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The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array.

First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments.

The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects.

Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects.

It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions.

The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials.

A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.

Selecting and scheduling optimal project portfolio simultaneously is a complex decision-making problem faced by organizations to realize the strategy. However, dynamic synergy relationships among projects complicate this problem. This study aims at constructing a project portfolio selection and scheduling (PPSS) model while quantifying the dynamic synergetic effects to provide decision support for managing PPSS problems.

This study develops a mathematical model for PPSS with the objective of maximal project portfolio benefits (PPBs). To make the results align with the strategy, comprehensive PPBs are divided into financial and non-financial aspects based on the balanced scorecard. Then, synergy benefits evolve dynamically in the time horizon, and system dynamics is employed to quantify them. Lastly, a case example is conducted to verify the applicability of the proposed model.

The proposed model is an applicable model for PPSS while incorporating dynamic synergy. It can help project managers obtain the results that which project should be selected and when it should start while achieving optimal PPBs.

This study complements prior PPSS research in two aspects. First, financial and non-financial PPBs are designed as new criteria for PPSS, making the results follow the strategy. Second, this study illuminates the dynamic characteristic of synergy and quantifies the synergetic effect. The proposed model provides insights into managing a PPSS effectively.

Blockchain technology (BCT) is considered a promising tool to improve the productivity of construction project management. Existing research has studied its potential costs and benefits for the construction industry. However, the potential costs and benefits of BCT failed to be compared as actual costs and benefits of specific applications for stakeholders. To fill this gap, this study seeks to analyze the cost-effectiveness of BCT-based applications in construction project management.

This study is conducted with a customized systematic literature review based on transaction cost theory to enable qualitative comparison. With a deliberately designed structure confining extraneous variables, the costs and benefits of BCT-based applications are identified and compared. The inherent dependent relations of processes and the evolution relations of functions are identified. The cost-effectiveness of blockchain adoption is then analyzed.

Seven functions and six challenges are identified within five processes. The result suggests all identified functions are cost-effective except for manual instruction (coding smart contracts manually). The smart contracts require explicit definition and logic to be effective. However, the construction projects essentially require the institution to be flexible due to unpredictability. The adoption of smart contracts and corresponding additional requirements can increase the transaction cost of bounded rationality.

As manual instruction is fundamental to realize other functions, and its advanced substitute relies on its broad adoption, its cost-effectiveness must be improved for applications to be acceptable to stakeholders. The establishment of a universal smart contract model and a universal, legitimate and efficient database structure are recommended to minimize the cost and maximize the effect of applications.

This study contributes to the knowledge by providing a comprehensive analysis of BCT adoption’s cost-effectiveness in construction project management. The adopted review structure can be extended to analyze the qualitative benefits and challenges of management automation in the early stages.

This study aimed to uncover the characteristics of both misinformation and refutations as well as the associations between different aspects of misinformation and corresponding ways of rebutting it.

Leveraging Hovland's persuasion theory as a research lens and taking data from two Chinese refutation platforms, we characterized the topics of COVID-19-related misinformation and refutations, misinformation communicator, persuasion strategies of misinformation, refutation communicators and refutation strategies based on content analysis. Then, logistic regressions were undertaken to examine how the characteristics of misinformation and refutation strategies interacted.

The investigation into the association between misinformation and refutations found that distinct refutation strategies are favored when debunking particular types of misinformation and by various kinds of refutation communicators. In addition, several patterns of persuasion strategies were identified.

This study had theoretical and practical implications. It emphasized how misinformation and refutations interacted from the perspective of Hovland's persuasion theory, extending the scope of the existing literature and expanding the classical theory to a new research scenario. In addition, several patterns of persuasion strategies used in misinformation and refutation were detected, which may contribute to the refutation practice and help people become immune to misinformation.

This research is among the first to analyze the relationships between misinformation and refutation strategies. Second, we investigated the persuasion strategies of misinformation and refutations, contributing to the concerning literature. Third, elaborating on Hovland’s persuasion theory, this study proposed a comprehensive framework for analyzing the misinformation and refutations in China during the COVID-19 pandemic.

This cross-sectional investigation explores the relative importance of specific psychological factors influencing adjustment in a sample of internationally mobile children and adolescents, commonly referred to as third culture kids (TCKs), living in Switzerland.

The study cohort comprised 126 participants aged 7–17 years, all of whom had experienced international relocations due to their parents' professional commitments. Participants were recruited from local and international schools in Switzerland, and data were collected through validated online questionnaires after obtaining informed consent. The study focused on proximal psychological factors, including emotion regulation strategies (such as cognitive reappraisal and emotion suppression), negative social cognition, resilience and perceived stress, assessing their impact on psychological adjustment. Outcomes were measured through indicators of well-being and mental health, including emotional and behavioral symptoms.

Commonality analysis was used to evaluate the relative contributions of each psychological factor to well-being and mental health in the sample. Perceived stress emerged as the most significant contributor to well-being, while resilience played a crucial buffering role against mental-health difficulties. These findings are consistent with Berry’s Acculturative Stress Model, which highlights the importance of managing stress in the context of cultural transitions. Additionally, the presence of well-established expatriate support networks in Switzerland may have mitigated the negative effects of stress, suggesting that similar patterns may not hold in less resource-rich environments.

The study offers important insights into the challenges faced by TCKs during international relocations, underscoring the need for further cross-cultural research. It remains essential to examine whether emotion regulation strategies such as cognitive reappraisal and resilience mechanisms function similarly in different cultural and socioeconomic contexts.

The findings also suggest the potential benefits of tailored psychotherapeutic interventions aimed at enhancing the well-being and adaptive capacity of TCKs.

Overall, this research contributes valuable knowledge to the understanding of the psychological mechanisms that can inform strategies for supporting TCKs as they navigate the complexities of global mobility.

This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.

Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.

This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.

This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.

Graded honeycombs are materials that exhibit better energy absorption performance compared to uniform honeycombs without adding additional weight. This paper introduces a novel modularized graded honeycomb into a commercial crash box to improve its crashworthiness.

A modularized graded honeycomb is inserted into a commercial crash box to develop a novel crash box. Finite element analyses are conducted to investigate the crashworthiness. Pareto cumulative influence analysis is conducted to rank the effects of design parameters on crashworthiness. A surrogate model-based multi-objective optimization is carried out to improve energy absorption while limiting the impact peak force. An optimal Pareto solution set is obtained.

Modularized honeycomb-filled crash box outperforms that of its corresponding uniform honeycomb-filled crash box and empty crash box in resisting impact. Pareto cumulative influence analysis reveals that for most crashworthiness indicators, cell-wall thicknesses of crash box tube contribute the most, followed by average relative density and graded coefficient of modularized honeycomb (MH). Graded coefficient contributes nearly 10% on mean force and maximum displacement, but it has insignificant influence on peak force and weight. Optimization results show that the optimal designs can not only absorb more energy but also limit the peak force compared with those of uniform honeycomb-filled crash box.

This paper fills a MH into a commercial crash box to propose a novel crash box and demonstrates the positive impact of modularized design on crashworthiness compared with that of uniform honeycomb-filled crash box. Moreover, modularizing honeycomb does not change the weight of the filler, and thus, the novel crash box would benefit development of crash box with lightweight and excellent energy absorption capacity.

This study aims to explore the traditional plant dyeing of Xinjiang Atlas silk fabrics, providing references for the comprehensive utilization of plant dyes in intangible cultural heritage.

The focus of this study is on dyeing experiments of Atlas silk fabrics using safflower extracts, constrained by regional resources. Safflower dry flowers grown in Xinjiang were selected, rinsed with pure water and rubbed. Yellow pigments were removed by adding edible white vinegar. Red pigments from safflower were extracted using an alkaline solution prepared with Populus euphratica ash, a special product of Xinjiang. The extraction rate was analyzed under varying material-to-liquor ratios, pH values, times and temperatures. Direct dyeing process experiments were conducted to obtain different colorimetric L, a, b and K/S values for comparison. Samples with good color development were selected to test the impact of dyeing immersions on color development, and their color fastness, UV protection and antibacterial effects were verified.

The dyeing experiments on silk fabrics confirmed their UV protection capabilities and antibacterial properties, demonstrating effectiveness against E. coli and Staphylococcus aureus. As a major producer of safflower, Xinjiang underscores the significance of safflower as an essential plant dyes on the Silk Road. This study reveals its market potential and suitability for use in the plant dyeing process of Atlas silk, producing vibrant red and pink colors.

The experiments indicated that after removing yellow pigments, the highest extraction rate of red pigment from safflower was achieved at a pH value of 10–11, a temperature of 30°C and an extraction time of 40 min. The best bright red color effect with strong color fastness was obtained with a material-to-liquor ratio of 1:20, a temperature of 40°C and three immersions. The best light pink color effect with strong color fastness was a material-to-liquor ratio of 1:80, a temperature of 30°C and two immersions.

Purpose: This study quantitatively assesses the impact of ESG profile on equity value and risk, as well as identifies potential differences occurring in different sectors, based on the data of the Nasdaq Nordic market.

Methodology: To reach this purpose, (i) the stock return and volatility analysis is being conducted (using the methods of paired sample t-test, correlation, etc.), and (ii) panel data models with constant, fixed and random effects are being constructed. The analysis is based not only on the company’s ESG performance but also on a cross-sectoral approach.

Findings: The results revealed that although ESG factors appeared to have a significant impact in most of the constructed models, the impact of these factors varies depending on the sector.

Implications: This research provides a comprehensive and comparative approach to the importance of the ESG profile for investment performance and therefore can be useful both for impact investors making investment decisions in dynamic global financial markets and for companies developing or reforming their ESG strategies.

Limitations: Due to the problem of data availability, the cross-sectoral comparison was performed based on the limited number of sectors. In addition, the limited availability of ESG data in the analysed market did not allow the use of additional methods to assess the impact of ESG.

Future Research: Expanding the data sample and assessing the impact of a company’s ESG profile not only in different sectors but also in different phases of the economic cycle might be the direction for future research.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.