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This study aimed to investigate EFL learner language anxiety and learning motivation of high school students. Subjects included 155 students from the same private senior high school in central Taiwan, 60 in academic track and 95 in vocational track. The majority of the participants started taking English lessons either before entering elementary school or during the first two years in elementary school. Statistical methods were conducted to investigate 1) whether learner motivation and language anxiety significantly vary between academic- and vocation-track high school students, 2) whether both academic- and vocational-track high school students feel an above-average level of language anxiety, and 3) whether there is a significant relationship between language anxiety and motivation among the EFL high school students. The findings of the study revealed that first, both groups of students felt moderate levels of language anxiety; there were no significant differences in anxiety level between the two groups of students. Second, students in the academic-track were also found to have higher extrinsic motivation and overall learning motivation than their vocational-track counterparts. Furthermore, a significant negative relationship was identified between the two important affective variables, motivation and anxiety. Important pedagogical implications for English teachers were discussed in the study.

In this article, a 0.7 nm thick monolayer MoS2 nanosheet gate-all-around field effect transistors (NS-GAAFETs) with conformal high- κ metal gate deposition are demonstrated. The device with 40 nm channel length exhibits a high on-state current density of ~410 μ A μ m−1 with a large on/off ratio of 6 × 108 at drain voltage = 1 V. The extracted contact resistance is 0.48 ± 0.1 kΩ μ m in monolayer MoS2 NS-GAAFETs, thereby showing the channel-dominated performance with the channel length scaling from 80 to 40 nm. The successful demonstration of device performance in this work verifies the integration potential of transition metal dichalcogenides for future logic transistor applications. [ABSTRACT FROM AUTHOR]

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of wastes-based bioethanol production.

The problem of hydrogen station location is often studied through understanding refueling behavior or reviewing the experience of gasoline stations. Driven by the notion "where you drive more is where you more likely need refueling", this paper develops a new approach where station siting is treated as a fuel-travel-back problem and the only required data is VMT distribution. Such a fuel-travel-back problem is a typical transportation problem and is solved as mix-integerprogramming model. When the total fuel-travel-back time is minimized, so is the average refueling travel time of a random motorist, for which theoretical deduction is provided. The model is applied to derive an optimal station roll-out scheme for Southern California. The results show that, if station size constraints are relaxed, only 18% of existing gas station number is needed to achieve the current fuel accessibility of gasoline in the region. Fewer stations lead to larger station size, suggesting a need to re-examine the current speculation on designs of hydrogen station and distribution system and to conduct more regional studies for discovery of optimistic and pessimistic regions for hydrogen. The results also indicate that early stations should be located strategically and even at low-demand locations, which is contradictory to existing proposition.

Southern California has been proposed as a likely site for developing a hydrogen refueling infrastructure. In this paper, we apply dynamic programming to identify optimized strategies for supplying hydrogen over time in Southern California. GIS-based traffic data are used to model the location and magnitude of hydrogen demand over time. Refueling stations are sited based on the location of demand and the trade-off between convenience and costs. We use engineering/economic models to describe a variety of possible hydrogen supply options. Both onsite and central production technologies including biomass gasification, coal gasification, natural gas reforming, and water electrolysis are investigated. For central production routes, several delivery modes are analyzed including liquid and compressed gas trucks, and gas pipelines. These technologies compete with each other to meet an exogenously estimated hydrogen demand over time at lowest cost. At each time step over a specified transition period, the model uses a dynamic programming algorithm to select the best strategy for building up the infrastructure. We find that 1) hydrogen could be cost competitive based on region-specific spatial optimization; 2) the best buildup strategy for Southern California could be industry hydrogen in early stage, bridged by onsite natural SMR and followed by biomass central gasification and then coal gasification with carbon capture and sequestration; 3) the feasibility of CCS is critical in CO2 emissions mitigation.

Southern California has been proposed as a likely site for developing a hydrogen refueling infrastructure. In this paper, we apply dynamic programming to identify optimized strategies for supplying hydrogen over time in Southern California. GIS-based traffic data are used to model the location and magnitude of hydrogen demand over time. Refueling stations are sited based on the location of demand and the trade-off between convenience and costs. We use engineering/economic models to describe a variety of possible hydrogen supply options. Both onsite and central production technologies including biomass gasification, coal gasification, natural gas reforming, and water electrolysis are investigated. For central production routes, several delivery modes are analyzed including liquid and compressed gas trucks, and gas pipelines. These technologies compete with each other to meet an exogenously estimated hydrogen demand over time at lowest cost. At each time step over a specified transition period, the model uses a dynamic programming algorithm to select the best strategy for building up the infrastructure. We find that 1) hydrogen could be cost competitive based on region-specific spatial optimization; 2) the best buildup strategy for Southern California could be industry hydrogen in early stage, bridged by onsite natural SMR and followed by biomass central gasification and then coal gasification with carbon capture and sequestration; 3) the feasibility of CCS is critical in CO2 emissions mitigation.

Aims: To evaluate the prevalence of pathogenic variants in genes associated with dilated cardiomyopathy (DCM) in a clinical trial population with heart failure and reduced ejection fraction (HFrEF) and describe the baseline characteristics by variant carrier status. Methods and results: This was a post hoc analysis of the Phase 3 PARADIGM‐HF trial. Forty‐four genes, divided into three tiers, based on definitive, moderate or limited evidence of association with DCM, were assessed for rare predicted loss‐of‐function (pLoF) variants, which were prioritized using ClinVar annotations, measures of gene transcriptional output and evolutionary constraint, and pLoF confidence predictions. Prevalence was reported for pLoF variant carriers based on DCM‐associated gene tiers. Clinical features were compared between carriers and non‐carriers. Of the 1412 HFrEF participants with whole‐exome sequence data, 68 (4.8%) had at least one pLoF variant in the 8 tier‐1 genes (definitive/strong association with DCM), with Titin being most commonly affected. The prevalence increased to 7.5% when considering all 44 genes. Among patients with idiopathic aetiology, 10.0% (23/229) had tier‐1 variants only and 12.6% (29/229) had tier‐1, ‐2 or ‐3 variants. Compared to non‐carriers, tier‐1 carriers were younger (4 years; adjusted p‐value [padj] = 4 × 10−3), leaner (27.8 kg/m2 vs. 29.4 kg/m2; padj = 3.2 × 10−3), had lower ejection fraction (27.3% vs. 29.8%; padj = 5.8 × 10−3), and less likely to have ischaemic aetiology (37.3% vs. 67.4%; padj = 4 × 10−4). Conclusion: Deleterious pLoF variants in genes with definitive/strong association with DCM were identified in ∼5% of HFrEF patients from a PARADIGM‐HF trial subset, who were younger, had lower ejection fraction and were less likely to have had an ischaemic aetiology. [ABSTRACT FROM AUTHOR]

Consumers replace their smartphones more frequently, with their increasing requirements for better functions, superior performance, and fancier appearance. The pursuit of replacement may also identify market opportunities and incur business risks. In line with the research streams in product replacement, this study focuses on the effect of recency on consumers' purchase timing in the context of replacing smartphones. The framework of this research accordingly stems from the rationale of consumer psychology, with concentration on the moderation of consumers' need for uniqueness, anticipated regret, and education level, besides the focal recency-purchase timing link. The corresponding hypotheses are therefore proposed. To test the hypotheses, we employed convenience sampling to collect data from a sample of smartphone users in China, and finally the valid sample size was 470. We analyzed the data collected in China for hypothesis testing. Based on the analysis of the data, our findings highlight the main contribution of this research, demonstrating that recency has a significant effect on the timing of consumers' purchasing smartphones and this effect is moderated by consumers' need for uniqueness and anticipated regret. [ABSTRACT FROM AUTHOR]