Empirical Study Research Articles

Has the Pilot Project of Innovative Cities Increased Economic Growth? An Empirical Study Based on Chinese Cities

Has the Pilot Project of Innovative Cities Increased Economic Growth? An Empirical Study Based on Chinese Cities

An Empirical Study of Entrepreneurial Intentions of the University Students from Mountainous Region of Pakistan.

An Empirical Study of Entrepreneurial Intentions of the University Students from Mountainous Region of Pakistan.

Green Remodeling: Empirical Study of Thermal Insulation Improvement Remodeling of Public Healthcare Center

This study investigates the energy performance improvement in an aging public healthcare center in South Korea through a comprehensive “Green Remodeling” project. The building, originally constructed in 2001 before the establishment of national energy-saving standards, exhibited substandard insulation performance in its walls, roof, floors, and windows. The remodeling was designed to meet the highest current energy-saving criteria, incorporating advanced insulation techniques and energy-efficient systems. The remodeling process achieved a significant improvement in heating efficiency, with the ECO2 simulation predicting a 50% reduction in energy consumption. However, actual post-remodeling savings were approximately 10%, influenced by factors such as varying occupancy patterns and construction challenges. Despite these obstacles, the project demonstrated the effectiveness of targeted energy-saving measures in enhancing the overall performance of the building. This research underscores the importance of green remodeling as a viable strategy for improving energy efficiency in aging buildings, particularly in the context of South Korea’s carbon reduction goals. This study provides practical insights into the design and implementation of energy-saving technologies, offering a model that can be adapted for similar projects in other contexts.

A Critical Systematic Review of Cultural and Community Practices in Integrated Reading and Writing Courses in Two-Year Colleges

ABSTRACT This study aimed to synthesize and critically analyze 24 empirical studies that focused on Students of Color (SOC) in Integrated Reading and Writing (IRW) classrooms in U.S. community colleges. Our review, through the cultural and community practices framing, examined IRW studies and how SOC, curriculum/institutional reform, and wise pedagogical practices for culturally diverse learning communities were portrayed. Our cultural and community practices framework was informed by culturally sustaining pedagogy and community cultural wealth framework in literacy. Through narratively reviewing studies, we generated the following six themes using our framework of cultural and community practices in classroom settings: ways to culturally diversify reading and writing text, strengthening students’ identity through curriculum, fostering community in the postsecondary classroom, supporting students’ mother tongue in the classroom, the need for diverse faculty representation, and the need for asset-based narratives in developmental education. Implications for asset-focused research and practice are discussed in postsecondary literacy spaces.

A systematic review and meta-analysis of intervention studies on mathematics and science pedagogical content knowledge

Pedagogical content knowledge (PCK) has been considered as professional knowledge that teachers need to effectively instruct students. Empirical studies have been conducted to enrich teachers' PCK through interventions such as professional development programs. In this study, we conducted a systematic review and meta-analysis of studies about PCK interventions and their quantitative evaluation among mathematics or science teachers at the elementary and secondary education levels. We identified 101 effect sizes (42 for multiple group comparison designs and 59 for multiple time point comparison designs) and found that the interventions had a positive effect in both designs. The results of the meta-regression analysis showed that in the multiple group design, the effects differed by intervention target, with higher scores for the PCK intervention than for the CK-only intervention, and in the multiple time point design, the effects differed by subjects treated, with higher scores for the science materials than for the mathematics materials. These results not only demonstrate the average effectiveness of previous studies aimed at improving teachers' PCK but also provide insights into effective designs for professional development methods that promote PCK acquisition.Systematic review registrationhttps://osf.io/vf4hq/?view_only=068483e4e82c42cd994e2c8174bd0a64.

International Transportation Infrastructure and China Enterprises Green Innovation: Evidence from the China Railway Express

The China Railway Express (CRExpress) is a vital international transportation infrastructure for the advancement of the Belt and Road Initiative and the expansion of international commerce, providing a significant opportunity for Chinese companies to undergo a sustainable transformation. We use a multi-period differences-in-differences model to investigate the relationship and mechanism between the opening of the CRExpress and green innovation of enterprises, based on the panel data of Chinese A-shared listed companies from 2007 to 2020. The results show that the opening of the CRExpress can significantly increase the logarithmic number of green patent applications by at least 5% in enterprises with a spoke radius of 250–450 km, and it also has a significant positive effect on different types of green patents to varying degrees. By examining the sub-items of ESG ratings, the opening of the CRExpress can promote the green innovation of enterprises through the improvement of E-score. Additionally, three pivotal mechanism pathways have been identified: the promotion of regional economic development, the alleviation of corporate financing constraints, and the reduction of corporate logistics expenses. Furthermore, we find that the effect of the CRExpress turns out larger on the green innovation of enterprises in the Secondary Industry and the Tertiary Industry, manufacturing industry, state-owned enterprises, and firms located in the eastern and central regions. Based on the empirical study, we provide policy implications for deepening the correlation between the CRExpress and green innovation.

Estimating Determinants of Contractualization of Workers in Organized Sector in India

The structural adjustment programme adopted in 1990 led to the removal of all existing pre-reform regulations and restrictions imposed on the Indian industries. This radically transformed India’s trade relations with international markets. The liberalization of Indian economy on one hand accelerated foreign capital as well as technology influx while on the other hand increased competition for the domestic economy. Thus the increased openness and flexibility arising from the economic transformation prompted the domestic producers to resort to alternative informal routes of employment thereby leading to increased contractualization of employment substituting of regular workers by contract workers in the manufacturing industries. Thus this paper is an empirical study which focuses on exploring and identifying the industry specific factors which are affecting the degree of casualization in Organized manufacturing sector of India. Panel regression using STATA has been used and the econometric analysis reveals that average firm size, value of output, capital intensity, profit per worker and welfare benefits for workers have considerable favourable effect on the employment of contractual workers while trade union activity and the ratio of male to female workers employed have significant negative impact on contract worker employment in the manufacturing industries of the organized sector in India. Keywords: contractualization, capital intensity, trade union activity, feminization, panel regression JEL Classification: C33, J21, L60

Exploring the Facilitating Environment for Mindfulness Meditation: Establishing a Framework through Analysis of Means and Exploratory Factor Analysis

Abstract Objective While mindfulness meditation is an established practice for improving well-being, the physical environment for this activity awaits further exploration. This research aimed to explore how the physical environment facilitates mindfulness meditation and to establish a framework through Analysis of Means (ANOM) and Exploratory Factor Analysis (EFA). The framework was intended to provide guidance for organisations and individuals to better design or modify physical space to support their mindfulness meditations. Method This study used methods to establish an initial research framework from the literature on mindfulness, architectural design, and environmental psychology. A survey of UK mindfulness practitioners was conducted in 2021–2022 to provide insights from a quantitative perspective. The data were analysed using ANOM and EFA to obtain a comprehensive framework based on the responses. Results Through a review of the literature and empirical study, nine physical factors with controllable elements were identified, ranked by their relative importance. Quietness, the use of supportive tools, and natural sounds were found to be the most important. Conclusion This research suggests the priority elements for groups and individuals to consider when adapting the physical environment for mindfulness meditation. The results revealed quietness as the most beneficial controllable element for its facilitation. Additionally, the use of supportive meditation tools was also important. Considering these top elements first could support practitioners' mindfulness meditation more effectively and efficiently. Preregistration This study was not preregistered.

Simulation-Based Optimization for Vertiport Location Selection: A Surrogate Model With Machine Learning Method

We present Vertiport-informed Surrogate-Based Optimization with Machine Learning Surrogates (VinS), a novel framework for solving the vertiport location problem for urban air mobility operations. The primary focus of this work is on the optimization of vertiport locations to facilitate efficient urban air transportation. Our framework helps choose not only the optimal vertiport locations but also the optimal number of vertiports. We develop a simulation model to analyze the impacts of various vertiport location configurations on the efficiency of the transportation network. To expedite the simulation process, a surrogate model is trained using machine learning algorithms, effectively reducing the computational time for evaluating a given vertiport location configuration. With the machine learning surrogate models, we apply a genetic algorithm to find the optimal set of vertiport locations. An empirical study was performed in the San Francisco Bay Area, from which we found that given the optimal set of vertiport locations, we further reduced the total travel time in the entire transportation system in the Bay Area compared with the sampled sets by 0.05% (400 h) on average. We release our framework at: https://github.com/Xuan-1998/LPSim .

Preliminary In silico Analysis of Adenylate Kinase 1 (ADK1) of Echinococcus granulosus as a Candidate for Vaccination against Cystic Echinococcosis

Background: A neglected zoonosis, Cystic Echinococcosis (CE), is most common in developing nations worldwide. Vaccination is, therefore, helpful in preventing the disease. Objective: Predicting the main biochemical properties of E. granulosus Adenylate Kinase 1 (ADK1) and its possible B-cell and T-cell-binding epitopes as a valuable candidate for immunization was the goal of the current study. Methods: Predictions were made to determine biochemical, antigenic, structural, and subcellular characteristics, along with the immunogenic epitopes, using several online servers. Results: The extracellular 22 KDa protein had no allergenicity, while it possessed hydrophilicity (GRAVY: -0.286), stability (instability: 17.48), tolerance to a wide range of temperatures (aliphatic: 93.45), and 17 post-translational modification sites. The secondary structure mostly comprised helices and random coils and the 3D model was generated using Robetta server (confidence: 0.88). Common B-cell epitopes were discovered by three servers and screened for antigenic, allergenic, and solubility traits. Moreover, MHC-associated epitopes for mice and humans were predicted in E. granulosus ADK1 with subsequent screening. Conclusion: This work offers a foundation for further investigation on designing an effective vaccination against CE. Further empirical research study with the examined protein solely or combined with other antigens is needed.

Is it Irrational to Assume Rationality in Business?

Purpose: Academic research continuously introduces new theories, methodologies, etc. to improve upon prior studies. Researchers commonly base their theories on the assumption that managers and stakeholders have calculated motives and make well-educated decisions. However, they ignore the fact that certain corporate decisions are completely random with no logical explanations. Researchers still are not comfortable with this idea and always try to justify business decisions by advancing new sophisticated theories while in fact, many executives and stakeholders are not that sophisticated. This paper discusses such instances of irrationality. Methodology: This paper discusses several instances of irrational business decisions in various aspects of finance including capital budgeting, capital structure, stock buybacks, executive compensation, overinvestment, and stock market irregularities. By examining a historical set of corporate events, it is shown that various forms of irrationality, when abundant, may cause empirical studies to refute theories. Findings: Theories cannot always be justified by empirical work not necessarily because of flaws in these theories but possibly because of irrational behavior of ill-informed executives and stakeholders. That is, if the assumption of rationality commonly imbedded in academic studies does not hold, then it may be the culprit, at least in part, behind the discrepancy between theory and empirical findings. Unique Contribution to Theory, Practice and Policy: This paper postulates that the discrepancy between theory and empirical findings may, in part, be caused by the common assumption of rationality that may not hold true. By discussing numerous instances of irrational behavior in various aspects of business, this paper attempts to show that irrationality is not uncommon and should be considered in the design of academic research.

Computer-assisted pronunciation training: A systematic review

Abstract This systematic review maps the trends of computer-assisted pronunciation training (CAPT) research based on the pedagogy of second language (L2) pronunciation instruction and assessment. The review was limited to empirical studies investigating the effects of CAPT on healthy L2 learners’ pronunciation. Thirty peer-reviewed journal articles published between 1999 and 2022 were selected based on specific inclusion and exclusion criteria. Data were collected about the studies’ contexts, participants, experimental designs, CAPT systems, pronunciation training scopes and approaches, pronunciation assessment practices, and learning measures. Using a pedagogically informed codebook, the pronunciation training and assessment practices were classified and evaluated based on established L2 pronunciation teaching guidelines. The findings indicated that most of the studies focused on the pronunciation training of adult English learners with an emphasis on the production of segmental features (i.e. vowels and consonants) rather than suprasegmental features (i.e. stress, intonation, and rhythm). Despite the innovation promised by CAPT technology, pronunciation practice in the studies reviewed was characterized by the predominant use of drilling through listen-and-repeat and read-aloud activities. As for assessment, most CAPT studies relied on human listeners to measure the accurate production of discrete pronunciation features (i.e. segmental and suprasegmental accuracy). Meanwhile, few studies employed global pronunciation learning measures such as intelligibility and comprehensibility. Recommendations for future research are provided based on the discussion of these results.

Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing

This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks.

Patient and public involvement in healthcare: a systematic mapping review of systematic reviews – identification of current research and possible directions for future research

ObjectivesTo provide an overview of patient and public involvement (PPI) in the mesolevel and macrolevel of healthcare (different from PPI in research) and identify directions for future research by mapping...

The impact of e-learning on students’ performance: the mediating role of sustainable education

PurposeThe study aims to assess the impact of electronic learning (e-learning) on students' performance (SP), with an emphasis on the mediating role of sustainable education (SE) at the Sharjah Police Sciences Academy (SPSA). The urgency of this study was highlighted by the global COVID-19 pandemic that happened between 2020 and 2022, which emphasized the need for e-learning as a tangible and imperative solution.Design/methodology/approachThe study applied a quantitative approach, targeting the 91 student population at SPSA in the winter semester of 2022/2023 who practiced the e-learning process. A comprehensive survey was implemented disseminating 91 questionnaires by Google Forms to all students. Of the total, 61 questionnaires were returned, resulting in a response rate of 67.03%.FindingsThe results revealed that e-learning predictors explained 85.4% of the variations in SP, and 46.4% of the variations in SE. The model demonstrated a strong fit and high predictive relevance. The indirect impact of e-learning on SP through SE was found to be positive and statistically significant at the 0.05 level. Consequently, the mediation effect was considered partial and statistically significant.Practical implicationsPolicymakers and educators should consider findings of the study when designing educational programs and policies. Accordingly, the SPSA management could gradually focus on safeguarding a sustainable e-learning environment and improving high-tech platforms. This would involve creating a sustainable e-learning curriculum to enable students to remain informed on the best e-learning practices.Originality/valueAn empirical study is essential for exploring the optimal answers to inquiries related to e-learning, SE and their impact on SP. The finding of the current study may contribute to the existing knowledge on e-learning, SE and SP. Additionally, they may improve learning activities and provide more sustainable learning experiences, leading to enhanced learning outcomes for students

ANALYTICALLY PRICING EUROPEAN OPTIONS UNDER A TWO-FACTOR STOCHASTIC INTEREST RATE MODEL WITH A STOCHASTIC LONG-RUN EQUILIBRIUM LEVEL

Abstract We construct a new stochastic interest rate model with two stochastic factors, by introducing a stochastic long-run equilibrium level into the Vasicek interest rate model which follows another Ornstein–Uhlenbeck process. With the interest rate under the Black–Scholes model being assumed to follow the newly proposed model, a closed-form representation of European option prices is successfully presented, when the analytical characteristic function of the underlying log-price under a forward measure is derived. To assess the model performance, a preliminary empirical study is conducted using S&P 500 index and its options, with the Vasicek model and an alternative two-factor Vasicek model taken as benchmarks.

Buying inferior to selling: Explore the impact of transaction direction on the effects of related-party transactions

Throughout, the effects of related-party transactions (RPTs) have been a hot topic in financial markets and corporate governance research. This paper analyzes the theoretical foundation of the effects of RPTs and constructs a new indicator, the quasi-profit margin, to assess the effects of RPTs by studying their impact on the quasi-profit margin. Based on the information asymmetry between transaction parties and the information screening theory, the paper proposes the buying inferior to selling theory, systematically explaining the impact of transaction direction on the effects of RPTs. Subsequently, using panel data from Chinese A-share listed companies from 2016 to 2021, the paper constructs fixed-effects models and conducts empirical studies from both exogenous and endogenous perspectives, employing estimation methods such as high dimensional fixed effects method, two-stage least squares method, and three-stage least squares method. The research indicates that RPTs of Chinese A-share listed companies generally exhibit a tunneling effect, and the transaction direction significantly affects the effects of RPTs. The higher the proportion of RPTs conducted as sellers to the total RPTs, the smaller the overall tunneling effect of the RPTs. This study has implications for reducing the tunneling risk of RPTs and improving corporate governance for listed companies, as well as providing some references for financial regulatory authorities to identify and rectify illegal RPTs.

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

AbstractLow‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.

Empirical Study on the HCI Characteristics of Business Model Innovation Based on AI-Powered CAD

Empirical Study on the HCI Characteristics of Business Model Innovation Based on AI-Powered CAD

Natural Frequencies Improve Public Understanding of Medical Test Results: An Experimental Study on Various Bayesian Inference Tasks with Multiple Scoring Methods and Non-Bayesian Reasoning Strategies.

It is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving. In a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods-strict rounding, loose rounding, absolute deviation, and 50-Split-were used to evaluate participants' estimations. The NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems. The NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks. The natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups.This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods.The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method.Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.