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A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified patterns that can yield new self-knowledge insights. This paper aims to discover the self-quantified patterns using multi-time window models.

This paper proposes a multi-time window analytical workflow developed to support the streaming k-means clustering algorithm, based on an online/offline approach that combines both sliding and damped time window models. An intervention experiment with 15 participants is used to gather Fitbit data logs and implement the proposed analytical workflow.

The clustering results reveal the impact of a time window model has on exploring the evolution of micro-clusters and the labelling of macro-clusters to accurately explain regular and irregular individual physical behaviour.

The preliminary results demonstrate the impact they have on finding meaningful patterns.

This paper aims to reveal the corrosion mechanism and corrosion development regulation of marine engineering structural steel in the marine environment and provide constructive suggestions for marine immersed tunnel engineering.

In this study, marine engineering structural steel’s behavior and corrosion prediction were carried out under the conditions of no cathodic protection and under-protection by artificially adding dissolved oxygen in a simulated seawater solution as a depolarizing agent.

Marine resources are rich in China. With the development of the economy and the improvement of engineering technology, marine engineering structural steel is used more and more widely. Engineering structural steel has a great risk of corrosion failure for long-term service in seawater, as seawater is a kind of corrosive medium containing various salts. At present, there are few projects and research studies available on the corrosion in the seawater environment of Q390C engineering structural steel, which is used in the Shenzhen–Zhongshan Link immersed tunnel steel shell at home and abroad. It cannot guide the corrosion of immersed tunnel steel shells in the ocean.

In this paper, the corrosion mechanism and corrosion development regulation of marine engineering structural steel in the marine environment are studied by accelerated corrosion test in the laboratory, which is of great significance to ensure the long-life durability of the immersed tunnel in marine engineering.

This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification.

This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts.

This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy.

This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

Selection of a gas sensor requires consideration of environmental effects that can significantly affect performance and cause false alarms. Metal‐oxide sensors have high sensitivity due to the specific interactions of gas molecules with thin metal‐oxide films, however, the films can also be sensitive to variations in temperature and humidity and some oxidizing and deoxidizing gases. The purpose of this paper is to evaluate the environmental effect on metal‐oxide nitrogen dioxide (NO₂) sensors quantitatively.

Three commercial metal‐oxide NO₂ sensors and one electrochemical sensor were tested simultaneously under controlled gas concentrations and various environmental conditions. For this test, a customized sensor testing setup was prepared including a gas mixer, heating module, gas chamber, electronics, and data acquisition units.

Based on the test results for NO₂ gas concentrations ranging from 0 to 10 ppm, the metal‐oxide sensors showed significant signal variations at elevated temperatures and humidity. The results provide overall sensor performance. Linearity, repeatability, selectivity and sensitivity of the metal‐oxide sensors were measured and compared to an electrochemical sensor.

A systematic evaluation to characterize metal‐oxide NO₂ sensors is presented, and their comparison regarding sensitivity, selectivity, linearity, and dependence on humidity and temperature is reported. The result provides sensor performance data and guideline for sensor evaluation.

This study explores the effect of local budget retention rate changes (RER) on total factor productivity (TFP) and its components in Vietnam.

The study employs a two-system generalized method of moments (GMM) estimator and data from 2012 to 2019 across all 63 provinces/cities of Vietnam.

The study finds that local budget retention rates significantly influence public investment, affecting scale and allocation efficiency. The reallocation of budgets between regions and from the central government to local levels incurs certain costs, often resulting in economically robust provinces experiencing reductions in their retention rates.

Recognizing the challenges of immediate structural budget changes due to cultural and historical factors, the study suggests a more gradual policy approach. It emphasizes the importance of policy predictability, as abrupt reductions in the retention rate lead to higher costs than gradual reductions, thus implementing budget policies with a clearer timeline. This study provides insight into local budget allocation regimes and their impact on productivity in transitioning countries.

First, the study provides fresh evidence of the impact of retention rate changes on TFP and its components in Vietnam. Second, the study provides insights into the mechanisms of the nexus of increased budget spending, capital efficiency and, most importantly, attaining improvement in education. We also offer further insights into inefficient budget allocation agents in Vietnam, especially in large cities, which should alert scholars to explore this topic further in the future.

This study aims to investigate the role of green strategic alliances (GSAs) in fostering a green shared vision (GSVis) and green shared value (GSV) and their impact on green organizational identity (GOI) and sustainability.

The authors employed structural equation modeling to analyze data collected through a 60-item questionnaire administered in Portugal and China, allowing the authors to test their theoretical model.

The findings of the authors' study indicate that green strategic alliances have a positive influence on the development of a GSVis and GSV in both countries. This, in turn, contributes to improved sustainability and the establishment of a GOI. Furthermore, the authors' results demonstrate that these alliances enhance GSV, resulting in enhanced sustainability performance and a stronger green identity, with a notable increase in awareness of environmental and social practices.

This article is innovative as it applies organizational learning and value creation theories to gain a deeper understanding of how alliances can shape the green identity of companies and contribute to their overall sustainability.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

The purpose of this paper is to test the dual frames of reference theory. For the test, the reference point hypothesis examines whether the difference in the quality of social conditions between the home and host countries determines the variations of immigrants’ trust in the institutions of their host country.

By using hierarchical linear modeling analysis, the current study examines whether “difference of homicide rate from South Korea” or “difference of level of democracy from South Korea” influences immigrants’ confidence in the police. Using data collected from nine different immigrant groups in South Korea, the current study examined both country-level factors as well as individual-level factors.

According to the analysis results, the difference of level of democracy from South Korea significantly influenced immigrants’ confidence in the South Korean police. Specifically, when immigrants came from democratically under-developed countries, they showed a relatively high level of confidence in the South Korean police.

Confidence in the police among the first generation of immigrants has recently attracted increasing attention. However, no previous studies have examined immigrants’ confidence in the police within an Asian country. Thus, the current study might contribute to generating better police performances toward first-generation immigrants who tend to be discriminated against and are also vulnerable to various forms of victimization because of their new status in a new society.

The purpose of this paper is to reveal the empirical issues of the implementation of coordination for textile‐apparel supply chains.

Employing case study, the paper examines three different types of coordination practice in three different structures of textile‐apparel supply chains: vertical integration chain, efficiency oriented chain and 3P‐hub chain. The coordinators are three leading Hong Kong based international textiles and apparel companies in these cases. The case sources are published articles, company web sites and some open seminars offered by the case companies.

In textile and apparel industries, brand owners generally coordinate the supply chain. There are also other coordination practices in industries. Through the research observations and analyses in the cases it is found that the integrated company, powerful garment manufacturer and trade agent play the role of coordinators in vertical integration chain, efficiency oriented chain and 3P‐hub chain, respectively. No matter what type of coordination practice, information sharing and product flow coordination should be comprehensive. Coordinators are the information centers of the whole supply chain. They should have power to manage the supply chain. They should actively integrate the whole chain for maximum total profitability.

This paper is just an overview of coordination practice in textile‐apparel supply chains. The case sources are published articles, company web sites and some open seminars made by the case companies. The methodology should be more systematic.

Coordination in textile‐apparel supply chains is still an unresolved question both from the theoretic and practical points of view. This paper fills in some of the gaps.

This study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices.

The authors used MultiFractal Detrended Fluctuation Cross-Correlation Analysis (MF-X-DFA) to explore the correlation behavior before and during conflict. The authors analyzed the price connections between future prices for crude oil and agricultural commodities. Data consists of daily futures price returns for agricultural commodities (Corn, Soybean and Wheat) and Crude Oil (Brent) traded on the Chicago Mercantile Exchange from Aug 3, 2020, to July 29, 2022.

The results suggest that cross-correlation behavior changed after the conflict. The multifractal behavior was observed in the cross correlations. The Russia–Ukraine conflict caused an increase in the series' fractal strength. The study findings showed that the correlations involving the wheat market were higher and anti-persistent behavior was observed.

The study was limited by the number of observations after the Russia–Ukraine conflict.

This study contributes to the literature that investigates the impact of the Russia–Ukraine conflict on the financial market. As this is a recent event, as far as we know, we did not find another study that investigated cross-correlation in agricultural commodities using multifractal analysis.