National Chengchi University

Wu Y.

Chu R.

Tu W.

Lin H., Hsiao Y., Yen Y.

Wong F., Lin C.

As the role of bibliographic databases in academic research continues to expand, understanding how different demographics utilize these tools becomes increasingly important. This study examines the relationship between demographic characteristics, use patterns of bibliographic databases, and research productivity among academics in Taiwan. Based on a survey of 338 respondents, the results reveal that Google Scholar, Web of Science, and PubMed are the most widely used databases. Most respondents frequently use databases for writing journal articles and teaching. Males spent more time using bibliographic databases than females. Younger and junior faculty members reported more frequent and longer use, while part-time staff exhibited significantly lower use. Additionally, researchers in fields like engineering used databases more extensively than those in the arts and humanities. A positive correlation was found between the frequency of database use and research productivity over the past three years. The findings highlight the critical role of bibliographic databases in academic work, with implications for institutional support and digital resource access. Limitations and recommendations for future research are discussed.

Hsin C., Lee C., Tsao Y.

Objectives: This study investigated the predictability effect on the N400 as an objective measure of listening-related fatigue during speech comprehension by: (1) examining how its characteristics (amplitude, latency, and topographic distribution) changed over time under clear versus noisy conditions to assess its utility as a marker for listening-related fatigue, and (2) evaluating whether these N400 parameters could assess the effectiveness of speech enhancement (SE) systems. Design: Two event-related potential experiments were conducted on 140 young adults (aged 20 to 30) assigned to four age-matched groups. Using a between-subjects design for listening conditions, participants comprehended spoken sentences ending in high- or low-predictability words while their brain activity was recorded using electroencephalography. Experiment 1 compared the predictability effect on the N400 in clear and noise-masked conditions, while experiment 2 examined this effect under two enhanced conditions (denoised using the transformer- and minimum mean square error-based SE models). Electroencephalography data were divided into two blocks to analyze the changes in the predictability effect on the N400 over time, including amplitude, latency, and topographic distributions. Results: Experiment 1 compared N400 effects across blocks under different clarity conditions. Clear speech in block 2 elicited a more anteriorly distributed N400 effect without reduction or delay compared with block 1. Noisy speech in block 2 showed a reduced, delayed, and posteriorly distributed effect compared with block 1. Experiment 2 examined N400 effects during enhanced speech processing. Transformer-enhanced speech in block 1 demonstrated significantly increased N400 effect amplitude compared to noisy speech. However, both enhancement methods showed delayed N400 effects in block 2. Conclusions: This study suggests that temporal changes in the N400 predictability effect might serve as objective markers of sustained speech processing under different clarity conditions. During clear speech comprehension, listeners appear to maintain efficient semantic processing through additional resource recruitment over time, while noisy speech leads to reduced processing efficiency. When applied to enhanced speech, these N400 patterns reveal both the immediate benefits of SE for semantic processing and potential limitations in supporting sustained listening. These findings demonstrate the potential utility of the N400 predictability effect for understanding sustained listening demands and evaluating SE effectiveness.

Peng Y., Li W., Chen Z.

Wang Y., Miao N., You M., Wang F.T., Hsu C.

ABSTRACTAimsThis study explored the factors associated with oncology nurses' behaviour and intention to provide cancer care to lesbian, gay, bisexual, and transgender (LGBT) individuals in Taiwan.DesignA qualitative descriptive study.MethodsSemi‐structured interviews were conducted with 25 oncology nurses between August and October 2023. The interview data were analysed using the framework method (the socio‐ecological model) and constant comparative techniques.ResultsFactors associated with oncology nurses' behaviour and intentions to provide cancer care for LGBT individuals were categorised and presented by the levels of the social‐ecological model: (1) intrapersonal level: oncology nurses' attitudes toward LGBT populations and their experiences, confidence, and beliefs in providing cancer care for LGBT individuals; (2) interpersonal level: concerns or interactions with oncology nurses' colleagues, other heterosexual cancer patients, and managers; (3) community and organisation levels: organisational climate and related training courses; and (4) societal and policy level: policy regarding sexual orientation, gender identity collection, and social atmosphere.ConclusionMultilevel barriers associated with oncology nurses' behaviours and intentions to provide cancer care to LGBT individuals were identified. The study findings emphasised the importance of related training courses offered by organisations to reduce cancer care disparities among LGBT individuals. These courses also aim to enhance oncology nurses' confidence and comfort in delivering holistic and patient‐centred cancer care for this population.ImpactThe study findings can be employed to assist in developing related training courses and understand the challenges oncology nurses face when providing cancer care to LGBT individuals.Patient or Public ContributionNo patient or public contributions.

Wen M., Zheng H., Chen J., Fong T.C., Li Y., Su D.

Cognitive decline is a global health concern that leads to significant impairment in daily functioning and quality of life. Limited large-scale cohort studies have concurrently examined the effects of lifestyle and social factors on cognitive status and the potential gender and age cohort differences in middle-aged and older adults. This longitudinal study examined the temporal associations between lifestyle and social factors and cognitive status in middle-aged and older adults in the United States. The sample comprised 2192 individuals (mean age = 71.6 years, SD = 6.6) drawn from two waves of the National Social Life, Health, and Aging Project conducted in 2010–11 and 2015–16. Lifestyle factors (vigorous physical activity, cigarette smoking, alcohol drinking, and sleep quality) and social factors (number of close social ties, frequency of volunteering, participation in group meetings, informal socialization, and religious services) were measured in 2010–11. Cognitive status was assessed via the Chicago Cognitive Function Measure at both waves. Regression analysis examined the effects of lifestyle and social factors on cognitive status and its change across the two waves. Past smoking and current drinking showed significant and positive effects on cognitive status. Maintaining robust social connections and active attendance in formal and informal social activities were significant predictors of healthier changes in cognitive status for those with normal cognition or mild cognitive impairment. Subgroup analysis revealed gender and age cohort differences in the effects of lifestyle and social factors on cognitive status, with females showing stronger effects for lifestyle and social factors than males. The present findings underscored the predominant influence of social factors over lifestyle factors on cognitive status and provided a more nuanced understanding of the social and lifestyle determinants across gender and age groups. Further research should aim to explore the causal direction of these associations, considering specific cognitive domains and underlying mechanisms.

Chen Y.

Abstract Mandarin ordinal phrases demonstrate an ambiguity between a definite reading and an indefinite quantity reading, first observed by Tsai (2011). However, contra Tsai (2011)’s lexical-ambiguity view, this paper presents a compositionally unified semantic analysis of the ambiguity of Mandarin ordinal phrases. Specifically, the ambiguity of Mandarin ordinals under discussion is derived from the semantic application of type-shifting rules and the pragmatic restriction on the domain of the ordinal morpheme di (which is reminiscent of the in-situ approach to the ambiguity of superlatives), coupled with the semantic requirements of di; therefore, a unified semantics of the ordinal morpheme di is maintained under either reading. Furthermore, this paper suggests two important felicity conditions in general on the use of an ordinal phrase in discourse. Besides theoretical contributions, this paper also presents empirical discoveries showing that some generalizations in Tsai (2011) are not entirely correct. For instance, it is false that all ordinal phrases in Mandarin Chinese must denote a singular entity (contra Tsai 2011). Instead, the semantics of classifiers matters for the denotation of an ordinal phrase: a Mandarin ordinal phrase can denote a plural entity when a group classifier is used. Furthermore, the presence of measure classifiers or certain predicates such as sheng ‘give birth’ is not a reliable diagnostic for teasing apart the two readings; the role of contexts and world knowledge in determining the (un)availability of a given reading is identified and discussed. Finally, this paper illustrates how the proposed analysis explains three puzzling properties of Mandarin ordinal phrases: (a) why the indefinite quantity reading is missing in the subject position; (b) why the indefinite quantity reading is missing when the experiential aspect marker guo occurs; and (c) why an ordinal phrase with canonical sortal classifiers has to denote a singular entity.

Chan *., Liu Y., Chen H.

Abstract Background Ketamine, an N-methyl-D-aspartate receptor (NMDAR) blocker, has been used as a dissociative anesthetic and currently become a popular recreational abused substance. Repeated and chronic use of ketamine leads to behavioral disturbances, including social deficits and cognitive impairments in memory. Previous studies indicate that enhancement of NMDAR function reveals the beneficial effects on ketamine-induced abnormal behavioral and neurochemical responses. Sodium benzoate (SB), a D-amino acid oxidase inhibitor, has been reported to improve several neurological disorders, such as depression and schizophrenia, which may be associated with increasing NMDAR function. Aims & Objectives This study aimed to investigate the therapeutic effects of SB on ketamine- induced psychotomimetic behaviors and neuronal activity. Method The male ICR mice received intraperitoneal injection (i.p.) of either ketamine (20 mg/kg) or vehicle twice per day successively during postnatal days (PN) 32-46. Subsequently, SB (30 &100 mg/kg, i.p.) was given during PN55-69. The novel location/object recognition task, reciprocal social interaction, three-chamber social test, tail suspension test and forced swimming test were monitored. Moreover, a serotonergic hallucinogen was administered to induce head-twitch responses. After animal behavioral tests, the hippocampal synaptic function was determined. Results The data showed that the enduring behavioral abnormalities after repeated ketamine exposure, including social deficits, recognition memory impairments, and increased depression- like and hallucinogen-induced head-twitch responses, were ameliorated by SB post-treatment. In addition, SB reversed the reduced hippocampal synaptic activity after repeated ketamine treatment. Discussion & Conclusion These results demonstrated that post-treatment with SB could effectively reverse the adverse behavioral manifestations and hippocampal synaptic plasticity after repeated ketamine use, suggesting that SB can be used as a novel adjunct therapy with ketamine for treatment- resistant depression and provide benefits for ketamine use disorders.

Kuo T.

We study the online minimum cost bipartite perfect matching with delays problem. In this problem, m servers and m requests arrive over time, and an online algorithm can delay the matching between servers and requests by paying the delay cost. The objective is to minimize the total distance and delay cost. When servers and requests lie in a known metric space, there is a randomized $$O(\log n)$$ -competitive algorithm, where n is the size of the metric space. When the metric space is unknown a priori, Azar and Jacob-Fanani proposed a deterministic $$O\left( \frac{1}{\epsilon }m^{\log \left( \frac{3+\epsilon }{2}\right) }\right) $$ -competitive algorithm for any fixed $$\epsilon > 0$$ . This competitive ratio is tight when $$n = 1$$ and becomes $$O(m^{0.59})$$ for sufficiently small $$\epsilon $$ . We improve upon the result of Azar and Jacob-Fanani for the case where servers and requests are on the real line, providing a deterministic $$\tilde{O}(m^{0.5})$$ -competitive algorithm. Our algorithm is based on the Robust Matching (RM) algorithm proposed by Raghvendra for the minimum cost bipartite perfect matching problem. In this problem, delay is not allowed, and all servers arrive in the beginning. When a request arrives, the RM algorithm immediately matches the request to a free server based on the request’s minimum t-net-cost augmenting path, where $$t > 1$$ is a constant. In our algorithm, we delay the matching of a request until its waiting time exceeds its minimum t-net-cost divided by t.

Hossen M.K., Peng Y., Chen M.C.

In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the temperatures in a city can vary from year to year due to various unclear reasons. In this paper, we utilized three distinct statistical techniques to analyze annual data drifting at various stations. These techniques calculate the P values for each station by comparing data from five years (2014-2018) to identify data drifting phenomena. To find out the data drifting scenario those statistical techniques and calculate the P value from those techniques to measure the data drifting in specific locations. From those statistical techniques, the highest drifting stations can be identified from the previous year’s datasets To identify data drifting and highlight areas with significant drift, we utilized meteorological air quality and weather data in this study. We proposed two models that consider the characteristics of data drifting for PM2.5 prediction and compared them with various deep learning models, such as Long Short-Term Memory (LSTM) and its variants, for predictions from the next hour to the 64th hour. Our proposed models significantly outperform traditional neural networks. Additionally, we introduced a wrapped loss function incorporated into a model, resulting in more accurate results compared to those using the original loss function alone and prediction has been evaluated by RMSE, MAE and MAPE metrics. The proposed Front-loaded connection model(FLC) and Back-loaded connection model (BLC) solve the data drifting issue and the wrap loss function also help alleviate the data drifting problem with model training and works for the neural network models to achieve more accurate results. Eventually, the experimental results have shown that the proposed model performance enhanced from 24.1% -16%, 12%-8.3% respectively at 1h-24h, 32h-64h with compared to baselines BILSTM model, by 24.6% -11.8%, 10%-10.2% respectively at 1h-24h, 32h-64h compared to CNN model in hourly PM2.5 predictions.

Cheng T., Chen T.

Lee P.

This study explores the design of digital strategies in museum education, utilizing the National Palace Museum in Taiwan as a case study and applying the resource-based view and value co-creation theories. It highlights the strategic use of unique resources and capabilities to gain a competitive edge, focusing on crafting unique, digital-enhanced learning experiences. Emphasizing the importance of visitor engagement in creating value, the research advocates for the development of interactive and personalized educational content. This methodology seeks to improve learning outcomes and deepen cultural comprehension, offering an advanced framework for developing digital strategies within museums. The study underscores the need for integrating technological advancements with immersive learning experiences to foster an engaging and informative environment.