National Kaohsiung University of Science and Technology

Alginate is a hydrocolloid from algae, specifically brown algae, which is a group that includes many of the seaweeds, like kelps and an extracellular polymer of some bacteria. Sodium alginate is one of the best-known members of the hydrogel group. The hydrogel is a water-swollen and cross-linked polymeric network produced by the simple reaction of one or more monomers. It has a linear (unbranched) structure based on d-mannuronic and l-guluronic acids. The placement of these monomers depending on the source of its production is alternating, sequential and random. The same arrangement of monomers can affect the physical and chemical properties of this polysaccharide. This polyuronide has a wide range of applications in various industries including the food industry, medicine, tissue engineering, wastewater treatment, the pharmaceutical industry and fuel. It is generally recognized as safe when used in accordance with good manufacturing or feeding practice. This review discusses its application in addition to its structural, physical, and chemical properties.

Social media has been playing an important role in marketing strategy. As a part of social media, social networking sites (SNS) can be utilized by enterprises to create direct communication and good relationships with their customers. Therefore, enterprises using SNS have to select the right marketing content to enhance strong customer relationships, which lead to their behavior generating sustainable performance for enterprises. This research considered social media marketing activity (SMMA) and Customer Experience (CX) to measure the customer’s relationship quality, which can impact customer behavioral outcomes, which are purchase intention, loyalty intention, and participation intention. The 413 online questionnaire surveys were measured and analyzed using SmartPLS 3. The results show that SMMA and CX have a significant influence on the customer relationship quality, which also leads to a positive impact on customer behavioral outcomes. This research guides enterprises that SNS’s marketing content has to follow SMMA and CX dimensions to achieve the marketing objective and generate sustainable performance for enterprises.

Fused Deposition Modeling (FDM) can be used to manufacture any complex geometry and internal structures, and it has been widely applied in many industries, such as the biomedical, manufacturing, aerospace, automobile, industrial, and building industries. The purpose of this research is to characterize the polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) materials of FDM under four loading conditions (tension, compression, bending, and thermal deformation), in order to obtain data regarding different printing temperatures and speeds. The results indicated that PLA and PETG materials exhibit an obvious tensile and compression asymmetry. It was observed that the mechanical properties (tension, compression, and bending) of PLA and PETG are increased at higher printing temperatures, and that the effect of speed on PLA and PETG shows different results. In addition, the mechanical properties of PLA are greater than those of PETG, but the thermal deformation is the opposite. The above results will be a great help for researchers who are working with polymers and FDM technology to achieve sustainability.

Artificial intelligence (AI) in the context of customer service, we define as a technology-enabled system for evaluating real-time service scenarios using data collected from digital and/or physical sources in order to provide personalised recommendations, alternatives, and solutions to customers’ enquiries or problems, even very complex ones. We examined, in a banking services context, whether consumers preferred AI or Human online customer service applications using an experimental design across three field-based experiments. The results show that, in the case of low-complexity tasks, consumers considered the problem-solving ability of AI to be greater than that of human customer service and were more likely to use AI while, conversely, for high-complexity tasks, they viewed human customer service as superior and were more likely to use it than AI. Moreover, we found that perceived problem-solving ability mediated the effects of customers’ service usage intentions (i.e., their preference for AI vs. Human) with task complexity serving as a boundary condition. Here we discuss our research and the results and conclude by offering practical suggestions for banks seeking to reach customers and engage with them more effectively by leveraging the distinctive features of AI customer service.

Strains of Acinetobacter baumannii are commensal and opportunistic pathogens that have emerged as problematic hospital pathogens due to its biofilm formation ability and multiple antibiotic resistances. The biofilm-associated pathogens usually exhibit dramatically decreased susceptibility to antibiotics. This study was aimed to investigate the correlation of biofilm-forming ability, antibiotic resistance and biofilm-related genes of 154 A. baumannii isolates which were collected from a teaching hospital in Taiwan. Biofilm-forming ability of the isolates was evaluated by crystal violet staining and observed by scanning electron microscopy. Antibiotic susceptibility was determined by disc diffusion method and minimum inhibitory concentration; the biofilm-related genes were screened by polymerase chain reaction. Results showed that among the 154 tested isolates, 15.6% of the clinical isolates were weak biofilm producers, while 32.5% and 45.4% of them possessed moderate and strong biofilm formation ability, respectively. The experimental results revealed that the multiple drug resistant isolates usually provided a higher biofilm formation. The prevalence of biofilm related genes including bap, blaPER-1, csuE and ompA among the isolated strains was 79.2%, 38.3%, 91.6%, and 68.8%, respectively. The results indicated that the antibiotic resistance, the formation of biofilm and the related genes were significantly correlated. The results of this study can effectively help to understand the antibiotic resistant mechanism and provides the valuable information to the screening, identification, diagnosis, treatment and control of clinical antibiotic-resistant pathogens.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

This study aimed to determine the efficacy of seven predominant wine terpenoids (i.e. α-pinene, limonene, myrcene, geraniol, linalool, nerol, and terpineol) against foodborne pathogenic bacteria, as well as to observe their antioxidant activities. Antibacterial activities were observed against foodborne pathogenic bacteria. MIC50 and MBC values for Escherichia coli, Salmonella enterica, and Staphylococcus aureus were in the ranges of 0.420–1.598 mg/mL and 0.673–3.432 mg/mL, respectively. The terpenoid α-pinene showed the strongest DPPH free radical scavenging (IC50 value = 12.57 ± 0.18 mg/mL) and the highest reducing power (213.7 ± 5.27 μg/mL of L-ascorbic acid equivalents). However, the DPPH free radical scavenging of the terpenoids was found to be lower than that of butylated hydroxytoluene, which is known to be a strong reducing agent. The seven predominant terpenoids in wines that were identified in this study could be new potential sources of natural antibacterial and antioxidant agents for use in the food industry.

With limited retrieval of reserves and restricted capability in plant pathology, automation of processes becomes essential. All over the world, farmers are struggling to prevent various harm from bacteria or pathogens such as viruses, fungi, worms, protozoa, and insects. Deep learning is currently widely used across a wide range of applications, including desktop, web, and mobile. In this study, the authors attempt to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases based on leaf image. A dataset with of 18,345 training data and 4,585 testing data was used to create the predictive model. The information is separated into ten labels for tomato leaf diseases, each with 64 × 64 RGB pixels. The best model using the Adam optimizer with a realizing rate of 0.0005, the number of epochs 75, batch size 128, and an uncompromising cross-entropy loss function, has a high model accuracy with an average of 98%, a strictness rate of 0.98, a recall value of 0.99, and an F1-count of 0.98 with a loss of 0.1331, so that the classification results are good and very precise.

To improve customer experience and achieve sustainable development, many industries, especially banking, have leveraged artificial intelligence to implement a chatbot into their customer service. By integrating DeLone and McLean’s information systems success (D&M ISS) model and the expectation confirmation model (ECM) with the factor of trust, the aim of this study was to investigate the determinants of users’ continuance intentions towards chatbot services in the context of banking in Vietnam. A total of 359 questionnaire surveys were collected from a real bank’s chatbot users and analyzed using structural equation modeling. The findings revealed that users’ continuance intentions towards the banks’ chatbot services were influenced by satisfaction, trust, and perceived usefulness, of which trust had the strongest effect. The results also indicate that information quality, system quality, service quality, and confirmation of expectations had significant effects on three drivers of continuance intention in different ways. Our study contributes to the literature by providing a more comprehensive viewpoint to understand the perceptions and reactions of chatbot users in the post-adoption stage. The results of this study also yield several key suggestions for banking service providers on how to increase their customers’ intentions to continue using chatbot services, serving as a basis for long-term and sustainable development strategies in the current digital era.

Abstract Manuscript Type Empirical Research Question/Issue Building on resource dependence theory, this paper examines the effect of board capital and the moderating effect of CEO power on R&D investment. Research Findings/Insights Based on a panel of electronics firms in T aiwan, the results indicate that board capital (directors' educational level, directors' industry‐specific experience and interlocking directorate ties) has a positive effect on R&D investment and that CEO power positively moderates this effect. The empirical evidence suggests that when powerful CEOs are present, directors with human and social capital will devote more effort to providing valuable strategic advice and resources and thus will support R&D investment to enhance innovative capabilities. Theoretical/Academic Implications This study contributes to knowledge on corporate governance by bridging the gap in the relationship between board capital and R&D investment via an empirical inquiry into the influence of CEO power on a board's resource provision. The findings suggest that research aiming to elucidate the resource dependence role of board capital in shaping R&D investment should consider the potential moderating role of CEO power. Thus, this study should not only supplement the resource dependence literature by providing a more thorough understanding of the relationship between board capital and R&D investment but also delve into the black box of CEO ‐board relations, an important topic within corporate governance research. Practitioner/Policy Implications This study suggests that when the boards of firms competing in innovation through R&D investment (e.g., electronics firms) search for new board members, they should consider the educational level, industry‐specific experience and interlocking directorate ties of potential directors and how those potential directors complement or reinforce the existing board in order to enhance their ability to obtain valuable strategic information and substantial resources that would facilitate better R&D investment decisions. Additionally, those R&D firms may be advised to have a combination of a powerful CEO and a board consisting of directors with more education, directors with industry‐specific experience and directors with interlocking directorate ties because the presence of a powerful CEO may motivate directors to provide ongoing advice and resources, leading to increased R&D investment necessary to enhance innovation capabilities.

The present research examined the effectiveness of digital storytelling (DST) on foreign language learners’ English speaking and creative thinking. In this study, DST was realized in the form of an interdisciplinary project integrated in a partnership between an English course and a computer course, with the class time of the former devoted to the content design and that of the latter to the multimedia design of learner-generated digital stories. The participants were required to work in small groups to create their digital stories in the target language, English, under an eight-week interdisciplinary curriculum. A two-group quasi-experiment with a pretest and posttest design was then conducted to compare the participants’ learning outcomes. The findings reveal the authentic and meaningful learning opportunities that DST has to offer for effectively fostering the students’ development of becoming proficient English speakers and creative thinkers. Future implementations on interdisciplinary DST projects are thus recommended for educators.

Human health/socioeconomic development is closely correlated to environmental pollution, highlighting the need to monitor contaminants in the real environment with reliable devices such as biosensors. Recently, variety of biosensors gained high attention and employed as in-situ application, in real-time, and cost-effective analytical tools for healthy environment. For continuous environmental monitoring, it is necessary for portable, cost-effective, quick, and flexible biosensing devices. These benefits of the biosensor strategy are related to the Sustainable Development Goals (SDGs) established by the United Nations (UN), especially with reference to clean water and sources of energy. However, the relationship between SDGs and biosensor application for environmental monitoring is not well understood. In addition, some limitations and challenges might hinder the biosensor application on environmental monitoring. Herein, we reviewed the different types of biosensors, principle and applications, and their correlation with SDG 6, 12, 13, 14, and 15 as a reference for related authorities and administrators to consider. In this review, biosensors for different pollutants such as heavy metals and organics were documented. The present study highlights the application of biosensor for achieving SDGs. Current advantages and future research aspects are summarized in this paper.Abbreviations: ATP: Adenosine triphosphate; BOD: Biological oxygen demand; COD: Chemical oxygen demand; Cu-TCPP: Cu-porphyrin; DNA: Deoxyribonucleic acid; EDCs: Endocrine disrupting chemicals; EPA: U.S. Environmental Protection Agency; Fc-HPNs: Ferrocene (Fc)-based hollow polymeric nanospheres; Fe3O4@3D-GO: Fe3O4@three-dimensional graphene oxide; GC: Gas chromatography; GCE: Glassy carbon electrode; GFP: Green fluorescent protein; GHGs: Greenhouse gases; HPLC: High performance liquid chromatography; ICP-MS: Inductively coupled plasma mass spectrometry; ITO: Indium tin oxide; LAS: Linear alkylbenzene sulfonate; LIG: Laser-induced graphene; LOD: Limit of detection; ME: Magnetoelastic; MFC: Microbial fuel cell; MIP: Molecular imprinting polymers; MWCNT: Multi-walled carbon nanotube; MXC: Microbial electrochemical cell-based; NA: Nucleic acid; OBP: Odorant binding protein; OPs: Organophosphorus; PAHs: Polycyclic aromatic hydrocarbons; PBBs: Polybrominated biphenyls; PBDEs: Polybrominated diphenyl ethers; PCBs: Polychlorinated biphenyls; PGE: Polycrystalline gold electrode; photoMFC: photosynthetic MFC; POPs: Persistent organic pollutants; rGO: Reduced graphene oxide; RNA: Ribonucleic acid; SDGs: Sustainable Development Goals; SERS: Surface enhancement Raman spectrum; SPGE: Screen-printed gold electrode; SPR: Surface plasmon resonance; SWCNTs: single-walled carbon nanotubes; TCPP: Tetrakis (4-carboxyphenyl) porphyrin; TIRF: Total internal reflection fluorescence; TIRF: Total internal reflection fluorescence; TOL: Toluene-catabolic; TPHs: Total petroleum hydrocarbons; UN: United Nations; VOCs: Volatile organic compounds

The grave environmental, social, and economic concerns over the unprecedented exploitation of non-renewable energy resources have drawn the attention of policy makers and research organizations towards the sustainable use of agro-industrial food and crop wastes. Enzymes are versatile biocatalysts with immense potential to transform the food industry and lignocellulosic biorefineries. Microbial enzymes offer cleaner and greener solutions to produce fine chemicals and compounds. The production of industrially important enzymes from abundantly present agro-industrial food waste offers economic solutions for the commercial production of value-added chemicals. The recent developments in biocatalytic systems are designed to either increase the catalytic capability of the commercial enzymes or create new enzymes with distinctive properties. The limitations of low catalytic efficiency and enzyme denaturation in ambient conditions can be mitigated by employing diverse and inexpensive immobilization carriers, such as agro-food based materials, biopolymers, and nanomaterials. Moreover, revolutionary protein engineering tools help in designing and constructing tailored enzymes with improved substrate specificity, catalytic activity, stability, and reaction product inhibition. This review discusses the recent developments in the production of essential industrial enzymes from agro-industrial food trash and the application of low-cost immobilization and enzyme engineering approaches for sustainable development.

This study investigates the impact on extemporaneous English-language first drafts by using Google Translate (GT) in three different tasks assigned to Chinese sophomore, junior, and senior students of English as a Foreign Language (EFL) majoring in English. Students wrote first in Chinese (Step 1), then drafted corresponding texts in English (Step 2), and translated the Chinese into English using the 2016 GT version (Step 3), and finally compared their self-written (SW) English texts drafted in Step 2 and their GT English texts translated from the Chinese texts in Step 3. Both English drafts were analyzed using two types of online computational assessments to compare and evaluate grammatical components of writing quality and lexical features. Results indicate that the GT English texts presented a number of components of significantly higher writing quality than those of students’ SW texts, by having more words, fewer mistakes in spelling and grammar, and fewer errors per words. In addition, there were more advanced-level words in the students’ GT texts than in their SW ones. A follow-up questionnaire survey indicated that EFL students found satisfaction with using Google Translate in their English writing, especially in finding vocabulary items and enhancing the completion of English writing.

Abstract Learning a foreign language requires interaction with language input while involved in a task. Given that problem‐based learning (PBL) offers hands‐on application in realistic contexts, and that virtual reality (VR) enables learners to interact with multiple modalities of information, this study examines how the integration of VR technology into PBL contexts affects students' motivation for, problem‐solving during, and vocabulary acquisition in learning English as a foreign language (EFL). A total of 84 engineering majors who enrolled in a course of English for specific purposes were randomly assigned to either an experimental group or a control group. Students in the experimental group participated in a VR‐assisted PBL context, in which they were to view a PBL scenario using VR technology and then to create VR videos about solving the given problems. Those in the control group participated in a PBL context without the use of VR technology for viewing and solving the identical scenario. After the intervention, all the students wrote a problem‐solving analysis, took a vocabulary knowledge test, completed a learning motivation questionnaire, and participated in individual interviews. The results showed that the students in the experimental group significantly outperformed those in the control group in terms of vocabulary acquisition, and were more motivated to learn English related to their future careers, whereas there was no significant difference in the problem‐solving performance of the two groups. Implications of these findings highlight the value of engaging EFL learners in immersive environments for contextualized learning through the integrated use of VR and PBL.

Recently, the application of renewable energy sources (RESs) for power distribution systems is growing immensely. This advancement brings several advantages, such as energy sustainability and reliability, easier maintenance, cost-effective energy sources, and ecofriendly. The application of RESs in maritime systems such as port microgrids massively improves energy efficiency and reduces the utilization of fossil fuels, which is a serious threat to the environment. Accordingly, ports are receiving several initiatives to improve their energy efficiency by deploying different types of RESs based on the power electronic converters. This paper conducts a systematic review to provide cutting-edge state-of-the-art on the modern electrification and infrastructure of seaports taking into account some challenges such as the environmental aspects, energy efficiency enhancement, renewable energy integration, and legislative and regulatory requirements. Moreover, the technological methods, including electrifications, digitalization, onshore power supply applications, and energy storage systems of ports, are addressed. Furthermore, details of some operational strategies such as energy-aware operations and peak-shaving are delivered. Besides, the infrastructure scheme to enhance the energy efficiency of modern ports, including port microgrids and seaport smart microgrids are delivered. Finally, the applications of nascent technologies in seaports are presented.

With the effects of the COVID-19 pandemic, the e-commerce trend is driving faster, significantly impacting supply chains around the world. Thus, the importance of logistics and supply chain functions has been amplified in almost every business that ships physical goods. In Vietnam, the logistics service sector has seen rapid expansion. Since more and more businesses are seeking third-party logistics (3PL) providers to outsource the logistics functions, this article aims to offer decision-makers an integrated and consistent model for evaluating and selecting the most efficient 3PLs. To this end, the authors exploit a hybrid multi-criteria method which is fuzzy analytic hierarchy process (FAHP) and fuzzy vlsekriterijumska optimizacija i kompromisno resenje (FVIKOR) while examining the most influential and conflicting criteria regarding economic, service level, environmental, social, and risk aspects. Fuzzy information in the natural decision-making process is considered, linguistic variables are used to mitigate the uncertain levels in the criteria weights. First, FAHP (the weighting method) is adopted to evaluate and calculate each criterion’s relative significant fuzzy weight. FVIKOR (the compromised ranking method) is then used to rank the alternatives. The combination of FAHP and FVIKOR methods provides more accurate ranking results. As a result, reliability and delivery time, voice of customer, logistics cost, network management, and quality of service are the most impactful factors to the logistics outsourcing problem. Eventually, the optimized 3PLs were determined that fully meet the criteria of sustainable development. The developed integrated model offers the complete and robust 3PLs evaluation and selection process and can also be a powerful decision support tool for other industries.

Nonthermal plasma (NTP) is an advanced technology that has gained extensive attention because of its capacity for decontaminating food from both biological and chemical sources. Plasma-activated water (PAW), a product of NTP's reaction with water containing a rich diversity of highly reactive oxygen species (ROS) and reactive nitrogen species (RNS), is now being considered as the primary reactive chemical component in food decontamination. Despite exciting developments in this field recently, at present there is no comprehensive review specifically focusing on the comprehensive effects of PAW on food safety and quality. Although PAW applications in biological decontamination have been extensively evaluated, a complete analysis of the most recent developments in PAW technology (e.g., PAW combined with other treatments, and PAW applications in chemical degradation and as curing agents) is nevertheless lacking. Therefore, this review focuses on PAW applications for enhanced food safety (both biological and chemical safeties) according to the latest studies. Further, the subsequent effects on food quality (chemical, physical, and sensory properties) are discussed in detail. In addition, several recent trends of PAW developments, such as curing agents, thawing media, preservation of aquatic products, and the synergistic effects of PAW in combination with other traditional treatments, are also presented. Finally, this review outlines several limitations presented by PAW treatment, suggesting several future research directions and challenges that may hinder the translation of these technologies into real-life applications.

Purpose Building supply chain (SC) resilience is crucial for business continuity given the ever-changing environmental conditions. Based on the resource orchestration and organizational culture theories, the purpose of this paper is to investigate the business value of SC resilience with the consideration of the roles of internal integration (II) and external integration (EI), risk management culture (RMC) and SC flexibility (SCF). Design/methodology/approach This study investigates how RMC, SCF and intra and interorganizational integration affect the performance of SC resilience. It collects primary and secondary data from 194 manufacturing firms listed in the Taiwan Stock Exchange and Taipei Exchange. Findings Results validate the authors’ hypothesis that RMC, SCF and II improve the financial performance of firms through SC resilience efforts. Research limitations/implications This study uses firms from Taiwan manufacturing industry, which might introduce country and industry bias. Practical implications This study helps managers improve the financial performance of their SC resilience efforts by developing RMC, SCF, II and IE across functions and partner firms. Originality/value This study contributes to the literature by empirically testing the relationship between SC resilience and financial performance, and how the relationship is moderated by RMC, SCF, II and EI based on the theories of organizational culture and resource orchestration.

Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for classification pneumonia X-rays images. Open-source thoracic X-ray images acquired from the Kaggle platform were employed for pneumonia recognition, but only a few data were obtained, and datasets were unbalanced after classification, either of which can result in extremely poor recognition from trained neural network models. Therefore, we applied augmentation pre-processing to compensate for low data volume and poorly balanced datasets. The original datasets without data augmentation were trained using the proposed and some well-known convolutional neural networks, such as LeNet AlexNet, GoogLeNet and VGG16. In the experimental results, the recognition rates and other evaluation criteria, such as precision, recall and f-measure, were evaluated for each model. This process was repeated for augmented and balanced datasets, with greatly improved metrics such as precision, recall and F1-measure. The proposed IVGG13 model produced superior outcomes with the F1-measure compared with the current best practice convolutional neural networks for medical image recognition, confirming data augmentation effectively improved model accuracy.