Primary Contribution Research Articles

Sustainable Development of China’s Intangible Cultural Heritage in Rural Revitalization based on Experience Perspective

The protection and inheritance of the local intangible cultural heritage (ICH) holds profound significance, which has the characteristics of oral transmission and the intertwining of time and space, is an important content and means of rural cultural governance in China in the new era. It is significant in value for promoting rural revitalization and passing on the cultural genes of China. As rural society transforms, the change of rural social structure and cultural order leads to some conflicts for rural ICHs in China, such as subjective crises, weakening of values and the impact of modernity. Therefore, this paper combines the advantages of experience with the inheritance needs of ICH and puts its inheritance and protection under the national strategy of rural revitalization. The study focuses on the function of ICH in rural revitalization from the perspective of experience and explores the topic from three levels: the government, universities, and villages. Moreover, we provide sustainable development strategies for integrating intangible cultural heritage into rural revitalization to promote the revival of rural culture. This article explores the mutually beneficial relationship between intangible cultural heritage and rural revitalization, enhancing our understanding of both aspects. The primary contribution lies in providing sustainable development strategies for integrating intangible cultural heritage into rural revitalization at the government, university, and village levels.

Humans and Automation: Augmenting Security Operation Centers

The continuous integration of automated tools into security operation centers (SOCs) increases the volume of alerts for security analysts. This amplifies the risk of automation bias and complacency to the point that security analysts have reported missing, ignoring, and not acting upon critical alerts. Enhancing the SOC environment has predominantly been researched from a technical standpoint, failing to consider the socio-technical elements adequately. However, our research fills this gap and provides practical insights for optimizing processes in SOCs. The synergy between security analysts and automation can potentially augment threat detection and response capabilities, ensuring a more robust defense if effective human-automation collaboration is established. A scoping review of 599 articles from four databases led to a final selection of 49 articles. Thematic analysis resulted in 609 coding references generated across four main themes: SOC automation challenges, automation application areas, implications on analysts, and human factor sentiment. Our findings emphasize the extent to which automation can be implemented across the incident response lifecycle. The SOC Automation Matrix represents our primary contribution to achieving a mutually beneficial relationship between analyst and machine. This matrix describes the properties of four distinct human-automation combinations. This is of practical value to SOCs striving to optimize their processes, as our matrix mentions socio-technical system characteristics for automated tools.

Trust in the pension system and housing investment

PurposeThis research contributes to the existing literature on the connection between trust and investment activities by exploring the effect of trust in the retirement system on dwelling investments.Design/methodology/approachThis study utilizes data including 28 OECD countries from 2009 to 2020, and employs panel fixed effects and GMM estimators.FindingsThe analysis reveals a negative relationship between trust in the retirement system and investment in dwellings. Notably, this is found to be more evident in countries that promote neo-liberalized welfare systems.Practical implicationsThe implications of our results are particularly relevant for policymakers and international construction firms.Originality/valueThe primary contribution of this paper extends the “trust–pension investment behavior” nexus. We explore whether individuals with diminished trust in the retirement system consider investing in the property market as an alternative means to safeguard their financial well-being during retirement.

STFGCN: Spatial–temporal fusion graph convolutional network for traffic prediction

Accurate traffic prediction plays a crucial role in improving traffic conditions and optimizing road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic spatial dependencies is crucial for accurate traffic prediction. These features can effectively reflect complex dynamic spatial–temporal processes, which have not been comprehensively addressed in most existing research work. Motivated by this issue, the primary contribution of this paper lies in proposing a novel Spatial–Temporal Fusion Graph Neural Network (STFGCN) for accurate traffic prediction, achieved by extracting multi-scale temporal dependencies from multiple semantic environments and constructing a dynamic adaptive graph to model spatial dependencies based on temporal characteristics. Specifically, to capture the multi-scale dynamic temporal dependencies effectively, a Multi-Scale Fusion Convolution (MSFC) module is designed, in which the temporal dependencies are extracted from multiple textual environments by utilizing multi-scale convolution. In order to model dynamic spatial dependencies, a Spatial Adaptive Fusion Convolution (SAFC) module is designed by combining the recent coherence and periodicity to infer dynamic graphs, which are then fused to model dynamic spatial dependencies. Extensive experimental results on five real-world datasets demonstrate that the proposed STFGCN has superior performance. Specifically, compared with the state-of-the-art baselines, STFGCN reduced 1.2% to 16.4% in RMSE measure.

Optimizing Droplet-Based Electricity Generator via a Low Sticky Hydrophobic Droplet-Impacted Surface.

Droplet-based electricity generators (DEGs) are increasingly recognized for their potential in converting renewable energy sources. This study explores the interplay of surface hydrophobicity and stickiness in improving DEG efficiency. It find that the high-performance C-WaxDEGs leverage both these properties. Specifically, DEGs incorporating polydimethylsiloxane (PDMS) with carnauba wax (C-wax) exhibit increased output as surface stickiness decreases. Through experimental comparisons, PDMS with 1wt.% C-wax demonstrated a significant power output increase from 0.07 to 1.2Wm- 2, which attribute to the minimized adhesion between water molecules and the polymer surface, achieved by embedding C-wax into PDMS surface to form microstructures. This improvement in DEG performance is notable even among samples with similar surface potentials and contact angles, suggesting that C-wax's primary contribution is in reducing surface stickiness rather than altering other surface properties. The further investigations into the C-WaxDEG variant with 1wt.% C-wax PDMS uncover its potential as a sensor for water quality parameters such as temperature, pH, and heavy metal ion concentration. These findings open avenues for the integration of C-WaxDEGs into flexible electronic devices aimed at environmental monitoring.

Almost sure detection of the presence of malicious components in cyber–physical systems

This article studies a fundamental problem of security of cyber–physical systems (CPSs): that of detecting, almost surely, the presence of malicious components in the CPS. We assume that some of the actuators may be malicious while all sensors are honest. We introduce a novel idea of separability of state trajectories generated by CPSs in two situations: those under the nominal no-attack situation and those under the influence of an attacker. We establish its connection to the security of CPSs, particularly in detecting the presence of malicious actuators (if any) in them. As primary contributions, we establish necessary and sufficient conditions for the aforementioned detection in CPSs modeled as Markov decision processes (MDPs). Moreover, we focus on the mechanism of perturbing the pre-determined control policies of the honest agents in CPSs modeled as stochastic linear systems, by injecting a certain class of random process called private excitation; sufficient conditions for detectability and non-detectability of the presence of malicious actuators, assuming that the policies are randomized history-dependent and randomized Markovian, are established. Several technical aspects of our results are discussed extensively.

The dual-edged sword effect of reciprocal information exchanges within partnerships on performance: The mediating role of creativity

This study investigates the impact of reciprocal information exchanges within partnerships on organizational performance, with a focus on the mediating role of organizational creativity. We conducted a quantitative investigation using a survey of 401 micro, small, and medium enterprises (MSMEs) in Brazil, and analyzed the data using multivariate regression analysis. Our primary contribution to the strategy field lies in the adoption of a knowledge-based view (KBV) to demonstrate how reciprocal information exchanges within partnerships can stimulate organizational creativity and positively impact performance, particularly in resource-scarce environments. A key managerial insight from our study is that reciprocal information exchanges serve as a dual-edged sword, enhancing both organizational creativity and performance. This is particularly relevant for firms in competitive ecosystems where the bidirectional flow of information can be critical to gaining competitive advantage.

Digital transformation and environmental, social, and governance greenwashing: Evidence from China

This paper examines the relationship between digital transformation(DT) and environmental, social, and governance(ESG) greenwashing using Chinese listed companies as a sample from 2012 to 2022. Furthermore, it analyzes the enterprise and regional heterogeneity as well as the influencing mechanisms on this relationship. The research results indicate that corporate digital transformation significantly inhibits ESG greenwashing, with a more pronounced effect on companies in non-high-pollution industries, high-tech industries, and the eastern region. In addition, mechanism tests reveal that internal control and financing constraints play a partial mediating role. Digital transformation suppresses ESG greenwashing by enhancing the quality of internal control and alleviating financing constraints. The primary contribution of this paper lies in demonstrating that digital transformation can serve as a strategic tool to mitigate ESG greenwashing. This enriches the research on the outcomes of digital transformation as well as the factors influencing ESG greenwashing. The conclusions of this paper provide theoretical foundations and policy recommendations for better ESG development by enterprises and governments in emerging markets. At the same time, this paper has a certain guiding role for the introduction and implementation of policies to encourage digital transformation.

Zero-Emission Heavy-Duty, Long-Haul Trucking: Obstacles and Opportunities for Logistics in North America

Background: Pressure is growing in North America for heavy-duty, long-haul trucking to reduce greenhouse gas (GHG) emissions, ultimately to zero. With freight volumes rising, improvement depends on zero-emissions technologies, e.g., battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs). However, emissions reductions are constrained by technological and commercial realities. BEVs and FCEVs are expensive. Further, BEVs depend on existing electricity grids and FCEVs rely on steam–methane reforming (SMR) or electrolysis using existing grids to produce hydrogen. Methods: This study assembles publicly available data from reputable sources to estimate breakeven vehicle purchase prices under various conditions to match conventional (diesel) truck prices. It also estimates GHG emissions reductions. Results: BEVs face numerous obstacles, including (1) limited range; (2) heavy batteries and reduced cargo capacity; (3) long recharging time; and (4) uncertain hours-of-service (HOS) implications. On the other hand, FCEVs face two primary obstacles: (1) cost and availability of hydrogen and (2) cost of fuel cells. Conclusions: In estimating emissions reductions and economic feasibility of BEVs and FCEVs versus diesel trucks, the primary contributions of this study involve its consideration of vehicle prices, carbon taxes, and electricity grid capacity constraints and demand fees. As electricity grids reduce their emissions intensity, grid congestion and capacity constraints, opportunities arise for BEVs. On the other hand, rising electricity demand fees benefit FCEVs, with SMR-produced hydrogen a logical starting point. Further, carbon taxation appears to be less important than other factors in the transition to zero-emission trucking.

Examining presence in immersive virtual reality and website interfaces through the cognitive fit and cognitive load theories

PurposeThe purpose of this paper is to assess the effects of presence on users’ information processing in mediated visualizations using immersive virtual reality (VR) and websites, focusing on information understanding and cognitive load. The impact of user interfaces on users’ perception of presence is also tested.Design/methodology/approachA between-subjects laboratory experiment using two user interfaces – immersive VR and website – is carried out. It is complemented by a self-administered survey on the participants.FindingsThe current results indicate that an immersive VR interface improves tourists’ information processing by producing a heightened sense of presence. The results confirm that presence facilitates tourists’ information processing by increasing information understanding and decreasing cognitive load. Finally, a negative relationship between cognitive load and information understanding is obtained.Practical implicationsVR user interfaces are not only visually appealing but also cognitively optimized. Immersive VR devices can be introduced in the specific sale process of cruise trips through both offline channels (physical stores and travel agencies) and online channels to facilitate tourists’ information processing during the prepurchase phase.Originality/valueThe primary contribution lies in the integration of the theory of presence with two influential information processing frameworks, namely, cognitive load theory and cognitive fit theory. This integration provides a holistic perspective on how user interfaces influence consumer information processing in the unique context of cruise tourism, particularly during the stage of a prepurchase information search.

Preparation, mechanical properties and strengthening mechanisms of T15 particles reinforced Cu matrix composites

High speed steel T15 particle reinforced copper matrix composites were prepared using powder metallurgy. A systematic investigation was conducted into the effects on the microstructure and mechanical properties of the composites of the sintering temperature, T15 hard particle content, and particle size. The results indicate that the grain size is efficiently refined by the addition of T15 hard particles. The decrease in grain size of 10 wt% T15/Cu composite is from 6.87 μm to 1.34 μm at the same sintering temperature, and the grain size is proportional to both the sintering temperature and the size of hard particles. As the sintering temperature increases, the relative density and hardness of the composite gradually increase, while the yield strength initially increases and then declines. When sintered at 790 °C, the 10 wt% T15/Cu composite achieved a maximum yield strength of 160.1 MPa, which is 178.9 % and 30.6 % higher than that of pure copper and 10 wt% TiC/Cu composites, respectively. There are no visible cracks after compression, and a good balance of strength and toughness is achieved. Meanwhile, the primary contributions of strengthening mechanisms such as grain refinement, thermal expansion mismatch and load transfer to the mechanical properties of T15/Cu composites were analyzed. It is anticipated that this will establish a foundation for the preparation and application of high-performance copper matrix composites reinforced with metal particles.

Maximum Lyapunov Exponent Based Nearest Neighbor Algorithm For Real-Time Transient Stability Assessment

In power systems, ensuring transient stability is paramount to prevent unforeseen blackouts and power failures. Transient stability assessment is crucial for the early detection and mitigation of instabilities, providing a rapid response to severe fault situations. The concept of the maximum Lyapunov exponent facilitates fast predictions for transient stability assessment after severe disturbances. This paper introduces an efficient maximum Lyapunov exponent algorithm for online transient stability assessment, representing the primary contribution of this work. This approach uses the time series data from the rotor angles of generators or the phase angles of generator terminal buses. Case studies are conducted on the Nordic Power System, with simulations performed in DigSilent PowerFactory. This study contributes by offering insights into the performance and adaptability of the proposed algorithm.

Exportation and Big Data: An Analysis of Kansas Trade Trends 1995 – 2025

The exportation of goods and services plays an essential part in the growth of an economy, exposing producers of goods and services to an external market(s) that fosters collaboration within and outside nations of the world. Some states rely more on exporting goods than others in the United States, and different products are exported at higher rates between states. The specialization of exports can benefit a state since those products help its economy succeed. Understanding what exported goods are essential to the economy allows people to decide what should be produced and exported. Kansas has its own set of goods that it exports across the world. By observing their exporting habits, policymakers in Kansas will know where they stand and how they can move forward. This information can help all stakeholders in Kansas improve the economy for everyone in the state. The study focuses on the 2000 – 2025 period, and its primary contribution is to identify what products (goods and services) the State of Kansas has a comparative advantage in production and what nation of the world Kansas has both a comparative and absolute advantage in exportation over the other 49 States and the District of Columbia.

MCV-UNet: a modified convolution & transformer hybrid encoder-decoder network with multi-scale information fusion for ultrasound image semantic segmentation.

In recent years, the growing importance of accurate semantic segmentation in ultrasound images has led to numerous advances in deep learning-based techniques. In this article, we introduce a novel hybrid network that synergistically combines convolutional neural networks (CNN) and Vision Transformers (ViT) for ultrasound image semantic segmentation. Our primary contribution is the incorporation of multi-scale CNN in both the encoder and decoder stages, enhancing feature learning capabilities across multiple scales. Further, the bottleneck of the network leverages the ViT to capture long-range high-dimension spatial dependencies, a critical factor often overlooked in conventional CNN-based approaches. We conducted extensive experiments using a public benchmark ultrasound nerve segmentation dataset. Our proposed method was benchmarked against 17 existing baseline methods, and the results underscored its superiority, as it outperformed all competing methods including a 4.6% improvement of Dice compared against TransUNet, 13.0% improvement of Dice against Attention UNet, 10.5% improvement of precision compared against UNet. This research offers significant potential for real-world applications in medical imaging, demonstrating the power of blending CNN and ViT in a unified framework.

Enhancing the interpretability of sentiment analysis with signatures

Sentiment analysis is a significant research area within the field of natural language processing, with applications in various social contexts. Recent advances in related research have led to notable improvements in accuracy, yet existing text sentiment analysis models continue to face challenges in terms of interpretability. This paper proposes a text sentiment classification method integrating BERT and BiLstm, comprising three stages: training, signature generation, and testing. Our primary contribution lies in the signature generation and testing stages. In the training stage, BERT is employed to generate word embeddings, while BiLstm is utilized for learning to construct the text sentiment classification model. In the signature generation stage, the training and validation sets are passed through the aforementioned classification model, the feature vectors are extracted following their passage through the fully-connected layer, and the feature vectors are utilized to generate the signature generation stage. The signature is then stored in the database. In the Testing Stage, the aforementioned operation is repeated on the test set in order to obtain the signatures. The signatures are then matched in the constructed signature database in order to obtain the final classification results. The matching degree is subsequently used as a threshold in order to be employed for subsequent evaluation of the results. The method enhances the interpretability of the classification task by matching through signatures. Two datasets, SST-2 and IMDB, were subjected to experimentation. Four quantitative metrics were employed for the evaluation of the results: accuracy, recall, precision, and F1-score. With a threshold of 0.99, the models optimal accuracy, recall, precision, and F1-score results reached 0.896, 1, 0.896, 0.945 and 0.847, 0.999, 0.848, 0.917, respectively.

Understanding the application of evolutionary psychology in consumer behavior: A review and future research agenda

AbstractThis paper is the first to offer a comprehensive literature review of the role of evolutionary psychology (EP) in marketing and consumer behavior. This study takes a holistic approach, combining techniques of a systematic review with bibliometric analysis, to provide a performance analysis and identify theories and methodologies used in the literature. Most importantly, by studying the current state of EP, we elucidate six major themes: the role of gender in families, the role of affect in consumer behavior, food preferences and shopping behavior, motivations for and consequences of status signaling, the impact of ovulation on consumer motives and behaviors, and contributions to the greater good. The findings enable researchers to understand the current state of the literature. Further, to advance the application of EP in consumer behavior, we identify gaps related to each theme and offer research questions that can serve as catalysts for future research. Thus, we offer two primary contributions: a comprehensive overview of the literature as it relates to methods, theories, and themes and detailed guidance that can be used to invigorate research on EP.

Robust predictive fault-tolerant control under actuator random failures

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation.

Sustainable Municipal Solid Waste Management Strategies and Practices: A review

Purpose: This paper introduces a multi-period, multi-objective model for creating an integrated, sustainable municipal solid waste management supply chain. The model includes source separation and a reward-penalty mechanism, aiming to minimize total system costs, greenhouse gas emissions, and environmental impacts on residential areas. Many countries strive to develop efficient solid waste management systems (SWMS) that handle and dispose of daily waste cost-effectively while maintaining sustainability. These systems include waste sources, collection stations, landfills, incinerators, recycling plants, and a transportation network. Methodology: A systematic literature review was conducted on existing literature from various studies. Recent studies were used for this systematic review of sustainable municipal solid waste management strategies and practices. Findings: Decision-makers must design or reconfigure a sustainable MSWM to determine the optimal supply chain network for treating and disposing of daily waste effectively. This paper suggests sustainable waste management strategies and practices from various countries, aiming to determine the best number and locations for facilities and optimal waste flow within the system to minimize daily costs. The primary contribution of this review is the theoretical development of sustainable MSWM practices applicable worldwide. Unique contribution to theory, policy and practice: Cooperation between the private and public sectors involved in solid waste management and the circular economy is imperative to ensure the technical, financial, and social sustainability of waste management systems. Large-scale awareness programs on solid waste management and the circular economy should be implemented, focusing on fostering environmentally responsible behaviors and attitudes, promoting sorting, selective collection, and the recovery of materials and energy from waste.

Numerical Investigation of the Seismic-Induced Rocking Behavior of Unbonded Post-Tensioned Bridge Piers

It is essential and convenient to use accurate and validated numerical models to simulate the seismic performance of post-tensioned (PT) rocking bridge piers, with a particular emphasis on accurately capturing rocking behavior. The primary contribution of this study is a comparison of the effectiveness of four commonly used numerical base rocking models (namely, the lumped plasticity (LP) model and the multi-contact spring (MCS) models with linear elastic (MCS-LE), bilinear elastic–plastic (MCS-EP) and nonlinear plastic (MCS-NP) material behavior, respectively) in modeling both the cyclic and seismic responses of PT rocking bridge piers. Also, this study validates the 3D contact stiffness equation for numerical models and assesses the differences between the dynamic and static stiffness values of the contact springs. Both quasi-static and shaking table tests of typical PT rocking piers are adopted to calibrate/validate these numerical models. These models describing the PT rocking piers’ seismic performance are formulated and calibrated, showing good agreement with test results for test specimens. Additionally, the suggested values of model spring stiffness for dynamic and quasi-static analyses are identified by parametric analysis. All base rocking models can predict the pier’s cyclic and seismic behavior after the calibration of contact spring stiffness values. The recommended contact stiffness for the dynamic analysis of PT rocking piers is smaller than that used for the quasi-static analysis. The results and findings provide a valuable reference and solution for the numerical simulation of PT rocking piers.

Diabetes mellitus and serum organochlorine pesticides mixtures in Mexican women

BackgroundVery recently, it has been reported that exposure to different mixtures of organochlorine pesticides (OCP) is associated with the development of diabetes mellitus (DM). In Mexico, DM is a public health problem that might be related to the historical intense use of OCP. We aimed to evaluate, the association between DM and serum concentrations of OCP mixtures, and identify the main contributors within them.MethodsWe conducted a secondary cross-sectional analysis on the control group from a breast cancer population-based case-control study conducted from 2007 to 2011 in Northern Mexico. We identified 214 self-reported diabetic women and 694 non-diabetics. We obtained direct information about sociodemographic, lifestyle and reproductive characteristics. We determined 24 OCP and metabolites in serum by gas chromatography using an electron capture micro detector. We used Weighted Quantile Sum regression to assess the association of DM and exposure to multiple OCP, and the contribution of each compound within the mixture.ResultsWe found a positive adjusted association between DM and an OCP mixture (OR: 2.63, 95%CI: 1.85, 3.74), whose primary contribution arose from p, p’-DDE (mean weight 23.3%), HCB (mean weight 17.3%), trans nonachlor (mean weight 15.4%), o, p’-DDE (mean weight 7.3%), heptachlor epoxide (mean weight 5.9%), oxychlordane (mean weight 4.7%), and heptachlor (mean weight 4.5%). In addition, these OCP along with p, p’-DDT and cis chlordane, were of concern and remained associated when excluding hypertensive women from the analysis (OR 2.55; 95% CI 1.56, 4.18).ConclusionsOur results indicate, for the first time in a Latin-American population, that the concomitant exposure to multiple OCP is associated with DM. Further research is needed since the composition of OCP mixtures may vary according to regional pesticides use patterns.