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This study used the subcritical hydrothermal liquefaction technique (SHLT) in the co- liquefaction of process rejects at a wastepaper-based paper mill (PRWPM) and waste soybean oil (WSO) for the production of biofuels and bio-char material. PRWPM emits complicated waste composed of cellulose, hemicellulose, lignin, and plastic from sealing film. The waste is produced from the recycled paper process of a mill plant located in central Taiwan. The source of WSO is the rejected organic waste from a cooking oil factory located in north Taiwan. PRWPM and WSO are suitable for use as fuels, but due to their high oxygen content, their use as commercial liquid fuels is not frequent, thus making deoxygenation and hydrogenation necessary. The temperature and pressure of SHLT were set at 523–643 K and 40–250 bar, respectively. The experimental conditions included solvent ratios of oil–water, temperature, reaction time, and ratios of solvent to PRWPM. The analysis results contained approximated components, heating values, elements, surface features, simulated distillations, product compositions, and recovery yields. The HHV of the product occurred at an oil–water ratio of 75:25, with a value of 38.04 MJ kg−1. At an oil–water ratio of 25:75, the liquid oil-phase product of SHTL has the highest heating value 42.02 MJ kg−1. Higher WSO content implies a lower heating value of the oil-phase product. The simulated distillation result of the oil-phase product with higher content of alcohol and alkanes obtained at the oil–water ratio of 25:75 is better than the other ratios. Here, the carbon number of the oil product is between C8–C36. The product conversion rate rises with an increase of the WSO ratio. It is proved that blending soybean oil with water can significantly enhance the quality of liquefied oil and the conversion rate of PRWPM. Therefore, the solid and liquid biomass wastes co-liquefaction to produce gas and liquid biofuels under SHLT are quite feasible. [ABSTRACT FROM AUTHOR]

Background: Social innovation is often used as a mechanism to jump-start public–private partnerships to leverage resources to achieve social impact; the analysis of sustainability and the impact of corporate social responsibility (CSR) cannot be emphasized enough. Due to advances in the information and communication technology industry in Taiwan, this paper aims to explore whether these advancements drive CSR as a form of social innovation to meet health needs in Taiwan. Methodology: This paper uses a case study to look at CSR programs in the health sector in Taiwan. Corporations with diverse missions and different CSR approaches that are available on the internet are selected. The analysis of the case study takes a qualitative, exploratory approach to shed light on current initiatives. Results: The majority of CSR programs in Taiwan are private sector activities that emerged during the COVID-19 pandemic; current CSR activities in Taiwan are driven by awards, public relations, and external interests. Corporations in Taiwan have the potential to address the health care gaps of urban–rural health utilization among Taiwanese indigenous communities. It is recommended for corporations to (1) develop partnerships with public health experts or to (2) employ CSR personnel with health care backgrounds who can navigate the intersection between health, business, and policies to develop CSR strategies. Conclusions: Further evaluation of the projects mentioned in this paper to assess the direct and indirect impact on health outcomes could provide a more comprehensive understanding of the field of CSR in the health sector in Taiwan. [ABSTRACT FROM AUTHOR]

Energy justice remains relatively under-researched outside of the Western context, especially in Asia. This paper addresses that gap by investigating the process of energy transition in Taiwan through the socio-spatial lens and institutional points of view. The paper underscores the urgency of recognizing and addressing the overlooked social injustices across different territories, advocating an integrated planning approach that incorporates a just energy transition perspective to reduce the uneven negative impacts of deploying renewable energies across communities and regional territories. Drawing on a case study of the Changhua region, the paper identifies conflicts arising from the rapid deployment of renewable energy, such as land use changes that displace farming activities and negatively affect rural stakeholders. The findings suggest the need to urgently address the gap between top–down goals in energy transition and bottom–up considerations to raise awareness and prevent injustices that risk deepening the existing socio-economic inequities. This paper also proposes a new framework for both new research and policy for (just) energy transition, incorporating distributional, procedural, and recognitional concerns together with a critical view on the cross-scale and cross-sectoral integration as part of the spatial planning process. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger life. As the situation continues to escalate, non-contact machines are used to assist patients at home to monitor their blood oxygen levels without encountering others. This paper uses a general network camera to capture the forehead area of a person's face, using the RPPG (remote photoplethysmography) principle. Then, image signal processing of red and blue light waves is carried out. By utilizing the principle of light reflection, the standard deviation and mean are calculated, and the blood oxygen saturation is computed. Finally, the effect of illuminance on the experimental values is discussed. The experimental results of this paper were compared with a blood oxygen meter certified by the Ministry of Health and Welfare in Taiwan, and the experimental results had only a maximum error of 2%, which is better than the 3% to 5% error rates in other studies The measurement time was only 30 s, which is better than the one minute reported using similar equipment in other studies. Therefore, this paper not only saves equipment expenses but also provides convenience and safety for those who need to monitor their blood oxygen levels at home. Future applications can combine the SpO2 detection software with camera-equipped devices such as smartphones and laptops. The public can detect SpO2 on their own mobile devices, providing a convenient and effective tool for personal health management. [ABSTRACT FROM AUTHOR]

This paper explores a decision analysis on product integration of family security services and residential fire insurance in the London and Taiwan markets by using the proposed mathematical models for counting sustainable value. This paper shows the five main different results between London and Taiwan markets with ten different parameters of the family security market, to find out the optimal number of family security integrated services for each security company in London. The improvement of the risk aversion effect based on risk and financial management will enhance the market share of the private security industries in the London and Taiwan markets. The results of this research can serve as a reference for the decision-making of private security industries on product integration under sustainable value consideration. The research findings highlight the potential benefits for both the private security industry and the insurance industry in their design and negotiation for product integration to improve both of business operation and achieve corporate social responsibility goals to match the sustainability in the future. [ABSTRACT FROM AUTHOR]

The investigation examines English proficiency levels across Taiwanese primary education, revealing a significant divergence in skills characterized by an M-shaped distribution among students in the fifth and sixth grades. To address this disparity, the study employs a performance evaluation method that combines importance–performance analysis (IPA) and the refined Kano model, aiming to identify and improve upon the current deficiencies in English language teaching. Further analytical depth is provided by applying a three-factor theory, which re-evaluates the traditional five-factor framework of the Kano model, informed by a survey of 443 respondents. The analysis identified that, out of twenty evaluative items, four are critical and demand targeted enhancement efforts. The paper has identified four items for enhancement to improve English language instruction quality. Specifically, it suggests that two items from the importance–performance analysis are critically under resourced and require immediate intervention. Additionally, the paper recognizes two items from the Kano model that also need focused improvement. The study recommends swift and targeted actions to address these areas, with the goal of raising the overall standard of teaching. Based on these findings, the study proposes a set of practical recommendations aimed at increasing the overall effectiveness of teaching and fostering an environment that enhances student motivation. The ultimate goal of this study is to support English teachers in primary schools by providing strategic insights that can lead to the enhancement of teaching practices and the amelioration of English language education deficiencies. [ABSTRACT FROM AUTHOR]

For a 50G passive optical network (PON) low-density parity-check (LDPC) decoder, decoding performance and area efficiency must be balanced. This paper adopts a layered decoder method to improve the area efficiency of the decoder. By parallel processing of three submatrices and storage reuse of node information, optimizing the matrix partitioning and processing order of the 50G-PON standard, the throughput of 1235 bps was reached under 100 MHz circuit frequency in field-programmable gate array (FPGA) implementation, and 9.864 Gbps was achieved based on Taiwan semiconductor manufacturing company (TSMC) 65 nm synthesis with 800 MHz circuit frequency in an area of 2.61 mm 2 by proposing a mechanism of spare decision storage to avoid errors caused by quantization overflow of the decoder and using full verification to terminate decoding in advance to improve performance. Finally, at an input bit error rate (BER) of 2.3 × 10 − 2 (signal-to-noise ratio (SNR) of about 3.72 dB), the output BER was lower than 10 − 12 , and the throughput area rate (TAR) also increased by 2 to 4 times compared with other papers. In conclusion, an area-efficient LDPC decoder without sacrificing decoding performance is made. [ABSTRACT FROM AUTHOR]

In this paper, an improved one-line evolution formulation is proposed and derived for the dynamic shoreline planforms of embayed beaches. Although embayed sandy beaches can perform several functions, serving as leisure spots and areas of coastal protection, shoreline advances and retreats occur continuously as a result of many natural forces, such as winds, waves, currents, tides, etc. The one-line evolution formulation for dynamic shoreline planforms based on the polar coordinate can be adopted to simulate high-planform-curvature shorelines and achieve better stability and simplicity in comparison with other description coordinates. While the polar coordinate and rectangular control volume are adopted to derive the one-line evolution formulation for dynamic shoreline planforms, the difference between the radial direction of the polar coordinate and the normal direction of the shoreline segment may result in inaccurate predictions of shoreline movements. In this study, a correction coefficient, which can adjust the influence of these two misaligned directions, is derived and included in the one-line evolution formulation, which is based on the polar coordinate. Thus, by considering the correction coefficient, an improved one-line evolution formulation for dynamic shoreline planforms of crenulate-shaped bays is proposed in this paper. Some numerical examples are provided to verify the merits of the proposed improved one-line evolution formulation. Moreover, the proposed numerical approach is applied to simulate the dynamic movements of the shoreline in Taitung—the southeastern part of Taiwan—and the effectiveness of the proposed formulation in solving realistic engineering applications is evidently verified. [ABSTRACT FROM AUTHOR]

This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management. [ABSTRACT FROM AUTHOR]

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive. [ABSTRACT FROM AUTHOR]

In current times that emphasize active aging, older adults are often encouraged to participate in interest classes at community centers. Adopting an ethnographic research approach to understand the subjectivities of older mothers, this paper not only sees community centers as a space for older adults for taking interest classes, but also as an infrastructure that defines relationship values. This paper examines the motivations of older mothers in urban Taiwan who have established a routine at a community center. By emphasizing the public space, that is, the values indoctrinated at the community centers, this paper investigates how community center activities offer self-care practices for older women, and how these women embody motherhood by taking care of themselves. This paper expands the notion of motherhood by investigating the subjectivities of older mothers as well as the effects of the global aging discourse on them. This study of urban Taiwan sheds light on understanding the subjectivity of aging mothers in other East Asian cities. The data of this paper were collected through participant observations at community centers, in-depth interviews, and analysis of newspaper archives. [ABSTRACT FROM AUTHOR]

Due to the economic inefficiency of material recycling of general industrial waste and urban waste, the use of solid recovered fuels (SRFs) not only mitigates the environmental loadings from waste incineration plants and sanitary landfills but also creates green electricity and/or heat and thus reduces the use of fossil fuels. In this regard, the Taiwan government formulated the "Solid Recovered Fuel Manufacturing Guidelines and Quality Standards" in 2020 to ensure the manufacturing quality of SRFs. This paper focused on the status of waste management and energy supply, the current regulations for adopting SRFs, and the challenges in the development of SRFs from the viewpoints (or life cycle) of the environmental, economic, and engineering (or technological) characters in Taiwan. Based on the database of the official handbook/yearbook, the energy supply from indigenous biomass and waste was 1678.7 × 103 kiloliters of oil equivalent (KLOE) in 2021, which only accounted for about 1.2% of the total energy supply. Obviously, available indigenous biomass and waste for producing SRFs were mostly from waste wood, sugarcane bagasse, and mixtures containing wood/paper. Finally, some suggestions for the increasing use of SRFs in the energy and industrial sectors were addressed to keep in step with the sustainable development goals (SDGs) in 2030, especially in the mitigation of GHG emissions. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines (FOWTs) are one of the innovative solutions to achieve net-zero emissions. Given that Taiwan has abundant wind power resources in its western waters and wind farms are evaluated as potential sites, a strategic emphasis on the extensive expansion of wind power is imperative. This paper chooses four different designs of hybrid mooring systems, comparing them with the conventional pure chain mooring arrangement in shallow water regions in Taiwan through integrated numerical simulations, ANSYS AQWA, and Orcina OrcaFlex. The use of synthetic fiber ropes in hybrid moorings provides substantial economic and operational advantages, making them the preferred alternative to traditional chains in deepwater offshore renewable energy (ORE) mooring configurations. Hybrid mooring enables the FOWT to survive in extreme sea conditions and is presented as the ultimate limit state (ULS) and fatigue limit state (FLS). In addition, hybrid mooring not only reduces mooring line costs but also minimizes the footprint area on the seabed, enhancing economic competitiveness and optimizing marine space utilization. However, it increases the entanglement risk that may pose a potential threat to marine mammals. Building on prior research, this paper proposes a unique approach to calculate the mooring line swept volume, which is essential for entanglement assessment and marine spatial planning. [ABSTRACT FROM AUTHOR]

Among various eye diseases, glaucoma is one of the leading causes of blindness. Glaucoma is also one of the most common eye diseases in Taiwan. Glaucoma screenings can use optical coherence tomography (OCT) to locate areas in which the retinal nerve fiber layer is thinning. However, because OCT equipment is costly, only large hospitals with well-equipped facilities will have OCT, and regular eye clinics cannot afford such expensive equipment. This has caused many glaucoma patients to worsen because they cannot get an early diagnosis in regular eye clinics in time. This paper proposes a method of using a generative adversarial network (GAN) to generate corresponding OCT images from fundus images to assist family doctors in judging whether further examination is needed based on the generated OCT images to achieve early detection and treatment of glaucoma. In addition, in order to improve the classification accuracy of the system deployed in different hospitals or clinics, this paper also proposes to use the incremental training method to fine-tune the model. The model can be quickly applied by adding a small number of images from a specific clinic or hospital. Experimental results show that the cosine similarity between the generated OCT image and the real OCT image is 97.8%. Combined with the proposed transfer learning method, the classification accuracy of the classification model reaches 83.17%. As well as the use of the incremental method, the accuracy of identifying glaucoma is approximately 78.94%, which is 8.77% higher than the 70.17% accuracy of the initial model. Experimental results show the effectiveness and feasibility of our proposed method. [ABSTRACT FROM AUTHOR]

Marine fisheries are undeniably important to Taiwan in terms of economic development and industrial strategies. In the past 10 years, Taiwan's fishery GDP accounted for 14% to 21% of all agriculture, forestry, fishery, and animal husbandry, which is higher than both animal husbandry and forestry. Since the domestic population structure of Taiwan has changed, the domestic fishery industrial labor force has gradually been replaced by migrant fishing workers. The issue of migrant fishing workers' protection has received attention from non-governmental organizations, and cross-national recruitment administration has become the greatest challenge faced by Taiwan's authority. Therefore, this paper describes the necessary protection of the labor conditions and interests of migrant fishermen by analyzing their status and human rights protection in the historical development of Taiwan's marine fisheries. In addition, considering the well-being of migrant fishing workers, this paper conducts a feasibility assessment on the operation of the "Nan-Fang-Ao Fishermen's Hostel" at the Nan-Fang-Ao Fishing Harbor and illustrates the urgent need to re-examine the migrant fishing workers' protection. This paper suggests that the government agencies should actively advise employers to accept migrant fishermen's requests for better living environments onshore and guarantee basic living conditions. [ABSTRACT FROM AUTHOR]

A large petrochemical complex, dubbed Petro–complex, situated in a rather rural region of Taiwan, was used as a test bed to detect emissions from the Petro–complex to its surroundings. Hourly observations of speciated non–methane hydrocarbons (NMHCs) by the photochemical assessment monitoring stations (PAMSs), as well as the total amounts of NMHCs, SO2, and NOx provided by the air quality stations (AQSs), were utilized to find useful petro–emission indication methods. The analytical aspect of NMHCs either as a speciated form or as total amounts was demonstrated through field comparison to illustrate data quality. Using ethyne to offset traffic influence, the ratios of ethene to ethyne (acetylene) (E/A) and propene to ethyne (P/A) were proven to be effective indicators of petro–emissions owing to pronounced emissions of ethene and propene, revealed as tall spikes in PAMS measurements. SO2 and NOx were also explored as petro–emission indicators mainly for stack sources. By coordinating with three–dimensional modeling, SO2 from petro–emissions could be distinguished from other prominent sources, such as coal–fired power plants. An attempt was also made to use SO2 and NOx as indicators of broader petro–emissions with stringent criteria to minimize traffic interference and increase specificity. Similar findings were observed with the three indicators, that is, volatile organic compounds (VOCs) ratios, SO2 and NOx, to identify the southwest area of the Petro–region as the most affected area, as represented by Taisi station (F2). The percent affected time of a year at F2 was 10%–14%, owing to the dominant wind field of northeast monsoonal (NEM) in the region, as compared with other sites in the east and north of 1–5%. Using VOC ratios as petro–emission indicators is more effective than using other gases, owing to the compositional advantage to minimize traffic interference. [ABSTRACT FROM AUTHOR]

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage. [ABSTRACT FROM AUTHOR]

Guided by entering the deep sea and achieving deep marine development in marine construction, the factors hindering marine construction cannot be ignored. Strong ocean winds have a devastating impact on tasks such as ship navigation, carrier aircraft take-off and landing, naval operations and military exercises, and affect the planning of sea routes and the development of the long-distance sea. This paper uses ERA5 wind field data and key climate indices to conduct a systematic analysis of catastrophic winds in the global ocean using methods such as climate statistical analysis, the Theil–Sen trend method, Pearson correlation and contribution rate calculation. It points out the spatiotemporal distribution, variation trend, climate index correlation and contribution rate characteristics of strong winds occurrence (SWO) and hopes that the results of this study can serve as a guide for maritime route planning and provide technical assistance and decision-making support for marine development and other needs. The results show the following: The high global SWO occurs in the Southern Ocean, the North Atlantic, the North Pacific, near Taiwan, China, the Arabian Sea and other locations, with the strongest SWO in summer. The growth trend of SWO in the Southern Ocean is strongest, with decreasing regions near the Arabian Sea and the Bay of Bengal, and the growth trend is reflected in all four seasons. The climate indices with the strongest correlation and highest contribution to the global SWO are AAO (Antarctic Oscillation) and EP–NP (East Pacific–North Pacific pattern) with a correlation between −0.5 and 0.5 and a contribution rate of up to −50%~50%. [ABSTRACT FROM AUTHOR]

This paper presents the verification results of an experimental site that employed a particle tracking algorithm to assess the transport of tracers through the composite formation of gravel and Cholan in northwest Taiwan. A suitable hydrogeological conceptual model that describes the flow characteristics of the gravel formation and Cholan formation is essential to evaluate groundwater flow and management at this site. Continuous porous medium (CPM) can be easily applied in the gravel formation, while the Cholan formation, characterized by argillaceous sandstone, is commonly treated as a porous medium. However, this study obtains its fracture distribution through geological surveys, and the key fracture parameters are also collected, analyzed, and incorporated into the model. Four hydrogeological conceptual models, including CPM, discrete fracture network (DFN), equivalent continuous porous medium (ECPM), and hybrid DFN/ECPM, are generated for this complex formation. This study combines the conceptual models of the gravel and Cholan formation into four cases to describe the characteristics of the composite formation. The groundwater flow field of four cases is simulated, and the particle tracking method is employed to model the tracer transport. Simulation results from the four hybrid models all yielded a breakthrough curve (BTC) for the first 15 h, indicating that the tracer arrived at the designated outlet within this timeframe and primarily flowed through the gravel formation, while long-time particle tracking revealed a possible flow path through the Cholan formation after 15 h. The breakthrough curve of the four cases shows that the ECPM model is more suitable for representing the heterogeneity of the Cholan formation than the common CPM model. This study provides a suitable numerical algorithm of the conceptual model of the Cholan formation based on strong evidence by considering different models and comparing them with in situ tracer tests. [ABSTRACT FROM AUTHOR]

In recent years, influencer marketing has taken over traditional brand advertisements on social media platforms, combining word-of-mouth marketing with celebrity endorsements. However, there has been limited academic research on the key success factors in influencer marketing. This paper used a hybrid MCDM model that integrates the Delphi method and the decision-making trial and evaluation laboratory (DEMATEL) approach. Through a two-stage empirical study, the research aims to explore the crucial success factors in influencer marketing. This study focuses on the Taiwanese market. The target respondents for the survey are consumers in Taiwan who have purchased products or services based on recommendations from key opinion leaders (KOLs) or internet celebrities. This systematic research framework not only pinpoints key factors that capture consumer attention towards influencers, but it also illustrates the inter-relationship of structure and improvement directions among these factors. According to results, the key factors include influencer reputation, credibility, degree of key opinion influence, attractiveness, popularity, consistency between influencers and brands, fan engagement level, and informativeness. Finally, businesses can consider five aspects to be the purpose of collaboration, product type, target audience for the product, characteristics of the influencer's creative content, and media attributes. By comprehensively considering these aspects, businesses can determine the appropriate format for collaborative content. This decision can then guide how influencers communicate with consumers, effectively conveying brand information to the relevant target audience. The research findings provide fresh and significant insights in the field of influencer marketing studies. [ABSTRACT FROM AUTHOR]

To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model's predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning. [ABSTRACT FROM AUTHOR]

Digital shearography is a technique that has recently been applied to material inspections that cannot be performed by the naked eyes, including the detection of air bubble defects in tires. Although digital shearography detects bubbles that are not visible to the naked eyes, the process of determining tire defects still relies on field operators, with inconsistent results depending on the experiences of the field operator personnel. New or different types of bubble defects that AI models have not previously recognized are often missed, resulting in an inadequate quality detection model. In this paper, we propose a bubble defect detection method based on an incremental YOLO architecture. The data for this research was provided by the largest tire manufacturer in Taiwan. In our research, we classify the defects into six distinct categories, pre-process the images to allow better detections of less-noticeable defects, increase the amount of training data used, and generate an initial training model with the YOLO framework. We also propose an incremental YOLO method using small-model training for previously unobserved defects to improve the model detection rate. We have observed detection accuracy and sensitivity of 98% and 90% in the experimental results, respectively. The methods proposed in this paper can assist tire manufacturers in achieving semi-automatic quality inspections and labor cost reductions. [ABSTRACT FROM AUTHOR]

Dividend tax policy is one of the important tools of government taxation. Observing the dividend tax policy and the behavior of stock prices around ex-rights will not only shed light on investment strategies, but also give us a clearer understanding of the microstructure of the capital market. Taiwan went through dividend tax policy and National Health Insurance (NHI) supplementary premium changes from 2014 to 2016. Therefore, this paper adopts the event study method to conduct empirical research on this major event period. The research conclusion points out: (1) During the research period, the company studied had a positive cumulative abnormal return before and on the ex-right day, and there was a negative cumulative abnormal return after the ex-right day. (2) When the tax reduction effect is more favorable to investors, there will be only a positive relationship with positive abnormal returns. (3) There is no statistical significance between the dividend tax reform policy and the negative abnormal return after ex-rights. The empirical results of this paper can help to better understand the pricing process of stocks by market microstructure systems such as dividend tax policies and help build a more stable stock market transaction structure. On the other hand, investors and companies can also gain their own investment or dividend policy inspiration from this research. [ABSTRACT FROM AUTHOR]

This paper studies the spatial forms and cultural sustainability of the Damazhan and Xiaomazhan historical area, the only area that reflects the development of the assembled-clan hall culture in Guangzhou. In the face of modernization and reconstruction, traditional culture plays an increasingly crucial role in modern cities, determining the expression of architectural uniqueness and continuous development that adapts to social needs. Therefore, preserving cultural characteristics is more relevant than ever. This study combines historical literature research with typo-morphological analysis to establish analytical strategies that link cultural sustainability with spatial evolution from the perspective of sustainable development. The analytical framework consists of three parts: First of all, through historical literature research, a thorough analysis is conducted on the cultural and social influencing factors from the Dynasties of Song, Yuan, Ming, Qing and the Republic of China to the reform and opening up period. Secondly, feature elements of the location are extracted, and a typo-morphological approach is used to analyze the sustained changes in spatial forms. Finally, the relationship between changes in block form and building types and the inheritance and development of traditional culture are demonstrated. The analytical results indicate that the continuity of cultural characteristics is embodied in the continuity of the block and architectural spatial characteristics, while cultural transformation and man-made social factors affect the changes in spatial forms. This paper combines the perspective of cultural sustainability analysis with the traditional typo-morphological approaches of urban spatial analysis, supplementing the existing typo-morphological methodology. In addition, guiding the development of urban morphology from the perspective of cultural sustainability is of prominent practical significance. [ABSTRACT FROM AUTHOR]

Studies have shown the abundance of fisheries resources in the waters of the northern part of the Philippines bordering southern Taiwan. However, discrepancies in legal frameworks, enforcement mechanisms, and cultural practices, as well as maritime boundary issues, contribute to complexities in collaboration. This paper thus aims to provide an understanding into the intricacies and challenges faced by both countries in managing their shared fishing resources. By analyzing the relevant international laws and instruments on fisheries cooperation, the paper shows what coastal states and entities fishing in the high seas could do to manage and conserve fishery resources in disputed areas. Existing fisheries agreements in the region such as the Taiwan–Japan Fisheries Cooperation provide a template of the kind of cooperation that can be concluded within the overlapping waters of both nations. Results of the analysis show how important it is for both nations to acknowledge the significance of fisheries cooperation in the overlapping waters. By recognizing the mutual benefits of sustainable resource management through peaceful dialogue, establishing a fisheries cooperation under the legal framework of the WCPFC is the logical solution. The findings contribute to understanding the complexities of cross-border fisheries cooperation and provide valuable insights for policymakers and stakeholders in the region. [ABSTRACT FROM AUTHOR]

Over the last 50 years since Whittingham created the world's first lithium-ion battery (LIB) in 1970, LIBs have continued to develop and have become mainstream for electric vehicle (EV) batteries. However, when an LIB for an EV reaches 80% of its state of health (SOH), although it still retains about 80% of its capacity, it is no longer suitable for use in general EVs and must be retired. This is problematic because not only is a retired LIB still viable for use and not totally obsolete, if not properly disposed of, a retired LIB may cause environmental pollution on top of being a waste of resources. Therefore, the reuse of retired LIBs from EVs is increasingly important. This paper uses circular economy (CE) and extended producer responsibility (EPR) as a theoretical basis to deal with the disposal of retired LIBs from EVs in Taiwan from legal, technical, and economic perspectives, and hopes to provide suggestions for the reuse of retired LIBs from EVs in Taiwan. [ABSTRACT FROM AUTHOR]

This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system. [ABSTRACT FROM AUTHOR]

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners' net income by 1.25 times compared to merely installing charging infrastructure. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to compare the differential impact of Taiwan High-speed Railway (T-HSR) and Hainan East Ring High-speed Railway (HER-HSR) stations on the surrounding construction land expansion, reveal the underlying institutional reasons, and then provide policy recommendations for coordinating the rational development of land resources and ecological environment protection around high-speed railway (HSR) stations. The research methods include the Institutional Analysis and Development (IAD) framework, buffer analysis, and GIS spatial analysis. The results show that (1) The spillover impact of HSR stations on the surrounding construction land expansion is the result of the game between different actors around the development and protection of land around HSR stations under the action scenarios of various external factors. The surrounding construction land of 12 stations of T-HSR increased by 269.98 km2 within the study scope and period, while the surrounding area of 10 stations in HER-HSR increased by 187.61 km2. (2) There are nine stations in T-HSR that affect the expansion of surrounding construction land within 1.0–3.0 km and lead to the expansion of construction land of 22.44 km2, while there are six stations in HER-HSR that affect the expansion of construction land within 1.5–3.5 km and lead to the expansion of construction land of 24.20 km2. Other stations cannot effectively identify their areas of influence due to other reasons. (3) Each station of HER-HSR has a stronger influence intensity and degree on the expansion of surrounding construction land than that of T-HSR, which is closely related to institutional factors such as land planning, land property rights, and public participation. In conclusion, the differences in land planning, land property rights, public participation, and other basic systems will lead to differences in the expansion of surrounding construction land for HSR stations. This research aims to provide institutional adjustment suggestions to achieve coordinated economic development and ecological protection around HSR stations in two Chinese islands with different institutional environments. [ABSTRACT FROM AUTHOR]

Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation, traditional methods relying on physical river gauges are becoming progressively unreliable. This paper presents an innovative methodology that leverages ResNet-50, a Convolutional Neural Network (CNN) model, to identify distinct water level features in Closed-Circuit Television (CCTV) river imagery of the Chengmei Bridge on the Keelung River in Neihu District, Taiwan, under various weather conditions. This methodology creates a virtual water gauge system for the precise and timely detection of water levels, thereby eliminating the need for dependable physical gauges. Our study utilized image data from 1 March 2022 to 28 February 2023. This river, crucial to the ecosystems and economies of numerous cities, could instigate a range of consequences due to rapid increases in water levels. The proposed system integrates grid-based methods with infrastructure like CCTV cameras and Raspberry Pi devices for data processing. This integration facilitates real-time water level monitoring, even without physical gauges, thus reducing deployment costs. Preliminary results indicate an accuracy range of 83.6% to 96%, with clear days providing the highest accuracy and heavy rainfall the lowest. Future work will refine the model to boost accuracy during rainy conditions. This research introduces a promising real-time river water level monitoring solution, significantly contributing to flood control and disaster management strategies. [ABSTRACT FROM AUTHOR]

With the accelerated digital transformation, food security data is exponentially growing, making it difficult to process and analyze data as the primary challenge for food security risk regulation. The promotion of "big data + food" safety supervision can effectively reduce supervision costs and improve the efficiency of risk detection and response. In order to improve the utilization of testing data and achieve rapid risk assessment, this paper proposes a rice security risk assessment method based on the fusion of multiple machine learning models, and conducts experimental validation based on rice hazard detection data from 31 provinces in China excluding Hong Kong, Macao and Taiwan in 2018. The model comparison verifies that the risk assessment model shows better performance than other mainstream machine learning algorithms, and its evaluation accuracy is as high as 99.54%, which verifies that the model proposed in this paper is more stable and accurate, and can provide accurate and efficient decision-making basis for regulatory authorities. [ABSTRACT FROM AUTHOR]

Due to an arising COVID-19 positive confirmed case in Taiwan, the screening of body temperature, mask wearing and quarantined violation is enhanced. A mobile robot that conducts this task is demanded to reduce the human labor. However, conventional robots suffer from several limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), different viewpoints, the scale of objects, low mobility, less functionality, and some environmental limitations. As for the thermal imager, it displays the current heat spectrum colors, and needs manual monitoring. This paper proposes applying Simultaneous Localization and Mapping in an unknown environment and using deep learning for detection of temperature, mask wearing, and human face on the Raspberry Pi to overcome these problems. It also uses the A* algorithm to do path planning and obstacle avoidance via 3D Light Detection and Ranging to make the robot move more smoothly. Evaluating and implementing different Simultaneous Localization and Mapping algorithms and deep learning models, then selecting the most suitable method. Root Mean Square Error of three Simultaneous Localization and Mapping algorithms are compared. The predictions of deep learning models are evaluated via the metrics (model speed, accuracy, complexity, precision, recall, precision–recall curve, F1 score). In conclusion, Google Cartographer for building a map, Convolutional Neural Network for mask wearing detection, and only looking once for human face detection achieve the best result among all algorithms. [ABSTRACT FROM AUTHOR]

This paper explores applications of the coordinated multi-point (CoMP) architecture operation of enhanced node B (eNB) in wireless communication networks featuring device-to-device (D2D) signaling. This is applied to cellular phone coverage for rapid mass transit systems, such as the Taiwan high speed rail transport system, and indoor public environments. The paper is based on formulas pertaining to the link between budget design and guidelines, as well as principles and theories of engineering practice, allowing designers to analyze and fully control the uplink and downlink signals and output power of fiber repeaters linking cellular phones to base stations. Finally, we employ easily installed cellular-over-fiber optic solutions for a small cell enhancement (SCE) system with novel architecture based on a leakage coaxial cable system using LTE-A technology. As a result, we successfully applied enhanced coverage designs for distributed antenna systems. These can be used to create self-organizing networks (SoN) for an Internet of Things. [ABSTRACT FROM AUTHOR]

Grouper aquaculture is a rapidly growing and dynamic industry within the Taiwan aquaculture sector that serves as an important source of income generation. Farming fishes at a better location (FFABL) is a promising production strategy. This study evaluated the production and economic performance of grouper farming in Taiwan. This investigation employed two steps: (1) a cost–benefit analysis to determine the main component affecting profitability; and (2) multivariate statistics to clarify the relationship between biological and economic factors. The findings suggest that the best choice of species is hybrid giant grouper that generates and highlights a positive income, viable benefit–cost ratio and profit rate. The study found the feed management of hybrid giant groupers requires more consideration due to its low feed conversion ratio. The producers need to replace trash fish in commercial feed during the production cycle to ensure better feed management. Finally, this paper argues that feed is the main constraint for grouper farming, which is sensitive to any change in feed productivity, and this constraint must be solved to sustain the grouper industry in Taiwan. [ABSTRACT FROM AUTHOR]

Colletotrichum, a genus within the phylum Ascomycota (Fungi) and family Glomerellaceae are important plant pathogens globally. In this paper, we detail four Colletotrichum species found in mangrove ecosystems. Two new species, Colletotrichum rhizophorae and C. thailandica, and a new host record for Colletotrichum fructicola were identified in Thailand. Colletotrichum tropicale was collected from Taiwan's mangroves and is a new record for Rhizophora mucronata. These identifications were established through a combination of molecular analysis and morphological characteristics. This expanded dataset for Colletotrichum enhances our understanding of the genetic diversity within this genus and its associations with mangrove ecosystems. The findings outlined herein provide data on our exploration of mangrove pathogens in Asia. [ABSTRACT FROM AUTHOR]

After the Taiwanese government launched the Blueprint for Developing Taiwan into a Bilingual Nation by 2030, the Implementation Project of Bilingual Instruction in several domains of primary and junior high school education was promoted by the Taiwan Ministry of Education. Content and language integrated learning (CLIL) is a dual-objective strategy in which students simultaneously acquire language skills and subject knowledge. CLIL has been widely implemented and proven to be successful in European countries. This strategy will become the primary method of bilingual education for instructors in grades K-12. Other non-European countries, such as Latin America and Indonesia, however, found difficulties applying CLIL in class. The issues need to be identified to encourage researchers and practitioners to find solutions. The purpose of this paper is to identify the barriers to implementing CLIL from the perspective of K-12 teachers. We surveyed 102 K-12 teachers in Keelung using a questionnaire. The findings indicate that (1) 49.0% of teachers feel concerned if they are required to teach half of their content in English, (2) 64.8% of teachers are willing to improve their English proficiency in their spare time for implementing CLIL in class, and (3) only 36.3% of teachers are willing to adopt CLIL in class, while 50.0% of teachers are willing to implement CLIL if a teaching assistant system supports them with English course materials. Teaching assistant systems that provide English course materials might influence teachers' willingness to implement CLIL in class. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy generation systems increases, traditional power generation facilities begin to face challenges, such as reduced output power and having the power turned off. The challenges are causing changes in the structure of the power system. Renewable energy sources, mainly wind and solar energy cannot provide stable inertia and frequency regulation capability. Ultimately, the power system's emergency response capability to face an N-1 is reduced, which leads to a reduction in system stability. Therefore, the application technology of the battery energy storage system is used to support the impact of changes in the new power system structure. This paper designed control technologies based on the WECC second-generation generic model, namely, dynamic regulation, steady regulation, and virtual inertia regulation. The models and control strategies are verified on Taiwan's 2025 power system target conditions, which consider the expected capacities for battery energy storage systems, and renewable energy sources with different load and N-1 fault levels. According to the simulation results, the capabilities of the RoCoF limitation, frequency nadir, frequency recovery, and system oscillation regulation are evaluated in the proposed strategies. Finally, the analysis results can help power operators make informed decisions when selecting and deploying battery energy storage systems. [ABSTRACT FROM AUTHOR]

Today, the various abilities that nurses require to meet patients' healthcare needs adequately are all affected by AI-enabled systems. This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (AI image e-book guidance) group or a control (text paper guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing AI image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery (p < 0.001). It was found that providing AI image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff. [ABSTRACT FROM AUTHOR]

Based on the absolute dynamic topography data from the Copernicus Marine Environment Monitoring Service, this paper applies the Topographic Position Index to develop a new approach for mapping the anticyclonic eddies in the South China Sea (SCS). The results show that anticyclonic eddies are active in the deep basin of SCS, and the five selected parameters (number or frequency, lifetime, kinetic energy, amplitude, and area or radius) of anticyclonic eddies have a similar temporal variation and a similar spatial distribution pattern. (1) As for monthly variations, anticyclonic eddies are active in late spring and most active in summer. (2) The El Niño–Southern Oscillation had a stronger impact on the inter-annual variations of anticyclonic eddies in the SCS before 2013, resulting in a significant transition of inter-annual variations of these five parameters in around 2004. After 2013, most of these five parameters had a minimum in 2015 and a maximum in 2017. (3) Analyses show that the eddy activities in the SCS are significantly influenced by the monsoon wind and the western boundary current like Kuroshio. Therefore, the areas southwest of Taiwan Island and east of Vietnam are the two areas where the anticyclonic eddies are most active, with much larger eddy kinetic energy and much higher eddy amplitude. [ABSTRACT FROM AUTHOR]

Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state's electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November. [ABSTRACT FROM AUTHOR]

The offshore China (OC) region is a significant sea area in the Western Pacific and many researchers have been interested in the distribution of sea surface temperature (SST) fronts in this area. In this study, the Cayula and Cornillon single image edge detection algorithm was used to detect SST fronts using the Daily Optimum Interpolation Sea Surface Temperature data from 1982 to 2021. The results revealed that there are eighteen SST fronts in OC— three in the Bohai Sea, seven in the Yellow Sea, two in the East China Sea, five in the South China Sea, one in the Pacific Ocean east of Taiwan province, China—and among them a new front was detected in the Yellow Sea and named the Yellow Sea Ring Front. The frequency of most fronts showed a tendency of initially increasing and then decreasing from January to September, followed by a trend of growing steadily from October to December. The frequency of a few fronts showed a decreasing tendency from January to September and an increasing tendency from October to December. The frequency of most fronts is highest in winter and lowest in summer. In spring and autumn, the frequency of most fronts is lower than that in winter and higher than that in summer. The annual average frontal probability of five-ninths of the fronts showed an upward trend, and the annual average frontal probability value of one-third of the fronts showed a downward trend. The rest of the fronts showed a stable trend. The results of this paper also showed that the Liaodong Bay Front and the Bohai-Laizhou Bay Front did not form a complete front, as previously reported. In addition, the frontal probability of the Bohai Front to the north of 39° N was in the tendency of decreasing. [ABSTRACT FROM AUTHOR]

Risk management has been an essential issue in the evolution of air-force flight safety. In this paper, the investigated risk management of air-force flight training in the Gangshan airbase, Republic of China (ROC) is the main field of study. The main goal of this paper is to conduct a series of risk identification and assessments of the flight training. Firstly, the 16 risk factors (RFs) of flight training were identified according to the related studies of flight safety and risk and three experts' interviews. Then, we created a fuzzy-analytic hierarchy process questionnaire and interviewed 20 flight instructors to obtain the weight of likelihood and consequence of the 16 RFs. Furthermore, a sequential assessment of the risk matrix was constructed to classify the 16 RFs into four groups, namely, extreme risk, high risk, medium risk, and low risk. As the results of the revised risk matrix, we provided four suggestions for the improvement of flight-training policy. These suggestions not only can facilitate the Gangshan airbase to smoothly transfer and reduce the deadly risk of flight training, but also provide exemplary risk management for other similar airbases. [ABSTRACT FROM AUTHOR]

This paper studies the actual electricity case of a national university in northern Taiwan, pointing out that many schools will face certain asymmetrical information and practical problems in the development of power systems, such as energy-savings and carbon-reduction policies, collecting electricity fees in each division, reducing the loss of power outages, expanding the power system capacity, and maintaining power distribution equipment. These problems are closely related to electricity costs, which include general electricity fees, unexpected losses caused by power outages, purchases of replacement power equipment, and maintenance fees of distribution equipment. This paper proposes corresponding improvement plans for each of the problems in the above-mentioned actual case studies and assists school power managers in using symmetrical information to formulate the best strategies to improve electricity costs. [ABSTRACT FROM AUTHOR]

Air pollution is a severe problem for the global environment. Most people spend 80% to 90% of the day indoors; therefore, indoor air pollution is as important as outdoor air pollution. The problem is more severe on school campuses. There are several ways to improve indoor air quality, such as air cleaners or ventilation. Air-quality sensors can be used to detect indoor air quality in real time to turn on air cleaner or ventilation. With an efficient and accurate clustering technique for indoor air-quality data, different ventilation strategies can be applied to achieve a better ventilation policy with accurate prediction results to improve indoor air quality. This study aims to cluster the indoor air quality data (i.e., CO2 level) collected from the school campus in Taiwan without other external information, such as geographical location or field usage. In this paper, we propose the Max Fast Fourier Transform (maxFFT) Clustering Approach to classify indoor air quality to improve the efficiency of the clustering and extract the required feature. The results show that without using geographical information or field usage, the clustering results can correctly reflect the ventilation condition of the space with low computation time. [ABSTRACT FROM AUTHOR]

This study explores the characteristics that influence the success factors of accounting firms in the introduction of a RPA system. The RPA system success factors used in this paper are based on the measurements of the Technology Acceptance Model (TAM) and the Information System Success Model (ISS). In this paper, a questionnaire survey method was used, and a total of 140 questionnaires were distributed to 70 small accounting firms in Taiwan. The results of this study showed that three characteristic factors—male, higher familiarity with the system and high CEO support—were significantly positively correlated with the success factors of the RPA system in accounting firms. Due to resource constraints, small- and medium-sized accounting firms have gone through a more difficult journey of digital transformation than the Big Four. Therefore, research on digital transformation with small- and medium-sized firms as samples is a topic worthy of attention. The research conclusions of this paper can provide a reference for the accounting digital transformation strategies of the accounting industry and educational institutions. [ABSTRACT FROM AUTHOR]

Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement. [ABSTRACT FROM AUTHOR]

Despite being a limited resource, pollution, poor management, and other drivers like climate change make available water unsuitable and insufficient for human consumption and ecosystem maintenance. Therefore, a transdisciplinary approach is needed for managing this precious resource. The overall aim of this paper is to address water inequalities and improve human well-being using an integrated approach of key informant interviews, hydrological modeling, and the payment of ecosystem services (PES) scheme in Pinglin District, Taiwan. This site is an upstream area of Feicui Reservoir, which protects the downstream tap water supply. Key informant interviews were conducted to identify the gaps in and challenges to water resource management. This was followed by a scenario-based hydrological simulation using a Water Evaluation and Planning (WEAP) tool to project future water quality by the year 2050 (using biochemical oxygen demand and total coliform content as key indicator parameters) and to trace the factors responsible for water pollution. Survey analysis of key informant interviews depicts that this area is facing several challenges, such as lack of water infrastructure, agricultural subsidy, construction restrictions, etc., which cumulatively cause water scarcity in the upstream regions. On the other hand, hydrological simulation results show that population decline and climate change under an RCP 8.5 scenario will have an enormous negative impact on water quality. The concentrations of BOD and E. coli in river water will expand by 110.1% and 117.3%, respectively, by 2050 compared to 2018. Finally, the results of the study suggest that the PES scheme can play a positive role in enabling integrated water resource management. For example construction of a small-scale wastewater treatment plant in the upstream area will reduce the total E. coli concentration by up to 90%. While the initial cost of construction will be taken care of by the government, the operation and management cost of this infrastructure will be covered by people living downstream, who need to pay only $0.10 per year per person. The obtained results should be vital for both the stakeholders and decision-makers in this region. [ABSTRACT FROM AUTHOR]

In today's digital age, the financial industry, a cornerstone of the global economy, is undergoing monumental shifts. While business performance hinges on proficient leadership, the seismic wave of digital transformation poses new challenges and magnifies the need for specific leadership competencies. The urgency is clear: adapt or become obsolete. However, there is a lack of clarity regarding which competencies are essential during such transformations. To address this gap, the purpose of this study is to identify the leadership competencies that leaders in the financial industry should possess during the digital transformation process and to determine the influential relationships among these competencies and which ones are highly influential. This paper extensively investigates the criteria for evaluating leadership competencies and integrates expert judgments to construct an evaluation framework for leadership competencies during digital transformation. The Z-based decision-making trial and evaluation laboratory (Z-DEMATEL) technique was applied to identify the influential relationships among the criteria and generate influence weights for each criterion. In addition, based on the results of Z-DEMATEL, an Influence Classification Map (ICM) was plotted that categorized the criteria into four groups: core, driving, independent, and impact. Management implications and improvement suggestions are provided accordingly. Z-DEMATEL enhances the general form of fuzzy DEMATEL. By integrating the Z-fuzzy theory, Z-DEMATEL not only accounts for the uncertainty of the evaluation environment but also measures the confidence level of experts. Taking Taiwan's financial industry as a case study, this study revealed that "vision and imagination," "critical analysis," and "strategic perspective" were the top three criteria with higher weights, while "critical analysis" and "interpersonal sensitivity" were the core criteria. Business managers can use the results to design leadership training programs that meet the requirements of digital transformation and ultimately improve the performance of organizations during the transformation. Moreover, the concepts and methods presented in this study can be applied to other industries that are embarking on their digital transformation journeys. [ABSTRACT FROM AUTHOR]

Background: Cigarette smoking is a serious global health issue. Limited studies previously analyzed health literacy components in patients undergoing smoking cessation interventions. This study focuses on individuals enrolled in smoking cessation services and investigates the distribution of health literacy in three domains (health care, disease prevention, and health promotion) and four abilities (access, understand, appraise, and apply health information). The study also explores the correlation between background factors (age, BMI, etc.) and health literacy, as well as the differences in health literacy levels among different background variables (gender, etc.). Methods: 228 individuals completed the health literacy questionnaire. Descriptive statistical analysis, Pearson Correlation, and a Chi-Squared Test were employed to investigate the various health literacy levels and background variables. Results: 68% had excellent or sufficient health literacy. A total of 32% were considered problematic or to have inadequate health literacy. Of the three domains of health literacy, participants performed better in the healthcare domain. More than one-third were problematic in accessing and appraising information. Conclusions: this paper, being the pilot study in providing an analysis of health literacy components in individuals undergoing smoking cessation, could serve as a useful reference for devising interventions for different population groups in trying to maximize successful cessation rates. [ABSTRACT FROM AUTHOR]