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The purpose of this paper is to make research on causing mechanism of meteorological disaster as well as the components of meteorological disaster system and their semantic relationships. It has important practical significance due to the urgent need of further providing support for pre-assessment of influences of disastrous weather/climate events and promoting the level of emergency management.

This paper analyses the occurrence regulations and components of meteorological disasters and proposes the concept of meta-action. Ontology modelling method is adopted to describe the components and relationships among different parts comprising meteorological disaster system, and semantic web rule language is selected to identify the implicit relationships among the domain knowledge explicitly defined in ontology model. Besides, a case is studied to elaborate how to provide logic and semantic information support for comprehensive risk assessment of disastrous weather/climate events based on rule-based ontology reasoning method. It proves that ontology modelling and reasoning method is effective in providing decision makings.

This paper provides deep analyses about causing mechanisms of meteorological disasters, and implements information fusion of the components of meteorological disaster system and acquisition of potential semantic relations among ontology components and their individuals.

In this paper, on the basis of analysing the disaster-causing mechanisms, the meteorological disaster ontology (MDO) model is proposed by using the ontology modelling and reasoning method. MDO can be applied to provide decision makings for meteorological departments.

This study aims to present a high-end lesson study (HELS) model to develop students' subject competency. Data were collected from a Beijing suburban key senior high school in China. How the subject competency framework (SCF) supports HELS and develops students' subject competency in practice are discussed in this study.

This study provides a four-dimensional SCF developed by the chemistry education research team at Beijing Normal University. Basic procedures of the HELS model involve the project plan, students' pre-test, lesson design workshop, first-round teaching implementation and improvement, second-round teaching implementation and evaluation, students' post-test, and results discussion. Data were collected from each of the procedures, and analysis of the data is conducted in both qualitative and quantitative approaches.

The results show that the SCF supports HELS implementation by (1) identifying key teaching objectives based on curriculum standard requirements and students' subject competency performance; (2) organizing teaching content based on the core knowledge to develop cognitive mode; (3) designing tasks and activities regarding understanding–applying–transferring and innovating categories and sub-categories of SCF; (4) establishing students' cognitive perspectives and reasoning paths to promote their subject competency by teacher–student interaction.

The HELS model provides theory-based pedagogical guidance for conducting lesson studies. It presents the SCF and orientation. The SCF is used throughout the entire process of HELS, including the identification of teaching objectives, the selection and organization of teaching content, and the design and implementation of teaching activities. It reflects a systematic instructional design–implementation–discussion–improvement–evaluation process. The SCF-based HELS can be applied to different topics and disciplines.

A lack of visibility into the manufacturer’s production cost information impedes a retailer’s ability to maximize her own profits, especially when market demand is uncertain. The purpose of this paper is to investigate the use of an option contract within a one-period two-echelon supply chain in the presence of asymmetric cost information.

Based on the principal-agent model, the retailer, acting as a Stackelberg leader, offers a menu of option contracts to mitigate the risk of uncertain demand and reveal asymmetric production cost information. The optimal contract in asymmetric and symmetric information scenarios is derived. Finally, the impact of production costs on the optimal contracts and the actors’ profits is explored by numerical experiments.

By comparing the optimal equilibrium solutions in two scenarios, the authors show that asymmetric cost information has a large impact on the optimal option contract and profits. In addition, information rent is affected by the type differential. The results prove that the level of information asymmetry plays a vital role in option contracts and profits.

Different from the existing literature on private demand information, this paper considers a supply chain with asymmetric cost information in the context of option contracts. Interestingly, not only the production cost but also the probability of a low production cost can affect the option strike price. In addition, from the perspective of the manufacturer, a high cost does not always bring a high information rent. These findings can provide some guidance to decision-makers.

Traditional analytical methods are often time-consuming and require bulky instruments, making their widespread implementation challenging. This paper aims to represent the principal concepts of biosensors as an introduction of this technology to readers and offers a comprehensive understanding of its functions.

The authors provide descriptions of the components, characteristics and advantages of biosensors along with the immobilization methods, followed by a brief discussion.

A biosensor is an analytical device comprising a specific biomolecule and a transducer in conjunction with an output system. The biomolecule recognizes a specific target which leads to a change in physicochemical properties of a system. This biorecognition phenomenon is later converted into a detectable signal by the transducer. Biosensors can essentially serve as rapid and cost-effective devices with excellent sensitivity and specificity for critical purposes in innumerable fields, ranging from scientific research to day-to-day applications.

Here, the authors explain and discuss the approaches and challenges with the aim of leading to an interest in biosensor development and improving their applications.

Student in-class participation is a crucial aspect in the learning process, including in synchronous learning process. Several variables may hinder students from actively participating in synchronous learning including communication anxiety. As behavior, student in-class participation can be modeled using theory of planned behavior (TPB) with communication anxiety as barrier variable. The current study aims to investigate student decision-making process to actively participate in synchronous online learning using TPB frameworks and examine the effect of communication anxiety as part of extended TPB.

This was quantitative nonexperimental research conducted by gathering cross-sectional data through electronic survey. A total of 1,009 Indonesian college students voluntarily participate in the survey. Structural equation modeling was used to analyze the data and test the hypothesis.

The results indicate that there was significant and positive effect between TPB-related variables: (1) attitude, (2) subjective norms, (3) behavioral control, (4) intention and (5) behavior. Communication anxiety, as an additional variable in the extended model, shows significant and negative effects on behavioral control and active participation behavior. However, the communication anxiety indicates insignificant effect on students’ intention to participate in online synchronous learning. The findings support the evidence that students’ intention to actively participate in synchronous learning can be described using TPB and communication anxiety hinders student to actively participate in synchronous learning.

The paper extends TPB on student active participation. Prior research limited to original theory of planned behavior (TPB) on student in-class and massive open online course (MOOC) participations, whereas the current research includes communication anxiety as additional variable on extended TPB to understand students’ intention to actively participate in synchronous online learning.

A widespread focus on the plant-based antimicrobial cotton fabric finishes has been accomplished with notable importance in recent times. The antimicrobials prevent microbial dwelling in fabrics, which causes severe infections to the fabric users. Chemical disinfectants were conventionally used in fabrics to address this challenge; however, they were found to be toxic to humans. Thus, the present study aims to deal with the utilization of phytochemical extracts from different parts of Pongamia pinnata as antimicrobial coatings in cotton fabrics.

The root, bark and stem were collected and washed several times using tap water. Then, the leaves were dried at room temperature and the root and bark were dried using an oven at 40ºC. After drying, they were ground into fine powder and extracted with ethanol using the Soxhlet apparatus. After that the extract was coated on the fabric tested for antimicrobial studies.

The results reported that the leaf extract of P. pinnata-coated fabric exhibited enhanced antibacterial property towards gram-negative Escherichia coli bacteria, followed by root, bark and stem. The wash durability test in the extract-coated fabric samples revealed that dip-coating retained antibacterial activity until five washes. Thus, the current study clearly suggests that the leaf extract from P. pinnata is highly useful to develop antibacterial cotton fabrics as health-care textiles.

The novelty of the present work is to obtain the crude extract from the leaves, bark, root and stem of P. pinnata and evaluate their antibacterial activity against E. coli, upon being coated on cotton fibres. In addition, the extracts were subjected to wash durability analysis to study the coating efficiency of the phytochemicals in cotton fabrics and a probable mechanism for the antibacterial activity of P. pinnata extracts was also presented.

This study aims to empirically examine how blockchain, one of the emerging Industry 4.0 technologies, can combat climate change by improving their green innovation performance, particularly under conditions of policy uncertainty.

This study utilizes the difference-in-difference-in-difference (DDD) method to explore the effect of blockchain on enterprises' green innovation performance. The analysis is based on data from Chinese-listed enterprises spanning the period from 2013 to 2021.

First, the adoption of blockchain in enterprises registered in areas designated as low-carbon pilot cities can significantly improve their green innovation performance. Second, the enhancement of green innovation efficiency emerges as the primary driving force behind the adoption of blockchain, thereby leading to improved green innovation performance. Lastly, it is observed that blockchain adoption has a greater positive impact on improving green efficiency in private enterprises compared to state-owned enterprises in China.

For managers, the findings can provide valuable insights to help them better prepare for the challenges and opportunities presented by the era of Industry 4.0. For policymakers, this study offers valuable insights into the interaction between new technologies in Industry 4.0 and the performance of green innovation, thereby aiding in the formulation of effective policies.

This study contributes to bridging the existing gap between the adoption of new technologies, such as blockchain, and their potential impact on climate change. Moreover, this research enriches practitioners' understanding of how new technologies in the era of Industry 4.0 can be applied to address significant challenges like climate change.

This study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.

The research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.

This study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.

This research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease which has become a public health concern, whose growing prevalence has been reported as around 33.9% in Iran. As oxidative stress plays a crucial role in the pathogenesis of NAFLD, antioxidant compounds such as quercetin could ameliorate the side effect of oxidative stress. The aim of the current study was to assess the effect of quercetin on lipid profile, liver enzymes and inflammatory indices in NAFLD patients.

In a randomized, double-blind, placebo-controlled trial conducted as a pilot study, 90 patients with NAFLD were supplemented with either a quercetin or a placebo capsule twice daily (500 mg) for 12 weeks. Both groups were advised to follow an energy-balanced diet with physical activity recommendations. Blood sample was obtained for laboratory parameters at baseline and the end of week 12.

At the end of the follow-up, quercetin group had significantly greater reduction in anthropometric parameters, cholesterol (−15 ± (−41, 0.00) in Q group versus −1± (−8, 2) in control group, p = 0.004), TG (−56.7 ± 22.7) in Q group versus −13.4 ± 27.7 in control group, p = 0.04), and tumor necrosis factor-α (TNF-α) (−49.5 ± (−99, 21) in Q group versus −5 ± (−21, 0.30) in the control group, p < 0.0001) compared to the control group. However, changes in fatty liver grade, liver enzymes, as well as high density lipoprotein-cholesterol and high-sensitivity C-reactive protein were not significantly different between the two groups.

To the best of the authors’ knowledge, this was the first study which assessed the effect of quercetin supplementation on liver enzymes, lipid profile and inflammatory indices of NAFLD patients as a double-blind placebo-controlled pilot study.

The purpose of this study is to investigate the cross-sectional associations between growth options and the peer pay–performance sensitivity of CEO compensation.

This study includes analytical analysis and multivariable regression analysis.

It is predicted in this study that there is a non-linear concave relation between peer pay–performance sensitivity and a firm’s growth options. Results based on the executive compensation data from ExecuComp are consistent with the hypothesis presented in this study.

Future scholars need to consider the non-linear impact of growth options on peer pay–performance sensitivity when they conduct research related to CEO compensation by differentiating the company’s growth options to be at a low, medium and high level. In an industry, when a compensation committee decides on the peers for performance comparison purposes, the committee needs to make sure that the peer firms they select have similar operational environments, for example, they face similar growth options (e.g. low, medium or high) and idiosyncratic variances.

This study contributes to the managerial compensation literature by revealing the important role growth options, as well as idiosyncratic variances, play on peer pay–performance sensitivity. The results of this study have implications for both future researchers as well as industrial practitioners.

It gives guidance on designing CEO compensation contracts.

This is an original work from the coauthors listed on this study.