Empirical Test Research Articles

Insights Into the Effectiveness of Advertising During the Recession Caused by COVID-19

This study employs an online experimental approach to explore why an appropriate message appeal that resonates with consumers is crucial during a recession, rather than reducing investments in marketing, such as advertising spending. Drawing upon congruity theory and the accessibility–diagnosticity framework, the results of this experimental study demonstrate an interaction effect between message appeals and the state of a recession on consumers’ booking intentions. Furthermore, this effect is mediated by perceived advertisement diagnosticity, but only in the context of a price discount appeal in advertisements during a recession. This research contributes to extending the existing literature by examining the dynamic relationship between message appeals and the state of a recession on consumers’ behavioral intentions, mediated by their perceived advertisement diagnosticity. This explanation plays a significant role in bridging the gap in the lack of theoretical reasoning regarding relationships beyond the conceptual threshold through empirical testing. Second, the results of this study suggest more effective and actionable propositions for marketing and advertising professionals.

Future of ADHD Care: Evaluating the Efficacy of ChatGPT in Therapy Enhancement.

This study explores the integration of large language models (LLMs), like ChatGPT, to improve attention deficit hyperactivity disorder (ADHD) treatments. Utilizing the Delphi method for its systematic forecasting capabilities, we gathered a panel of child ADHD therapy experts. These experts interacted with our custom ChatGPT through a specialized interface, thus engaging in simulated therapy scenarios with behavioral prompts and commands. Using empirical tests and expert feedback, we aimed to rigorously evaluate ChatGPT's effectiveness in therapy settings to integrate AI into healthcare responsibly. We sought to ensure that AI contributes positively and ethically to therapy and patient care, thus filling a gap in ADHD treatment methods. Findings show ChatGPT's empathy, adaptability, and communication strengths, thereby highlighting its potential to significantly improve ADHD care. The study points to ChatGPT's capacity to transform therapy practices through personalized and responsive patient care. However, it also notes the need for enhancements in privacy, cultural sensitivity, and interpreting nonverbal cues for ChatGPT's effective healthcare integration. Our research advocates for merging technological innovation with a comprehensive understanding of patient needs and ethical considerations, thereby aiming to pioneer a new era of AI-assisted therapy. We emphasize the ongoing refinement of AI tools like ChatGPT to meet ADHD therapy and patient care requirements more effectively.

Wind turbine load optimization control and verification based on wind speed estimator with time series broad learning system method

AbstractWith the rapid development of wind power, the power performance and mechanical load characteristics of wind turbine are simultaneously considered and focused. Normally, wind turbine senses the incoming flow characteristics through the nacelle mounted anemometer, due to the inability to perceive the characteristics of wind speed in advance, the control strategy makes the wind turbine itself to be at a passive state during the operation process. In this paper, a wind turbine mechanical load optimization control strategy based on an accurate wind speed estimator with time series Broad Learning System Method (BLSM) is designed, simulated and also verified. Firstly, the basic control theory of the BLSM and also a mechanical load optimization controller is designed. Then the OpenFAST is used to conduct a full‐life cycle simulation comparison study on mechanical load characteristics of wind turbine before and after the implementation of the optimization control strategy. Finally, a field empirical mechanical load test is performed on the wind turbine, which is configured with BLSM mechanical load optimization control technology. The findings indicate that the implementation of this control strategy can significantly mitigate the ultimate and fatigue loads of wind turbines, particularly the fatigue loads of tower base tilt and roll bending moments, with a reduction rate of approximately 6.2% and 4.3%, respectively.

Retail store-SKU level replenishment planning with attribute-space graph recurrent neural networks

Large retail chains routinely make replenishment decisions for inventory at the store Stock-Keeping-Unit (SKU) level, relying heavily on accurate demand forecasting to meet target service levels. We critically examine the current state-of-the-art in demand forecasting, revealing a notable gap in existing models, specifically the neglect of intricate cross-SKU promotional effects. To address this gap, we introduce an innovative solution: the Attributed Space Graph Recurrent Neural Network (ASG-RNN), which encodes demand history and drivers' history using a recurrent neural network, and captures cross-SKU promotional interactions through a graph convolution network. The graph convolutions operate on an attribute space graph constructed using K-nearest neighbor algorithm based on SKU-level static attributes. Initial empirical tests were conducted on a dataset comprising 200 SKUs in the Coffee category from a retail chain with 93 stores. The analysis demonstrates that ASG-RNN, on average, reduces loss by 4.1% compared to the best benchmark, resulting in average inventory cost savings of 4.94%. Notably, for promoted SKUs, ASG-RNN reduces loss by 13% on average, and it can achieve a 30% reduction in loss when the target service rate is high. Further experiments conducted on four additional product categories, including Beer, Laundry Detergent, Milk, and Mayo, yield similar results, showcasing the robustness of ASG-RNN's performance. These findings underscore the practical significance and cost-effectiveness of our innovative approach in enhancing demand forecasting and optimizing replenishment decisions for large retail chains.

Tests for homogeneity of distributions of component lifetimes from Type‐II censored system lifetime data with known system structures

AbstractIn system reliability analysis, researchers are interested in evaluating the characteristics of the lifetime distribution of components in a system based on system lifetime data. In collecting system lifetime data, a censoring scheme is often adopted due to time and budget constraints. In this article, we consider the situation where the system lifetime data from two different coherent systems are subjected to Type‐II right‐censoring, and we are interested in testing the homogeneity of component lifetime distributions based on Type‐II censored system lifetime data with known system structures. Based on the Mann‐Whitney test and empirical likelihood ratio tests developed for testing the homogeneity of component lifetime distributions with complete system lifetime data, we propose different non‐parametric test procedures using the permutation of the unobserved censored system lifetimes. We consider a restricted assumption on the censored lifetimes to reduce the permutations required in the computation. The computational approaches to obtain the critical values of the proposed test procedures are provided using the Monte Carlo method. A practical example is used to illustrate the proposed test procedures. Then, the power performance of the proposed test procedures is evaluated and compared through a Monte Carlo simulation study. The simulation results show that the proposed test procedures provide comparable power performance for Type‐II censored system lifetime data in contrast to the complete sample case under different scenarios. Finally, recommendations based on the simulation results and concluding remarks are provided.

Research on the impact of digital technology applications on firms' dual innovation in the digital economy context

In recent years, applications of digital technology have gradually become the primary driving force for enterprise innovation. Although the impact of digital technology applications on enterprise innovation has been recognized by the academic community, existing studies have failed to systematically reveal the mechanism of digital technology applications on firms' dual innovation, overlooking the data-driven dynamic characteristics of innovation development in the digital era, and neglecting external conditions such as environmental complexity. Therefore, based on dynamic capability theory and resource orchestration theory, this study adopts a “technology-economy” research paradigm to explore and analyze the impact of digital technology applications on dual innovation. It also analyzes the mediating role of data-driven dynamic capabilities and the moderating effect of environmental complexity. The article conducts an empirical test using microdata of Chinese Shanghai and Shenzhen A-share listed firms from 2010 to 2021.The research results show that digital technology applications positively promote dual innovation of enterprises and exhibit certain heterogeneity. Data-driven dynamic capabilities play a mediating role in the relationship between digital technology applications and dual innovation. The study also tests the moderating effect of environmental complexity and finds that the higher environmental complexity in which the company operates, the stronger the facilitating effect of digital technology applications.

An empirical study of the performance of the sixth generation ports model with smart ports with reference to major container ports in mainland China

Since the UNCTAD proposed the initial port model, called the first generation port model (1GP) in 1994, its adaptation and transformation have evolved, reflecting not only the evolution of port operations and technological advancements, but also the resilience of ports to natural disasters and supply disruptions. Recently, the trio of COVID-19, digitalization, and decarbonization (CDD) has emerged as core challenges whilst providing numerous opportunities for building adaption and resilience for the port industry. In light of this, the sixth generation ports (6GP) model has emerged. This paper addresses crucial research gaps by empirically testing the 6GP model, emphasizing the impact of emerging factors of the CDD, including 1) the lack of empirical testing for the 6GP model and the emphasis on the CDD; 2) the exploration of factors contributing to the significant development of Chinese container ports; 3) the investigation into how the CDD lead to the reversal of the port devolution continuum path. In doing so, an empirical study has been conducted, focusing on six major container ports in mainland China, all of which are among the world’s top 10 container ports in terms of container throughput. It evaluates the importance of the criteria of the 6GP model with the performance of the six ports. The findings of this research are discussed for managerial and policy implications, enriching the existing literature on port devolution, and providing the port industry with a holistic view of port development and strategy in the post-pandemic period.

Magmatic cyclicity and episodic continental growth of the Australian Tasmanides

The hafnium isotopes of zircon have recently been identified as a new means of detecting magmatic cyclicity. A hierarchy of possible magmatic cycles has been verified, ranging from the ∼600 Myr supercontinent cycle to 10–20 Myr plutonic cycles. While the supercontinent cycle is well-established, the shorter mantle convective and plutonic cycles require further work to understand how magmatic dynamics may give rise to such cyclicity. In this study, we analyze in detail the long-lived orogenic system of the Australian Tasmanides, and a hierarchy of cycles is visible, including a ∼ 290 Myr accretionary/orogenic cycle, a ∼ 65 Myr upper mantle cycle, and 20–10 Myr magmatic and plutonic cycles. These cycles correlate with the evolution of the orogens and episodic continental growth of eastern Gondwana. The surprising similarity in period length between the opening/closing of a typical Atlantic-like Wilson cycle to the case here of the lifecycle of a West Pacific-type arc system may imply that the two distinct tectonic settings have a similar mantle convective effect, which requires further empirical testing and theoretical study. Similar to magmatic cycles identified in the Hadean and in modern arc settings, the hierarchy of cycles in the Tasmanides implies a uniformitarian similarity in arc magmatism throughout Earth history.

Pengaruh Struktur Aset, Profitabilitas dan Ukuran Perusahaan terhadap Struktur Modal

Business growth and production expansion are basically the basis for companies to seek sources of income for their capital. This research aims to obtain empirical evidence and test related to how asset structure, profitability, and company size influence capital structure. By involving non-cyclical consumption sub-sector companies for 2019 - 2022 and registered on the IDX, 111 data were obtained using the purposive sampling method. The data analysis technique used is multiple linear regression with Spss 26. The research results show that asset structure and company size have a effect on capital structure. Meanwhile, the profitability variable has no effect on capital structure. The low adjusted r square in this study makes it have to be reviewed with other variables. The results showed that the companies taken preferred the use of external funds, namely debt. Keywords : Capital Structure, Asset Structure, Profitability, and Company Size.

Impacts of urban real-world labs: Insights from a co-evaluation process informed by structuration theory in Wuppertal-Mirke

To address the evaluation of the societal impact of real-world labs, we present a framework developed for the analysis of structure-agency dynamics: structuration theory. Using this tool on a neighbourhood level, we assess the outcomes of six projects on co-productive city-making.Ways of evaluating the societal impact of real-world labs as a transdisciplinary and transformative research format are under discussion. We present an evaluation approach rooted in structuration theory, with a focus on structure-agency dynamics at the science-society interface. We applied the theory with its four modalities (interpretation schemes, norms, allocative and authoritative resources) to the case of the Mirke neighbourhood in Wuppertal, Germany. Six projects promoted the capacity for co-productive city-making. The effects of the projects were jointly analysed in a co-evaluation process. Previously proposed subcategories of the modalities as an empirical operationalisation were tested and confirmed as being applicable. Five new subcategories were generated. The use of the modalities seems appropriate for co-evaluation processes. The tool is practical, focused on real-world effects, and suitable for transdisciplinary interpretation processes. We encourage further empirical testing of the tool, as well as development of the subcategories.

An interconnected system of energy

The purpose of this paper is to describe a system of interconnected energy models. These models produce inferences that may enhance our understanding of classical and quantum mechanics. From the formulated models, an empirical test is derived to validate the proposed light projection hypothesis.

Robotics, environmental regulation, and agricultural carbon emissions: an examination of the environmental Kuznets curve theory and moderating effects

IntroductionReducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?MethodsThis work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.ResultsThis study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.DiscussionThe findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.

A Study on the Impact of Green Finance on the High-Quality Economic Development of Beijing–Tianjin–Hebei Region

China’s economy has shifted to a new stage of high-quality development, which means that economic development is no longer simply pursuing the increase of quantity, but a balance of quality and quantity. High-quality economic development (HQED) has become essential for China to achieve sustainable economic and social advancement. This paper explores the influence of green finance on HQED, along with the mediating roles of green innovation and industrial structure upgrading. A fixed-effects model is employed to assess the relationship between green finance and HQED and conduct an empirical test in the Beijing–Tianjin–Hebei (BTH) region of China. The findings reveal that green finance significantly contributes to the HQED of the BTH region, and green innovation and industrial structure upgrading have intermediary effects in this process. Based on these insights, this paper proposes several strategies to improve HQED in China, including the development of a green financial system, the promotion of green innovation, and the acceleration of industrial structure optimization and upgrading in the BTH region.

Validation of a New Method for Continuous Flare Combustion Efficiency Monitoring

A new method is described for calculating flare combustion efficiency (CE) and destruction and removal efficiency (DRE) using a numerical parametric model. The method combines key variables that affect flare performance including the flare vent gas net heating value (NHV), flare design, flow rate, exit velocity, and inert gas composition, alongside the environmental influence of crosswind speed. Each effect is characterized using a parametric model derived from experimental testing data and computational fluid dynamics (CFD). The inclusion of CFD allows the model to be extended into the high-wind conditions that cannot be adequately controlled for in empirical testing yet represent some of the most challenging conditions in which to maintain good combustion. This new parametric model method (PMM) is coupled with ultrasonic flowmeters from which the molecular weight and net heating value of the flare gas can be derived using the vent gas speed of sound measurement. In doing so, this method provides a reliable continuous flare combustion efficiency measure that can be deployed at scale with minimum hardware updates. The system was verified using an extractive sampling method with tests conducted on three full-scale industrial flares including non-assisted, single-arm pressure-assisted, and multi-arm pressure-assisted flare designs. A total of seventy valid test points were carried out with varying flow rate and flare gas heating value, covering a CE range from 46–100%. The uncertainty of the method was assessed using both traditional error propagation and Monte Carlo methodology. The results from the new method agree with the extractive method to within 0.8% in the ≥98% DRE region where flares are expected to operate to limit the impacts of flaring as a source of methane as a greenhouse gas. Uncertainty analysis revealed that the larger DRE discrepancy for DRE ≤ 98% correlates to the measurement uncertainties for both methods.

I think you might like me: Emergence and change of meta-liking in initial social interactions.

Feeling accepted by others is a fundamental human motive and an important marker of successful social interactions. This interpersonal perception, known as meta-liking, is especially relevant during adolescence, when peer relationships deepen and expand. However, knowledge is limited regarding meta-liking formation in initial social interactions. This study investigated whether adolescents (N = 293, Mage = 15.48, 61.10% female) have default expectations for meta-liking at zero acquaintance and how these judgments are updated during initial group interactions. Specifically, we used latent change models to examine how personality traits predicted initial meta-liking and whether personality and social interaction experiences were linked to changes in meta-liking judgments throughout an interaction. Our findings revealed three key insights: First, meta-liking increased gradually over the course of the interaction, with substantial individual differences in both default meta-liking and change scores. Second, extraversion, neuroticism, and self-esteem predicted initial meta-liking. Third, liking others was also linked to initial meta-liking and early changes, while meta-liking changes toward the end of the interaction occurred independent of all these features and were not predicted by expressive behaviors of interaction partners. This study represents a first empirical test of default expectations and updates in meta-liking based on personality characteristics and social interaction experiences in initial social interactions. We discuss our results in terms of a broader framework for understanding how metaperceptions are formed and updated early in the acquaintance process. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Characteristics of Users of a Digital Hypnotherapy Intervention for Hot Flashes: Retrospective Study.

Hot flashes are associated with a lower quality of life and sleep disturbances. Given the many consequences of hot flashes, it is important to find treatments to reduce them. Hypnotherapy, the use of hypnosis for a medical disorder or concern, has been shown in clinical trials to be effective in reducing hot flashes, but it is not routinely used in clinical practice. One solution to close this implementation gap is to administer hypnotherapy for hot flashes via a smartphone app. Evia is a smartphone app that delivers hypnotherapy for hot flashes. Evia has made hypnotherapy more widely accessible for women who are experiencing hot flashes; however, the app has yet to undergo empirical testing. Additionally, research on user characteristics is lacking. This study aims to (1) determine the average age, stage of menopause, and length of menopause symptoms for users of the Evia app; (2) determine the characteristics of hot flashes and night sweats for users of the Evia app; (3) determine the self-reported sleep quality of users of the Evia app; (4) determine the self-reported mental health of users of the Evia app; and (5) determine the relationship between hot flash frequency and anxiety and depression for users of the Evia app. This study analyzed data collected from participants who have downloaded the Evia app. Data were collected at 1 time point from a self-report questionnaire that assessed the demographic and clinical characteristics of users. The questionnaire was given to users when they downloaded the Evia app. Users of the Evia app fill out a questionnaire upon enrolling in the program and prior to beginning the intervention. This included 9764 users. Results showed that the mean age of users was 49.31 years. A total of 41.6% (1942/4665) of users reported experiencing 5 or more hot flashes per day, while 51.2% (1473/2877) of users reported having difficulty falling asleep each night and 47.7% (1253/2626) of users reported their sleep quality to be terrible. In addition, 38.4% (1104/2877) of users reported that they often feel anxious or depressed. There was a small, significant, and negative correlation between hot flash frequency and self-report frequency of anxiety and depression (r=-0.09). This study showed that the average age of app users is in line with the median age of natural menopause. A large percentage of users reported experiencing 5 or more hot flashes per day, reported difficulties with sleep, and reported experiencing depression and anxiety. These findings are in line with previous studies that assessed hot flash frequency and the consequences of hot flashes. This was the first study to report on the characteristics of users of the Evia app. Results will be used to optimize the hypnotherapy program delivered via the Evia app.

Research on the relationship between CEO career variety, digital knowledge base extension, and digital transformation in the context of digital merger and acquisition: The case of China's new generation of information technology firms.

This study examines the relationship between CEO career variety, digital knowledge base extension, and digital transformation in a digital M&A context. An empirical test was conducted using regression analysis with the digital M&A events of the new generation of information technology firms in China as the research sample. The results reveal that CEO career variety has a positive effect on digital transformation in the digital M&A context and that digital knowledge-base extension plays a mediating role. Moreover, the heterogeneity impact analysis indicated that the moderating effects of geographical distance, knowledge disparity, and cultural difference between target and acquirer firms on the above relationships vary greatly: geographical distance has a negative moderating effect, cultural difference has a positive moderating effect, and the moderating effects of both geographical distance and cultural difference are realized through mediating effects, but none of the moderating effects of knowledge disparity are significant.

Caffeic acid phenethyl ester: an effective antiviral agent against porcine reproductive and Respiratory Syndrome Virus

Porcine Reproductive and Respiratory Syndrome (PRRS) presents a formidable viral challenge in swine husbandry. Confronting the constraints of existing veterinary pharmaceuticals and vaccines, this investigation centers on Caffeic Acid Phenethyl Ester (CAPE) as a prospective clinical suppressant for the Porcine Reproductive and Respiratory Syndrome Virus (PRRSV). The study adopts an integrated methodology to evaluate CAPE's antiviral attributes. This encompasses a dual-phase analysis of CAPE's interaction with PRRSV, both in vitro and in vivo, and an examination of its influence on viral replication. Varied dosages of CAPE were subjected to empirical testing in animal models to quantify its efficacy in combating PRRSV infections. The findings reveal a pronounced antiviral potency, notably in prophylactic scenarios. As a predominant component of propolis, CAPE stands out as a promising candidate for clinical suppression, showing exceptional effectiveness in pre-exposure prophylaxis regimes. This highlights the potential of CAPE in spearheading cutting-edge strategies for the management of future PRRSV outbreaks.

Cross-Site and Cross-Generation Knowledge Transfer in High-Tech After-Sales Service

Learning and knowledge transfer are crucial to organizational success. Knowledge can transfer between sites and generations, yet prior research has studied both types of knowledge transfer in isolation. Understanding their combined effect is essential because many manufacturing and service organizations have multiple sites and generations. In contrast to prior research, we study both types of knowledge transfer simultaneously in a high-tech after-sales service organization. In doing so, we provide new insights into the learning processes of high-tech firms providing global after-service. We leverage 10 years of weekly observations of after-sales service experience and performance of a firm in the semiconductor industry to test learning curve models with cross-site and cross-generation knowledge transfer and temporal distance between generations. Our empirical model tests the impact of these different sources of experience on the downtime performance of five machine generations serviced by 30 globally distributed service sites. We find knowledge transfer across sites and generations, except for cross-site knowledge transfer from newer to older generations. That is, the introduction of new products disturbs the cross-site learning process of older products. Furthermore, cross-generation knowledge transfer is conditional to the temporal distance between product generations. Knowledge transfer decreases when the temporal distance increases. Funding: This research was funded by the high-tech company that facilitated this study.

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues

N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection over the past decades, there were rooms left to improve the predictive accuracy and the efficiency. In this paper, we proposed an improved method for predicting m6A modification sites, which was based on bi-directional gated recurrent unit (Bi-GRU) and convolutional neural networks (CNN), called Deepm6A-MT. The Deepm6A-MT has two input channels. One is to use an embedding layer followed by the Bi-GRU and then by the CNN, and another is to use one-hot encoding, dinucleotide one-hot encoding, and nucleotide chemical property codes. We trained and evaluated the Deepm6A-MT both by the 5-fold cross-validation and the independent test. The empirical tests showed that the Deepm6A-MT achieved the state of the art performance. In addition, we also conducted the cross-species and the cross-tissues tests to further verify the Deepm6A-MT for effectiveness and efficiency. Finally, for the convenience of academic research, we deployed the Deepm6A-MT to the web server, which is accessed at the URL http://www.biolscience.cn/Deepm6A-MT/.