency of DER occurs.
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